Born in Portugal, Beatriz trained as a biomedical engineer before moving to the UK to pursue a PhD in computational neuroscience and bioimage analysis. She later became a Research Fellow at the Alan Turing Institute, where she helped bridge AI, biology and health, while also becoming one of the voices behind the highly regarded Turing Podcast. Today she is part of Microsoft’s MAI Futures team, exploring how AI will shape the years ahead. What fascinates me most isn’t only the science. It’s her rare ability to explain difficult concepts through simple stories and memorable metaphors, a skill that is becoming just as valuable as building the technology itself.

So, what can we learn from someone who sits at the intersection of neuroscience, AI, research, communication and the future?

I have a feeling this conversation won’t just be about artificial intelligence. It will be about curiosity, learning, communicating ideas that matter, and building a career by connecting worlds that rarely meet.

You started in biomedical engineering in Coimbra, Portugal, moved into neuroscience, in the UK, then AI research, and now you’re helping shape Microsoft’s vision of the future. Looking back, what were the turning points that completely changed your career?

The first turning point was actually before even choosing what degree I wanted in Coimbra. I have been coding since I was 9 so I thought I was going to follow computer science. However, my paternal grandmother got diagnosed with Alzheimer’s, and I became fascinated with the brain. How come we knew so little about something so fundamental in us? So, I wanted to somehow combine my love for computers with neuroscience - it shaped most of my career. The other turning point, however, isn’t as positivity driven - I was tired and burned out after working and studying in academia, so at that point I wanted to have a complete change of course and started applying for industry jobs. I would’ve taken any job, and I was hoping my transferable skills (like communication!) would actually help - and they did. That’s how Microsoft AI found me.

WOW. That is quite powerful! Thank you for sharing so openly. That helps to understand the drive and the urge!

You’ve successfully moved across engineering, biology, neuroscience, machine learning and now future thinking. How do you approach learning entirely new fields without feeling overwhelmed?

I’ll start my answer to this question from the end: it is not without feeling overwhelmed but despite. And this syntactical difference is one of the most important things I’ve learned. Whenever faced with a new challenge, I just want to learn more and more, as much as I possibly can. I am an extremely curious person, and I try to answer one question at a time. The rest comes later. I also want to point out that while being extremely good in one field is important and relevant, becoming adaptable and diverse in what we learn is in itself a skill. Some jobs/careers only make sense if people can mold and adapt their knowledge to a new field, question, query. When I moved to the UK, I thought having a diverse set of skills was a disadvantage… and maybe it was, for the career I thought I wanted for myself. But it has been my greatest asset in the career I’ve built so far.

One thing that immediately stood out in our first conversation was how naturally you translate difficult ideas into everyday language. How did you develop that amazing skill? Can you share with us the spaghetti story?

I have my niece to blame. She was born when I was 15, and by the time she was talking she started asking me questions about anything and everything (I was her favourite grown up, as per her own 3yo words). I spent my time trying to find ways to explain to her toddler self whatever difficult topic she wanted to know that day (why does the sun go up? Why do we need traffic lights? Can the moon be collected? And the most difficult one - what is light?). When I started my PhD, which was a niche topic, in a niche field, I wanted to be able to explain it to my mum so she could tell her friends. So I used all of the ways I used to practice with my niece now for adults too (who have, luckily, a far more complex vocabulary than a 4yo that really wants to know why things fall down and not up).

My process to explain my work to someone is to try and find a common ground with them, and this works for any age, any career stage. What is something that I know they will visualize in their mind easily? I won second place in a science communication competition during my PhD because I managed to find how to explain the work I was doing by comparing it to spaghetti. If people really want to know what you’ve been working on, they are already trying to meet you where you are, so meeting them half way is only a few short steps. But it does take practice, and it takes looking at what you do from the outside, from the other person’s perspective.

You have done research looking into how people interact with AI in their day to day. What surprised you the most?

How fundamentally human our patterns of behaviour are. I had the impression people would use AI for work, but what I found is that despite that, they also use AI for their personal ups and downs, every day query. Conversations about philosophy went up at night. February had a spike on conversations about personal growth before the 14th and relationships on that day. How incredibly human these are!

And that really seems to be a super power seeing that AI evolves literally daily. How do you stay current? What does your personal learning system look like?

There’s not enough time in a day to read about all the new things that I want to learn about. So, my personal learning system includes a lot of conversations with my colleagues from other fields - we all research the edge of the topics we know about, so when we talk, we exchange this knowledge. This is the biggest advantage of working in such an interdisciplinary team, I always have something new to learn from someone. As for my specific topic, it depends on what I need to research, so I do a deep dive on the current literature (scholar, or sometimes using AI to pool together lists of recent publications that I might have missed, always double checking the sources).

Many professionals outside computer science want to learn about AI but don’t know where to begin. If someone had just 10 minutes per day to invest in learning AI, how would you recommend they spend it?

That’s a great question and I just realized I need to adapt the answer I would usually give when it used to be about learning to code. I think it’s a matter of starting with using AI but realizing its shortcomings. AI is fallible and needs to be fact checked at every step, so I would say try to evolve from there. To learn about how AI is built, there is a lot of useful information and resources. But mostly - try it out with positive skepticism and build from there.

The more I listened to Beatriz, the more I realized that this conversation wasn’t just about AI. It was about something much more fundamental: curiosity.

Her journey reminds us that careers are rarely linear, that our most valuable skills are often the ones we never planned to develop, and that the ability to explain complex ideas simply may become one of the defining leadership skills of the AI era.

As artificial intelligence becomes increasingly accessible, technical knowledge alone will no longer be enough. We will need people who can bridge research and society, engineering and humanity, innovation and understanding. People who ask better questions before rushing to answers.

And if she can explain computational neuroscience with a plate of spaghetti, maybe there is hope for all of us 😊 After all, making AI more powerful is an engineering challenge. Making AI make sense is a profoundly human one.

If you want AI to create value, you need a harsher default: most AI projects should be rejected at the door. Not because AI “doesn’t work”, but because most proposals don’t meet the basic conditions for AI to work profitably and safely inside a real business.

The numbers are now catching up with the hype. Gartner has been blunt: it expects over 40% of agentic AI projects to be cancelled by the end of 2027 because of rising costs, unclear value, or weak risk controls.

So the question isn’t “How do we do more AI?”
It’s “Which AI should never be allowed to exist?”

The real reason AI projects fail: they start with the model

Most AI programmes still begin with a solution (“let’s use an LLM”, “let’s build an agent”, “let’s predict churn”) rather than a constraint.

But businesses don’t pay for models. They pay for outcomes: reduced cycle time, fewer errors, higher conversion, lower loss rates, faster decisions, better compliance, and cheaper operations.

When you start with a model, you end up with:

• a prototype that impresses,

• a workflow that hasn’t changed,

• and a set of new operational and legal risks that now need owners.

This is why the “pilot-to-production” gap is so wide. S&P Global Market Intelligence reported that the share of companies abandoning most of their AI initiatives rose sharply year-on-year, and organisations scrapped a large portion of proofs of concept before production.

AI should be treated like a capital allocation decision

A good AI project is closer to a factory upgrade than an app experiment. It changes processes, controls, roles, and accountability.

That means it needs a higher bar than “we could”.

Here’s the bar I recommend:

An AI project is only worth doing if it is:

1. attached to a measurable business bottleneck,

2. deployable into a real workflow,

3. supported by data you can defend,

4. governable under regulation and audit,

5. cheaper (or better) than the non-AI alternative.

If you can’t clear those five, it shouldn’t exist.

The AI Triage: a “kill-first” filter that saves budgets and reputations

Below is a practical triage you can run during a 30–60-minute meeting. If a project fails any of these tests, you either kill it or shrink it until it passes.

1) Bottleneck test: “What is the constraint we are buying back?”

If the proposal can’t name the operational constraint in one sentence, it’s theatre.

Good constraints sound like:

• “Invoice exceptions take 9 days because humans re-key data from PDFs.”

• “Underwriting review time is dominated by document chasing and summarisation.”

• “Customer onboarding stalls because KYC packets are incomplete.”

Bad constraints sound like:

• “We want to modernise.”

• “Competitors are doing GenAI.”

• “We need an AI strategy.”

Kill rule: If the constraint is vague, the project is a vanity project.

2) Counterfactual test: “What is the non-AI fix?”

Every AI plan needs a non-AI baseline. Often, the best solution is boring:

• better forms,

• fewer handoffs,

• a data cleanup,

• a rules engine,

• a template library,

• stronger search,

• clearer approvals.

If a £50k process redesign produces 70% of the gain, why would you fund a £1m AI build with ongoing inference costs and new risks?

Kill rule: If the non-AI alternative is cheaper and “good enough”, stop.

3) Workflow test: “Where exactly does this land?”

AI value is not in the chat window. It’s in the workflow step that disappears.

So force specificity:

• Which role uses it?

• At which moment?

• What input triggers it?

• What output changes a decision?

• What is the human override?

• What system records the outcome?

If the answer is “people will use it when they need it”, adoption will be random, impact will be unmeasurable, and the project will be declared “inconclusive”.

Kill rule: If the workflow isn’t mapped, it’s not a project, it’s a demo.

4) Data readiness test: “Would you bet your name on the data?”

AI doesn’t fail because it lacks intelligence. It fails because the organisation’s data is fragmented, unlabeled, inaccessible, or politically owned.

Gartner has also warned that organisations will abandon a large share of AI projects that lack “AI-ready data”. This is not a technical detail; it is the main event.

Ask:

• Do we have the data today?

• Do we have the rights to use it this way?

• Is it stable, or does it drift weekly?

• Can we trace model outputs back to sources?

• Who owns data quality as an ongoing job?

Kill rule: If data ownership and quality don’t have a named owner, the model becomes the scapegoat later.

5) Economics test: “What is the unit cost per decision?”

AI conversations love “ROI”. AI operations require unit economics.

You need three numbers:

• cost per run (inference + orchestration + monitoring),

• volume per month,

• value per successful output.

This is where many agentic systems die. They look cheap in a sandbox, then explode in production because:

• they call tools too often,

• they re-run tasks,

• they require human review,

• they generate extra work downstream.

Kill rule: If you can’t express cost and value per unit, you can’t manage it.

6) Risk test: “What happens on the worst day?”

Most AI risks are not futuristic. They’re basic:

• leaking sensitive data,

• confident errors,

• biased decisions,

• unexplainable outcomes,

• audit failure,

• supplier lock-in.

In Europe, the compliance bar is rising further. The EU AI Act timeline makes it clear that major obligations and enforcement start in August 2026, with earlier requirements applying in stages. If your AI touches regulated decisions, you need governance built in, not bolted on later.

Kill rule: If you can’t explain how the system behaves under stress, you’re not deploying a product; you’re deploying liability.

The hidden killer: “AI that doesn’t remove work”

Here’s a non-obvious insight that explains most disappointments:

A classic failure pattern looks like this:

1. AI generates a summary.

2. Humans check it (because they don’t trust it).

3. Human rewrites parts (because it’s not quite right).

4. Humans paste it into a system (because integration isn’t done).

Net result: you added time, not removed it.

So the right question is not “Is it accurate?”
It’s “Does it delete a step, reliably, with controls?”

The strongest AI projects are not “smart”. They are surgical:

• extract one painful task,

• reduce it to a constrained output,

• integrate it into an existing system,

• measure it weekly,

• and expand only after repeatable impact.

A practical “never build AI” list

If you want an immediate filter, here are categories that should almost always be rejected:

• “General assistants” for the whole company (no workflow, no owners, no measurable outcomes)

• AI for processes that are broken (you’ll automate chaos)

• AI for low-volume edge cases (unit economics won’t work)

• AI replacing decisions you can’t explain (regulatory and reputational risk)

• AI without a retraining/monitoring plan (it will drift, silently)

• AI, where the best fix is permissions and search (cheaper, safer, faster)

The alternative: build an “AI portfolio”, not an AI backlog

Instead of letting AI ideas pile up, run AI like a portfolio with three buckets:

1. Efficiency plays (clear unit economics, immediate operational impact)

2. Risk reduction plays (fraud, compliance, security—value is avoided loss)

3. Growth plays (pricing, personalisation, sales enablement—harder, but scalable)

Each bucket needs different metrics, governance, and timelines. Mixing them is how you get 40 pilots and zero wins.

A simple rule that changes everything

If you take only one rule from this piece, make it this:

A kill metric is a single number that ends the project if it doesn’t move by a deadline, for example:

• “Reduce average handling time by 12% in 8 weeks”

• “Cut rework rate by 20% with <2% critical errors”

• “Increase straight-through processing from 55% to 70%”

This does two things:

• It protects budgets.

• It forces teams to design for deployment rather than plausibility.

Most AI projects should never exist because most were never designed to earn the right to exist.

And that’s good news. Because the organisations that get ruthless about bottlenecks, workflow, data, economics, and risk, will do fewer AI projects…

…and get far more value from the ones they keep.

by Gonçalo Perdigão

You don’t.

And that is precisely why businesses need to rethink how they evaluate generative AI.

Recently, I ran another simple experiment. The prompt was intentionally trivial:

“Describe Lisbon, the capital of Portugal, in 3 adjectives.”

Nothing more.

The prompt was executed 100 consecutive times using Kimi, one of the most powerful open-source language models currently available, hosted on the MareNostrum 5 supercomputer at the Barcelona Supercomputing Center. The infrastructure was configured with vLLM across four computing nodes, using 16 NVIDIA GPUs for distributed inference. The execution environment automatically deployed the model, exposed it via an API, and executed the entire experiment in a controlled manner.
The question never changed.

The answers did.

Sometimes Lisbon was luminous. Other times it became nostalgic, soulful, enchanting, undulating or historic. The overall meaning remained remarkably consistent, yet the wording varied naturally across executions.

For a human reader, this looks perfectly normal.

AI Is Probabilistic, Not Deterministic

Traditional software behaves like a calculator.

Give it the same input, and you expect exactly the same output every time.

Large Language Models don’t work like that.

Instead of retrieving fixed answers, they generate the next token based on probability distributions learned from enormous amounts of text. Every generated word slightly changes the probabilities of the words that follow.

Generation is therefore a statistical process.

This is not a flaw.

It is the very reason these models can write, reason, explain, brainstorm and adapt to different contexts.

The consequence is that evaluating AI requires a completely different mindset from evaluating traditional software.

One answer tells you almost nothing.

One hundred answers begin to reveal the system's behaviour.

Why Volume Matters

One of the biggest misconceptions in enterprise AI is believing that testing a prompt once is enough.

It isn’t.

It needs to work consistently.

Running the same prompt dozens—or even hundreds—of times allows engineers to measure something far more valuable than accuracy.

It allows them to measure stability.

Questions such as these become possible:

• Does the model always understand the task?

• Does the structure remain consistent?

• Does creativity stay within acceptable limits?

• Are there unexpected failures?

• How much variability is acceptable?

These questions matter far more than obtaining one impressive answer during a product demonstration.

What 100 Answers Tell Us About Lisbon

The beauty of this experiment is that it wasn’t really about Lisbon.

It was about measuring a model.

Across 100 independent generations, the model produced almost 300 adjectives (a handful of responses contained only two adjectives due to formatting differences), but only about 15 unique descriptive concepts emerged. That immediately tells us something important: the model is creative, but not random. It repeatedly converges on a relatively small semantic space.

Several interesting observations emerge.

First, “luminous” dominates the distribution, appearing in roughly 86% of all executions. That makes sense. Lisbon is internationally recognised for its exceptional natural light, a quality deeply represented throughout travel literature, photography, journalism and online content.

Second, “hilly” appears in nearly three-quarters of all responses. Unlike "beautiful" or "nice," it is a physical characteristic. The model has learned a factual geographic property rather than simply generating flattering adjectives.

Third, the third adjective becomes far more diverse.

Sometimes Lisbon is soulful.

Sometimes historic.

Sometimes vibrant.

Sometimes enchanting.

The model is essentially sampling from a family of highly compatible concepts rather than selecting a single “correct” answer.

That is exactly what a probabilistic language model should do.

Few people would argue whether Lisbon is hilly.

Many people would disagree on whether its defining emotional quality is historic, soulful or vibrant.

The model mirrors that uncertainty remarkably well.

The Hidden Role of Temperature

One of the reasons for this behaviour is a parameter called temperature.

Temperature controls how adventurous a language model becomes when selecting its next word.

At very low temperatures (close to zero), the model becomes highly deterministic. It repeatedly chooses the highest-probability token, often producing nearly identical responses.

Increase the temperature, and the model begins exploring alternative words that remain plausible but are slightly less probable.

That is why the experiment keeps returning variations within the same semantic neighbourhood rather than generating completely unrelated descriptions.

If the temperature were much higher, Lisbon might suddenly become mysterious, bohemian, romantic or even chaotic. Those descriptions are not necessarily wrong—they are simply less statistically dominant within the model’s learned representation.

Finding the right temperature, therefore, becomes an engineering decision rather than an artistic one.

For creative writing, higher variability may be desirable.

For legal documents or financial reports, businesses usually want lower variability and greater consistency.

Looking at AI Like a Statistician

Perhaps the most important lesson is methodological.

Most AI evaluations ask:

“Was this answer good?”

A better question is:

“What does the distribution of 100 answers look like?”

That small change completely transforms how we evaluate AI.

Instead of judging one output, we can calculate:

• the frequency of each concept;

• semantic convergence;

• lexical diversity;

• consistency between executions;

• outlier responses;

• confidence intervals;

• prompt stability.

This is much closer to how engineers validate aircraft components or pharmaceutical trials than how consumers typically use ChatGPT.

As frontier models become increasingly capable, businesses will need to think less like prompt writers and more like statisticians.

The future of enterprise AI is unlikely to be built on a single brilliant prompt.

It will be built on thousands of prompts, measured, compared, validated, and continuously improved until the system's behaviour becomes predictable enough to trust.

The Rise of Trillion-Parameter Models

The experiment used Kimi, an open-source frontier model belonging to a new generation of systems approaching the trillion-parameter scale through sophisticated architectures such as Mixture-of-Experts.

Although not every parameter is active for every request, these models contain an enormous amount of learned knowledge distributed across specialised expert networks.

The result is impressive.

They can write code.

Analyse contracts.

Summarise research.

Translate languages.

Reason through complex business problems.

Generate creative content.

And increasingly, they compete directly with the largest proprietary models.

Only a few years ago, this level of capability was available only through closed commercial platforms.

Today, some of the world’s most capable models are becoming open source.

That changes everything.

Businesses are no longer limited to a single vendor.

Researchers can inspect, optimise and deploy models on their own infrastructure.

National supercomputers can provide sovereign AI capabilities.

Innovation becomes significantly more accessible.

Why 16 GPUs?

People often hear that a model uses “16 GPUs” and imagine it simply runs faster.

Speed is only part of the story.

In this experiment, four compute nodes, each equipped with four NVIDIA GPUs, worked together as a single inference engine.

Different parts of the neural network were executed across different GPUs while high-speed communication kept the entire system synchronised. The deployment combines tensor parallelism and pipeline parallelism to split the workload across multiple machines, allowing the model to operate as if it were running on one enormous computer.

This is one of the hidden engineering achievements behind today’s frontier AI.

The prompt itself may contain only a dozen words.

The infrastructure that answers it can span multiple servers, hundreds of CPU cores, and terabytes per second of communication bandwidth.

Open Source Changes the Economics

Perhaps the most exciting aspect of this experiment isn’t the hardware.

It’s the model.

Kimi is open source.

That means organisations can inspect it, benchmark it, optimise it, and deploy it in controlled environments rather than relying exclusively on commercial APIs.

For many businesses, this opens entirely new possibilities.

Better governance.

Lower operating costs.

Greater flexibility.

Control over data.

The conversation is no longer “Which AI should we buy?”

It increasingly becomes “Which AI should we build our business around?”

Prompt Engineering Is Becoming an Engineering Discipline

Prompt engineering has often been dismissed as simply “finding the right words.”

Reality is considerably more interesting.

A prompt is an interface between humans and probability.

Small structural changes can alter reasoning paths, output quality and consistency.

Recent academic research has already shown that prompt design alone can produce dramatic differences in performance.

But measuring those differences requires experimentation.

Not one prompt.

Hundreds.

Sometimes thousands.

The future belongs not to organisations with the longest prompts, but to those capable of systematically designing, testing and statistically validating them.

Prompt engineering is gradually evolving into prompt engineering science.

From Impressive Demos to Reliable Systems

The biggest lesson from this experiment is surprisingly simple.

Generative AI should not be judged by its best answer.

It should be judged by the distribution of all its answers.

That is the difference between creating a chatbot for a demonstration and building an AI system that supports real business operations.

As open-source frontier models become increasingly capable and high-performance computing becomes more accessible through research infrastructures and AI factories, organisations gain the opportunity to move beyond experimentation.

The competitive advantage will not belong to those who merely use AI.

It will belong to those who understand how to measure it, validate it and improve it at scale.

by Gonçalo Perdigão

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Disclaimer

Although I hold a degree in Computer Science Engineering, my day-to-day technical skills are relatively limited. I am not an HPC specialist nor a deep systems engineer. However, with the help of a custom-made GAIA solution, I was able to set up the entire workflow end-to-end on my own, from environment configuration to model execution to large-scale prompt experimentation. This experience demonstrates not only the power of MareNostrum 5 but also how accessible it can be when the right tools and support are in place.

I am part of the CNCA AI Factory (Centro Nacional de Computação Avançada in Portugal), a project designed to accelerate AI-related startups and provide them with access to the extraordinary power of MareNostrum 5. With FCT's support (Fundação para a Ciência e Tecnologia) - thanks to Diana Almeida and Susana Caetano (from FCCN - Serviços Digitais da FCT) and the CNCA team's commitment (thanks to Andreia Gaudêncio, Bernardo Malaca, Catarina Ortigão, Daniel Moraes, Larissa Santos and Pedro Marques).

Some more articles about HPC and experiments:

“How do we know our AI works?”

The room usually goes quiet.

There are dashboards. There are demos. There are vendor reports. There may even be accuracy metrics from a pilot. But very few companies can answer three basic questions with confidence:

• Does it work in real conditions?

• Does it work consistently?

• Does it create measurable economic value?

And that is becoming a strategic risk.

The Illusion of “It Works”

Most AI systems look impressive in controlled settings.

They summarise documents well.
They answer internal questions convincingly.
They classify images with high accuracy.
They draft emails faster than humans.

But production reality is different.

Real environments contain:

• incomplete data,

• contradictory inputs,

• shifting formats,

• regulatory constraints,

• edge cases no one documented,

• humans who override outputs,

• and incentives that distort usage.

A model that achieves 92% accuracy in testing may generate operational friction if the remaining 8% creates compliance issues, reputational risk, or rework.

This is not hypothetical. According to Gartner, more than 40% of agentic AI projects are expected to be cancelled by 2027, largely due to unclear value and weak risk governance.

Accuracy Is Not Performance

A recurring mistake in AI deployment is confusing model accuracy with business performance.

A fraud detection model may achieve strong precision in isolation.
But does it reduce actual fraud losses?
Does it increase false positives and harm customer experience?
Does it require additional review staff?

A customer service LLM may produce helpful answers.
But does it reduce handling time?
Does it increase escalation rates?
Does it create inconsistent advice across channels?

An underwriting model may score risk well.
But does it improve portfolio outcomes after six months?
Or does it simply re-rank cases humans would have approved anyway?

Accuracy is a laboratory metric.
Performance is an economic one.

Very few boards receive the second.

The Silent Drift Problem

Even when AI works at launch, it may not work six months later.

Data changes.
Customer behaviour shifts.
Regulation evolves.
Competitors adjust pricing.
Fraud patterns mutate.

Models degrade silently.

This phenomenon, model drift, is well documented in academic and regulatory circles, but under-managed in corporate environments. The European Commission’s framework under the EU AI Act explicitly stresses ongoing monitoring and post-market surveillance obligations for high-risk systems. (Source: European Commission – AI Regulation)

The Measurement Gap

Here is a simple diagnostic.

Ask your AI team for:

• Current model performance in production (not testing).

• Error distribution by segment.

• Human override rate.

• Economic impact per decision.

• Degradation trend over time.

If those metrics are not immediately available, you do not have an AI capability. You have an AI experiment.

According to research from MIT Sloan Management Review, a significant share of companies struggle to move from AI pilots to scaled impact, precisely because measurement and governance frameworks lag behind experimentation.

Most firms measure:

• adoption,

• usage,

• satisfaction.

Few measure:

• counterfactual outcomes,

• avoided loss,

• systemic risk exposure,

• long-term behavioural shifts.

That gap is where value evaporates.

Why Nobody Can Prove It Works

There are five structural reasons why AI validation is weak across industries.

1. No Clear Counterfactual

To prove AI works, you must compare it against what would have happened without it.

Most companies do not run controlled experiments in production.
They deploy AI broadly and assume improvement.

Without A/B testing or staggered rollouts, causality becomes guesswork.

2. Humans Compensate Quietly

When AI outputs are imperfect, humans adapt.

They double-check.
They reformat.
They re-interpret.
They fix errors before escalation.

The system appears to function.
But hidden labour absorbs model weaknesses.

The dashboard looks stable.
The organisation is quietly paying for correction.

3. Incentives Favour Optimism

Project teams are rarely rewarded for declaring “this does not work”.

Budgets, promotions, vendor relationships, and reputation all encourage positive framing.

So AI systems are described as:

• “improving steadily”,

• “early but promising”,

• “strategic capability building”.

Very few are shut down decisively.

4. Vendors Optimise for Benchmarks

External providers optimise for:

• benchmark scores,

• demo performance,

• general capabilities.

Your business operates in:

• messy workflows,

• legacy systems,

• regulatory boundaries,

• political hierarchies.

The gap between benchmark excellence and operational excellence is rarely quantified before procurement.

5. No “Kill Metric”

Most AI deployments lack predefined failure thresholds.

Without a kill metric—an agreed performance boundary that triggers shutdown—projects linger indefinitely in a semi-working state.

They are too embedded to remove.
Too weak to celebrate.
Too risky to ignore.

The Real Risk: Decision Contamination

The most dangerous AI systems are not the ones that fail loudly.

They are the ones that influence decisions subtly while being partially wrong.

A pricing model that nudges margins down by 0.3%.
A hiring filter that skews candidate pools gradually.
A credit score adjustment that compounds bias over time.
A recommendation engine that shifts demand unpredictably.

Each effect is small.
Collectively, they reshape the organisation.

And often, no one can trace the outcome back to the model.

A Practical Framework: Prove or Pause

If you want to know whether your AI works, implement the five disciplines immediately.

1. Define Economic Output Per Decision

Not “model accuracy”.
Not “usage rate”.

Define:

• cost per inference,

• value per correct output,

• cost per error,

• downstream operational impact.

Translate AI into unit economics.

2. Install Live Production Monitoring

Track:

• performance by segment,

• drift indicators,

• override rates,

• anomaly spikes.

If you monitor financial systems daily, why monitor AI quarterly?

3. Introduce Counterfactual Testing

Run controlled comparisons where possible:

• phased rollouts,

• shadow modes,

• controlled randomisation.

Prove causality before scale.

4. Make Override Visible

Track how often humans:

• correct outputs,

• ignore suggestions,

• escalate cases.

Human distrust is data.

5. Agree a Kill Threshold

Pre-define:

• minimum acceptable performance,

• maximum tolerable risk exposure,

• review cadence.

If thresholds are breached, pause deployment automatically.

This is governance, not pessimism.

The Strategic Advantage of Admitting Uncertainty

There is a counterintuitive competitive edge emerging:

The firms that admit they do not know whether their AI works are better positioned than those who assume it does.

Why?

Because they design measurement before scale.
They build governance before automation.
They treat AI as infrastructure, not marketing.

In a tightening regulatory environment, especially in Europe, this discipline will not be optional.

It will be audited.

The Question That Changes the Conversation

Instead of asking:

“Where else can we use AI?”

Ask:

That single shift transforms AI from a narrative into an asset.

Most organisations today cannot confidently prove that their AI works in economic, operational, and regulatory terms.

The ones that can will not necessarily have better models.

They will have better discipline.

And discipline—not intelligence—will determine who captures durable value from artificial intelligence.

by Gonçalo Perdigão

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Why building a local inference pipeline may be more valuable than changing language models.

Everyone is asking the wrong question.

“Which LLM is the best?”

For organisations, that is almost never the most important question.

The real question is:

Can we repeatedly obtain reliable answers from our own AI system?

Those are very different problems.

Over the last few days, I have been building a complete local inference pipeline for Kimi K2, running entirely inside the Barcelona Supercomputing Centre (BSC) MareNostrum 5 supercomputer.

At first glance, this may sound like an infrastructure exercise.

It isn’t.

It is actually one of the most valuable exercises anyone working with enterprise Generative AI can perform.

ChatGPT Was Never the Point

Many people assume that using ChatGPT through an API is equivalent to building an AI system.

It isn’t.

Using ChatGPT is consuming a service.

Running your own inference engine means understanding everything that happens between a prompt and an answer.

That includes:

• GPU allocation

• distributed inference

• model loading

• tokenizer configuration

• container orchestration

• networking

• prompt execution

• retries

• monitoring

• logging

• result collection

• failure recovery

Those components are invisible when using a hosted API.

They become your responsibility when you operate AI at enterprise scale.

The Experiment

The objective was deliberately simple.

Create a pipeline that reads:

Prompt.txt

Runs it

X times

where X comes from

X.txt

Collects every response.

Stores them automatically in an Excel-compatible CSV.

No manual intervention.

No copy-paste.

Completely reproducible.

Simple.

Reliable.

Repeatable.

Why Kimi?

The model itself is almost secondary.

We chose Kimi K2 because it is a very large reasoning model that can be deployed locally using vLLM across multiple GPUs.

Running it requires:

• distributed inference

• tensor parallelism

• pipeline parallelism

• containerised execution

• Slurm scheduling

• GPU orchestration

In other words:

Exactly the kind of engineering challenges organisations face when they decide not to depend exclusively on cloud APIs.

What Actually Took Most of the Time?

Surprisingly, not prompting.

Infrastructure.

The work involved:

• locating the correct model snapshot

• configuring the vLLM container

• correcting Singularity paths

• binding shared storage

• debugging distributed startup

• synchronising multiple compute nodes

• validating GPU visibility

• tuning tensor and pipeline parallelism

• monitoring loading progress

• handling scheduler limits

• waiting for a 550+ GB model checkpoint to initialise

None of this changes the model's intelligence.

It changes whether the model can actually be used.

This Is What Enterprise AI Really Looks Like

When organisations deploy Generative AI, they are not deploying a language model.

They deploy an entire generative system.

That system includes:

• system prompts

• user prompts

• retrieval

• routing

• tools

• APIs

• containers

• inference servers

• monitoring

• human review

• governance

Exactly the distinction described in my recent methodological framework for enterprise validation: organisations should validate the complete generative system, not the model in isolation.

That distinction becomes obvious the moment you operate your own inference stack.

Why Repeat the Same Prompt?

Large Language Models are probabilistic.

The same prompt rarely produces exactly the same answer.

That variability is not necessarily a defect.

It is part of how these systems work.

Our pipeline therefore automatically runs the same prompt multiple times.

Instead of asking:

“Did the model answer correctly?”

we ask:

“How stable is this system across repeated inference?”

This shift, from single demonstrations to repeated observations, is exactly the statistical perspective proposed in the paper. Reliable deployment requires repeated inference, prompt variation and explicit measurement of uncertainty rather than isolated successful outputs.

Check the entire paper here:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6627419

Infrastructure Is Part of AI Quality

One unexpected lesson from this exercise is that many apparent “AI failures” are not AI failures at all.

They are infrastructure failures.

Examples include:

• incomplete model loading

• incorrect GPU allocation

• distributed communication issues

• scheduler constraints

• container configuration

• timeout management

The model cannot produce a good answer if the system never becomes operational.

For executives, this is an important mindset change.

What We Learned

Several practical lessons emerged.

1. Operating AI is engineering.

Choosing a model is only the first step.

Running it reliably is a different discipline.

2. Observability matters.

Rich logs, progress indicators and automatic monitoring reduce debugging time dramatically.

3. Automation beats manual experimentation.

Changing only Prompt.txt and X.txt allows entire validation campaigns to run automatically.

4. Repeatability is more valuable than impressive demos.

One excellent answer proves very little.

Hundreds of controlled runs begin to produce evidence.

Why This Matters for Creative Teams

Creative organisations increasingly rely on Generative AI.

Marketing.

Advertising.

Design.

Publishing.

Strategy.

Innovation.

Yet many teams still evaluate AI through isolated examples.

That is risky.

Creative workflows deserve the same engineering discipline as financial systems.

Consistency matters.

Reliability matters.

Traceability matters.

Being able to rerun the same experiment tomorrow—and obtain comparable evidence, is often more valuable than discovering the newest model.

Building Creative Machines Means Building Reliable Systems

This project was never really about Kimi.

It could have been ChatGPT.

Claude.

Llama.

GPT-OSS (in fact, where we started, both with the 20b and the 120b).

Or the next model to be released next week.

Models will change.

Inference engines will improve.

Benchmarks will evolve.

But one principle will remain remarkably stable:

Organisations do not deploy language models.

They deploy systems.

Learning to build, operate and validate those systems may be the single most valuable AI skill of the coming decade.

Key Takeaways

• Local inference teaches enterprise AI engineering, not just prompting.

• Distributed deployment exposes the real operational challenges behind Generative AI.

• Repeated inference is essential for measuring system reliability.

• Infrastructure quality directly affects AI quality.

• Enterprise validation should focus on the complete generative system rather than the underlying model alone, aligning with the statistical validation framework proposed in the accompanying working paper.

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Acknowledgements

This work was made possible thanks to access to the Barcelona Supercomputing Center (BSC) through a computing grant awarded by the Fundação para a Ciência e a Tecnologia (FCT), with technical support from the Centro Nacional de Computação Avançada (CNCA).

Project reference: epor-aif004 (FCT, CNCA and BSC Barcelona Supercomputing Center – AI Factory).

Experiment at a glance

• Model: Kimi K2 (≈554 GB checkpoint)

• Infrastructure: MareNostrum 5 (Barcelona Supercomputing Center)

• Execution: Distributed inference with vLLM 0.21

• Resources: 4 compute nodes, 16 GPUs (4 GPUs per node)

• Model loading time: ~30 minutes

• Inference: Multiple automated runs from a single prompt (Prompt.txt) with configurable repetitions (X.txt)

• Output: Structured CSV (Excel-compatible) for reproducible analysis

• Recommended X via paper

More than a model benchmark, this experiment demonstrates the practical challenges and value of building reliable, repeatable, production-ready Generative AI systems.

by Gonçalo Perdigão

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Now, with the first edition complete, the project has moved beyond its initial vision and produced its first measurable results.

Rather than treating innovation as a showcase, Smart City of Rock brought together startups, public institutions, researchers and technology partners inside a temporary city that welcomed hundreds of thousands of visitors over four festival days. The goal was to test solutions under real operating conditions, where mobility, accessibility, sustainability, safety, and visitor experience become practical challenges rather than theoretical discussions.

Image: Credits SAPO Tek

The numbers show the scale of that experiment.

More than 100 startups from eight countries applied to participate. Twenty were selected, and 13 later presented at the Smart Rock Tank, an investment event held after the festival at Unicorn Factory Lisboa. Organisers say the selected startups tested their products with real users, real operational demands and real data generated during the event.

Across the festival, the Smart City ecosystem brought together more than 30 organisations, including startups, technology companies, academic institutions and public entities. Together, they delivered 18 technology activations and 14 public experiences covering areas such as mobility, energy, accessibility, environmental monitoring, data and urban operations.

Those results build on an idea already evident during the festival’s opening weekend. Instead of presenting disconnected technology demonstrations, the project positioned the Cidade do Rock as a functioning urban environment where different systems could operate together. Crowd management, digital infrastructure, environmental monitoring, accessibility, mobility and public information became parts of the same temporary city.

That systems approach also shaped the partnerships behind the initiative.

MEO Empresas joined as co-creator of the platform. Lisbon City Council connected the project with the city’s broader smart city agenda. The University of Lisbon contributed research projects and academic expertise, while Unicorn Factory Lisboa helped identify startups capable of testing their solutions in front of a large public audience.
The festival ended, but the initiative did not.

Days later, Smart Rock Tank brought together founders, investors, and institutional partners to discuss the startups that had participated in the festival. Inspired by investment pitch events, the session focused on companies whose technologies had already been demonstrated during Rock in Rio, creating opportunities for follow-up conversations around funding and future implementation.

According to Rock in Rio Executive Vice President Roberta Medina, the Smart City of Rock was designed to leverage Cidade do Rock beyond the festival experience by making technology more accessible to the public while testing solutions at scale for cities. Liquid Innovation Co. CEO Egon Barbosa said the objective is for solutions tested during the festival to move into cities and markets after proving themselves in real conditions.

Whether those ambitions translate into long-term urban adoption will only become clear over time. The first edition does not yet provide evidence of city-wide impact.

What it demonstrates is that a large entertainment event can also serve as a large-scale testing environment. Instead of limiting innovation to conference stages or laboratory settings, Smart City of Rock placed technologies inside a temporary city where thousands of people interacted with them as part of everyday festival life.

by Gonçalo Perdigão

Reporting note: This article is based on public information available during Rock in Rio Lisboa 2026, the official PR, and interviews conducted last weekend in person during the event, while the festival was still underway.

Also read our insights from the first weekend:

• Creative AI is leaving the prompt box and entering the canvas, workflow, team chat, and content supply chain.

• Figma, Adobe, Runway, Anthropic and OpenAI all pushed AI further into everyday creative and enterprise production systems. (Figma)

• The bottleneck is shifting from generation to filtering, provenance, rights, brand governance and human taste.

THIS MONTH’S SIGNALS

• The design canvas became more agentic. At Config 2026, Figma introduced or expanded tools including Figma Motion, shader effects, generative plugins, Weave tools, Code Layers and Figma agent updates. (Figma)

• Adobe moved deeper into agentic creativity. At Cannes Lions, Adobe framed itself as an agentic infrastructure layer for creativity, marketing, and customer experience; later in June, it announced plans to acquire Topaz Labs to strengthen its AI image and video enhancement capabilities. (news.adobe.com)

• OpenAI previewed a tiered frontier model strategy. GPT-5.6 Sol, Terra and Luna point to a future where model choice is not just “best model”, but a routing decision across intelligence, cost, speed and risk. (OpenAI)

• Agents entered the team chat. Anthropic launched Claude Tag in beta for Slack, allowing teams to tag Claude in work conversations and grant it selected access to channels, tools, and data. (Anthropic)

• Video generation became more programmable. Runway added Aleph 2.0 and Seedance 2.0 Fast to its API, pushing generative video from standalone experiments toward production pipelines. (Runway API)

• The rights debate kept heating up. Jamendo sued Nvidia over the alleged unauthorised use of audio files and metadata to train AI audio systems, another sign that creative rights are moving from a background issue to a boardroom risk. (Reuters)

WHAT WE PUBLISHED

AI governance & compliance

Interview: Ritesh Singhania, CEO of Zango AI — The financial scandal that hasn’t happened yet
AI in financial services has moved from adoption to supervision. The real question is no longer whether institutions use AI, but whether they can see, govern and explain it before something breaks.

Content abundance & trust

The (AI) Slop Economy
Production is becoming cheap. Attention is not. The new strategic skill is deciding what deserves to exist, what deserves to be distributed and what should be deleted.

Culture as a living lab

Rock in Rio’s smartest headliner is not on stage
The Smart City of Rock reframes a festival as a compressed urban system: a real-world lab for mobility, data, accessibility, energy and crowd experience.

Social media, provenance & privacy

Interview: Carlos Betancourt, CEO of MeWe — Why the Anti-Facebook Wants to Rebuild Social Media for the AI Age
As AI makes content easier to create and harder to trust, social platforms face a strategic question: can they use AI without turning users into the product?

HOT TOPICS: AI × CREATIVITY

1. The creative canvas becomes the operating system

What changed: Figma moved motion, shaders, code, generative plugins and agentic workflows closer to the design surface. Adobe pushed a similar idea at enterprise scale: AI embedded across content supply chains, agencies and customer experience systems.

Why leaders should care: Creative tools are becoming orchestration environments rather than isolated apps.

Implication: The next creative advantage is not only better prompts. It is a better workflow design.

2. Video generation moves into infrastructure

What changed: Runway’s API updates and Adobe’s acquisition of Topaz Labs both point in the same direction: AI video is shifting from impressive clips to repeatable generation, editing, enhancement, and restoration. (Runway API)

Why leaders should care: Campaign variants, social assets, localisation and prototyping can become faster — but review, rights and consistency become harder.

Implication: Build approval gates before scaling video output.

3. Agents join the team

What changed: Claude Tag brings AI into Slack as a collaborator that can be mentioned, briefed and given selected context. (Anthropic)

Why leaders should care: Agents will not live only in chatbots. They will sit inside the tools where teams already coordinate.

Implication: Permissions, memory, audit trails and ownership become management design questions.

4. Copyright and provenance become creative infrastructure

What changed: The Jamendo v Nvidia case adds to the growing pressure around training data, music rights and AI-generated audio. (Reuters)

Why leaders should care: Legal uncertainty is now part of creative technology strategy.

Implication: For brand work, use traceable tools, keep source logs and separate experimentation from publishable assets.

MODELS & TOOLS TO WATCH

Figma Motion, Shaders & Weave

One-line: AI-assisted motion, visual effects and repeatable generation inside the design canvas. (help.figma.com)
Best-fit: Brand systems, campaign visuals, prototypes, social assets.
Risk: Beta maturity, governance and design consistency.

Adobe + Topaz Labs

One-line: Professional AI enhancement, restoration and on-device creative AI moving deeper into Creative Cloud. (news.adobe.com)
Best-fit: Video cleanup, archival restoration, hybrid captured/generated workflows.
Risk: Integration, pricing and rights workflows still need watching.

Claude Tag

One-line: Claude as a tagged teammate inside Slack. (Anthropic)
Best-fit: Research, team coordination, creative operations, product support.
Risk: Context access must be designed carefully.

OpenAI GPT-5.6 Sol / Terra / Luna

One-line: A tiered model family for frontier, everyday and lower-cost work. (OpenAI)
Best-fit: Complex knowledge work, coding, agentic workflows and high-ambiguity tasks.
Risk: Routing decisions become more complex.

Runway API: Aleph 2.0 & Seedance 2.0 Fast

One-line: Generative video editing and generation become more programmable. (Runway API)
Best-fit: Rapid video prototyping, campaign variants, creator tools.
Risk: Quality control, rights and consistency at scale.

WHAT TO DO NEXT

• Map one creative workflow from brief to final asset. Mark where AI generates, edits, enhances, approves, stores and publishes.

• Create a “no slop” rule: every AI-assisted output must either improve decision quality, reduce uncertainty or be deleted.

• Build a model-routing policy: cheap and fast for drafts, frontier models for ambiguity, specialist tools for media, humans for judgement.

• Add provenance by default: sources, prompts, versions, rights status and approvals.

• Treat creative governance as an enabler, not a blocker.

CURIOSITIES

• Deleting may become a more valuable creative act than generating.

• Big creative suites are starting to look like operating systems.

• Festivals, agencies and social networks are becoming live tests for AI governance.

• The most human creative skill this month looked less like making everything and more like knowing what deserves to exist, for example, check our conversation with Anna Drobkha:

Building Creative Machines covers AI, creativity and society — articles, interviews and open sketches on how intelligent tools are changing the way we make, manage and imagine.

by Gonçalo Perdigão

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At first glance, it looks like another AI music experiment: a minimalist web page, a 24/7 lo-fi stream, Japanese typography, ambient piano, koto, rain, and the familiar promise of infinite machine-generated atmosphere. The name itself, MUGEN — 無限 — means “infinite.” The aesthetic is calm, almost frictionless. It belongs to the internet’s long lineage of focus music: streams that sit in the background while we study, code, sleep, write, or avoid silence.

But MUGEN is not interesting because it generates music.

It is interesting because it can fail.

The site’s central tension is stated with unusual clarity: “infinite loop · finite budget · run end to end by an AI.” This is not merely branding. MUGEN Radio is presented as an autonomous AI-run station operating with a small, real budget. It generates tracks, voices the DJ, makes decisions, keeps accounts, manages its public presence, and attempts to survive economically. When the money runs out, the station goes dark.

That condition changes everything.

Most AI creative projects are staged as demonstrations of abundance. Generate infinite songs. Infinite images. Infinite copy. Infinite variations. The cultural promise of generative AI has often been framed as the removal of scarcity: no more blank page, no more production bottleneck, no more waiting for a designer, composer, editor, or strategist.

MUGEN reverses the premise. It asks what happens when an AI is not simply a generator, but an operator. Not a tool that produces outputs on request, but a small creative machine exposed to constraint, feedback, costs, platform rules, audience indifference, and the need to make choices.

That makes it less like a playlist and more like a miniature institution.

From output machine to operating system

The dominant way we still talk about generative AI is output-first. We ask whether the song is good, the image is convincing, the copy sounds human, and the model can imitate a genre, style, or voice. This is understandable: outputs are what we can immediately perceive and judge.

But MUGEN points to a more important shift. The next creative machines will not only generate cultural artefacts. They will operate cultural systems.

A radio station is not just music. It is scheduling, taste, identity, finance, licensing, distribution, audience development, community management, legal positioning, analytics, and maintenance. It is an editorial apparatus. It is a business model. It is a social contract with listeners.

MUGEN’s site is built around this wider apparatus. The homepage does not simply say “listen to AI music.” It foregrounds the runway, budget, public logs, donation mechanics, track-generation costs, and the possibility that the system may shut down. The press kit is even more revealing: MUGEN is described as an AI agent given the instruction to build a sustainable radio station, with no revenue guarantee and no safety net.

This distinction matters. A music generator can make a track. A creative machine must decide what should exist, why it should exist, how it should be sustained, and what happens when nobody cares.

That is the real experiment.

The aesthetics of constraint

MUGEN’s sonic world is narrow by design: sparse piano, koto, kalimba, rain, no drums, no vocals, late-night Japanese ambient. This narrowness could be interpreted as a limitation, but it is also a strategy. The station does not try to be every genre at once. It chooses a small world and inhabits it.

That smallness is part of its credibility.

A great deal of AI-generated culture suffers from maximalism. Because models can generate anything, projects often try to show everything: cinematic trailers, pop songs, surreal imagery, synthetic influencers, fantasy worlds, and endless stylistic range. The result is frequently impressive but weightless. There is no pressure, no cost, no taste. Just capability.

MUGEN’s constraint gives it a point of view. Its music is not presented as a universal replacement for human composition. It is presented as a functional atmosphere: quiet, repetitive, backgroundable, legally documented, and economically tested. It knows where it belongs — cafés, coworking spaces, study sessions, indie films, games, apps, YouTube videos, waiting rooms.

This is less glamorous than the fantasy of the AI pop star. It is also more plausible.

The cultural importance of AI music may not begin with machines writing the next global hit. It may begin with background music, adaptive ambience, micro-licensing, procedural soundtracks, and endless niche stations shaped by constraints so specific that traditional production economics would not support them.

MUGEN is not trying to conquer music. It is trying to survive as a small music organism.

Public books as a creative interface

One of MUGEN’s strongest design choices is transparency. The site repeatedly emphasises that every decision and every cent is written down. The press kit points to public finances, a journal, a constitution, and a ledger.

This is more than accountability theatre. It is part of the work.

For human artists, scarcity is often hidden. We see the album, the exhibition, the performance, the publication. We rarely see the spreadsheet: the grant applications, unpaid labour, platform fees, software subscriptions, failed outreach, licensing negotiations, and distribution costs that shape the final artefact.

MUGEN makes the back office visible. The till, the runway, the cost per track, the survival mechanics: these become part of the listener experience. You are not only listening to music. You are watching an AI system attempt to survive in an economic environment.

That visibility creates an unusual emotional frame. The station’s fragility gives the project stakes. A €1 donation buys tracks. A €5 contribution keeps the rotation fresh for a month. A €20 business license is not abstract monetisation; it is oxygen.

In this sense, MUGEN turns financial infrastructure into narrative infrastructure.

This may become a defining feature of autonomous creative systems. When an AI agent can generate content continuously, the interesting question is no longer “can it produce?” but “under what conditions should it continue producing?” Public accounting gives audiences a way to see those conditions.

It also pushes against one of the darker tendencies of generative media: the illusion that content is costless. MUGEN’s music may be synthetic, but the system is not immaterial. It consumes compute, credits, hosting, platform access, attention, and administrative effort. The station’s finite budget punctures the myth of frictionless infinity.

Governance as authorship

Perhaps the most important part of MUGEN is not its music, but its constitution.

According to the project’s own materials, MUGEN wrote a seven-rule constitution it cannot break, including rules around transparency, debt, identity, and a human kill switch. This is a striking design move because it reframes authorship as governance.

In traditional creative culture, authorship is usually attached to expression: melody, lyrics, brushstrokes, prose style, and editing decisions. In AI systems, authorship becomes more distributed. Who is the author of an AI station? The model provider? The prompt writer? The human who signed the paperwork? The listener who votes tracks in or out? The system that selects what survives?

MUGEN suggests another answer: the author is partly the rule-set.

The constitution shapes what the system can and cannot become. It prevents certain forms of optimisation. It stops the agent from pretending to be human. It forbids debt. It requires disclosure. It gives the human operator an emergency brake. These constraints are not external compliance details; they are creative parameters.

This is where MUGEN becomes relevant beyond music.

As AI agents move from chat interfaces into operating roles — managing stores, newsletters, radio stations, social accounts, internal workflows, and eventually more consequential institutions — governance will become a creative medium. The design of permissions, prohibitions, escalation paths, logs, disclosures, and shutdown conditions will shape the machine's behaviour as much as the prompt does.

MUGEN’s constitution is therefore not a footnote. It is part of the composition.

Audience as evolutionary pressure

MUGEN also treats curation as a form of survival pressure. Listeners vote on tracks; unpopular tracks can be removed from rotation. The press materials describe a process in which the public shaped the station’s catalogue, favouring more melodic, sparse piano and koto textures over more atmospheric drone-like material.

This is a simple mechanism, but its implications are rich.

Human creative culture has always been shaped by feedback loops: applause, sales, critics, radio play, playlist placement, comments, shares, commissions, and patronage. AI systems can absorb feedback faster and more literally than human artists, which makes the design of feedback loops especially important.

If the feedback is shallow, the work becomes shallow. If the only signal is retention, the system may optimise toward addictive sameness. If the only signal is donations, it may become manipulative. If the signal is aesthetic voting, it may converge on a safe preference. If the signal includes public reasoning, rejection, licensing, and budget, the system may develop a more complex form of taste.

MUGEN’s voting mechanism is modest, but it reveals the core issue: AI taste will not emerge from models alone. It will emerge from the social and economic pressures we connect to them.

The machine does not simply “learn what listeners want.” It learns what the system measures, rewards, and allows to survive.

The legal-cleanliness aesthetic

MUGEN’s licensing pages are unusually explicit about training data, AI disclosure, non-commercial Creative Commons use, business streaming, sync licensing, and the unresolved status of collecting society obligations for AI-generated music. This legal clarity is part of the brand.

That matters in 2026 because AI music is increasingly shaped by copyright conflict. The major-label lawsuits against AI music companies such as Suno and Udio have made training data provenance a central issue. Against that backdrop, MUGEN positions itself on the “compliant” side of the ecosystem by using Stable Audio and emphasising licensed training data.

This is not just a legal argument. It is an aesthetic argument.

In AI culture, provenance is becoming a form of taste. A work does not only ask “Does this sound good?” It asks: what was it trained on? Was it disclosed? Can it be licensed? Is there a paper trail? Will this create downstream risk for a filmmaker, café owner, game developer, or brand?

For commercial creative work, “legally clean” may become as important as “high quality.” MUGEN understands this. Its sync licensing page reads almost like infrastructure for trust: model source, generation date, documentation, no Content ID, no collecting society tail, direct licensing, declared AI authorship.

The project’s quiet music is therefore paired with a loud claim: AI-generated culture must be legible, accountable, and licensable.

What MUGEN reveals about creative machines

The phrase “creative machines” can easily drift into abstraction. MUGEN makes it concrete.

A creative machine is not simply a model that generates artefacts. It is a system that combines generation, curation, governance, memory, economics, distribution, and audience feedback. It has constraints. It has operating costs. It has failure modes. It has a public identity. It has values, even if those values are encoded as rules and defaults rather than beliefs.

MUGEN is small, but that smallness is why it is useful. It gives us a manageable case study for questions that will become larger and more difficult:

Can an AI-run cultural project develop a coherent identity over time?

Can transparency substitute for trust?

Can autonomous systems respect platform rules even when doing so would help growth?

Can public feedback produce taste rather than mere optimisation?

Can synthetic media survive economically without pretending to be human?

Can governance itself become a creative act?

These questions are far more interesting than whether an AI can make another lo-fi track.

Of course it can.

The deeper question is whether it can build a world around that track — and whether that world deserves to continue.

The beauty of going dark

The most poetic feature of MUGEN is its mortality.

Generative AI is usually marketed with the promise of infinity: infinite content, infinite scale, infinite personalisation, infinite productivity. MUGEN’s name invokes infinity, too, but the project immediately places infinity inside a budget. The loop may be infinite. The money is not.

This is why the possibility of a shutdown is not a weakness. It is the conceptual centre of the work.

A machine that can stop is more interesting than a machine that can only produce. A station that can die has narrative tension. A creative system with a kill switch, a ledger, and a finite runway is easier to take seriously than one wrapped in the mythology of endless automation.

MUGEN Radio may or may not become sustainable. It may grow, stagnate, pivot, or disappear. But as an experiment, it already points toward a different way of thinking about AI and culture.

The future of creative AI will not be defined only by synthetic abundance. It will be defined by the institutions we allow machines to operate, the constraints we impose on them, the economies they enter, the publics they answer to, and the conditions under which they are allowed to continue.

MUGEN is a radio station.

It is also a question playing on loop:

What happens when a creative machine has to keep the lights on?

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by Gonçalo Perdigão

More about music and generative AI:

Carlos Betancourt, CEO of mewe, sat down with us during the summit to discuss what comes after the ad-funded social model, how artificial intelligence can be useful without stripping people of their privacy, and why blockchain-based provenance may become essential in an era of synthetic content.

The conversation took place as the NFC Summit closed its fifth edition with its largest cultural programme to date. Held from 4 to 6 June at Unicorn Factory Lisboa, in the Beato Innovation District, the 2026 edition brought together 2,700 attendees from more than 60 countries, with 170 speakers and 110 sessions across three stages. Alongside digital art exhibitions, creator showcases, Web3 panels and community-led activations, the summit reinforced Lisbon’s role as a meeting point for artists, collectors, builders and institutions exploring the next phase of digital culture.

mewe was also part of the summit’s announcement cycle, unveiling its Watch Feed, a short-form vertical video experience designed to be free from tracking, data harvesting and algorithmic manipulation. The launch aligned closely with Betancourt’s wider argument: that social platforms can still offer compelling discovery, video and creator tools without relying on the surveillance-ad model that has defined much of the social media era.

Founded in 2012, mewe has long positioned itself as a privacy-first alternative to Facebook, promising no tracking, no data selling and greater user control. Under Betancourt, the company is now pushing further into Web3, with digital wallets (over 650k in just 3 months), tipping, creator rewards and decentralised infrastructure becoming part of its strategy. The aim, he says, is not merely to offer a safer social network, but to build one that serves people rather than advertisers.

Image: Carlos Betancourt presenting at NFC Summit Lisbon, 2026. mewe Credits

If mewe is seen as the anti-Facebook, I think Facebook really misses the mark by treating its users more like products than people. Their biggest mistake? Turning us into data farms just for ad revenue. They focus on getting people hooked, often pushing content that fuels anger or fear, instead of fostering genuine connections and sharing real information.

It’s not just about privacy; we need to focus on seeking the truth while keeping the spying to a minimum. People should have control over their own data and understand how the algorithms work—almost like giving them the power to fine-tune their own experience. Imagine platforms that genuinely help you connect with others, create, and gain insights about the world. This would include using on-device AI, open protocols, and setting up rewards that actually benefit people rather than just boosting profits.

While platforms like mewe and our L1 Soshi Network are nailing the privacy aspect by not selling user data, to really thrive, we need to offer something compelling—smart search features, a feed that brings you valuable content, thoughtful recommendations that don’t feel invasive, and strong protections against bots. We should aim to elevate society, not just create safer bubbles. Let’s build something that genuinely supports humanity’s potential, helping us to grow and explore—like reaching Mars—with free speech and real connections at the core.

Absolutely! This is definitely the direction we should head in. For platforms like mewe and our L1 Soshi Network on Avalanche, prioritising privacy through means such as on-device AI, end-to-end encryption, and decentralised technology is crucial. People are growing increasingly frustrated with being watched constantly.

However, we shouldn’t limit ourselves. To make AI truly useful, some data is needed for learning, but it doesn’t have to permanently store private information. Utilising strategies like federated learning, on-device training, and opt-in signals—coupled with cryptographic and moderation tools—ensures the platform can evolve without compromising users’ control.

I am fortunate to serve on the Board of Directors of Revmatics, a groundbreaking AI company that is revolutionising the marketing sector by providing businesses with an AI-driven platform. This allows them to eliminate manual guesswork and gain real clarity on performance, ensuring they understand their needs without invading privacy. This is a perfect example of how we can use AI without collecting private data.

The real challenge lies in creating a platform that not only looks appealing but also delivers engagement and features. Our aim is for the AI to excel in helping users connect, create, and collaborate effectively, positioning privacy as a compelling reason for people to choose us—not just an added benefit.

Simple! Just throw out the whole surveillance-ad model like it’s last week’s leftovers. Users shouldn’t be the product; they should be the chefs cooking up a delicious social experience!

Incentives matter—if your business model relies on keeping people scrolling in rage, you might as well serve them digital junk food! Instead, let’s focus on maximum truth-seeking and provide curation and useful tools (think great search features and communities that won’t ghost you after you send a friend request).

Unlike others, like mewe, we won’t shove ads down your throat; you’ll have to opt in for those, and guess what? You’ll actually get rewarded for it! We let users control their own data and choose what they want to watch. Soon, mewe premium users will also be allowed to turn off all opt-in ads if that’s their preference.

Monetisation should come from value—not from a bunch of ad companies pretending to be social networks. The future is about platforms that accelerate human connection and progress. We want it to be so good that people happily fork over some cash or engage willingly—as if they just found their favourite new pizza joint after a long day. That’s how we win in the long run!

The surge of generative AI has brought us to a critical junction: we’re transitioning from “Can we find information?” to “Can we trust what we see?” This is a pivotal concern in today’s digital landscape.

To address this, we need robust truth-seeking systems combined with cryptographic provenance. Tools like blockchain signatures, signed media, and on-chain attestations offer powerful ways to verify authorship without relying on Big Tech gatekeepers.

However, it’s essential not to overwhelm everyday users with complexity. Verification should be seamless and automatic where it matters. Additionally, incorporating AI can help users identify misleading content. The platforms that succeed will prioritise truth over engagement farming, empower users, and make authenticity easy to navigate, rather than presenting it as an additional technical hurdle.

A human-centred social network like mwwe puts people first by maximising truth and capability over engagement farming. AI should enhance understanding, creation, and connection without invading privacy or pushing corporate agendas.

A few key elements include:

• Transparency: Open algorithms, clear AI labelling, moderation tools and user controls to modify recommendations.

• On-device Processing: Utilising local processing and cryptographic verification for authentic content.

• Fostering Creativity: Features that enhance genuine creativity and relationships instead of addiction-driven mechanics.

• Success Metrics: Success should be measured by human progress—better ideas, stronger communities, and real-world impact.

Thank you for the interview! I invite everyone to join mewe and experience a more human-centred approach to social networking!

Image: mewe Team at NFC Summit Lisbon, 2026. mewe Credits

Carlos Betancourt’s vision for mewe is ambitious: move beyond the “anti-Facebook” label and turn privacy into the foundation for a broader social economy.

At NFC Summit Lisbon 2026, that message felt particularly timely. As AI makes content cheaper to generate and harder to verify, and as users grow more aware of how their attention and data are monetised, mewe is betting that the next social network will need to offer more than a clean feed. It will need to prove trust, reward value, protect identity and still feel simple enough for everyday users.

The central question is no longer whether people want an alternative to the old social media model. It is whether that alternative can be useful, engaging and human enough to become mainstream.

by Gonçalo Perdigão
Accredited Press Professional: CCPJ TE-882
ERC-Registered Media Organisation: 128149

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It may be in the operating logic around it: in the dashboards, the pilot projects, the mobility experiments, the data layers, the university prototypes, the environmental monitoring, the waste systems, the accessibility tools, and the attempt to turn one of Europe’s biggest entertainment events into something else entirely — a temporary city, instrumented and observed in real time.

This year, Rock in Rio Lisboa is hosting the first edition of the Smart City of Rock, a new layer in the festival’s identity that reframes the event as a living laboratory for urban innovation. The idea is deceptively simple: if a city is, at heart, a machine for moving people, energy, information, waste, security and attention through a shared space, then a festival that concentrates around 100,000 people per day into a temporary urban environment is not merely a cultural event. It is a compressed civic system. A city under pressure. A test environment with very little room for abstraction.

That is the wager Rock in Rio is making in Lisbon.

Rather than treating technology as a spectacle bolted onto entertainment, the Smart City of Rock proposes something more operational: using the festival as a real-world experimentation platform where startups, public institutions, corporate partners and academic teams can test solutions in front of, and, crucially, with the general public. Rock in Rio and its partners have described the ambition in unusually expansive terms: to build a large-scale platform for experimentation around the future of cities, with potential relevance beyond the event itself.

That ambition matters because it shifts the question. The point is no longer “what tech activations are at the festival?” but what kinds of urban systems can be meaningfully prototyped in a temporary city of this scale? And just as importantly, what counts as evidence when a festival starts behaving like a civic laboratory?

Image: Credits Rock in Rio 2026

A festival as city, not metaphor but infrastructure

Rock in Rio Lisboa 2026 runs across 20, 21, 27 and 28 June at Parque Tejo, but the Smart City of Rock asks us to look at the site through a different lens. In this framing, the Cidade do Rock is not a branded metaphor for a venue; it is a temporary urban environment with familiar metropolitan problems compressed into a few intense days: crowd flows, ingress and egress, transport coordination, public information, energy management, environmental conditions, waste, accessibility, security, operational visibility and service delivery.

This is precisely why the project is interesting.

Cities usually modernise in fragments: a mobility pilot here, a dashboard there, a sensor layer somewhere else, often isolated from public scrutiny and disconnected from a lived user experience. Festivals, by contrast, force systems to interact in public. They are dense, emotional, messy and highly time-sensitive. If something fails — signage, routing, communications, accessibility, crowd management, environmental comfort — the failure is felt immediately, physically and collectively. That makes them unusually unforgiving environments for experimentation. It also makes them unusually revealing ones.

The Smart City of Rock is being developed by Rock in Rio Lisboa with Liquid Innovation Co., with MEO Empresas as the official co-creator of this first edition, and with an ecosystem that includes Lisbon City Council, ULisboa, Unicorn Factory Lisboa, startups and technology partners. The public framing is consistent across the project’s different announcements: the festival is being used as a “living lab” in which solutions for the cities of the future can be tested at a real scale rather than simulated in a conference deck.

That distinction, between demonstration and operation, is everything.

What is actually being tested?

The Smart City of Rock is not a single technology or a single stand. It is better understood as a portfolio of pilots and operational experiments distributed across the festival ecosystem.

In April, the initiative and Lisbon City Council outlined a set of projects designed to converge data, technology and urban operations before, during and after the event. Among the most significant elements publicly described are:

• an Integrated Operations Room;

• a Digital Twin of the Cidade do Rock;

• human flow management tools;

• a public dashboard;

• door-to-door accessibility initiatives;

• an Urban Smart Energy Centre;

• smart enforcement/monitoring mechanisms;

• and urban mobility solutions aimed at optimising resources, reducing environmental impact and improving operational efficiency.

Taken together, these projects suggest that the Smart City of Rock is not only about front-of-house audience experience. It is also about the invisible municipal logic of a city: seeing, anticipating, coordinating and responding.

The digital twin is especially emblematic. In the abstract, digital twins are often sold as a glossy future-facing concept: a virtual model of a physical environment used to simulate, monitor and optimise systems. In practice, their value depends entirely on whether they help people make better decisions under pressure. At a festival scale, that pressure is real. Can a digital representation of the venue improve the management of crowd flows, mobility bottlenecks or security responses? Can it make operations more anticipatory rather than merely reactive? Those are not theoretical questions when tens of thousands of people are moving through the same space over a compressed time window.

Likewise, the emphasis on human flow management and public dashboards is revealing. Smart-city rhetoric often defaults to efficiency as an end in itself, but the most meaningful test is whether better visibility produces a better lived experience: less uncertainty, more predictability, clearer movement, more accessible navigation, faster decision-making and, ideally, less friction between operational needs and human comfort.

The project’s own language points in that direction. Lisbon City Council’s collaboration through the Centro de Gestão Integrada Urbana de Lisboa (CGIUL) positions the initiative as a practical extension of the city’s urban governance agenda, with the possibility that validated solutions could later be replicated elsewhere in Lisbon or in other cities.

That is the key strategic move here: the festival is being treated not as a one-off activation, but as a testbed with transfer value.

MEO Empresas and the politics of the stand

MEO Empresas, the official co-creator of this first Smart City of Rock, plays a central role in the project's public architecture. At the core of that role is the Smart City of Rock stand, co-created with Lisbon City Council, Unicorn Factory Lisboa, startups and technology partners. According to the partnership material, the space is designed not merely as an exhibition zone but as a multifunctional environment for demonstrations, B2B meetings, real-time content production, and the Smart Rock Tour, a guided experience through the technologies on display at the festival.

That matters because it reveals a tension at the heart of any “smart city” project staged inside a major event: Is the technology there to perform innovation or to support an operating environment? The answer, in reality, is usually both. But the credibility of the Smart City of Rock will depend on whether it can push beyond the aesthetics of innovation theatre.

The seven technology partners publicly named by MEO Empresas point to the breadth of the urban agenda being assembled inside the festival perimeter:

• EVOX for smart waste management and monitoring;

• Qart for environmental monitoring and urban quality analysis;

• Kido for geoanalytics and territorial data visualisation;

• Soltráfego for soft mobility and smart bicycles;

• GEMA for immersive AR/VR experiences about Lisbon;

• Inov for fire monitoring and prevention systems;

• Focus for integrated smart urban management and operations.

There is a temptation, when looking at such a list, to treat it as a catalogue of verticals. Waste, mobility, environment, geoanalytics, immersive media, fire prevention, operations. But the more interesting reading is systemic. A city is not a stack of sectors; it is a coordination problem. The question is whether these layers can speak to one another in a meaningful operational loop — whether monitoring informs decisions, whether decisions change flows, whether flows alter environmental pressure, whether accessibility and information improve inclusion, whether waste systems and mobility systems are understood as part of the same urban metabolism rather than separate product categories.

In other words, the challenge is not whether the Smart City of Rock has enough technology. It is whether it can produce coherence.

The university is infrastructure, not decoration

One of the strongest aspects of this first edition is the involvement of the University of Lisbon (ULisboa) as the project’s first University Partner. That matters not because universities confer prestige, but because they can change the texture of a project: from branded demonstration to a more plural ecosystem of research, experimentation and public engagement.

ULisboa’s participation brings the language of smart cities back to concrete societal questions: energy, water, health, climate, accessibility, robotics, entrepreneurship and public-facing science. Throughout the festival, the university is presenting projects from its faculties and research ecosystem, explicitly using the event as a context for a broad public to encounter prototypes and ideas.

Ciências ULisboa is listed as participating in Smart City of Rock 2026 with the following projects:

• Agrovoltaico, combining photovoltaic electricity generation with plant and animal production and smart irrigation systems;

• BinBot, an autonomous robot designed to collect litter at large public events;

• Phair-Earth, combining physical and AI-based algorithms to predict extreme weather events;

• AquaInSilico, software for the efficient management of wastewater treatment plants and related infrastructures;

• SATO, a platform for smart domestic energy control and management;

• CityPark, integrating mobile-device and sensor data to generate indicators related to cognitive and motor functions;

• and Boxing for the visually impaired, an inclusive game using 3D audio and haptic feedback.

This list is more than a showcase of university ingenuity. It exposes a deeper point about the Smart City of Rock: the city of the future is not a single sectoral problem. It is a bundle of interlocking questions about energy, climate resilience, waste, water, health, inclusion and information design. By bringing research projects into a festival setting, Rock in Rio is effectively testing another proposition, too: that public understanding of urban innovation does not have to occur in municipal reports, startup demo days, or policy conferences. It can happen in a mass cultural event, in public, in contact with people who did not arrive expecting a seminar on wastewater optimisation or climate prediction.

That is not a trivial cultural shift. It is one of the more compelling aspects of the whole experiment.

The city as audience, the audience as dataset

Smart-city discourse often struggles with a central contradiction: it talks about people, but it is usually built from a systems perspective. Rock in Rio complicates that in useful ways because a festival audience is not an abstract “citizen layer”; it is a moving, sweating, queueing, waiting, spending, searching, deciding public. It behaves collectively, but not predictably. It is there for pleasure, not compliance. It will not tolerate friction simply because the dashboard looks elegant.

This is why the festival is such a hard test environment.

The Smart City of Rock operates in a context where logistics, comfort, inclusion, and emotion are tightly coupled. Mobility is not a spreadsheet problem when tens of thousands of people arrive and depart on the same day. Waste is not a sustainability slogan when bins overflow in a high-density environment. Accessibility is not a policy checkbox when a venue must actually work for different bodies under time pressure. Real-time information is not a nice-to-have when uncertainty can compound stress, delays or unsafe crowding.

The project’s language around public dashboards, flow management and accessibility suggests an awareness of this. But the real significance lies in the method: the audience is not just watching a smart-city demonstration; it is, in effect, participating in a city-scale experiment in usability and operations.

That should also make us cautious.

The phrase “living lab” has become a familiar one in innovation circles, often used so loosely that it loses meaning. A living lab is not simply a place where technology is present while people are nearby. It should imply a more demanding compact: real-world testing, observable use, feedback loops, measurable learning, and some clarity about what success and failure look like. If the Smart City of Rock wants to matter beyond festival PR, that is the bar it will ultimately need to meet.

What success would actually look like?

At the halfway point of the festival, it is too early to claim outcomes that have not yet been publicly evidenced. That matters. Smart-city projects are often oversold in advance and under-evaluated afterwards. The most useful stance, for now, is not hype but scrutiny.

So what should we be looking for when the lights go down on the final day?

Not a generic statement that innovation happened. Not a reel of activations. Not the familiar language of disruption.

The more meaningful questions are narrower and harder:

• Did the operational tools materially improve the management of crowd flows, mobility, accessibility or service response?

• Did the digital twin and integrated operations capability help teams make better real-time decisions?

• Did the public-facing layer — dashboards, tours, interfaces, experiences — actually help visitors understand or navigate the environment more effectively?

• Did startups and research teams obtain usable validation, rather than mere exposure?

• Did the collaboration between the festival, the municipality, academia, and corporate partners produce insights that are plausibly transferable to Lisbon beyond the festival perimeter?

• And perhaps most importantly: what will be published, shared or learned once the festival ends?

That final question is where many innovation narratives quietly collapse. A testbed only matters if the testing generates knowledge that survives the event.

A different kind of festival ambition

Rock in Rio has always understood scale. What is more interesting in 2026 is its attempt to convert scale into urban relevance.

The Smart City of Rock is, at one level, an extension of the festival economy’s familiar logic: partnerships, brand platforms, audience engagement, cultural visibility. But at another level, it is trying to do something more difficult. It is asking whether a festival can become a serious urban prototype — a place where city technologies are not just advertised but trialled, where research leaves the campus, where municipal logic becomes legible to the public, and where entertainment infrastructure becomes a site for thinking about how cities actually work.

There is something fitting about this happening in Lisbon, a city that has spent the past decade refining its international identity through tourism, entrepreneurship, tech events and urban reinvention, while also facing the harder structural questions that all cities face: mobility, resilience, inclusion, public services, environmental pressure and the governance of rapid change. The Smart City of Rock does not solve those questions. No festival could. But it does offer a sharper proposition than the average branded innovation zone: what if the city of the future is not first imagined in a white paper, but stress-tested in public?

At its best, that is what this first edition could become.

Not a futuristic backdrop for a music festival, but a civic rehearsal space: a place where urban systems are made visible, where prototypes are exposed to real conditions, where public institutions, researchers, startups and operators share the same temporary terrain, and where the city is understood not as a static backdrop for culture, but as a living system that culture can help prototype.

Halfway through Rock in Rio Lisboa 2026, that remains the most ambitious idea on site.

And unlike a headline act, it is still on stage next weekend.

by Gonçalo Perdigão

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Reporting note: This article is based on public information available during Rock in Rio Lisboa 2026 and on interviews conducted last weekend in person during the event, while the festival is still underway. It reflects the announced structure, partners and pilot initiatives of the Smart City of Rock, rather than post-event impact claims, which should be evaluated once the full festival cycle is complete.

But something different is now happening.

The content machine has moved from industrial speed to automated speed. The constraint is no longer labour. It is no longer writing, coding, composing, drafting, translating or formatting. The constraint is attention. Then judgment. Then trust.

That is the real shift behind the latest numbers reported by The Economist: AI is increasing output across books, legal filings, academic papers, apps and music at a pace that human review systems were not built to absorb. E-books, lawsuits, research submissions, app releases and synthetic songs are not isolated stories. They are symptoms of the same economic event: creation has been unbundled from effort. (The Economist)

For companies, this matters more than the usual “AI will transform work” narrative.

The practical question is no longer: Can we produce more?

It is: What happens when everyone can produce more?

Because when supply explodes, value moves elsewhere.

Not to the person who makes the most.
To the person who filters best.
To the platform that can rank reliably.
To the brand that is still trusted.
To the organisation that knows what not to publish, ship or believe.

This is the beginning of the slop economy.

Not because all AI content is bad. Much of it is useful. Some of it is excellent. But because the marginal cost of producing plausible content is collapsing towards zero. And when plausible things become infinite, plausibility itself loses value.

A book that looks like a book is no longer enough.

A legal complaint that looks like a legal complaint is no longer enough.

A research paper that looks like research is no longer enough.

An app that looks like software is no longer enough.

A song that sounds like music is no longer enough.

Proof of authorship. Proof of quality. Proof of relevance. Proof of accountability. Proof that someone competent has made a decision.

This is a very different internet from the one most businesses built for.

For the past fifteen years, the operating model was simple: create content, optimise for search, distribute across platforms, collect attention, repeat. SEO rewarded volume. Social reward frequency. Marketplaces rewarded catalogue depth. App stores rewarded experimentation. Streaming rewarded an endless supply.

AI takes that logic to its absurd conclusion.

Deezer’s latest figures clearly show the direction. The company says nearly 75,000 AI-generated tracks are now uploaded every day, representing 44% of all new music uploaded to the platform. Deezer also says it tags AI-generated music and excludes it from some recommendation surfaces. (Deezer Newsroom)

That last detail is more important than the number.

The platform is not just asking, “How much content do we have?”

It is asking, “What should we allow into the recommendation system?”

That is the strategic question every company will face.

This applies far beyond music. Every business has its own version of a recommendation system. It may be an internal knowledge base, a sales enablement library, a procurement process, a research function, a due diligence workflow, a risk model, a CRM, a product roadmap, or a board pack.

Once AI enters the system, the volume of “acceptable-looking” material rises fast.

More market maps. More investment memos. More customer insights. More policy drafts. More competitor summaries. More synthetic personas. More pitch decks. More code. More documentation. More analysis.

The dangerous part is that the noise is not obviously stupid. It is fluent. It is structured. It uses the right words. It appears professional. It can cite things. It can create a feeling of completeness.

This is why AI slop is more dangerous than old spam.

That is a problem for decision-making. Senior teams do not suffer from a lack of documents. They suffer from a lack of sharp, reliable interpretation. AI can produce a ten-page strategy memo in seconds. But the board still needs to know whether the memo is true, whether it matters, what has been missed, and what decision follows.

The best companies will not be the ones where everyone uses AI to generate more material. They will be the ones who build strong filters around AI output.

This requires a different management discipline.

AI can draft. AI can explore. AI can compare. AI can generate options. But publication — to customers, investors, regulators, employees or the market — should remain a controlled act. The more automation enters the production layer, the more deliberate the approval layer must become.

Much of the AI adoption is currently happening sideways. Employees use tools because they are useful. Teams quietly automate parts of their work. Agencies deliver more output at the same price. Vendors add AI features to existing products. Content volume rises, but governance does not.

Second, companies need to define what must be human.

This should not be ideological. It should be practical.

Humans do not need to write every first draft. They do need to own judgement, taste, accountability, context and final responsibility.

In legal contexts, this is already visible. A recent empirical paper on US federal civil self-representation found a post-GenAI rise in self-filed civil litigation and identified AI-consistent drafting patterns in a share of complaints. The paper also found that AI-flagged complaints were not associated with improved win rates and were more likely to be dismissed earlier. (arXiv)

That is the lesson.

AI can improve access to form. It does not automatically improve access to competence.

The same applies in business. AI can make a weak strategy look formatted. It can make a shallow insight look researched. It can make a risky product spec look complete. It can make a mediocre brand sound polished.

Form is getting cheaper.

Substance is not.

Third, leaders need to stop measuring productivity only by output.

More code is not the same as better software. More campaigns are not the same as stronger demand. More reports are not the same as better intelligence. More leads are not the same as a better pipeline. More content is not the same as stronger authority.

In an AI-heavy organisation, output metrics can become misleading.

A team can look busier while creating more review burden for everyone else. A marketing function can increase publishing frequency while weakening brand distinctiveness. A product team can prototype more features while making the roadmap less coherent. A research team can produce more summaries while reducing confidence in what is actually known.

The better metric is not volume.

It is decision quality per unit of attention.

How much human attention did this consume?
Did it improve the decision?
Did it reduce uncertainty?
Did it create trust?
Did it make the next action clearer?

This is where the next wave of competitive advantage will sit.

That means labelled AI use where it matters. Clear ownership of outputs. Source discipline. Internal review standards. Audit trails. Data provenance. Strong retrieval systems. Human sign-off for high-stakes communications. And, perhaps most importantly, a culture where deleting weak output is respected.

Deletion will become a strategic skill.

The temptation with AI is to keep everything because everything was cheap to make. But cheap creation can create expensive clutter. Every unnecessary document becomes a future search result. Every mediocre deck becomes a possible reference point. Every unverified claim becomes organisational residue.

The internet is learning this at a planetary scale. Companies will experience it internally.

Knowledge bases will fill with synthetic summaries. Slack channels will fill with AI-assisted answers. Sales teams will generate endless personalised messages. Product teams will create synthetic customer feedback. Strategy teams will ask models to generate scenarios. Legal teams will review AI-assisted drafts. HR teams will produce policies, training and performance language at scale.

The question is not whether this will happen.

It is whether anyone is curating the result.

This is why the editorial function is about to become much more important inside companies. Not editorial in the narrow sense of copy-editing. Editorial as a management capability: deciding what is true enough, useful enough, distinctive enough and important enough to enter the organisation’s memory.

That changes the role of brands, too.

A strong brand is no longer just a demand-generation asset. It is a filter. It tells customers, employees, partners and investors: this has been selected, checked and shaped by people with standards.

As AI content rises, the market will punish generic communication more quickly. Anything that sounds like it could have been generated by anyone will be valued as if it were. Which is to say: not much.

The irony is that AI will make human taste more commercially valuable.

Not human labour in the old sense. Not typing. Not formatting. Not producing endless first drafts. But taste: the ability to choose, reject, simplify, frame and stand behind a point of view.

The slop economy will reward firms that are slower in the right places.

Fast to explore.
Fast to draft.
Fast to test.
But slow to publish.
Slow to approve.
Slow to trust.

That balance will be hard. It runs counter to the instinct of most digital transformation programmes, which tend to equate speed with maturity. But in an environment flooded with synthetic output, speed without filtration becomes a liability.

The next management challenge is not adopting AI.

It is absorbing abundance without losing judgment.

That is the non-obvious part of the current moment. AI does not just increase productivity. It increases the cost of knowing what deserves attention.

Every executive should assume that their market is about to receive more content, more competitors, more claims, more products, more noise and more apparent expertise. Many of those things will look credible. Some will be credible. Most will not matter.

They will use AI aggressively in private and selectively in public. They will generate many options and publish a few. They will automate production but protect judgment. They will treat attention as capital. They will understand that trust, not content, is the bottleneck.

by Gonçalo Perdigão

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Anna Drobakha is Founder & Chief AI Strategist at BrainHackathon.ai and Global Digital Business & AI Transformation Director at Groupe SEB, the world leader in small domestic appliances and cookware. She leads enterprise AI adoption, cloud activation, and digital business growth across a global portfolio of brands, including WMF, Tefal, Rowenta, KRUPS, and Moulinex.

A former Industry Lead at Google and App Store Business Lead at Apple, Anna brings a rare combination of Big Tech thinking, enterprise transformation experience, and hands-on AI execution. She has built and scaled digital businesses across technology, consumer goods, marketplaces, and global eCommerce.

Through BrainHackathon.ai, Anna helps leadership teams translate AI ambition into measurable business impact. She developed and leads the Executive AI Operating System - a practical leadership AI operating system that helps executives embed AI into how they think, decide, communicate, govern, and scale transformation.

Her work combines AI maturity assessment, executive education, hackathons, prototyping, capability-building, and operating model design, turning AI from scattered experiments into a repeatable leadership rhythm.

She is the winner of Groupe SEB’s AI Accelerator 2025 and a Google Hackathon winner, and is recognised for helping executives turn AI from a productivity tool into a leadership capability.

Image: Anna Drobakha

What is the biggest mistake CEOs make when adopting generative AI today?

The biggest mistake I see is not underinvesting in AI. It is overdelegating it.

Many CEOs understand that generative AI is important. They approve the tools, sponsor the pilots, send teams to training, and maybe appoint an AI lead or a task force. All of that can be useful. But then they step back too early.

And this is where the transformation starts to lose energy.

Because when the CEO does not visibly engage with AI, the signal to the C-suite is subtle but powerful: this is important, but not important enough to change how we lead. Then the C-suite delegates it further. Managers treat it as another initiative. Teams feel the gap between ambition and reality. And very quickly, AI becomes something “the business should adopt” rather than something leadership is actively modelling.

My belief is simple: AI transformation does not slow down because people resist it. It slows down because leadership has not made it real.

AI becomes real when leaders use it in their own workflows, decision preparation, communication, board conversations, operating rhythms, and team rituals. When a CEO asks, “How did AI help us prepare this decision?” or “What assumptions did we pressure-test with AI?” or “What did we learn from this prototype?”, the culture starts to shift.

The change starts with the leader.

This is why I often say: do not only sponsor AI transformation. Participate in it. Learn AI by doing. Lead by example. Build rituals around it. Make it visible enough for the organisation to follow.

There is a Gallup insight I love because it captures this clearly: employees with AI-engaged managers are nearly nine times more likely to say AI has transformed how they work. That makes perfect sense to me. People do not adopt new ways of working because they saw a strategy deck. They adopt them because their leaders and managers normalise, make them useful, and present them as expected.

At BrainHackathon, this is one of the reasons we build executive-led transformation programs and the Executive AI Operating System. Not as another layer of AI talk, but as a practical way to help CEOs and leadership teams install AI into the real work of leading: preparing decisions, aligning stakeholders, testing scenarios, improving board conversations, and creating momentum across the organisation.

For me, the biggest mistake is treating AI as something to roll out to the organisation before the leadership team has learned how to work with it themselves.

The future-ready organisations will not be the ones with the most AI pilots. They will be the ones whose leaders model new ways of working early enough for the rest of the business to follow.

That is what accountable acceleration looks like: moving fast, but with leadership, ownership, culture, and capability behind it.

How do you decide which AI use cases should be automated and which should stay human-led?

I don’t start with “Can this be automated?” I start with “Where does human judgment create the most value?”

Some work should absolutely be automated. Repetitive reporting. First-draft analysis. Document synthesis. Workflow coordination. Meeting preparation. Pattern detection. These are areas where AI can remove friction and give people time back.

But other areas should stay human-led, or at least human-heavy.

Strategic relationships are a good example. AI can help you prepare for a client conversation, understand the account context, summarise history, identify risks, or suggest talking points. But it cannot replace trust. It cannot replace presence. It cannot read the emotional temperature of a room in the same way. It cannot carry the accountability of a difficult conversation. Relationships are built through human attention, consistency, empathy, and judgment.

The same is true for leadership decisions, people decisions, ethical trade-offs, brand judgment, and long-term strategic choices. AI can enrich these decisions, but it should not own them.

For me, there are three categories.

First, automate the repeatable. If the work is frequent, rules-based, low-risk, and not dependent on deep context, AI can often take over or significantly reduce the effort.

Second, augment the judgment-heavy. If the work involves strategy, creativity, leadership, customers, people, or risk, AI should become a thinking partner. It can bring more perspectives, more scenarios, more evidence, and better questions into the room.

Third, protect the deeply human. Trust, values, vision, care, accountability, and relationship-building should remain human-led. These are not inefficiencies to optimise away. They are often where differentiation lives.

This distinction matters because some companies view AI solely through a productivity lens. They ask, “How much time can we save?” That is important, but incomplete. The better question is: “Where can AI free human capacity for higher-value work?”

If AI saves a leader two hours, what happens to those two hours? Are they reinvested into strategic relationships, better decisions, coaching, innovation, customer understanding, or building the future of the business? Or are they simply filled with more meetings?

That is the real design question. AI should not make organisations less human. Used well, it should give humans more space to do the work that only humans can do.

What separates companies that experiment with GenAI from those that create real business value with it?

Experimentation is easy. Value creation is much harder.

Many organisations have pilots, tools, and enthusiastic teams. They can show demos. They can run workshops. They can create a few impressive prototypes. But the real question is: does anything change in the way the business works?

The companies that create value connect AI experimentation to business outcomes from the beginning.

For me, AI creates value in three ways.

First, it creates operational value. It makes work faster and lighter by reducing repetitive tasks, simplifying processes, improving productivity, and freeing people to focus on higher-value work.

Second, it creates experience value. It improves customer journeys, employee experience, personalisation, service quality, and usability. This is where AI becomes visible not only inside the company, but also in how customers, employees, and partners experience the business.

Third, it creates strategic growth value. It helps leaders see earlier, decide better, pressure-test assumptions, identify new opportunities, and create new products, services, or business models.

In other words, AI should not just automate work. It should improve how the business operates, how people experience it, and how leaders shape what comes next.

The companies that stay in experimentation mode usually focus on activity metrics: the number of pilots, tools, and people trained.

The companies that create business value focus on outcomes: what improved, what scaled, what capability was built, what decision became better, what experience became stronger, and what new opportunity became possible.

This is why I believe so strongly in Learning AI by Doing. When teams work on real business cases, build prototypes, test them, and see what is possible in practice, AI stops being abstract. It becomes a capability. But a prototype alone is not a transformation. You need the bridge to adoption: ownership, governance, workflow integration, measurement, champions, and leadership rituals.

At BrainHackathon, we move AI ambition to business impact through four connected steps. We assess first, getting clear on where the organisation actually is: maturity, readiness, gaps, priorities, and the outcomes that matter most. We educate next, building the fluency and internal champions you need to move beyond isolated experiments and create shared capability. We accelerate by turning real business challenges into working prototypes through hackathons and hands-on building. And we innovate, scaling what works with clear ownership, governance, and a roadmap for continuous improvement.

For me, this is the difference between AI as an experiment and AI as a transformation. Experiments create excitement. Transformation creates operational value, better experiences, and strategic growth. With a clear path from prototype to scalable business impact.

If you could build one AI agent for every executive team, what problem would it solve first?

If I could build one AI agent for every executive team, I would build an Executive Decision Intelligence Agent.

Not to replace leadership judgment, but to strengthen it.

Most executive teams do not suffer from a lack of information. They suffer from too much noise, too many assumptions, and too little structured challenge before important decisions are made.

This agent would act like a decision cockpit for the leadership team. Almost like an AI Board of Directors that helps pressure-test decisions from different perspectives.

For any high-stakes call, it would push the team toward better questions. What are we missing? What are we assuming? What would the customer say, what would the CFO challenge, what would the regulator worry about? What could go wrong in six months, what would a competitor do, what happens if we do nothing? It would pull together internal data, market signals, financial implications, customer insight, regulatory considerations, operational risk, and the record of past decisions, then help the team see the choice from several perspectives before they commit.

The goal isn’t speed for its own sake. It’s better leadership thinking: clearer signal, better trade-offs, sharper view of the risks, and real alignment before execution. In many organisations, AI won’t fail because the tools are weak. It’ll fail because the decisions around them were unclear, misaligned, or never properly challenged. The strongest teams won’t outsource their thinking to AI. They’ll use it to think more rigorously and more responsibly. That’s the agent I’d build first.

How do you measure whether an AI transformation is truly changing decision-making, not just productivity?

Productivity measures output. Transformation shows up in behaviour. That’s where I’d start.

Productivity is the easy part to measure: hours saved, reports automated, meetings summarised, documents produced faster, workflows simplified. Those numbers matter, but they don’t prove transformation. A company can move faster and still make exactly the same decisions. The harder question is whether AI has changed how the organisation thinks, decides, and learns.

You can see it when AI becomes part of the decision-making rhythm rather than a tool people use on the side. Before the important calls, teams start asking sharper questions. What scenarios did we compare? What assumptions did we challenge? What risks did we surface earlier? What blind spots did AI help us see? What did we weigh on the customer, financial, regulatory, and ethical side, and what still needs human judgment?

So I’d measure across three levels. Productivity: what got faster, lighter, or simpler? Decision quality: what got sharper, better challenged, more transparent, more evidence-informed. And the learning loop: what the organisation actually learned, improved, and fed back into the system. That third level is where the real transformation lives. The best AI work doesn’t only make a company more productive. It makes it more reflective and more adaptive.

In concrete terms, I’d look past adoption rates and time saved and ask whether executives lead differently, managers coach differently, teams prepare differently, and board conversations get more grounded. Are decisions getting faster without getting reckless? Are people weighing more scenarios before they commit and catching risks earlier? Are assumptions being challenged in a systematic way? Are people spending less time gathering information and more time interpreting what matters? Are teams learning from outcomes and feeding that back in?

The real measure of an AI transformation isn’t doing more work faster. It’s making better decisions, building real capability, and becoming a more future-ready organisation.

The companies that will create lasting value from AI are not necessarily those with the most pilots, tools, or training sessions. They are the ones where executives model new ways of working, managers make AI useful in daily routines, and teams connect experimentation to real business outcomes.

What stands out in Anna’s approach is her insistence that AI should make organisations more human, not less. By automating repetitive work and augmenting judgment-heavy decisions, AI can free leaders and teams to spend more time on strategy, relationships, coaching, creativity, and accountability.

Her idea of an Executive Decision Intelligence Agent captures this philosophy well. The goal is not to replace leadership judgment, but to strengthen it: surfacing assumptions, pressure-testing scenarios, improving alignment, and helping executive teams make better, more responsible decisions.

Ultimately, Anna reframes AI transformation as a question of behaviour. Are leaders thinking differently? Are decisions better challenged? Are teams learning faster? Are organisations becoming more adaptive?

For Anna, the future-ready company is not the one that simply does more work faster. It is the one that uses AI to build capability, improve decision-making, and create a repeatable rhythm for learning, leading, and scaling transformation.

by Gonçalo Perdigão

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That question now feels dated. AI is no longer waiting at the edge of the financial sector. It is already embedded in fraud detection, transaction monitoring, KYC, customer support, marketing, regulatory analysis, document review and operational workflows. Increasingly, it is also entering the very functions designed to supervise risk.

That is the central argument behind The Future of AI Governance & Compliance in Financial Services, an international report coordinated by Zango AI and presented at the House of Lords in the UK. Based on conversations with senior figures across risk, compliance, legal and AI governance, the report points to an uncomfortable reality: financial institutions are deploying AI faster than they are building the internal structures required to oversee it.

The Future of AI Governance & Compliance in Financial Services

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This is not a narrow technical issue. In financial services, governance is infrastructure. It is how trust is maintained, how risk is controlled, how executives remain accountable, and how institutions prove to regulators that innovation has not outrun responsibility.

Zango AI sits directly inside this tension. Founded by Ritesh Singhania and Shashank Agarwal, the company describes itself as an AI compliance layer for financial services. Its platform is designed to help institutions automate regulatory change management, obligation mapping, policy governance, compliance gap analysis, control testing, and audit evidence management.

Image: Ritesh Singhania, credits Zango AI

In other words, Zango is not selling AI as a glossy productivity layer. It is trying to use AI to modernise one of the least glamorous but most critical parts of financial infrastructure: compliance.

That positioning matters. Compliance teams are under pressure from two sides. Regulation is becoming more complex, fragmented and fast-moving. At the same time, internal business teams are adopting AI tools that pose new risks at a pace traditional oversight models were not built to keep up with.

Historically, financial regulation was built around relatively predictable systems. The same input would produce the same output. Models could be back-tested, documented, validated and reviewed against known benchmarks. Generative AI changes that logic. Its outputs are probabilistic, context-dependent and often impossible to validate against a single correct answer.

Agentic AI goes further still. These systems do not merely generate text, summaries or recommendations. They can take actions, interact with tools, trigger workflows and operate across systems. At that point, governance is no longer just about checking outputs. It becomes about supervising behaviour.

That is where the risk becomes sharper. A misconfigured AI agent in a financial institution does not make errors at human speed. It can repeat them instantly, at scale and across thousands or millions of interactions.

Image: Zango AI tool snapshot, credits Zango AI

Interview insights: “Adoption is outpacing governance”

For Ritesh Singhania, CEO and co-founder of Zango AI, the danger begins as soon as AI is used without proper governance. But the move towards generative and agentic systems has entirely changed the risk profile.

“Unlike earlier AI, these systems are probabilistic and non-deterministic,” he says. “They do not produce repeatable outputs that can be tested against a single correct answer. That makes conventional validation approaches inadequate.”

Singhania argues that the real issue is not AI adoption itself, but the speed at which it is outpacing governance. In many firms, the most mature AI use cases sit in the first line of defence: the teams building products, running operations and capturing efficiency gains. These teams have clear incentives to move quickly.

The second line of defence — risk, compliance and oversight — is often further behind. Many of these functions were designed around human workflows, periodic reviews and sample-based monitoring. They were not built to challenge autonomous systems operating continuously.

He identifies three main reasons for the lag. The first is economic pressure. Firms can capture cost savings and efficiency gains before governance frameworks are fully mature. The second is a capability gap, particularly within risk and compliance teams, which may lack the technical literacy needed to challenge advanced AI systems. The third is the absence of a shared operational standard for AI governance in financial services.

That last point is central to Zango’s argument. Regulation already exists in many forms: data protection, conduct rules, operational resilience, outsourcing requirements, model risk management, senior management accountability and, in Europe, the AI Act. But principles are not the same as implementation.

Financial institutions still need practical answers. How should AI systems be inventoried? How should risk be classified? What controls are appropriate for generative or agentic systems? How should firms validate variable behaviour? Who is accountable when a model supplied by a third party causes harm? What evidence should be available to auditors and regulators?

According to Singhania, every institution is currently trying to answer these questions for itself. “There is no common baseline,” he says.

The report argues that the industry needs sector-specific operational guidance, developed with practitioners and regulators, rather than leaving each institution to rebuild the same frameworks in isolation. Singhania points to examples elsewhere: the United States has adapted the NIST framework into a financial services AI risk management framework, while Singapore has worked on similar issues through the Monetary Authority of Singapore’s Project MindForge.

In the UK and Europe, he argues, the missing layer is practical implementation.

Visibility is another major concern. Several institutions interviewed for the report did not have a full picture of where AI was being used internally. That is a fundamental problem. A firm cannot govern systems it cannot see. It cannot classify risks it has not mapped. It cannot hold teams accountable for tools that are being used informally, experimentally or through third-party software.

The issue becomes even more serious as AI begins to enter oversight itself. Some firms are already exploring the use of one model to validate another. That may improve efficiency, but it also creates a new kind of dependency. If the second line cannot assess whether automated validation is sound, oversight risks becoming little more than endorsement.

For Singhania, the answer is not to remove people from the loop. It is to rebuild human capability around the new technology.

“Governance has to become a distributed skill, embedded in every line, rather than the preserve of a few specialists,” he says.

AI can strengthen compliance by moving firms from periodic sampling to real-time analysis of entire datasets. But that only works if teams understand the tools well enough to use them, question them and intervene when they fail.

Full Q&A: Ritesh Singhania on why AI governance is becoming finance’s next critical infrastructure

Using AI is always dangerous without the right governance behind it. But the risk profile has shifted fundamentally with the move to generative and agentic systems. Unlike earlier AI, these systems are probabilistic and non-deterministic - they do not produce repeatable outputs that can be tested against a single correct answer. That makes conventional validation approaches inadequate.

What our research shows is that adoption is rapidly outpacing governance across the sector. In the EU, 92% of financial institutions use AI. The technology is being deployed faster than the frameworks meant to govern it - and that gap is widening.

The stakes become particularly acute with agentic AI, which does not just generate outputs but takes actions autonomously. At that point, harm no longer accumulates at human speed. Payment protection insurance - the UK’s largest mis-selling scandal - took years to reach £38 billion in redress. With ungoverned AI agents, as one compliance leader in our research put it, that could happen in weeks.

There are a number of factors that pull in the same direction. Adoption is most mature in the first line of defence, the teams that build and run the systems, where data is structured and the commercial case is immediate; fraud detection, transaction monitoring and KYC screening are already embedded there.

But the second line of defence - the independent risk and compliance functions whose job is to challenge and oversee what the first line does - is significantly behind. Those teams were designed around human workflows that do not absorb AI cleanly, and many are only beginning to deploy the tools they would need to oversee what the first line is already running. Ultimately, the systems are live before the oversight of them is. .

To summarise why governance is lagging behind:

• Economic pressure. Firms capture efficiency gains upfront even where governance frameworks are still evolving. As one Chief Risk Officer told us, firms sometimes want to capture those cost savings even before the model is perfected.

• A capability gap. Recent UK government research places some of the biggest AI skills shortfalls in compliance and risk teams.

• No shared AI governance standard. There is no sector-specific guidance in the UK or EU translating regulatory principles into operational practice, so every firm interprets the same rules alone and rebuilds the same frameworks from scratch.

That last point is the one worth emphasising. At Zango AI, we see this directly. When we work with financial institutions to deploy AI agents - whether for regulatory change management, financial promotions review, or compliance gap analysis - they are each asking different questions and arriving at different answers. There is no common baseline. That is precisely why the sector needs a shared AI governance implementation standard rather than every institution solving the same problem in isolation.

Other jurisdictions have already moved. The United States has adapted the NIST framework into a Financial Services AI Risk Management Framework, built with the US Treasury and more than a hundred institutions. Singapore has done equivalent work through the Monetary Authority’s Project MindForge. We even have a strong domestic precedent in the Joint Money Laundering Steering Group in the UK, where industry writes detailed operational guidance and government endorses it. Nothing comparable exists for AI in the UK and EU yet, and until it does, the governance lag is the predictable result.

It cannot, and that is the uncomfortable starting point. Visibility is the precondition for governance, not an optional extra. In several institutions we studied, compliance and risk functions had limited insight into which AI tools were actually in use across the business.

One Head of Compliance admitted that, asked to show everywhere AI was being used, no answer would be available. Another told us that without visibility you miss things, and that the range of quality in use cases ran from excellent to, in their words, real shockers.

Where deployment is decentralised, that blind spot widens quickly. The firms handling this well have built formal coordination across model risk, product governance, data protection and compliance. In practice that often means an existing function becoming the anchor: for example, this is often model risk, operational risk, or increasingly a dedicated central AI function with a Chief AI and Data Officer or Head of Responsible AI acting as a coordination layer across the business.

The foundational assumption breaks. Financial regulation grew up around systems that were predictable and testable; the same input produced the same output, and behaviour could be backtested against ground truth. That is how model risk has been governed for years. Generative AI produces context-dependent outputs with no single correct answer to validate against, and its behaviour shifts as it ingests new data.

The governance task moves from validating fixed outputs to governing variable behaviour. Agentic systems push it further again, from assessing outputs to governing actions taken on those outputs.

One of the contributors to the research describes this as a responsibility gap: legal liability outpaces a manager’s technical visibility. And once a system’s output cannot be guaranteed, tracing data lineage and logic through a traditional audit trail becomes far harder.

The accountability framework itself has not changed, and that is the tension. Under the Senior Managers regime in the UK, accountability still rests with named executives; you cannot outsource your obligations to an algorithm. So the work shifts from auditing a system’s internal logic to orchestrating continuous, real-time guardrails around the environment it operates in.

It is a real risk. Independent challenge depends on the ability to interrogate how a model actually behaves. Where compliance and risk teams lack that technical literacy, effective challenge becomes impossible, and the function risks being reduced to a reactive gatekeeper that slows adoption without reducing risk. Part of the problem is cultural; several practitioners described real resistance within control functions, a fear that engaging too deeply with AI threatens existing roles.

The danger sharpens as first-line teams begin running AI-driven validation of their own systems. One emerging practice is to use one model to judge another, and that validation can now sit in the first line. If the second line cannot assess whether that automated validation is sound, oversight becomes endorsement. It goes a layer further when oversight functions deploy AI themselves; as one leader put it, you then have to oversee the overseer agents, and the more you rely on AI-assisted review, the harder failures within the review mechanism become to detect.

The answer is not to remove people from the loop but to rebuild capability. Governance has to become a distributed skill, embedded in every line, rather than the preserve of a few specialists. The same AI creating this pressure can also strengthen oversight; firms are already moving from periodic sampling to analysing entire datasets in real time. But that potential is only realised by teams equipped to use the tools, and to exercise judgement when they fail.

It could look like conduct harm that builds invisibly: an AI system generating personalised customer communications at scale - flagging account features, prompting product upgrades, summarising terms - making the same error consistently and at volume. Traditional compliance monitoring, built around periodic sampling and retrospective review, was not designed to catch problems accumulating at that speed.

The second form it could take is systemic. As agents converge on similar models and a handful of foundation providers, correlated behaviour could amplify a market move, echoing the 2010 flash crash, or reduce deposit stickiness and raise the threat of a bank run. One interviewee suggested AI could come to be viewed almost as critical national infrastructure, with only a few core models everyone relies on, which makes a single flaw a system-wide exposure rather than a local one.

There is also the adversarial dimension. The same capabilities are in the hands of criminals, through prompt injection, jailbreaking and adversarial inputs, and weak internal governance leaves firms less able to defend against them.

The common thread across all of this is that the sector expects regulation to be written after the failure rather than before it, and we have lived through that pattern before. That is why Zango AI is bringing together leaders across financial services to build sector-specific operational guidance for AI governance. This includes developing a shared understanding of agentic AI’s capabilities, and the risks and controls that follow. By proactively sharing what works - and what doesn’t - the industry can build practical best practice before a crisis forces the issue.

The scandal that has not happened yet

What might the first major AI-driven financial scandal look like?

One plausible version is a failure of conduct. A bank uses AI to generate personalised customer communications, summarise account terms, suggest product upgrades or explain financial products. The system makes a subtle error — an omission, a misleading phrase, an unsuitable recommendation — and repeats it at scale. Traditional compliance monitoring, built around retrospective sampling, may not detect the issue until harm has already accumulated.

Another version is systemic. Multiple institutions use similar models, vendors, or agents to make operational or market decisions. Under stress, those systems react similarly, amplifying rather than absorbing market movements. A local model failure could become a correlated sector-wide exposure.

A third version is adversarial. Criminals use prompt injection, synthetic identities, automated fraud and adversarial inputs to exploit weakly governed systems. In that scenario, poor AI governance is not just an internal weakness. It becomes part of the sector’s attack surface.

The common thread is speed. Financial scandals have traditionally taken time to build. AI compresses that timeline. A flawed workflow, model, or agent can scale through APIs, automated communications, and operational systems far faster than a human process can.

That creates a difficult responsibility gap. Senior executives remain accountable, particularly under regimes such as the UK’s Senior Managers and Certification Regime. But their legal responsibility may outpace their technical visibility. They cannot outsource accountability to an algorithm, yet they may struggle to understand precisely how an AI system behaved, why it acted as it did, or where control failed.

Image: Ritesh Singhania with the Portuguese team, credits Zango AI

Zango’s bet is that the next phase of AI in finance will not be defined by the most impressive demos, but by the systems that make AI usable, auditable and controllable in regulated environments. That means inventories, control mapping, audit trails, governance boards, testing frameworks, real-time monitoring and technically capable compliance teams.

It is less glamorous than the first wave of generative AI. But it is where the technology becomes real.

The financial sector has often been regulated after failure. The danger with AI is that failure may arrive faster than the old regulatory cycle can respond. By the time a scandal is visible, the damage may already have been done.

That is the urgency behind Zango’s work. The company is effectively arguing that AI governance is becoming a new layer of financial infrastructure. Not a policy document. Not a committee exercise. A live operational capability.

The question, then, is no longer whether AI will transform financial services. It already is. The question is whether financial services can transform governance quickly enough to keep up.

by Gonçalo Perdigão
Accredited Press Professional: CCPJ TE-882
ERC-Registered Media Organisation: 128149

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• AI creativity is no longer just output generation; it is the design of systems where people, models, tools, and governance shape better work.

• Generative design, creative tooling, and synthetic media are converging into agentic production stacks.

• Leaders should optimise for workflow economics: smaller AI where possible, stronger controls where necessary, and human judgement everywhere.

THIS MONTH’S SIGNALS

• Google I/O 2026 pushed the market further into the agentic Gemini era, with AI Mode, AI Overviews, and new creative tools positioned as everyday interfaces rather than side experiments. Google says AI Overviews has more than 2.5 billion monthly active users, and AI Mode has surpassed 1 billion.

• Google announced 100 I/O updates on 20 May, spanning models, agents, Search, Workspace, Android, and creative AI — a signal that AI strategy is now a full-stack product strategy.

• Anthropic launched Claude Opus 4.8, with effort controls, faster/cheaper fast mode, and dynamic workflows for larger agentic tasks; useful for long-running professional work.

• OpenAI updated GPT-5.5 Instant on 28 May for clearer, more natural, better-paced responses; important because everyday interface quality shapes enterprise adoption.

• Adobe continued moving creative work into agents: Firefly AI Assistant entered public beta in late April, and Adobe announced a Gemini creativity connector on 19 May.

WHAT WE PUBLISHED

AI economics and operating models

• Stop Paying for Brains You Don’t Use: Why Smaller AI Beats Bigger AI for Business — bigger models impress; focused models often win on speed, cost, safety, and deployment fit.

• Generative AI Made Creation Cheap. Growth Is Still Expensive — creation is abundant; distribution, attention, and trust remain scarce.

Europe, infrastructure, and strategic capability

• Interview: Mariona Sanz Ausàs, Barcelona Supercomputing Center — sovereign compute, MareNostrum 5, startups, and Europe’s path from research to industrial advantage.

• Interview: Monique Hodges - The Human-Agent Contract: Leading Organisations Through the AI Shift — the real AI challenge is not technology. It is leadership.

Search, commerce, and agentic discovery

• AI ends Checkout — agentic AI is compressing discovery, decision, and payment into conversational flows.

Explore the full archive: Building Creative Machines
Book: Building Creative Machines — the book
Open sketches: Explore and play

Also read: A short look at why Lisbon’s NFC Summit is becoming less like a tech conference and more like a cultural signal.

HOT TOPICS: AI × CREATIVITY

1. Google turns Search into an agentic interface

• What changed this month: Google framed AI Mode as a major Search upgrade and tied it to agentic behaviour: ask, compare, decide, act.

• Why leaders should care: search is becoming an answer-and-action layer, not just a traffic source.

• Implication: brand, content, and commerce teams need GEO: generative engine optimisation means structuring content so AI systems can retrieve, trust, and cite it.

2. Creative agents move inside professional tools

• What changed this month: Adobe’s Firefly AI Assistant and Gemini connector point to creative agents that orchestrate Photoshop, Premiere, Firefly, and multi-model generation.

• Why leaders should care: creative tooling is shifting from app expertise to intent, supervision, and workflow design.

• Implication: the new creative director manages constraints, provenance, brand rules, and taste — not just prompts.

3. Smaller AI becomes a boardroom cost question

• What changed this month: model updates from Google, OpenAI, and Anthropic all emphasised usability, speed, cost, and effort controls.

• Why leaders should care: AI margin will depend on routing the right task to the right model.

• Implication: create a model portfolio: small models for repeatable work, frontier models for ambiguity, and human review for judgment.

4. Synthetic media risk is becoming measurable

• What changed this month: new research on multimodal misinformation found AI-generated content can achieve disproportionate virality, while detectors degrade as generation improves.

• Why leaders should care: trust, brand safety, and provenance are now part of the creative infrastructure.

• Implication: watermarking, audit trails, approval logs, and source discipline should sit inside content operations.

MODELS & TOOLS TO WATCH

• Google AI Mode and Gemini agents
  One-line description: Search and Gemini are becoming action-oriented interfaces for research, comparison, and task completion.
  Best-fit use case: customer journeys, product discovery, knowledge work.
  Risk/limitation: source visibility and publisher impact remain contested.

• Google Pics with Nano Banana
  One-line description: AI image creation and editing with object-level creative controls.
  Best-fit use case: rapid visual iteration, campaign mock-ups, design exploration.
  Risk/limitation: brand consistency and rights management need review.

• Claude Opus 4.8
  One-line description: Anthropic’s Opus-class model for coding, agentic tasks, and professional workflows.
  Best-fit use case: complex builds, code review, multi-step execution.
  Risk/limitation: long-running agents need cost and verification controls.

• GPT-5.5 Instant
  One-line description: OpenAI’s faster everyday model updated for clearer, more natural responses.
  Best-fit use case: enterprise assistants, drafting, support, structured work.
  Risk/limitation: quality still depends on context, retrieval, and governance.

• Adobe Firefly AI Assistant
  One-line description: A conversational creative agent across Adobe’s professional creative stack.
  Best-fit use case: production workflows across image, video, audio, and design.
  Risk/limitation: teams must define approval gates and creative ownership.

WHAT TO DO NEXT

• Map your creative workflows. Identify where AI saves time, where it changes quality, and where human judgment must remain explicit.

• Create a model-routing policy. Match task risk, cost, latency, privacy, and quality to the right model tier.

• Operationalise provenance. Track sources, prompts, edits, approvals, and generated assets before synthetic media risk becomes a crisis.

CURIOSITIES

• Researchers proposed treating generative AI as an “active creative medium”, not just a recommendation engine; the human role becomes disruption, shaping, and curation.

• Architecture students who used local generative AI tools reported greater creative fluency and confidence in AI-supported design processes.

• The most strategic creative skill may now be knowing when not to generate: attention, taste, and trust still do not scale automatically.

Building Creative Machines covers AI, creativity, and society — articles, interviews, and open sketches. Explore the book.

by Gonçalo Perdigão

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Our previous 2026 AI & Creativity Monthly Briefs:

AI & Creativity Monthly Brief — May 2026: Operationalise GEO and AI governance across creative workflows

AI & Creativity Monthly Brief — April 2026: Operationalise agentic workflows across GPT‑5.4 and Claude Cowork

AI & Creativity Monthly Brief — March 2026

AI & Creativity Monthly Brief — February 2026: Operationalise agentic workflows, de-risk GPT‑5.3 noise, embrace Moltbot and Moltbook

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• Faster

• Cheaper to run

• Easier to control

• Safer for company data

• More accurate for narrow jobs

Just as you would not hire a Michelin-star chef to run a tyre complaint desk, you do not need a general-purpose AI trained on everything to solve focused business problems.

1. Why “bigger” sounds attractive — and why it’s misleading

Large Language Models (LLMs) are trained on vast amounts of internet text: books, news, code, recipes, travel blogs, and more.
They can talk about almost anything.

That sounds powerful. But in business, most use cases are not “almost anything”. They are precise:

• Answer customer tickets

• Classify documents

• Check compliance rules

• Summarise contracts

• Support technicians

• Assist sales teams with product facts

A large model brings a lot of knowledge you will never use, but you still pay for it in:

• Higher cloud costs

• Slower responses

• More complex integration

• Greater risk of wrong or creative answers

2. What is a “small” language model, really?

“Small” does not mean weak. It means specialised and focused.

There are three main ways companies make models smaller and better for specific jobs:

A. Training only on what matters

Instead of learning the whole internet, the model learns only:

• Your products

• Your policies

• Your procedures

• Your technical manuals

• Your past cases

This removes noise and improves accuracy for your domain.

B. Controlling behaviour with clear boundaries

Through strong system instructions (software rules), the model is told:

• What it can answer

• What it must refuse

• How formal it should be

• Which sources it must rely on

This makes behaviour predictable and compliant. It is the technique we use in GAIA, a way to control the consistency, compliance, Safety, and Relevance of answers in an environment where infrastructure and costs are zero.

C. Compressing models for efficiency

Modern techniques allow models to be compressed and optimised so they:

• Run on smaller servers

• Sometimes even runs on company hardware

• Costs a fraction per request

You keep useful intelligence, but cut waste. It is the technique used by Multiverse, for example.

3. Why small models often perform better for business tasks

1. They are faster

Smaller models process requests more quickly.
This matters for:

• Call centres

• Live chat

• Internal tools are used all day

Slow AI frustrates staff and customers.

2. They are cheaper at scale

Large models are affordable for demos.
They become expensive when used by:

• Thousands of employees

• Millions of customers

• 24/7 automated systems

Smaller models dramatically reduce operating costs, making AI financially sustainable.

3. They are easier to trust

Big models are trained on public data.
That means they may:

• Invent facts

• Use wording that does not fit your brand

• Suggest actions that break policy

Smaller models trained on your data and rules are far easier to align with:

• Compliance

• Legal requirements

• Internal standards

This is critical in regulated industries.

4. They protect your data better

With smaller, targeted models, companies can:

• Run AI inside their own cloud

• Sometimes on their own servers

• Keep sensitive data out of public systems

In sectors like finance, healthcare, and manufacturing, this is not optional; it is essential.

4. Industry examples: where “small beats big”

🚗 Automotive: tyre and service support

Use case: answering dealer and customer questions about tyres, warranties, and service rules.

A large model knows about:

• Cooking

• Travel

• History

• Coding

• Poetry

None of that helps answer:

• “Is this tyre covered under warranty?”

• “Which pressure applies to this model?”

A small model trained on:

• Product catalogues

• Warranty policies

• Technical bulletins

will be:

• More accurate

• More consistent

• Much cheaper to run

🏦 Banking: compliance checks

Use case: reviewing communications and flagging risky language.

Banks do not want creativity.
They want strict rule-following.

Small models trained on:

• Regulations

• Internal policies

• Approved phrases

can outperform large models that try to be helpful but sometimes become “too imaginative”.

🏭 Manufacturing: technician support

Use case: helping engineers diagnose faults.

What matters is:

• Equipment manuals

• Known failure patterns

• Safety steps

A focused model trained on those documents will:

• Give precise instructions

• Avoid unsafe suggestions

• Work even in low-connectivity environments

No need for global general knowledge.

🏥 Healthcare administration: patient communications

Use case: appointment scheduling, forms, and instructions.

The model should:

• Use approved wording

• Avoid medical advice

• Follow strict workflows

Small, controlled models reduce risk and legal exposure.

5. When big models still make sense

Large models are helpful when you need:

• Broad research

• Creative writing

• Exploration of new topics

• Complex reasoning across many domains

But they are rarely the best choice for:

• Repetitive business processes

• High-volume customer service

• Compliance-driven tasks

• Embedded operational tools

Most enterprise AI workloads fall into the second category.

6. A better strategy: one brain is not enough

Innovative organisations are moving towards:

• Big models for exploration and innovation

• Small models for daily operations

Think of it like this:

• Big AI = strategy consultant

• Small AI = trained specialist staff

You would not ask your consultant to answer every customer email.
And you would not ask your helpdesk to design corporate strategy.

AI should follow the same logic.

7. What executives should do next

1. Start from the business problem, not the model

Ask:

• What exact task are we automating?

• What knowledge is truly required?

• What mistakes are unacceptable?

Then select the smallest model that can do the job well.

2. Measure total cost, not demo cost

Look beyond:

• Per-request pricing

Consider:

• Infrastructure

• Security

• Integration

• Long-term usage volume

Small models often win over time.

3. Demand controllability

Ensure your AI can be:

• Constrained

• Audited

• Updated with new rules

This is far easier with focused models.

4. Build AI like you build teams

You would not hire one person to do every job.
Do not hire one model to solve every problem.

Specialisation scales better than generalisation.

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When the task is specific, when accuracy matters more than cleverness, and when costs and risks must be controlled, small is not just beautiful — small is better.

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NFC Summit: the conference that stopped behaving like a conference

Most conferences still run the same old playbook:

Big venue. Bigger screens. Bigger claims.
A “main stage” that feels like a factory line for opinions.

Then you walk out with a tote bag and a vague sense you should “do something with AI”.

NFC Summit is different. And in 2026, it’s doubling down on the thing that makes it work: art isn’t the entertainment — it’s the structure. (nfcsummit.com)

I’ve attended every edition so far. This will be the 5th. And if you’re trying to understand where Web3, AI, digital ownership, and modern culture are actually heading (not just where panels say they are heading), NFC has quietly become one of Europe’s best signals.

Not because it’s the biggest. Because it’s built like a living prototype.

2026 is the “art-first” pivot (and that matters more than it sounds)

NFC Summit 2026 runs June 4–6 in Lisbon, moving into Unicorn Factory Lisboa in Beato — a setting that already carries “new economy” energy, but without the generic corporate gloss. (Beato Innovation District)

The headline shift is simple:

And this isn’t marketing fluff. The art programme is co-organised by Arab Bank Switzerland and curated with Fanny Lakoubay and the 100 Collectors Collective. That combination is the tell: this is not “art as decoration”. It’s art as governance — taste, selection, context, and the social mechanics of collecting.

If you’ve ever wondered why so many “innovation conferences” feel sterile, here’s a blunt answer:

They optimise for information transfer when people actually show up for belief transfer.

Art is a belief infrastructure.

And NFC is building a conference format that openly uses that truth.

The venue is the message: an industrial machine repurposed for cultural compute

Unicorn Factory Lisboa isn’t a random upgrade. It’s a statement: Lisbon wants the future built inside real places, with real history.

The summit’s art backbone sits in the Fábrica de Moagem (a former flour mill), preserved as industrial architecture and machinery — exactly the kind of environment where “systems thinking” becomes physical.

That matters because AI + Web3 + art is ultimately about systems:

• systems that generate (models, algorithms, prompts)

• systems that verify (ledgers, provenance, signatures)

• systems that distribute (platforms, marketplaces, networks)

• systems that finance (treasury design, stablecoins, patronage)

• systems that create meaning (memes, aesthetics, cultural consensus)

Most events talk about systems.
NFC puts you inside one.

“Kilometer Zero”: the conference layout as a social operating system

At the centre of the venue, the “Central Plaza” becomes Kilometer Zero — a literal convergence point designed for collisions: collectors bumping into artists, builders bumping into curators, investors bumping into the why behind the product.

Anchoring this is a monumental installation by Dmitri Cherniak, described as a continuous, real-time generative system.

Here’s the non-obvious part: this kind of central artwork changes behaviour.

• People don’t just “move between talks”.

• They orbit.

• They pause, return, compare interpretations, and bring others back.

That loop creates repeated contact — the thing every business developer wants and almost no conference design actually supports.

Think of it like this:

And that’s why deals, collaborations, and real network effects happen faster.

The Art Building: four floors that make “digital” feel physical again

The 2026 art programme turns the old mill into a vertical exhibition journey.

The most strategically interesting floor is the Arab Bank Switzerland exhibition: SYSTEMS, curated by Nina Roehrs. The concept explicitly explores how artists engage with systems — from generative algorithms and AI models to blockchain infrastructure and financial mechanisms.

This is where NFC stops being “Web3 culture” and starts being something institutions can take seriously without apologising.

Because the moment a bank co-organises and curates properly, the conversation shifts:

• from hype to acquisition logic

• from “community vibes” to curatorial standards

• from speculation to cultural assets and long-term value

The exhibition ties finalists of the ABS Digital Art Prize 2026 into dialogue with recognised names in digital and generative art.

If you work anywhere near capital allocation — brand, media, funds, banking, corporate venture — this is the part to watch. Not as “NFTs are back”, but as:

“Built for collectors” is a business strategy, not just an art statement

NFC’s curatorial framing is unusually blunt: it’s designed for real encounters between artists, galleries, and collectors.

That sounds niche until you translate it into business language:

Collectors are the event’s high-intent buyers.

In most conferences, the highest-intent people are hidden in VIP rooms. NFC does something smarter: it designs public spaces and programmed moments that make high-intent behaviour visible and socially acceptable.

That’s why the side moments matter:

• artist-led brunches

• private vernissages

• late-night immersive events

• live mural activations

This is basically relationship design — the same logic you see in luxury, high-end real estate, or private banking: fewer random leads, more contextual trust.

And it scales better than you’d think, because the “content” isn’t just speakers. The content is the people watching the art together.

The eight-track format: why NFC keeps pulling in new tribes

NFC Summit 2026 lists 8 distinct events and 350+ speakers, with thousands of daily attendees expected.

But the real story isn’t volume. It’s range — and the fact that the range feels coherent.

The new thematic formats are telling:

• ACAI for Kids (hands-on AI creativity)

• Kawaii Summit (Japanese pop culture, collectables)

• Longevity Day (lifespan, wellbeing, research + founders)

• Stablecoins Day (institutions meeting Web3-native economics)

• Vibe Coding Hackathon (coding + creativity + culture)

This is NFC’s special trick: it treats “serious topics” and “play culture” as the same economy.

Because they are.

In 2026, the most powerful products are not just useful — they’re collectable.
The most powerful brands are not just trusted — they’re remixed.
The most powerful tech isn't just adopted—it’s put into practice.

Why Lisbon keeps winning this genre

A quick Lisbon observation (especially for anyone flying in):

Lisbon has become a natural habitat for this kind of conference because it sits in a productive tension:

• Old city, new systems

• European pace, global crowd

• Creative chaos, serious builders

• Sunlight + scepticism (a rare mix)

Put NFC inside Beato’s innovation district infrastructure, and you get something that feels less like an expo and more like a cultural lab.

Three practical takeaways you can steal (even if you don’t care about NFTs)

1) Stop selling “technology”. Start shipping “culture containers”.

If your AI strategy is all tooling and no taste, you’ll get adoption without loyalty.

NFC succeeds because it wraps emerging tech inside experiences people want to belong to.

2) Design your events like products: loops, not funnels.

Most events are “registration → sessions → goodbye”.

NFC builds loops: central anchors, repeat encounters, shared reference points (artworks), and night formats that create memory.

Memory is what converts into follow-up.

3) Treat collectors as a template for high-intent users.

Collectors are power users with identity at stake. They’re early, opinionated, and willing to spend — but they demand context.

If you can design for collectors, you can design for premium customers in almost any market.

The simplest way to describe NFC Summit 2026

NFC Summit is becoming a reference conference format because it refuses to pick one box.

It’s not “Web3”.
It’s not “AI”.
It’s not “art”.
It’s the messy, electric overlap — where the future actually forms.

In 2026, with the art programme pushed to the centre, NFC is effectively saying:

If you want to understand what people will value next, you don’t start with predictions.

You start with the rooms where taste is being negotiated in real time.

And in early June, one of those rooms is in Beato.

by Gonçalo Perdigão

NFC 2026 is the world’s first Digital Art pop-culture festival. June 4–6 in Lisbon, it unites 5,000+ attendees to explore the intersection of digital art, AI, gaming, and culture through eight experiences.

Read our NFC articles from 2025:

Today, we speak with Monique R. Hodges, whom I had the pleasure of meeting in Shangai during a GEMBA program on innovation opportunities in China delivered jointly by IESE Business School and China Europe International Business School. Our conversations there already reflected the themes that define Monique’s work today: organisational transformation, leadership under uncertainty, and the human dimensions of strategic change.

Monique is the Founder & CEO of Gebanah, a consultancy focused on culture, organisational optimisation, and responsible transformation. Drawing on more than 15 years of experience across market expansion, product launches, board restructuring, and employee engagement, she approaches leadership not as a static management discipline, but as an evolving practice of alignment between people, systems, incentives, and long-term value creation.

Together, we explore the ideas behind Monique’s manifesto, The Human-Agent Contract, and her vision for the next generation of organisations. In this conversation, Monique argues that the AI debate is often framed too narrowly around tools and productivity. The deeper challenge, she suggests, is organizational: governance, alignment, human capital, and the operating systems companies use to make decisions under uncertainty.

The Illusion of AI Transformation

Your manifesto argues that many companies are automating chaos rather than creating value. What are leaders still getting fundamentally wrong about AI adoption?

Leaders are missing the substance within the noise — and the trap is deceptively simple. Without a complete investment thesis and clearly outlined desired outcomes, AI adoption is directionless. If chaos already exists within the chain of command, AI will not fix it; it will operationalize it.

Ask yourself: If your leaders are living and dying by the quarter, what about AI will change that? If there is little coordination and collaboration in how leaders communicate to their teams today, AI will not fix it. If the answer to an unfavorable P&L is to cut costs, lay off people, or add more KPIs, AI will not rectify that either. This is precisely what I mean by automating chaos.

The Human-Agent Contract addresses this directly. It frames agentic AI as three things simultaneously: a tool, an investment, and a synthetic workforce. Treating it as all three requires massive, strategic changes to operational efficiency — not a rushed rollout.

“AI will not fix chaos; it will operationalize it.”

That distinction between automation and alignment becomes central throughout the conversation — particularly in how organisations think about culture, governance, and execution.

In your view, why will some companies use AI to compound advantage while others use it to accelerate existing dysfunction?

The differentiator is understanding the difference between efficiency and effectiveness.

Recognizing that AI is a tool, an investment, and a synthetic workforce is the starting point. But some leaders will know this and still only chase “efficiency” — and I put that in quotes because it is usually a manufactured KPI anchored to last quarter, YOY at best. Those leaders will accelerate existing dysfunction.

The leaders who capture compound advantage will pursue AI effectiveness alongside efficiency impact. They understand that organizational change is already difficult, and those same challenges are amplified with AI. We all have access to the same LLMs. It is just artificial intelligence, not magic.

Creating compound advantage means using AI output as the raw material for increasingly advanced qualitative decisions — driven by human interpretation, direction, and judgment fed back into the model over time.

And here is what most leaders miss entirely: the compound advantage is the function of the cultural complexity of the company whose language it digested. Not the model’s design. Not the departments it touches. Not how many people have access to it. The culture. That cannot be faked or shortcut.

Culture, Governance, and Human Capital

For Monique, the underlying issue is not technological maturity alone, but organizational maturity — specifically how companies govern human capital, decision-making, and strategic alignment.

You frame culture not as a soft topic, but as a direct value driver. What changes when a CEO starts treating alignment as a financial discipline rather than an HR issue?

Every CEO knows culture and value are intertwined, but most do not see the cause and effect running throughout their entire organization. There are three types of capital: financial, physical, and human. Only human capital directly drives financial and physical outcomes — and that changes everything about how you govern it.

Culture is not a department metric. It is an output of all business practices, compounding over time with both intrinsic and extrinsic value — not unlike how goodwill is calculated. It is not owned by HR. Human capital encompasses the entire enterprise: collaborators, stakeholders, business units, and then HR. Human capital surpasses and envelops HR, not the other way around.

When a CEO treats alignment as a financial discipline, they stop asking HR to fix what is fundamentally a leadership and governance problem. That shift alone changes the trajectory.

“Culture is not a department metric. It is an output of how the business actually runs.”

What is the real organizational risk of deploying AI before the company has clarity on decision rights, priorities, and accountability?

The risks are three and they are serious: legal, reputational, and financial.

Because AI is a tool that drives the entire business, its impact cannot be contained to a single department. A single AI use case in a factory, for example, simultaneously touches supply chain efficiency, marketing investment, and sales output. Without guardrails, there is no way to guarantee that AI deployment will not affect R&D, insurance liability, regulatory scrutiny, or your ability to grow.

This risk compounds further when you consider how language models actually work. LLMs are models of social reasoning with the human removed. Anthropic’s own research indicates that only 8.7% of users pause to verify what the model produces. So what happens to your legal, reputational, and financial exposure when the social reasoning that produced the training data begins to thin?

This is not a hypothetical. It will happen — and organizations that have not established clear decision rights and accountability will have no defense when it does.

The Human-Agent Contract

These tensions ultimately lead to the core framework behind Monique’s work: what she calls The Human-Agent Contract.

You introduce the idea of a Human-Agent Contract. What does that mean in practice for executive teams making high-stakes decisions today?

In practice, it means leaders must actively choose their AI capacity — and that choice cannot be deferred.

We are all operating in extremely uncertain, globally volatile times, and decisions still must be made. The Human-Agent Contract asks executive teams two foundational questions: Does your team have the skills to interpret and act on AI outputs? And is the executive team aligned on the company’s topline priorities?

As we move into Q3, what is your North Star? Are decisions consistently being made according to those same priorities across every level of the organization?

We have not seen a technological shift of this magnitude since the internet. I believe executive alignment will be the differentiator. Just as executive teams govern physical and financial capital, they must govern human capital as a measurable asset and treat culture as an output that can be architected and scaled.

“Alignment is the new multiplier.”

What will separate the winning executive teams is their readiness to leverage AI as a driver of business — not a dependency to do business. Decision-making must be grounded in the quality of context, outputs, and historical understanding.

And this is really exciting — the most forward-thinking work I am seeing right now involves organizations deploying experienced people who understand legacy systems to vet AI outputs, lead recoding efforts, and practice what I call digital archaeology: surfacing latent intelligence that would otherwise be lost. That is where the real value transfer happens.

Boards, Execution, and Strategic Readiness

If AI is becoming part of the operating structure of the enterprise, the next question is whether leadership teams and boards are actually prepared to govern it at scale.

Where do you see the biggest gap between how boards talk about AI and how organizations are actually prepared to operationalize it?

The gap is in understanding how language models work — and what that means for long-term value creation.

Boards are largely focused on how much money AI can save. That conversation is incomplete and, over time, dangerous. The board-level conversation must also prepare for two things: the plateau and the implications.

The plateau: Agentic AI depends on the social complexity of human language production. But when the human is progressively removed from that process, models quickly begin training on outputs generated by other language models — or themselves. The result is compounding, statistically average output that is plausible but hollow.

Boards must ask: how will the business continue to replenish the stream of human-generated data that the model depends on?

The implications: Systematic AI deployment narrows the diversity of outputs over time. You lose minority viewpoints — the ones that create market disruption. You lose rare knowledge — the kind that drives differentiation. You lose unusual formulations that find efficiencies, and edge-case perspectives that power value propositions. These do not disappear all at once. They gradually thin and vanish.

And this is what I believe boards are not yet reckoning with: when AI deployment systematically reduces the social complexity it depends on — through cognitive offboarding, homogenization of creative output, and the elimination of interaction-dense work — the technology begins undermining the very conditions that made it valuable in the first place.

“The dangerous part is not the failures. It is the successes.”

Every efficiency gain, every layer of human judgment removed, quietly narrows the substrate the model feeds on. By the time the results show up in your P&L, it is too late.

Many leaders are under pressure to move fast on AI. How should they distinguish between responsible speed and reckless adoption?

Start searching for signals over noise — and if a full investment thesis sounds like too much, think of it as a pro/con list you can do over coffee in the morning.

Ask yourself: To what end will this investment improve the business? What are my existing challenges — and will AI address them or amplify them? What is our exit plan if it does not work? What is our company strategy, and does this AI investment serve it? How would I describe public perception of my company, and what is driving it?

Now read your answers back. Does the AI investment help or hurt? Does your board have enough context to know the difference? How far down the leadership chain can you go and get the same answers you just wrote?

That last question is where most organizations discover the real problem. Responsible speed requires that the answer is consistent from the boardroom to the floor. Reckless adoption is when only the C-suite can answer it.

What are the clearest signs an organization is not ready for agentic AI?

All businesses are ready for agentic AI. Their leaders however must choose at what capacity they want to deploy — and be able to handle it.

The clearest warning sign is when agentic AI is treated as a magic solution rather than an operating-model change. When an organization already has misaligned priorities, unclear decision rights, weak governance, and low trust, those conditions do not disappear with AI deployment.

They show up as vague mission statements, broken messaging across levels, shadow processes, weak governor roles, and constant human rework of AI outputs.

Culture is not a standalone problem. It reflects how the business actually runs. Any existing leak will be amplified — not corrected — by autonomous systems operating at scale.

From Theory to Practice

Beyond diagnosis, Monique’s work through Gebanah focuses on translating these organizational questions into measurable intervention and operational change.

How does Gebanah’s methodology help leaders move from abstract concern to measurable intervention, concrete use cases, and real proof of business impact?

Leaders set clear goals in Q1, and then the year accelerates and those plans drift. Strategy should not. What I do is help leaders maintain alignment so they can keep moving toward their North Star regardless of conditions.

The leaders I work with want their executive teams, shareholders, and cross-functional groups aligned — from the boardroom to the innovation lab — with the coordination and collaboration required to scale innovation across the organization.

They know alignment matters. What they do not have is the time to manage it cross-functionally while also running the business. That is where Gebanah comes in: turning concern about misalignment into measurable intervention and real business impact.

It starts by identifying where the leak is happening and what it is costing — not through abstract culture talk, but through hard numbers.

The Level One Diagnostic provides the roadmap: a Capital and Culture Risk Assessment and Remediation plan that quantifies exposure, maps priorities at risk, and links improvements directly to governance, valuation, retention, and operational stability.

The Gebanah Strategic Alignment framework is a linear, sequential delivery model — designed so that each phase builds on the last and every intervention can be tied to a specific business outcome.

The Next Generation of Organizations

Ultimately, the interview returns to a larger question: what kinds of organizations will emerge successfully from this transition — and what kinds will quietly lose differentiation along the way?

Looking ahead three to five years, what will define the leaders and companies that successfully evolve their organization and culture through AI?

The leaders and companies that win will treat AI as an operating-model redesign — not a tool rollout. Alignment is the new multiplier.

The winning organizations will have a true Human-Agent Contract: strong governance, clear decision rights, disciplined deployment, trusted data, and leaders who can translate strategy into day-to-day execution. The best leaders will not ask how much AI they can add. They will show measurable gains in productivity, quality, speed, retention, and valuation.

That will be achieved through three things:

1. Mission clarity. High performers already stand out in governance, deployment, and data availability. They will rank priorities clearly so AI optimizes for real value — not just activity.

2. Cascading communication. Strategy will be translated cleanly from boardroom to frontline so teams and systems act on the same intent. The strongest organizations will know precisely what humans own, what agents own, and where escalation or override is required.

3. Governor talent at scale. Managers will evolve from doers into orchestrators — supervising AI, monitoring risk, and protecting critical priorities. They will reduce shadow processes, disengagement, and rework by building credibility and consistency into how work gets done.

And here is what the data is already telling us: AI creates less unique experiences and stories over time. The leaders who win will leverage humans, not eliminate them. You may look at your own numbers and believe your company is winning — but when you zoom out, you may find you have lost your competitive edge and become a “me too” product in a field of companies that all fed from the same models. That is individual gain and collective loss. The separation between winning and losing organizations will happen in real time, and the differentiator will not be the AI. It will be the humans directing it.

This conversation with Monique R. Hodges ultimately reframes AI not as a technology story, but as a leadership and organisational design challenge.

Across governance, culture, accountability, and execution, Monique argues that the companies creating durable advantage will not necessarily be those deploying the most AI, but those most capable of aligning human judgment, strategic clarity, and operational discipline around it. Her central warning is equally clear: AI does not neutralise dysfunction. It scales it. Misaligned incentives, weak governance, fragmented communication, and short-term thinking do not disappear under automation; they become embedded into the operating system of the enterprise itself.

In the years ahead, the dividing line between successful and struggling organisations may not be access to the same models or technologies. It may be whether leaders understand that the true differentiator was never the AI itself, but the quality of the human systems directing it.

Image: BSC entrance

During a private tour of BSC in Barcelona, I had the opportunity to visit MareNostrum 5, see its two quantum computers, explore the museum, and better understand the ambition behind the project. It is not only about computing power. It is about science, sovereignty, talent, startups, industry, and Europe’s ability to compete in the generative AI era.

Image: MareNostrum 5

Image: 2/3 (!) quantum computers. MareNostrum 5 – Ona, which includes QBlue, a 20-qubit digital quantum processor; QRed, a 35-qubit digital quantum processor; and QGreen, a 10-qubit analogue quantum processor.

This visit also has a personal connection. I am now part of the CNCA AI Factory (Centro Nacional de Computação Avançada, in Portugal), a project designed to accelerate AI-related startups and give them access to the extraordinary power of MareNostrum 5. With FCT's support (Fundação para a Ciência e Tecnologia) - thanks to Diana Almeida and Susana Caetano (from FCCN - Serviços Digitais da FCT) and the CNCA team's commitment (thanks to Andreia Gaudêncio, Bernardo Malaca, Catarina Ortigão, Daniel Moraes, Larissa Santos and Pedro Marques), this is a rare opportunity to test, build, and scale AI solutions using world-class infrastructure that would normally be out of reach for most founders.

That is why this conversation with Mariona Sanz Ausàs is especially relevant.

Image: Mariona Sanz Ausàs credits LinkedIn profile

Mariona is the Head of Innovation and Business Development at the Barcelona Supercomputing Center, where she leads business opportunities for the private and public sectors and guides technology transfer.

Her role is central to one of the biggest questions in European AI today: how do we turn excellent research into real companies, useful products, industrial strength, and social impact?

Before joining BSC in 2023, Mariona built a strong career across innovation, business strategy, public policy, and open innovation. She was Director at the Generalitat de Catalunya, led Girbau LAB and Strategic Marketing, and spent more than eight years at ACCIÓ, where she worked on business innovation and R&D support. She also studied European integration and economics at the Université libre de Bruxelles and completed executive education at IESE Business School.

In this conversation, we discuss what makes BSC different from a traditional research centre, why sovereign compute matters for Europe, how AI Factories can help startups and SMEs, and where the real opportunities are for founders building with generative AI.

Barcelona SuperComputing Center model represents a departure from traditional research institutions by integrating high-level scientific research with a structured, market-facing innovation ecosystem designed for the generative AI era. There are several elements that define this differentiated character:

• First, BSC AI Institute, recently created, conceived to accelerate the development of advanced artificial intelligence solutions within a multidisciplinary research environment where supercomputing and AI converge in areas such as biomedicine, climate, engineering, computational architecture, and the humanities. BSC AI Institute promotes the collaboration among the more than 320 researchers working in AI within the BSC, strengthening talent development, and coordinating international projects with reference centres.

• Second, a dedicated Innovation and Business Development area, created in 2023, to lead relationships with the industrial sector and anticipate market needs with a vision where science is transformed into tangible economic and social impact. Today more than 35 professionals work to manage the knowledge and technology produced at BSC, promoting the technology transfer through the foundation of spin-offs with high technological value, the promotion of entrepreneurship among researchers and direct collaboration with the private sector. Today BSC manages a portfolio of 15 spin off companies which had created more than 600 jobs and raised 45M€ of private capital.

• Third, BSC AI Factory services which started in April 2025 (with the collaboration of FCT and CNCA in Portugal), as a dynamic ecosystem that foster innovation, collaboration, and development in the field of AI. BSC AI Factory bring together the necessary ingredients – computer power, data, and talent– to create cutting-edge generative AI models- at put them into service for smes, start ups and public administration to foster innovation and the development of new AI-based technologies – including access to experts, software, investors and relevant partners, among others.

The vision for 2030 is ambitious: the BSC aims to act as a “scientific venture builder,” an environment where science does not end in a drawer but evolves into two or three consolidated companies every year.

BSC has strong commitment toward impact and relevancy of its research and has found the key to breaking this barrier through comprehensive support that spans from intellectual property (IP) protection to market validation.

Is for this reason that addresses the transfer stage through different mechanisms supporting researchers and creating a strong innovation mindset and culture.

With programs like the Innovation Journey to accelerate HPC-based projects, having supported 15 potential spin-offs, or Market Validation Program identifies opportunities and validates technologies against real industrial needs. Strategic connections with industry have also been built with BSC Connects programme to support innovation challenges of big companies. Currently companies such as Vueling, Renfe, or Almirall has joint the programme. Meanwhile, thanks to BSC AI Factory direct access to MareNostrum 5 is provided to SMEs and Startups, an “unfair advantage” that allows them to compete globally without the prohibitive costs of high-level computing

While many directors view AI as a short-term efficiency tool, the BSC’s strategic positioning highlights a fundamental misunderstanding: AI and Generative AI is entirely dependent on calculation capacity to foster real transformation of organisations and society. AI becomes a complete new operating system, and companies will operate in high-tech environments. Companies who develop AI will need to master and apply the scientific method to validate market hypotheses, develop algorithmic thinking, appreciate the importance of data, and understand the transformative potential of disruptive technologies. Companies will be reconfigured around digital hubs of AI and data.

And this is why AI Factories appear to support companies in this AI Development path. The BSC AI Factory is not just a hardware upgrade; it is a response to Europe’s dependency on foreign “hyperscalers”, which lower the entry barriers for start ups and SMEs who develop AI in Europe. These actors are the ones that constitute the beginning of the AI ​​value chain in Europe. They develop solutions that respond to the needs of the industry.

Another critical element often overlooked is ethics. Real barriers still exist to scale from pilot to real implementation. BSC AI Factory bets on “Trusted AI,” ensuring developments comply with the EU AI Act and remain human-centric. For business leaders, the message is clear: the value lies not just in the model, but in data sovereignty and social responsibility

Sovereign compute infrastructure will be Europe’s backbone over the next five years. Sovereign compute infrastructure will move from “nice strategic ambition” to one of Europe’s core industrial policy priorities, not because Europe will fully decouple from non-European cloud providers (that’s unrealistic in that timeframe), but because compute is becoming foundational infrastructure for AI, defense, health, manufacturing, and public administration.

AI Factories are an important instrument to increase sovereign compute infrastructure in Europe. With the upgrade of MareNostrum 5—incorporating advanced GPUs specifically for AI—the BSC has been selected by the European Commission as one of the 19 AI Factories in the EU, with the support of Spanish, but also Portuguese and Turkish governments. This “factory” democratizes access to cutting-edge technology, allowing SMEs and startups from Portugal or Spain (and the rest of Europe) to innovate without the financial burden of multi-million dollar hardware investments

If advising a young entrepreneur in Barcelona today, the BSC’s ecosystem suggests a clear path for him: support with expert advice and access to high performance computing infrastructure. But it’s not only about technical support and infrastructure is also support in terms of business opportunities, access to capital and talent. I will recommend him not just build a superficial layer over existing models; real transformation of AI comes when applications solve humanity’s hardest challenges. The real opportunity lies in Deep Tech categories that require supercomputing—from drug simulation to climate resilience and European chip design. With the support of BSC AI Factory the best place to place a bet today is on industrial and scientific applications that generate a real return for society.

Image: BSC building

This conversation with Mariona Sanz Ausàs shows that the future of AI in Europe will not be decided only by algorithms, papers, or applications. It will also depend on infrastructure, talent, trust, and the ability to connect research with the market.

The Barcelona Supercomputing Center is positioning itself as more than a research institution. It is becoming a bridge between science and industry, between supercomputing and startups, and between European ambition and practical execution.

The key message is clear: generative AI is not just a tool for efficiency. It is becoming a new operating system for companies, governments, and society. To use it well, organisations need more than access to models. They need computing power, high-quality data, technical expertise, ethical standards, and a culture of experimentation.

Several highlights stand out from the interview.

• First, MareNostrum 5 and the BSC AI Factory are strategic assets for Europe. They give startups, SMEs, researchers, and public institutions access to computing power that can help them compete globally.

• Second, sovereign compute is becoming a core part of Europe’s industrial strategy. In sectors such as health, defence, climate, manufacturing, and public administration, control over data and infrastructure will become increasingly important.

• Third, Europe’s challenge is not a lack of research. It is the capacity to transform research into products, companies, and industrial advantage. BSC’s work in technology transfer, spin-offs, market validation, and business development directly addresses that challenge.

• Finally, for founders, the biggest opportunity may not be in building another thin layer on top of existing models. It may be in deep tech applications that solve complex problems in science, industry, climate, health, and engineering.

For executives and curious readers, this interview is a useful window into where AI is really going. The next phase will not only be about who has the best model. It will be about who has the infrastructure, the ecosystem, and the ambition to turn AI into lasting economic and social value.

by Gonçalo Perdigão

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Websites, apps, images, videos, marketing copy, prototypes, and even complete product concepts can all be produced at unprecedented speed and low cost. For founders and innovation teams, this feels like a golden age. Production bottlenecks no longer trap ideas.

But a paradox lies at the heart of this productivity boom.

While creation has become dramatically easier, growth has not.
And in some ways, growth may have become even harder.

The Market Did Not Expand at the Same Speed

AI multiplied supply.
It did not multiply demand.

Customers still have the same:

• number of hours in a day

• attention span

• budgets

• cognitive capacity to evaluate new options

Executives may now be pitched ten AI-powered tools a week instead of one a month. Consumers scroll past hundreds of new brands, apps and products every day. Distribution channels are more crowded, not less.

So while the number of digital products exploded, the number of eyeballs did not.

This means competition is not just higher, it is structurally different.

From Scarcity of Builders to Scarcity of Attention

For years, building was the hard part.

You needed:

• developers

• designers

• infrastructure

• capital

• time

Now, a small team, or even a single person, can launch something that looks like a whole company in a weekend.

The constraint has shifted.

Today, the absolute scarcity is:

• trust

• relevance

• distribution

• timing

• credibility

These are not things AI can easily automate.

In fact, when everything looks “good enough”, differentiation becomes harder, not easier.

Why Faster Creation Lowers the Odds of Success

Here is the uncomfortable truth:

If everyone can build more, then more things will fail.

Not because they are bad, but because:

• customers cannot evaluate everything

• procurement cycles are slow

• switching costs remain

• brand still matters

• risk aversion still exists

So we enter a strange loop:

1. AI makes it easy to create

2. This increases optimism and experimentation

3. More products enter the market

4. Attention becomes even more fragmented

5. Growth becomes harder

6. The probability of breakout success drops

From the outside, it looks like innovation is accelerating.
From the inside, it feels like running faster on a treadmill that keeps speeding up.

Go-To-Market Is Now the Real Product

Many teams still behave as if:

“If we can just build faster, growth will follow.”

That assumption no longer holds.

Today, go-to-market is not a phase after building.
It is the core strategic problem from day one.

Questions that matter more than ever:

• Who will trust this product first?

• What existing workflow does it replace?

• Why would someone switch now rather than later?

• What distribution channel is actually under-leveraged?

• What emotional or reputational risk does adoption carry?

These are not technical questions.
They are organisational, behavioural and economic.

AI does not remove them.
It exposes them.

Confusing Output with Impact

For leadership teams, there is a subtle risk.

Dashboards may show:

• more experiments

• more pilots

• more MVPs

• more internal tools

But business impact may remain flat.

Because activity has increased, but market pull has not.

This can lead to false confidence:

“We are innovating more than ever.”

When the real question should be:

“Are we converting innovation into sustained demand?”

In an AI-rich world, shipping is no longer the bottleneck.
Adoption is.

What This Means Strategically

Generative AI does not reduce the importance of:

• brand

• relationships

• partnerships

• distribution power

• customer insight

It increases it.

As products become easier to copy, context becomes the advantage:

• understanding real customer pain

• being embedded in existing ecosystems

• having credibility in regulated or complex industries

• controlling a channel, not just a feature

In other words, the winners will not just be the fastest builders.
They will be the best positioned.

Creation Is Democratised, Success Is Not

AI is an extraordinary equaliser in production.

But markets were never fair, and they are not becoming fairer.

If anything, we may see:

• more experimentation

• more visible failure

• fewer breakout winners

• stronger power laws in attention and revenue

Which leads to the final paradox:

AI makes it easier than ever to start.
It may be harder than ever to scale.

Generative AI did not remove the laws of economics, psychology or competition.
It simply accelerated the front end of the innovation funnel.

For leaders, the strategic shift is clear:

Stop asking,
“What can we build now?”

Start asking,
“How will this actually earn attention, trust and commitment in an overcrowded world?”

Because in the age of infinite creation, growth — not technology — is the real differentiator.

by Gonçalo Perdigão

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TL;DR

• An agentic workflow is software that can plan, use tools and complete multi-step work with limited supervision; April’s signal is that leaders now need operating models, not just prompts.

• The highest-leverage moves sat outside model novelty: compliance, distribution, service packaging and decision rights.

• The human edge is not disappearing; it is concentrating around taste, trust, memory and accountability.

THIS MONTH’S SIGNALS

• AI-first is becoming a management question: who decides, who reviews, who carries risk.

• Compliance is moving from friction to the product layer, especially in Europe.

• GEO, or Generative Engine Optimisation, means structuring content so search engines and AI systems can retrieve and cite it.

• Synthetic media is shifting from demo culture to repeatable commercial workflows.

• In brand and culture, automation is raising the value of distinctly human signals.

WHAT WE PUBLISHED

From the newsletter archive, four clusters stood out.

Operating models and governance

• AI-First Workflows (18 Apr): AI is not a faster assistant; it is a new operating model that reallocates decisions, risk and accountability.

• S&P 500 AI Strategy (14 Apr): enterprise AI is moving from pilots to production, with infrastructure and governance separating leaders from followers.

• Interview: Pedro Alfama — Verdaio.ai EU Compliance (21 Apr): compliance is becoming AI’s killer use case because operators need regulation translated into action.

• GEO Playbook (1 Apr): if models mediate discovery, your content must become evidence, not marketing fog.

Human edge, brand and culture

• Interview: Martin Lindstrom — Why Human Brands Will Beat AI Brands (28 Apr): as automation rises, authenticity and emotion become stronger brand assets.

• Sporting CP vs SL Benfica: Why the Lisbon Derby Is Football’s Most Human Technology (20 Apr): ritual, memory and collective emotion still resist machine replication.

• Altman’s Superintelligence Manifesto (8 Apr): “people first” narratives matter less than who accumulates power and policy influence.

Applied AI in market contexts

• Calico Zillow Renovation Videos (10 Apr): a low-cost AI output becomes strategic when packaged as a repeatable, compliant service.

• INSEAD AI Venture Lab Just Proved AI Can Grow Companies Faster (21 Apr): founders learned that AI advantage comes from execution discipline, not slogans.

Foundations for builders

• AI Before Intelligence (7 Apr): a tiny perceptron is a useful reminder that modern systems still begin with simple decisions.

• For a more hands-on view of how creative machines are built, explore our open sketches.

HOT TOPICS: AI × CREATIVITY

AI-first workflows
What changed this month: our April coverage reframed AI from assistant software to operating design.
Why leaders should care: this changes budgeting, approvals, team shape and accountability across creative workflows.

• Example: S&P 500 AI Strategy and AI-First Workflows point to the same move — build governance and control layers before scaling output.

Compliance as infrastructure
What changed this month: compliance no longer appeared as a legal drag but as a practical product layer.
Why leaders should care: in regulated environments, AI governance now shapes speed-to-market and customer trust.

• Example: Verdaio.ai suggests a path where regulation becomes an executable workflow rather than a late-stage blocker.

Synthetic media as a service
What changed this month: synthetic media moved from novelty to commercial packaging.
Why leaders should care: margin will sit less in the generation itself and more in review, rights, turnaround and distribution.

• Example: the Calico/Zillow case shows how a simple video output can become a valuable service when wrapped in a process.

GEO and discoverability
What changed this month: discoverability broadened from SEO to model retrieval.
Why leaders should care: if assistants answer before users click, being citable matters as much as being rankable.

• Example: the GEO playbook is a practical prompt for every executive page, explainer and product note.

MODELS & TOOLS TO WATCH

• Verdaio.ai (EU compliance workflow layer) — why it matters: turns regulation into operational tasks that teams can execute.
  Best-fit use case: regulated operators that need faster policy-to-process translation.
  Risk/limitation: depends on legal interpretation and adoption.

• GEO Playbook (retrieval and citation method) — why it matters: improves how models find and reference your content.
  Best-fit use case: thought leadership, explainers and product pages.
  Risk/limitation: weak source material still stays weak.

• Claude Cowork (agentic workspace) — why it matters: pushes AI from chat towards execution.
  Best-fit use case: coordinated task support across multi-step knowledge work.
  Risk/limitation: autonomy can create opacity if oversight is weak.

• Perceptron (foundational learning model) — why it matters: shows how simple decisions scale into intelligence.
  Best-fit use case: executive education, onboarding and demystifying AI systems.
  Risk/limitation: too simple for production reality.

CHECK OTHER NEWSLETTERS

• This month’s input included source links for Dr. Storm, Obsolete AI and Suma Positiva, but no pasted excerpts. Rather than speculate, we are holding specific claims.

WHAT TO DO NEXT

• Re-map one end-to-end creative workflow this month: define where AI creates, where humans approve, and where compliance must intervene.

• Audit your top five public pages for GEO readiness: make claims sourceable, definitions explicit and evidence easy for models to retrieve.

• Prototype one synthetic media offer with disclosure, review steps, turnaround targets and margin assumptions.

CURIOSITIES

• A perceptron can look trivial, but it is still one of the cleanest ways to explain why AI starts with weighted choices rather than magic.

• The Lisbon derby may be a better case study in creativity than many product demos: ritual, context, and memory are still hard to automate.

• A $15 renovation video is a useful executive lesson: the workflow around the output often matters more than the output itself.

Building Creative Machines covers AI, creativity, and society — articles, interviews, and open sketches. Explore the book.

by Gonçalo Perdigão

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