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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Carts abandoned. Forms half-filled. Passwords forgotten. Payment pages that feel like crossing a border.

What Google just outlined with its push toward agentic commerce hints at something much bigger than better shopping bots. It points to a structural change in how commerce works at all.

We are moving toward the end of checkout as a distinct step.

From “browse → cart → checkout” to “ask → decide → done”

Large language models are quietly collapsing the entire e-commerce funnel.

Discovery no longer starts with keywords, filters, and pagination. It begins with intent expressed in natural language.

A decision no longer means comparing 10 tabs. It means agents narrowing choices instantly.

When Google talks about agents completing purchases without leaving the conversation, that’s not a UX improvement; it’s a category shift.

Zero clicks doesn’t mean zero commerce

We’ve spent two decades optimising for clicks: CTRs, funnels, conversion rates. But agentic commerce flips the metric entirely.

0 clicks → more sales is not a paradox anymore.

In an agent-driven world:

• The “page” is a conversation

• The “storefront” is context

• The “CTA” is trust

If an AI agent already knows:

• who the customer is

• what they’ve bought before

• their preferences, size, budget, loyalty status

• their payment credentials

Then the highest-converting interface is… no interface at all.

Just confirmation.

“Yes, that works.”

That’s it. Sale completed.

Merchants still matter, but differently

A common fear is that platforms will own the customer and squeeze retailers out. What’s interesting about Google’s direction is the opposite emphasis: merchant-of-record stays with the retailer.

That’s crucial.

In agentic commerce, power shifts from traffic ownership to relationship ownership:

• Customer history

• Loyalty logic

• Personal pricing

• Bundling intelligence

• Post-purchase experience

Retailers who understand this will stop obsessing over homepage design and start obsessing over:

• clean product data with Generative Engine Optimisation (GEO) on top of SEO

• real-time inventory

• composable pricing rules

• agent-readable APIs

Payments become background infrastructure

Once agents handle intent and identity, payments become plumbing.

Think about what’s happening:

• Wallets already store cards

• Platforms already authenticate users

• Agents already act on behalf of customers

The logical outcome is ambient payments:

• No forms

• No redirects

• No explicit checkout page

Just consent.

Physical retail isn’t immune; it’s merging

This shift doesn’t stop online.

In-store associates are becoming agents.
Apps are becoming personal shoppers.
Stores are becoming fulfilment nodes in an AI-orchestrated network.

Imagine:

• walking into a store where your agent already knows why you’re there

• inventory dynamically adjusted to predicted intent

• delivery options decided before you ask

Retail becomes continuous, not channel-based.

What changes now for e-commerce teams

The stack is forming quickly.

Practical shifts happening right now:

• SEO gives way to answer optimisation, or Generative Engine Optimisation (GEO)

• PDPs give way to structured product knowledge

• CRO gives way to agent success rates

• UX gives way to conversation quality

• Checkout optimisation gives way to trust optimisation

The brands that win won’t be the loudest; they’ll be the most legible to machines.

Takeaway

Agentic AI isn’t just improving e-commerce.

It’s ending the era where buying is a process.

Buying becomes a moment inside a conversation.
Sales happen without clicks.
Checkout becomes invisible.

The question for retailers isn’t if this happens.

It’s whether your business is ready to sell when no one ever visits your site again.

by Gonçalo Perdigão

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It was a pleasure to be there and experience the event's energy firsthand. The conversations were rich, multidimensional, and refreshingly practical, crossing industries and perspectives rather than getting stuck in traditional marketing language. This was a serious discussion about decision-making, trust, culture, competitiveness, and the future of business.

One of the most anticipated speakers was Martin Lindstrom, a globally recognised leader in branding, consumer behaviour, and business transformation. Over the years, he has advised iconic companies such as LEGO, Disney, and PepsiCo, helping them reconnect with customers and reinvent relevance. He is widely known for pioneering the concept of “small data”, the human observations and behavioural clues that reveal what consumers truly feel, often more accurately than large spreadsheets ever can.

Martin Lindstrom and Carlos Coelho (from Ivity Brand Corp) at the Branding & Business Summit

Lindstrom is also the author of several influential books. Buyology explored how neuroscience shapes purchasing decisions and why emotion drives choice more than logic. Brandwashed examined how companies influence habits and desires in everyday life. Small Data argued that tiny observations often unlock the biggest business opportunities. The Ministry of Common Sense focused on how bureaucracy harms innovation and customer experience, and on how organisations can regain agility and humanity.

During the summit, I had the opportunity to sit down with Martin for a direct, fast-paced conversation about AI, authenticity, consumer behaviour, brand growth, and what will define the strongest brands over the next five years. His answers were sharp, provocative, and deeply human, exactly what one would expect from someone who has spent decades studying why people connect with certain brands and reject others.

Photo Credits: Carob Mill

Interview with Martin Lindstrom

Not really. I mean, at the end of the day, we’re all brands, right? As soon as you’re a living person, you have an identity. You can choose to amplify it or choose not to build your brand.

I tend to say we all have three bank accounts. One is where you get your salary. One is where you educate and train yourself. And then you have your brand account.

The brand account is where you build brand equity. Because in the future, we won’t stay in the same job all our lives. We may be in one job for a year and then move on to somewhere else. The brand equity you’ve built has to go into that account, because then you’re safe when you move on. If you don’t do that, you have to start all over again.

The biggest branding mistake is very simple: they believe AI is sophisticated enough to create all communication.

That means things become shallow. You can still sense it’s AI, so you never become emotionally attached to what you see.

We’ll get there eventually. But it will take time. If you jump on it now, you’re wasting your media spend, because people can feel it. They’ll say: I don’t want to be influenced by an AI robot trying to make me buy something.

That’s the biggest mistake.

I think we are in a transition period right now.

In the past, branding had already been going downhill in quality. People believed it was all about the logo and its size. They forgot the emotional side. Then AI came in, and the emotional side continued declining.

But at some point, people get tired of technology. We already see the reputation of technology in the US declining significantly, alongside healthcare. A recent study showed that.

What we’ll see is technology becoming more of an enemy than a friend, as it was perceived until recently. And once that happens, you can’t use technology to communicate a perfect image anymore. People will want the opposite. They’ll want authenticity.

So that migration will happen. AI and technology will be used to distribute, personalise, animate, and make things beautiful, but not yet to create truly emotional messages.

The first thing they’re ignoring is that you cannot read in a report how people feel.

You have to get your hands dirty and get into homes, into real life, into reality.

The world has never changed this fast—and it will never change this slowly again. Because of that, the consumer you had five years ago before COVID, the consumer you had three years ago before Ukraine, and the consumer you had one year ago before the wars in Iran, have all completely changed.

Yet we’re stuck with the mindset that people still behave the same way.

So get your hands dirty quickly. Once you touch that nerve and bring it into your product innovation and service innovation, that’s where you stand apart from everyone else right now.

Yes, they can. They just need to be clever.

Let me give you an example: LEGO first became hugely successful with boys. They tried three times to enter the girls’ segment. They failed twice. The third time was a home run.

The brand returned to form after nearly going bankrupt 16 years earlier. Then everyone said: Okay, that’s it. LEGO is doing well now, but growth will slow down.

Guess what? Through some of our research, we discovered that adults were playing with LEGO—and paying for it.

Today, that represents around 60% of total revenue. They discovered they could create an adult LEGO line—a whole new business.

They thought they were done. They thought there was no more room to grow. But small data helped them uncover a completely untapped opportunity.

The strongest brands will have their own communities, and those communities will be highly specific.

They’ll be able to speak to very individual people in exactly the tone of voice those people resonate with. And that voice will be consistent across every touchpoint: from store design to customer service to packaging.

I also think brands will become much more authentic. They will intentionally celebrate mistakes.

And I think brands will begin to question why they exist. Because of that, they’ll need to become much better at usability, much better at connecting with people where they are—instead of forcing consumers to adapt to bureaucracy and compliance.

There will be a lot of change in the next five years.

Take your entire team, leave work for one day, move in with consumers, and watch how they interact with your brand or product. Then come back, look at each other, and ask: Are we heading in the right direction?

Photo by Catarina Carvalho: Martin Lindstrom and Gonçalo Perdigão after the interview, at the Branding & Business Summit

What became clear throughout both the summit and this interview is that branding is no longer a communications function; it is a leadership function. It is not about logos, slogans, or visual identity alone. It is about trust, coherence, relevance, emotional connection, and the ability to create meaning across every touchpoint of an organisation.

Martin Lindstrom’s central message was especially timely: while businesses race toward automation and AI, the brands that will truly win are those that become more human, not less. Technology can help distribute, personalise, and scale experiences, but it cannot replace empathy, intuition, authenticity, or genuine understanding of people. Consumers increasingly sense when something is artificial, shallow, or manufactured, and they respond accordingly.

His reflections on “small data” were equally powerful. In a world obsessed with dashboards, metrics, and algorithms, Lindstrom reminded us that many of the most valuable insights still come from observation: watching how people live, struggle, choose, complain, improvise, and aspire. Those signals rarely appear in reports, yet they often contain the blueprint for innovation.

The broader atmosphere of The Branding & Business Summit reinforced the same idea. Across panels on geopolitics, talent, leadership, creativity, and transformation, one theme emerged repeatedly: the future belongs to organisations that can combine intelligence with imagination, scale with sensitivity, and growth with authenticity.

Lisbon proved to be the right setting for this conversation, a city increasingly connected to entrepreneurship, global business, and creative energy. Leaving the summit, one thing felt certain: brands that listen more carefully, act more courageously, and stay closer to human reality will define the next decade. And Martin Lindstrom remains one of the clearest voices explaining why.

by Gonçalo Perdigão

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Disclosure: This interview was recorded using the Xiaomi 17 and its built-in real-time recording and automatic transcription features, which performed impressively in a noisy live-event setting. Xiaomi provided the device for testing purposes only, with no editorial input, sponsorship, or content approval involved. Any future sponsored content will always be clearly disclosed.

At Building Creative Machines, we have dozens of exclusive interviews with the leaders transforming our days. Here you can find past interviews with leaders from Anthropic, Google, IESE, Microsoft, Harvard, TikTok…

What followed is now reflected in a newly released academic working paper from INSEAD and Harvard Business School: “Mapping AI into Production: A Field Experiment on Firm Performance.” The paper studies 515 high-growth startups that participated in the programme and offers rare causal evidence that AI can improve real company performance, not just productivity on isolated tasks.

For anyone serious about AI strategy, entrepreneurship or the future of work, the findings deserve attention. Here is the paper, by Hyunjin Kim INSEAD, Dahyeon Kim INSEAD and Rembrand Koning Harvard Business School, to whom I much appreciate the time spent and the dedication to the program:

Mapping AI into Production: A Field Experiment on Firm Performance

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What Is the INSEAD AI Venture Lab?

The INSEAD AI Venture Lab is a global entrepreneurship initiative created by INSEAD, one of the world’s leading business schools.

The programme is designed to help early-stage founders build and scale ventures using artificial intelligence as a core capability. Unlike many startup accelerators that focus mostly on fundraising and pitch decks, the AI Venture Lab is deeply operational: it focuses on how founders can actually use AI to redesign products, workflows, teams and business models.

The 2025 cohort was global, remote-first (with time in Abu Dhabi and Singapore) and highly selective. Startups came from Europe, Asia-Pacific, the Americas, the Middle East and Africa. According to the paper, the median company was founded in 2024 and had a team of 4 people. Many already had products, customers or early revenue.

What Participants Received

The programme included six major pillars:

• Access to frontier AI tools and API credits

• Weekly technical training sessions

• Founder workshops led by faculty and operators

• Peer groups and mentor office hours

• Investor exposure and demo days

• Opportunities for non-dilutive funding prizes

Partners included Google Cloud, OpenAI, Manus, and NVIDIA.

This made the programme one of the most practical founder environments I have seen for testing AI in real business conditions.

Why This Accelerator Matters

There is endless discussion about AI tools increasing productivity. But most of the evidence so far has focused on individuals performing specific tasks faster: writing emails, coding, researching, or providing customer support.

That is useful, but incomplete.

The harder question is:

That means more customers, more revenue, faster execution, less capital required and stronger business outcomes.

This is exactly what the INSEAD research set out to measure.

The Core Idea: “The Mapping Problem”

The paper introduces a powerful concept: the mapping problem.

Many companies have access to AI tools. But access is not the real bottleneck.

The real challenge is discovering:

• where AI creates value inside the business

• which workflows should change

• what tasks should be automated

• how teams should reorganise

• how products themselves should evolve

In simple terms, most companies know AI exists but do not know how to redesign their operations around it. That distinction is critical.

Image: Prof. Ethan Mollick was one of the researchers who boosted the cohort

What the Researchers Tested

During the accelerator, startups were randomly split into treatment and control groups.

Both groups received normal accelerator support.

But one group also received structured examples and case studies showing how other companies were reorganising production around AI.

This gave researchers a way to measure whether improved thinking about AI deployment affects business outcomes.

That is rare in entrepreneurship research, and even rarer in AI research.

Main Findings: AI Improved Real Company Performance

The results were striking.

1. Startups Found More AI Use Cases

Companies that received the additional AI mapping guidance identified 44% more AI use cases than the control group.

Not just chatbots or copywriting, but product development, operations, strategy and workflow redesign.

2. Teams Completed More Work

Treated firms completed 12% more tasks overall.

This suggests AI was increasing execution speed, especially on internal work.

3. More Paying Customers

They were 18% more likely to acquire paying customers.

This is one of the most important metrics in any startup.

4. Revenue Nearly Doubled

The treated group generated 1.9 times the revenue of control firms.

That moves AI from “interesting tool” to a real commercial lever.

5. Less Need for Capital

Perhaps the most underrated result:

Companies exposed to stronger AI operating models demanded 39.5% less external capital.

This could become one of the defining shifts of the AI era.

Why This Changes Startup Economics

Traditionally, startups scale through:

• hiring more people

• raising more money

• extending the burn runway

• slowly building products

AI changes this formula.

The paper suggests companies can:

• ship faster

• operate leaner

• test more ideas

• reach customers earlier

• reduce dependence on fundraising

That is a structural change in entrepreneurship.

My Personal Experience Inside the Programme

Having participated in the 2025 cohort, I saw firsthand that this was not a superficial AI trend exercise.

The programme pushed founders to think beyond prompts and tools.

The real challenge was:

• rethinking production systems

• compressing decision cycles

• removing internal bottlenecks

• using AI across multiple functions simultaneously

• building businesses that would have been too expensive or slow only a few years ago

Why Corporations Should Pay Attention

This research is not only for startups.

Large organisations often have:

• more data

• more budget

• more processes

• more inefficiencies

• more bureaucracy

Which means the upside may be even larger if they solve the mapping problem.

Buying licences for AI tools is not enough.

The real advantage comes from redesigning how the company works.

What Leaders Should Do

1. Audit Internal Bottlenecks

Where does work slow down today?

2. Map AI Across Functions

Not one department. Every function.

3. Redesign Workflows

AI layered onto broken processes creates limited gains.

4. Measure Commercial Outcomes

Revenue, customers, margin, speed.

5. Build Capability Early

The learning curve compounds.

Final Thought

The biggest misconception in AI today is that success comes from having access to the best models.

This paper suggests something more important:

That is a management challenge, not a technology challenge.

And from inside the INSEAD AI Venture Lab, it was clear this future is already underway.

by Gonçalo Perdigão

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Read more about the participants, the tech stream, the mentors and what happened at INSEAD AI Venture Lab:

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Pedro’s path makes sense once you zoom out. He’s done the hard yards in high-volume, real-world environments: Ford, IVECO, then into product roles where execution matters more than theory. The turning point, at least for this story, was his time at TLScontact: building technology products that sit right on top of highly sensitive data, documents, passports, biometrics, and that must work inside strict operational and regulatory constraints. That’s a brutal training ground. You either learn to build responsibly, or you learn what breaks first.

Fast-forward to today, and we’re suddenly operating in the same arena again: AI, regulation, and the messy reality of shipping products into Europe. Pedro has launched Verdaio.ai, a compliance intelligence platform designed for companies that don’t have a legal department on standby but still need to navigate the EU’s fast-expanding rulebook, across sustainability, privacy, AI, technology, and cybersecurity.

I tested Verdaio myself. What surprised me wasn’t just the breadth of tools, it was how “operational” the product feels. It doesn’t speak like a consultant’s slide deck. It behaves as a product leader built it: clear prompts, structured outputs, practical next steps, and an obvious bias toward getting teams unstuck quickly. That’s why I’m sharing this conversation.

Pedro isn’t positioning himself as a lawyer, and Verdaio isn’t giving legal advice. The value is different: it turns regulation into a set of concrete questions, artefacts, and decisions that teams can actually execute, before the procurement questionnaire lands, before the auditor asks, before the first real fine hits the market.

Pedro is the founder and product leader at Verdaio.ai

Before I started building my own products, I worked on different projects, one of which was highly compliance-sensitive. I was a Product Manager for an Outsourced Visa Service Provider, a company that runs visa application centres on behalf of governments. My product involved collecting all documents submitted online by the user, as well as passport and fingerprint scans, and a photo taken at the Visa Application Centres. I find it difficult to imagine more sensitive data.

After that experience, I built some products and MVPs, but I always felt I lacked full control as AI moved so fast.

So I started doing more research and decided to build a product about compliance because I realized how little I knew, how time-consuming it is, and how necessary it already is in 2026. What I want to explain is why I believed it was worth a product, especially for small and mid-sized companies. The current alternatives focus on only one area, are too expensive or require serious integration.

Also, most current discussions focus on new AI models, tools, token efficiency, faster building, and so on. That seems to be all we need to know.

I wanted to add “making it compliant” to this scene.

A disclaimer up front: I’m not a lawyer, and I have never worked in governance or compliance. I’m a product builder and I focus on solutions.
What I have built is Verdaio, a product that makes it simple for a company to find where it falls short of European regulation. It is not legal advice. It sits between a company knowing nothing about the applicable rules and that same company paying thousands of euros to lawyers and consultants to find out. And when a lawyer or consultant is still needed afterwards, their work is usually cheaper, because the groundwork is already done.

When I looked at the European regulatory landscape - the AI Act, the Cyber Resilience Act, NIS2, DORA, the post-Omnibus versions of CSRD and CS3D, I noticed something uncomfortable.
Big companies have legal teams, compliance officers, and consultancy retainers. They can absorb the cost of new regulation. Even big fines. Small and mid-sized companies, who are in scope of most of the same rules, mostly don’t have any of that. No compliance officer, no legal budget, no one whose job it is to read EUR-Lex on a Tuesday. They’re expected to comply anyway. A small company doesn’t need to build AI. But if they use some provider that does, there are already rules for that.

That’s the gap I built Verdaio to address. Not because I had a market study in hand - I didn’t - but because I couldn’t see how an SME like mine, in Portugal or anywhere in the world that does business in Europe, was meant to navigate, say, the AI Act, without spending money it doesn’t have on consultants. The work had to get cheaper or it wouldn’t get done. AI was the obvious lever to make it cheaper because compliance work is structured, text-heavy, jurisdiction-specific, and largely involves synthesis. Exactly what LLMs are good at.

The product itself took months to develop. But the sharpest test and evidence for this came when I pointed our own AI Act Readiness tool at Verdaio as a subject. The first offline pass came back around 45 out of 100, “Developing” , a humbling result for a compliance product.
Then came around fifteen days of fixing the gaps the tool had just surfaced. The most intense stretch was a five-day sprint writing every artefact the tool flagged as missing:classification doc, model card, risk register, technical documentation, AI literacy record, incident log, accuracy methodology, scope-exclusion notes, supplier DPA register. The rest of the fortnight went into rolling out completion logging across every tool, building a benchmark harness to make the outputs reproducible, hardening prompts against injection, tightening the model temperature to close output variance, and redoing the legal pack end to end. After the sprint we hit 85 out of 100, “Advanced”. Today, on a reproducible run, we sit at 92. I suspect some much larger companies, even with full legal teams, would run Verdaio against themselves and find gaps they hadn’t noticed either.

That’s the shape of the use case. A solo founder, months to build the product focused on compliance and around fifteen days to fix the compliance gaps it surfaced - moved from “developing” to “advanced” on a concrete regulatory readiness scale. Five years ago that journey would have required a consultancy engagement and five-figure fees. Today it doesn’t have to. But “doable” is not “prompt a generic chatbot and call it done” - that would produce confident-sounding nonsense against regulation this dense. What made the work possible was a tool that already had the regulatory framework built in - the articles, the classification boundaries, the obligation grid, the scoring methodology - and someone whose job is to keep that framework current as the law moves. That is what Verdaio is, and it is why I could use my own product to audit itself. For the SMEs I built Verdaio for, the availability of that kind of specialised tooling is the difference between compliance being realistic and remaining a consultancy luxury they cannot afford.

I don’t have much hard evidence here. These are just patterns I’ve noticed while talking to people and exploring during development.
But I’ll give you a practical example. GDPR has been around for years. I suggest a simple test for anyone building products: Ask an LLM to conduct full research on how compliant your website is regarding GDPR.
It’s very common to find gaps when you least expect them.

So, if this happens under GDPR, with the EU AI Act being so new, I wouldn’t expect better results.

Another piece of hard evidence I have was found on the frontier models. They are making it visible that a Human-in-the-Loop (HITL) is now a reality in their chats.
This change is due to regulation. Other businesses might also need to make changes, but I see little evidence they are aware.

However, the biggest evidence is that AI sometimes doesn’t arrive in companies labelled “AI”. It arrives as a feature inside a SaaS tool. Your sales team turns on an AI lead scorer. Your support team deploys a chatbot. Your dashboard starts generating “AI insights”. Few businesses procure and assess AI systems. They procure a CRM upgrade. So when regulation starts asking about the AI systems you operate, most companies don’t even know where their inventory is.

Another personal experience serves as evidence. I studied machine learning and I focus on EU regulations daily for my project. Even so, I had to re-read the Provider/Deployer definitions in the AI Act several times to figure out which one Verdaio was. (Both, it turns out, for different tools.) If that’s me, I don’t blame a product manager at a mid-sized SaaS for not having figured it out yet.

And the fourth, which I suspect matters most, is that there’s no forcing function yet for most companies. The fines are theoretical until someone gets fined. What I’m starting to see change that is enterprise procurement. When you try to sell into a bank, an insurer, or a public authority, their procurement team is now asking hard questions about AI governance. That’s what pulls the issue from “someday” to “this week” for a lot of teams. At least that’s what’s happening in the few enterprise conversations I’ve had.

Honestly, I think so because the focus will shift in that direction.

Two things shifted my view.

The first was watching what Omnibus I did to CSRD scope earlier this year. Thousands of SMEs got cut out of the mandatory reporting perimeter. Most saw that as relief. The sharper ones I’ve talked to saw it as an opening. They now voluntarily publish under the VSME standard, because their larger customers still have to report and need sustainability data from suppliers. Suddenly “voluntary” reporting becomes a commercial weapon. The supplier that hands over a clean VSME pack wins the contract. That’s not theory. It’s been a real pattern in how people describe their CSRD conversations to me.

The second was more personal. After I finished building Verdaio’s internal compliance pack, with all the documentation the regulation expects, I realised I had something I could show. When someone asks “how do you handle AI governance?”, I can send them a folder. That changes the sales conversation. The quality of the conversation changes immediately.

So my honest answer: I’ve seen hints that regulation can be an advantage, but it’s an advantage for the companies that treat their compliance work as evidence they show, not credentials they earn. Evidence beats credentials once the market matures. That’s my guess.

This one I feel more confident about, because I lived it. Governance, almost every time.

AI products in production usually ship with unexamined defaults. What parameters are set, what gets logged, which outputs are benchmarked, and how reproducibility is proven. Any one of those is an audit problem later if it was never written down.

Verdaio had exactly this problem during development. No temperature policy. No reproducibility benchmark. No change log. No test harness. I fixed it in an afternoon. Benchmark at 0, production at 0.3. It has been stable since. The model wasn’t broken. The process around the model was.

I tell the story because it’s the cleanest version of a pattern I think is everywhere. The model does what it was trained to do, roughly. The decisions around how you call it, what you log, how you version prompts, when you re-benchmark: that is where things silently rot. And when something goes wrong publicly, the company that can point to a written decision with a date on it is fine. The one that can’t is in real trouble.

The unglamorous version of the answer: most of the value of “good AI governance” is just having written down what you decided and why. That part isn’t hard. It’s just not done.

To correctly address that question, we first need to answer: In five years, will every company be using AI? I would say it’s almost unavoidable at this point.

A lot could be said about reality five years from now, especially around AI. But I’m confident that “doing and using AI the right way” will be mandatory.

Let’s look at the regulation plans.

The enforcement calendar is crowded and moving. The AI Act’s high-risk rules are scheduled for August 2026 and August 2027, though a proposed Digital Omnibus could push those dates to December 2027 and August 2028 if adopted. The Cyber Resilience Act’s main obligations take effect at the end of 2027. The revised Product Liability Directive starts covering software and AI from December 2026. CS3D is transposed by mid-2028, applied mid-2029, with first disclosures in 2030. CSRD Wave 2 reporting arrives in 2028. PSD3 and the Payment Services Regulation are expected to apply around 2028. Any European company, or any company selling into Europe, will meet at least one of these in the next five years. Most will encounter several.

If AI is almost unavoidable, and the regulatory calendar is what I just described, then yes, every company will need some version of an AI compliance layer. Just as every company has accounting, bookkeeping, and payroll because those obligations didn’t become optional once they were written into law, AI governance is becoming a non-negotiable function for companies that make decisions about people. Hiring, pricing, credit scoring, content moderation, support routing, lead qualification. If your software affects a human outcome, regulators will likely increasingly want to know how.

What Verdaio Changed in 7 Days

Pedro ran the same EU AI Act Assessment twice, one week apart, using Verdaio itself as the subject.

Before (12 April 2026): “Progressing” — 65/100
The first report classified Verdaio’s system as Minimal Risk (Art. 6 not triggered) and described a company with “strong foundations” but several missing pieces. The gaps were practical and familiar to anyone shipping AI fast: incomplete risk management documentation, thin data/input governance notes, and weak human oversight / override mechanics. In short: nothing “broken” in the product — but the evidence layer was patchy.

After (19 April 2026): “Advanced” — 92/100
Seven days later, the picture changes completely. The second report classifies Verdaio as Limited Risk under Article 50 (because AI-generated content reaches natural persons) and shows an Advanced posture: risk management, technical documentation, logging, transparency disclosures, human oversight, and post-market monitoring are reported as in place.

The real “upgrade” wasn’t the model, it was the governance wrapper

Across the two PDFs, you can see the shift from “we basically do this” to “we can prove this”. The improvement is driven by very operational moves:

• Art. 50 transparency tightened: making AI disclosure clearer and positioned correctly in the user journey and outputs.

• Risk work formalised: turning partial risk thinking into an actual risk register with owners, decisions, and review cadence.

• GPAI supply-chain trail documented: model/version tracking and retaining upstream documentation (Anthropic + Amazon Bedrock) as a durable record.

• Human oversight made explicit: simple mechanisms to flag issues, halt/withdraw, and reinforce “advisory-only” boundaries.

• Monitoring and reproducibility are treated as first-class: not “nice to have”, but part of how you defend the system over time.

Why this matters (even if you’re not “an AI company”)

This is the part most teams miss: compliance is rarely blocked by a single catastrophic flaw. It’s blocked by missing artefacts and undocumented decisions.

And that’s exactly why compliance is emerging as one of AI’s most valuable real-world uses: it can convert dense regulation into structured work, and structured work into evidence. The “before” report shows what happens when a product is ahead of its paperwork. The “after” report shows what happens when the paperwork becomes part of the product.

If you sell into Europe — or sell to anyone who sells into Europe — this “evidence layer” will increasingly decide whether you pass procurement, shorten security review cycles, or get stalled for months.

The Before sample:

Verdaio AI Act Assessment 2026 04 12 Before

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The After sample:

Verdaio AI Act Assessment 2026 04 19 After

520KB ∙ PDF file

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How ready is your company?

What I like about Pedro’s thinking is that it removes the drama from compliance without underestimating the risk.

A few ideas from this conversation will stick with me:

• Compliance is becoming a mainstream AI use case not because it’s exciting, but because it’s structured, text-heavy, and full of checklists — exactly where specialised AI systems can compress cost and time.

• Most organisations don’t understand their exposure because AI arrives sideways: via CRM features, support tools, analytics add-ons. If you don’t know your AI inventory, you can’t govern it.

• Governance breaks before models do. Temperature policies, benchmarking, logging, versioning, and incident records — unglamorous, but decisive when scrutiny shows up.

• Regulation can become a commercial weapon when compliance is treated as evidence you can show, not a certificate you claim. In procurement-heavy markets, that distinction changes conversations fast.

The practical takeaway is simple: the EU’s regulatory calendar is no longer “future”. It’s becoming an operating environment. The companies that win won’t be the ones with the best intentions; they’ll be the ones with the cleanest artefacts, the clearest accountability, and the fastest path from “we should” to “we did”.

That’s the bet Verdaio is making: not replacing lawyers, but making the first 80% of compliance work achievable for teams that would otherwise postpone it forever. And if you’re selling into regulated buyers, banks, insurers, public sector, enterprise procurement, “achievable” is often the difference between closing a deal and never making it past the questionnaire.

by Gonçalo Perdigão

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I did not go as a supporter.
I did not go searching for tactics, statistics, or controversy.

I went as an observer.

And from that distance, something became clear very quickly: the most important part of the evening was never confined to the pitch.

The match was only one layer of the event.

What unfolded around it was something far older, deeper, and more revealing about human nature.

Before the first whistle, the stadium was already alive. Fireworks cut through the Lisbon sky. Vast banners moved across the stands like ceremonial fabrics. Scarves rose in synchrony. Songs echoed from one side, answered by the other. Tens of thousands of people, many of whom had never met, behaved as if connected by a single nervous system.

Modern society often describes football as entertainment.

That definition feels far too small.

It is a ritual disguised as sport.

It is identity expressed through colour, sound, memory, and movement.

It is one of the few places left where strangers willingly gather to feel something together.

As the scoreline changed, so did the stadium's emotional climate. Confidence turned to anxiety. Relief turned to tension. Hope returned unexpectedly. Whole sections of the crowd fell silent at once, then erupted seconds later with explosive force.

The crowd did not merely watch the match.

The crowd became part of it.

AI can analyse player positioning in real time.
It can predict probabilities of victory.
It can optimise substitutions.
It can personalise highlights.
It can monetise attention more efficiently than any broadcaster in history.

It can explain what happened.

But it cannot inhabit what happened.

It cannot inherit rivalry from a grandparent.

It cannot understand why a shirt colour can hold emotional weight across generations.

It cannot feel why one chant can contain thirty years of memory.

It cannot know why defeat ruins a weekend, or why victory makes strangers embrace.

Those things are irrational.

And precisely because they are irrational, they are deeply human.

They emerge through context.
Through memory.
Through belonging.
Through contradiction.
Through emotion.

A derby is built from all five.

Context lives in every reference outsiders miss.

Memory lives in every supporter who remembers previous battles.

Belonging lives in every shared chant.

Contradiction lives in loving something that so often causes pain.

Emotion lives everywhere.

No machine arrives carrying childhood memories of a parent taking them to the stadium.

No model understands what it means to care without reason.

No system feels loyalty despite repeated disappointment.

That matters.

Not because football rejects technology, it does not. VAR, analytics, data science, and algorithmic media are already embedded in the game.

But because meaning still exceeds measurement.

When the evening ended, the results were entered into the databases instantly.

Statistics were updated.

Tables shifted.

Commentary multiplied online.

Yet none of those systems fully captured what actually happened.

Thousands of bodies moved through cycles of fear, hope, pride, anger, joy, and relief in shared time.

That experience will be remembered far longer than the numbers.

And in an era increasingly shaped by machines, perhaps that is exactly why it matters so much.

by Gonçalo Perdigão

Most organisations talk about AI as if it were a tool upgrade.

That shift matters because most “AI projects” fail for a boring reason: teams automate the wrong things, keep the same handoffs, and add new complexity on top. They end up with more drafts, more reviews, more Slack messages, and the same bottlenecks, just faster.

The winners do something different. They redesign workflows so the right “agent” handles each decision:

• Autonomous AI for low-risk, fully specified work

• AI + human in the loop for quality control at scale

• Human + AI assist for judgement-heavy, high-stakes decisions

Below is a simple, structured way to move from “workflow” to “AI-first workflow”, using six steps and a few hard questions that make allocation obvious. They are based on a very insightful webinar I attended from the Board of Innovation, along with several personal notes.

Key takeaways

1. AI doesn’t optimise workflows—it redesigns them. The biggest gains come from changing decision paths, not speeding up tasks.

2. The problem is allocation, not automation. Performance improves when each decision is routed to the cheapest agent that still meets the quality requirements.

3. Advantage comes from system design, not tools. Your edge lies at the interface between AI and humans—especially in escalation rules.

The AI-first workflow in six steps

Think of this as a loop, not a one-off transformation. You map, allocate, operate, measure, and then reallocate as models and confidence improve.

Step 0 — Prioritise: choose what to redesign first

Not every workflow is worth touching. Pick one where:

• There is volume (enough repetition to matter)

• There are clear inputs and outputs

• The workflow has visible wait states (handoffs, approvals, queueing)

• The outcome affects cost, revenue, risk, or customer experience

Use a simple 2x2 to decide:

• Readiness (data quality, standardisation, stable process)

• Strategic leverage (impact if improved)

Start here: high readiness + high leverage.
Quick wins: high readiness + low stakes.
Invest to unlock: low readiness + high leverage (often data/process clean-up first).
Deprioritise: low readiness + low leverage.

Step 1 — Map the decision flow (not the process chart)

Process maps often lie because they show the “happy path”.

You want a decision map:

• Every decision point (approve, reject, classify, escalate, price, route, interpret)

• Every handoff (who receives it next, and why)

• Every wait state (queues, missing info, ambiguity, policy questions)

A practical method: take 20 real cases from the last month and trace them end-to-end. Highlight:

• Where people ask for clarification

• Where work bounces back

• Where quality is checked

• Where exceptions appear

Step 2 — Define constraints and allocate work

Now allocate each decision to one of three archetypes.

The three archetypes

1. Autonomous AI
   AI runs end-to-end. No human in the loop. Best for low-risk, fully specifiable tasks with rules.

2. AI + human in the loop
   AI produces an output; a human reviews/approves before final. Best when you need speed and control.

3. Human + AI assist
   Humans lead; AI supports with research, options, drafting, and comparisons. Best for high-stakes, judgment-heavy work.

The four questions that make allocation obvious

For each decision, answer these in plain language:

1. Cost of error: what happens if it’s wrong? Does it need to be right the first time?

2. Determinism: can you fully specify the task with clear inputs/outputs and rules?

3. Judgement: is tacit knowledge required (context, politics, risk appetite, relationship nuance)?

4. Recurrence: does this repeat with patterns, or is it mostly one-off?

A simple rule:

• Low cost of error + deterministic + recurring → Autonomous AI

• Medium cost of error or partial determinism → AI + human in the loop

• High judgment or high cost of error → Human + AI assist

This is where most organisations unlock value: not by buying a better model, but by properly routing decisions.

Step 3 — Define escalation logic: design the AI–human interface

This is the centrepiece.

If Step 2 decides who should own a decision, Step 3 decides when ownership switches.

Borrow a proven principle from operational excellence: Jidoka—build quality into the process by detecting abnormal conditions and stopping/escalating immediately. In classic operations, it means the line can stop when something is wrong; the system prevents defects from flowing downstream. (トヨタ自動車株式会社 公式企業サイト)

In AI-first workflows, “stop the line” becomes: escalate to a human when a trigger fires.

The escalation design template

For each trigger, define three things:

1. Trigger condition (what the AI detects)

2. Escalate to (role, not person)

3. Information package (what the human needs to decide fast)

Example: compliance verification workflow

• Trigger: “Unknown requirement” (policy gap)
  Escalate to: compliance lead
  Package: relevant clause + closest matches + confidence score + source references

• Trigger: “Borderline match” (high consequence if wrong)
  Escalate to: bid/transaction owner
  Package: exact gap + risk exposure + options + recommended wording

• Trigger: “Contradictory clauses” (logic conflict)
  Escalate to: proposal/legal owner
  Package: conflicting sections + page refs + suggested resolution paths

The key insight: the trigger is designed, not accidental. If you do not design escalation, you get random escalations—people lose trust and revert to manual work.

This is also where governance becomes real. A framework like NIST’s AI RMF treats AI risk as a socio-technical issue: not only the model, but also how people deploy and oversee it across its lifecycle. (Publicações Técnicas NIST)

Step 4 — Design the human role (it changes)

In AI-first workflows, humans should not be “executors”.

They become:

• Architects (define constraints, policies, routing)

• Judges (handle exceptions and edge cases)

• Trainers (label outcomes, correct patterns, improve prompts/rules)

• Owners (accountable for outcomes, not outputs)

This is a shift in capability. You need fewer people doing routine production work and more people doing quality control, exception handling, and system tuning.

A practical move: rewrite role expectations in one sentence:

• Old: “produce and process”

• New: “supervise, decide, and improve the machine”

Step 5 — Measure outcomes and reallocate continuously

AI-first workflows create a new advantage: every AI node is measurable by default.

But you must measure the right things. The goal is not “more AI usage”. The goal is outcomes at lower cost and controlled risk.

Metrics that matter (simple and operational)

• Quality: error rate, rework rate, audit findings

• Speed: cycle time, time-to-first-draft, time-to-decision

• Cost: cost per decision, review hours per 100 cases

• Risk control: escalation rate, high-severity incident count

• Trust: human override rate (and why), user satisfaction

Then do the most important part: reallocation.

As AI capability improves and your escalation design becomes reliable, tasks should migrate:

This is how the feedback loop compresses. It is also why process redesign beats “tool rollout”: it keeps paying you as models improve.

Recent thinking on scaling AI increasingly points to this same idea: redesign end-to-end processes and define human–AI roles, rather than chasing incremental automation. (BCG Global)

Your moat is the routing layer

Most competitors can access similar models.

Your durable advantage becomes:

• your decision map,

• your allocation logic,

• your escalation triggers,

• your measurement loop.

If you treat AI as a chatbot bolted onto yesterday’s process, you get demos.

If you treat AI as a decision-routing system with designed escalation and continuous reallocation, you get compounding performance.

by Gonçalo Perdigão

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For the last few years, most big companies treated AI as a set of experiments: a pilot here, a “copilot” feature there, a few integrations, a press release. That era is ending. The next era is harder: putting AI into real workflows, with real customers, under real regulation, with real consequences.

A recent CB Insights + Human[X] report makes this shift very clear. It tracks S&P 500 activity across partnerships, investments, acquisitions, hiring, and earnings calls from 2023 to 2025.

Download the report from CB Insights + Human[X] here:

CB Insights Human[X]: The Future Of The Enterprise AI Buildout

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The market looks broad, but power is concentrated

The report finds that nearly 70% of the S&P 500 showed some external AI activity over the period. That sounds like a flood. But zoom in, and you see the real shape: five companies (NVIDIA, Microsoft, Amazon, Alphabet, Salesforce) account for 32% of all documented AI activity across relationships, investments, and acquisitions.

This matters because it tells you what “AI leadership” actually means in practice.

It is not just about using AI internally. It is controlling the ecosystem: distribution, compute, models, tooling, and the deal flow around them. In other words, some firms are building the rails everyone else will ride.

The report also notes something easy to miss: 30% of the S&P 500 had no documented external AI activity, yet many of them are still hiring heavily for AI roles and discussing AI on earnings calls. So the outside signals help you see visible leaders, but they do not capture the full internal buildout.

Partnerships are rising faster than revenue

One of the cleanest signals in the report is partnership growth. Startup partnerships involving S&P 500 companies rose from 835 in 2023 to 1,031 in 2025.

But most of these partnerships are not the kind your CFO loves.

Nearly 68% are “ecosystem-building” relationships: integrations, pilots, and co-marketing. Only about 12% are client relationships, and 12% are vendor relationships.

That mix tells you the market is still in an “alignment” phase. Companies are deciding:

• Which platforms will they build around

• Which tools will they standardise on

• Which model providers do they trust

• How they will connect AI to data, security, and operations

This also explains why partnership hubs matter. The report highlights NVIDIA, Microsoft, and Amazon as the main hubs — with NVIDIA’s advantage tied to its deep embed in the infrastructure layer, and Microsoft/Amazon extending reach through cloud distribution and enterprise access.

Money still flows to infrastructure, not apps

If you want to know what enterprises really believe, watch where they place capital.

The report shows enterprise investment concentrating in infrastructure and model-layer companies: Databricks, OpenAI, CoreWeave, Anthropic, xAI, Cohere, Scale, and Groq. Infrastructure/model plays account for over half of enterprise investment rounds.

It even draws a useful analogy: this pattern resembles the earlier cloud buildout, where money went first to the enabling layers, and applications scaled later.

This has a strategic implication that many boards still underestimate:

If your AI strategy is “buy applications,” you are arriving late to the part of the market where defensibility will sit. The durable bottlenecks are still compute, data plumbing, orchestration, evaluation, and governance.

The real shift: from capability to control

Here is the most important part of the report — and the part many companies are least prepared for.

As AI moves from assistive tools to more autonomous systems (agents), the question changes from:

“What can AI do?”

to:

“How do we control what it does?”

The report highlights rising interest in governance and agent-related markets, including the “Know Your Agent” (KYA) concept: identity, permissions, monitoring, and control for agents operating in production environments.

That framing lines up with where standards and best practices are going:

• NIST’s AI Risk Management Framework gives a practical structure for identifying and managing AI risks across the lifecycle (not just at launch). (nist.gov)

• ISO/IEC 42001 sets requirements for an AI management system, including risk assessment, impact assessment, lifecycle management, and supplier oversight. (ISO)

In plain terms, “AI governance” is becoming operational work, not policy work.

A practical playbook for the next 12 months

If you strip away the hype, the report points to a very practical plan. Here is what tends to work in companies that are moving from pilots to production.

1) Treat AI like a production system, not a feature

Pilots fail because they stay “special.” Production wins when AI becomes normal IT.

Do three basic things:

• Put AI behind the same release gates as software (testing, approvals, rollback plans)

• Measure performance over time (drift, error rates, cost per task)

• Assign an owner with operational responsibility (not just innovation responsibility)

2) Build your “agent perimeter”

If you are deploying agents (or plan to), you need a perimeter: identity, permissions, audit logs, and monitoring.

A simple starting checklist:

• Unique identity for each agent (and for each tool it can use)

• Least-privilege permissions (what it can access, change, spend)

• Full traceability (what it saw, what it did, why it did it)

• Continuous evaluation (before and after deployment)

This is exactly the kind of control layer the report is signalling with KYA and governance markets.

3) Standardise your stack before you scale spend

Partnerships are rising, but most are still ecosystem-building. That means many firms haven’t locked in their stack.

Your job is to reduce variance:

• Choose a small set of approved model endpoints

• Choose one orchestration pattern (so teams don’t invent ten)

• Define how data is accessed (and what never leaves the boundary)

• Decide how you observe systems (metrics, logs, red-teaming outputs)

The fastest way to burn money is to let every business unit buy its own AI universe.

4) Assume “infrastructure first” in your deal radar

The report shows capital and activity clustering at the infrastructure and model layers.

So if you are in corporate development or strategy, flip the usual scanning order:

1. Governance and control (audit, evaluation, permissions, monitoring)

2. Orchestration and workflow tooling

3. Data and inference infrastructure

4. Vertical applications (only once the above is stable)

This is where M&A may open up “below the biggest winners,” as the report suggests, especially in governance and infrastructure enablers.

5) Make ROI easier by starting with low-risk, high-volume work

The report’s “looking ahead” section is blunt: the bottleneck is shifting from access to AI to operationalising it inside real workflows.

So pick work that is:

• Repeatable (high volume)

• Observable (clear success metrics)

• Contained (limited blast radius)

• Easy to revert (humans can take over fast)

Examples: internal knowledge retrieval, ticket triage, document drafting with approval gates, forecasting support with human sign-off.

Insight

Many companies think the AI race is about picking the “best model.”

The report suggests something different: the next advantage comes from building the systems that make AI safe, governable, and scalable inside the enterprise, while everyone else is still announcing pilots.

And that is why the enterprise AI buildout is becoming less like buying software, and more like building a modern operating system for the firm.

by Gonçalo Perdigão

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Key sources: CB Insights + Human[X] report on enterprise AI buildout (CB Insights), NIST AI RMF (nist.gov), ISO/IEC 42001 (ISO)

Read the latest AI & Creativity insights in our monthly briefing:

Stay part of the conversation, explore our updated 2026 AI Glossary:

There’s a very specific kind of content that makes people stop scrolling: a room “fixing itself”.

Not a static before/after. Not a slideshow. A transformation that feels like a film trailer—dust, light, movement, a clear “future state”. And now you can build that from nothing but a listing’s existing photos, plus Calico AI, and deliver it as a productised service to local agents.

The clever part isn’t the AI. It’s the packaging.

Because most agents don’t want “a cool video”. They want:

• more buyer enquiries this week

• more listing appointments next week

• something they can post without thinking

• and a story they can tell sellers: “Here’s how we’ll market your home differently.”

This is how you turn the workflow you shared into a small, repeatable business line—without drowning in revisions, legal risk, or endless custom work.

1) Sell the outcome, not the process

If you pitch “AI renovation videos”, you’ll get curiosity. If you pitch “seller-winning listing content”, you’ll get budgets.

A simple framing that works:

• For sellers: “We show buyers what the home could be—without staging or construction.”

• For buyers: “You’ll see the potential instantly.”

• For the agent: “It helps you win the listing and increases qualified calls.”

Keep it boringly commercial. Your offer is not art. It’s a listing conversion asset.

2) Create a “style bible” once and reuse it forever

Your biggest enemy is inconsistency. The second biggest enemy is endless back-and-forth about taste.

So you set a house style that becomes your default, and you only deviate for a premium fee.

Example style packages (name them like product tiers):

• Scandi Bright (white oak, warm neutrals, soft daylight)

• Modern Premium (stone, matte black accents, hotel lighting)

• Family Ready (cosy, clean, practical, light staging cues)

Why this matters: the “after” images are doing two jobs at once.

1. They make the transformation feel real.

2. They signal the price bracket and buyer type.

Calico itself publishes a prompt guide specifically for turning listing photos into cinematic renovation transformations—use that as your baseline and standardise from there. (Calico AI)

3) Don’t renovate everything—renovate what moves the needle

A mistake beginners make: they try to transform every room.

In practice, you get the most impact from three moments:

• Kitchen (value perception)

• Primary living space (emotion + lifestyle)

• Kerb appeal/façade (click-through and drive-bys)

If the listing has a truly ugly bathroom, that can replace one of the above. But keep it to 2–4 transformations max.

This keeps costs down, production predictable, and the video punchy.

4) Build the “shot list” like a movie trailer

Your deliverable should feel like a trailer, not a walkthrough.

A reliable structure:

1. Cold open (0–2s): worst “before” shot, fast.

2. Promise (2–4s): one line of on-screen text: “Imagine this home, renovated.”

3. Transformations (4–18s): 2–4 room morphs, each with a clear beat.

4. Payoff (18–24s): best “after” hero shot, linger a touch longer.

5. Close (24–30s): agent branding + call to action.

This format works because it compresses narrative:

• problem → possibility → proof → action

And it fits social platforms where attention is rented by the second.

5) Price it like a product, not like a creative project

Agents are used to paying:

• photographers,

• video people,

• stagers,

• editors.

What they hate is ambiguous creative billing.

So you offer fixed packages:

• Starter: 1 transformation + branded close (short-form)

• Standard: 3 transformations + music + captions (short-form)

• Premium: Standard + 1 extra format cut (e.g., vertical + square) + 24h delivery

The numbers depend on your market, but the logic doesn’t: fixed scope, clear turnaround, limited revisions.

6) Handle the boring legal bits up front (or you’ll regret it)

You’re using listing photos. That raises two practical issues:

A) Rights to use the photos
Just because photos are on a portal doesn’t mean anyone can reuse them freely. Platforms have terms, photographers have rights, and the industry is actively litigating image usage. Zillow’s own terms and related licensing language are worth reading carefully. (Zillow)
Separately, the scale of photo-related disputes in real estate marketplaces has been highlighted in mainstream photography press as well.

Practical rule: only do this for the listing agent (or with their written permission). If you’re selling as a service, you can bake a simple clause into your agreement: “Client confirms they have the right to provide and use the photos for marketing and derivative works.”

B) Disclosure: “this is a visualisation”
You are showing a future state. That can mislead buyers if you imply it’s real.

So add a small, consistent caption:

• “AI renovation visualisation. For marketing only.”

It builds trust, reduces risk, and actually makes the content feel more premium—like an architect’s render, not a trick.

7) Reduce revisions with one simple trick: “one decision point”

If you let agents review every intermediate step, you’ll get trapped in taste debates.

Instead, you give them a single decision point:

• They pick the style package.

• You deliver the finished cut.

• Revisions are limited to typos, branding, or swapping one photo.

If they want a different aesthetic halfway through, that’s a new order.

8) Distribution is the real moat

The video is the hook. The system around it is the business.

Give agents a posting kit:

• 3 caption options (short, medium, story-led)

• 10 hashtags tailored to the area

• a suggested posting schedule (e.g., Tuesday 6 pm + Saturday 11 am)

• a “seller pitch” paragraph they can paste into emails

9) How to find buyers without cold email misery

Start with local leverage:

• Find agents who already post reels weekly (they value content).

• Offer one “demo” using an old listing (so no risk to their current seller).

• Put your watermark in the corner, small.

• Ask them to post it, tag you, and include a soft CTA.

Then turn that into a loop:

• 1 demo → 1 post → 1 inbound from another agent → repeat.

10) The opportunity: contractors and developers

Agents are the obvious customers. But the higher-margin buyer is often:

• renovation contractors,

• small developers,

• architects doing extensions,

• property investors pitching partners.

They don’t just want attention. They want funding, approvals, and confidence.

Same workflow. Different wrapper:

• “Here’s what this becomes.”

• “Here’s the uplift story.”

• “Here’s the vision in 20 seconds.”

The real takeaway

Calico-style renovation videos are not a “content trend”. There’s a change in who gets to produce persuasion.

When a solo operator can create cinematic transformation media from existing listing photos, the bottleneck shifts:

• away from cameras and crews,

• towards taste, packaging, compliance, and distribution.

That’s good news—because those are business skills, not film-school skills.

by Gonçalo Perdigão

P.S. Also read our last interview on Real Estate:

Sam Altman has started saying the quiet part out loud: superintelligence is close, disruption is unavoidable, and politics will have to catch up fast. Axios framed it as a kind of “New Deal” for the superintelligence era. (Axios)

OpenAI’s 13-page document, “Industrial Policy for the Intelligence Age: Ideas to Keep People First” (April 2026), is designed as a conversation starter but also an attempt to define the agenda before governments do. Download it here:

Industrial Policy For The Intelligence Age

3.85MB ∙ PDF file

Download

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The plan clusters around three headline ideas that sound worker-friendly:

• Public Wealth Fund (a national fund investing in AI-era growth, paying citizens dividends)

• Robot / automated labour tax (modernise the tax base as payroll taxes erode)

• Four-day workweek pilots funded by “efficiency dividends” (32 hours, no pay cut, maintain output)

If you’re running a business, advising one, or financing one, the practical question is not “is this kind?” It’s:

1) Public Wealth Fund: the dividend is the marketing; the governance is the battleground

The Public Wealth Fund proposal is simple: seed a large national fund, invest in diversified assets (including AI companies and AI adopters), and distribute returns to citizens.

It becomes a social contract for accepting concentration

A public dividend can make the public tolerate a winner-takes-most AI economy, especially if the narrative is: “Yes, a few firms will capture huge value, but everyone gets a cheque.”

The key design question: what is the fund actually buying?

If the fund mostly holds public equities and broad indices, it will behave like a national pension-style portfolio. Fine—but then it’s not directly correcting AI concentration. It’s just letting citizens ride the same market wave.

If the fund is seeded with special access—equity, warrants, compute credits, or licensing rights from the most powerful AI builders, it becomes much more consequential. But that is where governance becomes explosive:

• Who decides which models count as “frontier” and therefore owe contributions?

• Who values private stakes in fast-moving AI companies?

• Who prevents regulatory capture (the fund becoming dependent on the firms it should discipline)?

A “people first” version that actually bites

If policymakers take this seriously, the fund should be paired with competition rules and public option capacity; it’s a legitimacy shield.

A practical blueprint executives should anticipate:

• Contribution triggers tied to measurable thresholds: revenue share, compute scale, or model capability tiers (rather than voluntary pledges).

• Anti-capture governance: independent board, transparent mandate, mandatory disclosures on holdings and conflicts.

• Use dividends as stabilisers, not bribes: e.g., automatic top-ups when displacement metrics spike (mirroring the paper’s preference for automatic stabilisers in safety nets).

2) Robot tax: the revenue isn’t the point; the incentive design is

OpenAI argues that the tax base may shift away from labour income (and payroll taxes) towards corporate profits and capital gains as AI reshapes work, so tax systems must adapt—including by exploring taxes on automated labour.

The robot tax debate is old. The hard part has always been implementation: what counts as a “robot”, and how do you avoid punishing productivity? A useful primer on the pitfalls is the Tax Policy Centre’s analysis of “robot tax” logic.

Companies will automate in ways that are hard to tax

If you tax “robots” as physical assets, automation shifts into software and process redesign. If you tax “AI usage”, firms route usage through vendors, offshore entities, or bundled services.

So the most workable versions tend to tax outcomes, not “robots”:

• Excess profits in specific sectors with rapid AI substitution

• Windfall gains linked to AI deployment at scale

• Capital income at the top end (where the upside concentrates)

A “people first” version that doesn’t kill adoption

If you’re advising boards, assume policy will move towards “retain, retrain, or pay” structures.

Concretely, expect mixes of:

• Wage-linked credits for retention and retraining (the paper explicitly points to R&D-style incentives).

• Transition levies that activate only when layoffs exceed thresholds (so you’re not taxing every efficiency improvement).

• Sector-specific schemes (customer service, back-office operations, basic analytics) where displacement is most measurable.

For companies, this becomes a finance-and-operating-model issue:

• Build a workforce transition P&L line now (training, redeployment, severance, outplacement) so you’re not improvising under a future levy.

• Treat “automation ROI” as after-policy ROI—scenario it like you would carbon pricing.

3) Four-day workweek: This is less about kindness than about control of productivity gains

The document proposes “efficiency dividends”: convert AI-driven efficiency gains into better benefits and time back—explicitly including time-bound 32-hour/four-day workweek pilots with no loss in pay, maintaining output and service levels.

This is not fringe. The UK has already produced credible evidence from large trials that many organisations can hold output steady with fewer hours, often by cutting meeting waste and tightening workflows. The UKRI summary of research outcomes is a useful public-sector-facing reference point. (The Autonomy Institute)

A four-day work week becomes a stealth work-intensification tool

Plenty of firms “compress” work into fewer days by increasing pace and surveillance. Employees get a “benefit” that quietly demands always-on performance.

That’s why OpenAI’s framing is revealing: pilots must “hold output and service levels constant”.

The corporate reality: You will be asked to prove where the productivity went

In an AI-heavy organisation, a four-day work week is a governance mechanism: it forces leadership to decide whether AI productivity gains flow to:

• shareholders (margin expansion),

• customers (price cuts),

• employees (time back/benefits), or

• reinvestment (growth).

The “people first” promise only works if you can credibly measure and share gains.

Practical playbook:

• Run 90-day pilots in functions with measurable throughput (support, finance ops, reporting, marketing ops).

• Define “output” properly (quality-adjusted, not just volume).

• Use the pilot to delete work, not compress it: meeting bans, decision rights, template-first documentation, fewer approvals.

• Hard-stop AI-induced scope creep (“because it’s faster, do more”)—this is the classic failure mode.

The hidden centre of gravity: infrastructure, energy, and bargaining power

One of the most concrete parts of the manifesto is not dividends or taxes—it’s infrastructure: grid expansion and powering AI.
That’s where the real leverage lies, because whoever controls compute supply chains (energy, chips, data centres, permitting) controls the pace of AI deployment.

For investors and corporate strategists, the manifesto is signalling where political capital may go:

• grid acceleration

• public-private financing models

• “AI should pay its way” narratives around energy costs

What to watch next (and what to do now)

If Altman’s framing sticks, you should expect policy packages, not single policies: wealth fund + tax base shift + labour standards + safety nets + energy buildout.

Three immediate “board-level” actions that are low-regret:

1. Model your “AI displacement exposure” by role family (task substitution, time-to-automate, redeployability).

2. Pre-build your response: retraining pathways, internal talent marketplaces, redeployment budgets.

3. Track policy triggers: where you operate, which jurisdictions are likely to pilot automated-labour taxes or incentivised reduced-hours schemes.

The point is not to predict one law. It’s to avoid being the company that looks surprised when the social licence for automation gets renegotiated.

by Gonçalo Perdigão

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It stayed with me longer than most books about artificial intelligence. Not because it was technical, but because it was human.

The demo you see here is my way of translating one big idea from the book into something you can see and feel.

About the Book: “Why Machines Learn”

Why Machines Learn is written by Anil Ananthaswamy, a science journalist known for making complex ideas accessible without dumbing them down. He has written for New Scientist, Nature, and The New York Times, and his work often sits at the intersection of science, philosophy, and culture.

This book is not about hype. It does not promise that AI will replace everyone, nor does it drown the reader in maths. Instead, it asks a deeper question:

Why do machines learn the way they do?

Ananthaswamy explains how learning systems came to be, what ideas shaped them, and what assumptions we quietly embedded into machines long before “AI” became a buzzword.

For me, one message was very clear:

To understand modern AI, you must understand its origins.

A Short History: The Perceptron

At the heart of the book is a humble idea from the late 1950s: the perceptron.

The perceptron was introduced by Frank Rosenblatt in 1957. It was one of the first attempts to build a machine inspired by how the brain works — not in detail, but in spirit.

At its core, a perceptron does something extremely simple:

• It receives a few inputs

• It gives each input a “strength” (a weight)

• It adds everything together

• It makes a yes/no decision

That’s it.

No imagination. No understanding. Just a rule.

And yet, this tiny idea is the ancestor of today’s neural networks, deep learning systems, and large language models (LLMs).

Why the Perceptron Still Matters

The perceptron matters because it shows us something important:

Intelligence does not start as intelligence.
It starts as decisions.

Before AI could write text, generate images, or drive cars, it had to answer a much simpler question:

“Should this be 0 or 1?”

The perceptron is not smart. It is not creative.
But it learns by adjusting itself when it is wrong.

That learning rule — adjust, try again, improve — is still the foundation of AI today.

Understanding this helps remove some of the mystery around AI. These systems are not magical minds. They are layered decision machines, built on very old ideas.

About This Sketch

This sketch is a visual, minimal demo of a perceptron, designed so that:

• A 12-year-old can understand it

• A creative can grasp the idea without maths

• A decision-maker can see what “learning” actually means

You will see:

• Two simple switches (inputs)

• A “brain” that adds things up

• An output that turns on or off

When you press TRAIN, the system slightly adjusts itself if it gets the answer wrong. Over time, it improves.

Nothing is hidden.
Nothing is abstract.
What you see is what is happening.

Why I Think This Matters Now

We talk about AI mostly at the level of products, risks, or promises. Rarely at the level of origins.

But understanding where AI comes from changes how we talk about it:

• It reduces fear

• It reduces hype

• It increases responsibility

And that reminder started with a book, a conversation, and a very small idea from more than 60 years ago.

Built after reading Why Machines Learn, by Anil Ananthaswamy — thanks to Ricardo Chaves for the nudge.

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P.S. Here is the code in JavaScript I used to simulate the Perceptron:

let w1, w2, b;
let lr = 0.25;

let x1 = 0, x2 = 0;         // inputs (0/1)
let target = 0;             // OR target
let yhat = 0;               // prediction (0/1)
let z = 0;                  // sum

function setup() {
  createCanvas(windowWidth, windowHeight);
  pixelDensity(1);
  resetBrain();
}

function windowResized() {
  resizeCanvas(windowWidth, windowHeight);
}

function resetBrain() {
  w1 = random(-1, 1);
  w2 = random(-1, 1);
  b  = random(-0.5, 0.5);
  updateAll();
}

function draw() {
  background(250);

  const s = min(width, height);
  const big = max(18, s * 0.04);
  const med = max(14, s * 0.03);

  // Layout
  const leftX = width * 0.22;
  const rightX = width * 0.78;
  const midX = width * 0.50;
  const topY = height * 0.22;
  const midY = height * 0.52;
  const botY = height * 0.82;

  // Title
  noStroke();
  fill(20);
  textAlign(CENTER, TOP);
  textSize(big);
  text("Perceptron: tiny brain that learns", width / 2, 12);

  // Two big input switches
  drawSwitch(leftX, topY, "Switch 1", x1, big, med);
  drawSwitch(leftX, midY, "Switch 2", x2, big, med);

  // Wires to brain (thickness shows weight strength)
  const brainX = midX;
  const brainY = height * 0.37;
  drawWire(leftX + s * 0.10, topY, brainX - s * 0.06, brainY, w1, s);
  drawWire(leftX + s * 0.10, midY, brainX - s * 0.06, brainY, w2, s);

  // Brain dot (sum + step)
  drawBrain(brainX, brainY, s);

  // Output light
  drawOutput(rightX, height * 0.37, yhat, big, med);

  // Big readable info (very short!)
  target = OR(x1, x2);
  const ok = (yhat === target);

  fill(ok ? 20 : 120);
  textAlign(CENTER, CENTER);
  textSize(big * 0.9);
  text(ok ? "✅ Correct" : "❌ Wrong", width / 2, height * 0.62);

  fill(40);
  textSize(med);
  text(`Goal (OR): ${target}   •   Brain says: ${yhat}`, width / 2, height * 0.68);

  // Buttons
  drawButton(width * 0.35, botY, "TRAIN", big, med);
  drawButton(width * 0.65, botY, "RESET", big, med);

  // Tiny hint
  fill(90);
  textSize(max(12, s * 0.02));
  text("Tap switches. Tap TRAIN a few times.", width / 2, height - 18);

  // Keep values updated
  updateAll();
}

function updateAll() {
  z = w1 * x1 + w2 * x2 + b;
  yhat = step(z);
}

function step(v) {
  return v >= 0 ? 1 : 0;
}

function OR(a, c) {
  return (a === 1 || c === 1) ? 1 : 0;
}

// ---------- Drawing helpers ----------

function drawSwitch(x, y, label, value, big, med) {
  const s = min(width, height);
  const r = s * 0.07;

  // circle
  stroke(20);
  strokeWeight(max(2, s * 0.004));
  fill(value === 1 ? 255 : 245);
  circle(x, y, r * 2);

  // value
  noStroke();
  fill(20);
  textAlign(CENTER, CENTER);
  textSize(big * 1.1);
  text(value, x, y);

  // label
  fill(40);
  textSize(med);
  textAlign(CENTER, TOP);
  text(label, x, y + r + 10);
}

function drawWire(x1p, y1p, x2p, y2p, w, s) {
  const thick = map(constrain(abs(w), 0, 2), 0, 2, 2, 10);
  stroke(20);
  strokeWeight(thick);
  line(x1p, y1p, x2p, y2p);
}

function drawBrain(x, y, s) {
  const r = s * 0.085;

  // body
  stroke(20);
  strokeWeight(max(2, s * 0.004));
  fill(255);
  circle(x, y, r * 2);

  // tiny face: sum -> step
  noStroke();
  fill(20);
  textAlign(CENTER, CENTER);
  textSize(max(16, s * 0.03));
  text("Σ", x, y - r * 0.1);

  // arrow to output (simple)
  stroke(20);
  strokeWeight(max(2, s * 0.004));
  const outX = width * 0.68;
  line(x + r, y, outX - r * 0.7, y);

  // little arrow head
  const ah = r * 0.25;
  line(outX - r * 0.7, y, outX - r * 0.7 - ah, y - ah * 0.6);
  line(outX - r * 0.7, y, outX - r * 0.7 - ah, y + ah * 0.6);
}

function drawOutput(x, y, value, big, med) {
  const s = min(width, height);
  const r = s * 0.08;

  stroke(20);
  strokeWeight(max(2, s * 0.004));
  fill(value === 1 ? 255 : 245);
  circle(x, y, r * 2);

  // big output number
  noStroke();
  fill(20);
  textAlign(CENTER, CENTER);
  textSize(big * 1.2);
  text(value, x, y);

  // label
  fill(40);
  textAlign(CENTER, TOP);
  textSize(med);
  text("Output", x, y + r + 10);
}

function drawButton(x, y, label, big, med) {
  const s = min(width, height);
  const w = s * 0.26;
  const h = s * 0.09;
  const r = h * 0.35;

  rectMode(CENTER);
  stroke(20);
  strokeWeight(max(2, s * 0.004));
  fill(255);
  rect(x, y, w, h, r);

  noStroke();
  fill(20);
  textAlign(CENTER, CENTER);
  textSize(big * 0.85);
  text(label, x, y + 1);
}

// ---------- Interaction ----------

function mousePressed() {
  const s = min(width, height);
  const leftX = width * 0.22;
  const topY = height * 0.22;
  const midY = height * 0.52;

  // switches
  const rSwitch = s * 0.07;
  if (dist(mouseX, mouseY, leftX, topY) < rSwitch) {
    x1 = 1 - x1;
    return;
  }
  if (dist(mouseX, mouseY, leftX, midY) < rSwitch) {
    x2 = 1 - x2;
    return;
  }

  // buttons
  const botY = height * 0.82;
  if (hitButton(width * 0.35, botY)) {
    trainOnce();
    return;
  }
  if (hitButton(width * 0.65, botY)) {
    resetBrain();
    return;
  }
}

function touchStarted() {
  mousePressed();
  return false;
}

function hitButton(cx, cy) {
  const s = min(width, height);
  const w = s * 0.26;
  const h = s * 0.09;
  return (abs(mouseX - cx) < w / 2 && abs(mouseY - cy) < h / 2);
}

function trainOnce() {
  // learn OR for the current switches
  const y = OR(x1, x2);
  const err = y - yhat;

  // classic perceptron update
  w1 += lr * err * x1;
  w2 += lr * err * x2;
  b  += lr * err;
}

• “Computer use” became a real product surface area: shipping agents now touch desktops, files, and workflows (not just chat).

• Integration is consolidating around MCP-style connectors: design systems and tool access are becoming governance problems.

• Synthetic media moved from “content” to “infrastructure”: watermarking, identity consistency, and provenance are now operational.

THIS MONTH’S SIGNALS

• Desktop execution is now a default capability, not a novelty: the competitive edge shifts to permissions, review gates, and auditability.

• AI literacy shows compounding returns: experienced users are measurably more successful, so training becomes a productivity lever rather than HR hygiene (Anthropic Economic Index, Mar 2026).

• Model Context Protocol (MCP) is quietly becoming a standard. Definition: MCP is a common interface that lets models use tools and context safely via “servers”, reducing bespoke integrations (Figma on agents + MCP, Mar 24).

• The “prompt layer” is now a crown jewel: system prompts + RAG configs + tool permissions are where attacks and failures will concentrate (Mckinsey’s Lilli (Reportedly) Hacked).

• GEO is moving from marketing tactic to executive risk control. Definition: GEO (Generative Engine Optimisation) means structuring content so LLMs retrieve and repeat it accurately—facts, claims, disclaimers, provenance, and sources.

WHAT WE PUBLISHED

Theme: Agentic execution (chat → deliverables)

• Claude Cowork vs Claude Dispatch vs OpenClaw (Mar 26) — a clean operating-model map for desktop agents.
  Why it matters: prevents “agent adoption” without clear risk boundaries.

• GPT 5.4 to 5.5: what’s being said, what’s actually known, and why OpenAI still feels pressure to move fast (Mar 25) — shipped GPT‑5.4 vs GPT‑5.5 chatter (unconfirmed).
  Why it matters: keeps roadmaps anchored to verified capability.

Theme: Governance, security, and maturity

• The AI Glossary Update 2026 (Mar 24) — the vocabulary stack for agents, security, governance, and cost.
  Why it matters: shared language speeds decisions and reduces misalignment.

• Mckinsey’s Lilli (Reportedly) Hacked (Mar 12) — the prompt layer as an enterprise weak point (claims require caution).
  Why it matters: security shifts from models to systems and workflows.

• KPMG Maturity Gap (Mar 5) — agent ambition is outrunning organisational readiness.
  Why it matters: execution discipline becomes the differentiator in 2026.

Theme: Distribution, GEO, and brand surfaces

• Brand GPTs (Mar 19) — the GPT Store as a “behaviour store”, not an app store.
  Why it matters: distribution shifts from apps to answers (and risks).

• iPhone 17e Intelligence (Mar 3) — on-device AI becomes procurement logic, not a feature.
  Why it matters: AI moves from pilot to pocket-scale default access.

Theme: Society, trust, and the human edge

• Interview: Maria Empis — Real Estate, Risk and the Generative AI Gap (Mar 25) — data quality + trust + judgement as competitive edge.
  Why it matters: the bottleneck is governance-ready data, not model IQ.

Photo: Maria Empis by JLL Portugal

• Interview: Adib Bamieh — The Future Looks a Lot Like the Past (Mar 2) — what humans are for in an agentic economy.
  Why it matters: strategy needs a human value thesis, not just tooling.

Photo: Adib Bamieh

• Your AI Slop Bores Me (Mar 10) — a cultural shift: abundance is cheap, “aliveness” is scarce.
  Why it matters: taste and trust become the premium creative outputs.

• When did generative AI become popular? (Mar 17) — two adoption waves: viral 2022, utility 2023.
  Why it matters: adoption follows distribution, not just capability.

HOT TOPICS: AI × CREATIVITY

Computer use becomes the new creative interface — why it matters: agents ship work, not text.

• What changed this month: GPT‑5.4 launched with native computer-use and 1M context (Mar 5) and Claude added computer use + Dispatch (Mar 23), turning “assistants” into workflow actors.

• Why leaders should care: once agents click and send, brand and operational mistakes scale faster than approvals.

• Example/implication: designate one “safe” workflow (e.g., weekly briefing pack) where an agent can draft—but must request approval before any external action.

Prompt-layer security becomes board-level — why it matters: prompts are now operational IP.

• What changed this month: agent platforms make system prompts, tool permissions, and RAG configurations the new control plane (Lilli prompt-layer framing).

• Why leaders should care: “we secured the model” is not the same as “we secured the system”.

• Example/implication: treat system prompts like secrets—version them, restrict access, and log every change.

MCP turns into a governance decision — why it matters: integrations stop being bespoke glue.

• What changed this month: Figma expanded agent access via MCP so clients like Codex and Claude Code can operate with design context (Agents meet the Figma canvas).

• Why leaders should care: “which tools can the agent touch?” becomes an executive control question, not an engineering footnote.

• Example/implication: create an MCP allow-list (approved servers, scopes, data permissions) and enforce it centrally.

Synthetic media enters the identity + provenance phase — why it matters: trust becomes infrastructure.

• What changed this month: “synthetic media” (content generated or materially altered by AI) is being shipped with stronger provenance signals—e.g. watermarked audio in Gemini 3.1 Flash Live (Mar 26), plus identity-preserving image workflows highlighted in Phota Studio.

• Why leaders should care: identity-consistent generation boosts creative speed and fraud risk simultaneously.

• Example/implication: require explicit consent for identity use, and store provenance logs (inputs, prompts, approvals) for anything public.

Learning curves become a measurable advantage — why it matters: AI literacy compounds like a flywheel.

• What changed this month: Anthropic reports higher-tenure users show materially higher success rates (Economic Index: Learning curves).

• Why leaders should care: talent strategy becomes “who can run agentic workflows reliably”, not “who has access to a model”.

• Example/implication: measure “successful workflow completions per week”, not “prompt usage”.

MODELS & TOOLS TO WATCH

GPT‑5.4 (frontier, computer-use, 1M context)

• Why it matters: brings agent execution into mainstream professional work.

• One-line description: OpenAI’s new flagship for knowledge work, tools, and computer-use agents (launch post).

• Best-fit use case: workflow-heavy deliverables (docs, spreadsheets, presentations) with toolchains.

• Risk/limitation: cost, evaluation overhead, and “wrong-but-confident” outputs without review.

GPT‑5.4 mini + nano (small models for subagents)

• Why it matters: makes multi-agent systems economically viable at scale.

• One-line description: fast, efficient models designed for high-volume workloads and subagent execution (release).

• Best-fit use case: classification, extraction, routing, and background tasks under a larger planner model.

• Risk/limitation: smaller models still need guardrails, tests, and monitoring.

Claude Cowork + Dispatch (desktop agent workspace + remote tasking)

• Why it matters: shifts agents from chat to desktop labour.

• One-line description: Claude can point, click, and complete tasks on macOS in research preview, with Dispatch to assign work from your phone (Anthropic post).

• Best-fit use case: document-heavy internal workflows with explicit approvals.

• Risk/limitation: “computer use is still early”; avoid sensitive data and high-blast-radius apps.

Gemini 3.1 Flash Live (real-time voice model)

• Why it matters: makes voice-first creative workflows feel responsive.

• One-line description: low-latency audio model for natural real-time dialogue and task execution (Google overview).

• Best-fit use case: voice agents for customer experience, live brainstorming, and assistive workflows.

• Risk/limitation: real-time systems compress the distance between error and impact.

Figma MCP server + agent canvas access (design-system-aware agents)

• Why it matters: connects design intent directly into code generation.

• One-line description: agents can operate on live Figma files using MCP and use_figma, carrying design-system context (Figma post).

• Best-fit use case: accelerating UI component work while staying on-brand.

• Risk/limitation: design access is sensitive; permissioning and change control are mandatory.

Voxtral TTS (open-weight multilingual text-to-speech)

• Why it matters: audio becomes a controllable creative interface.

• One-line description: a low-latency, multilingual TTS model positioned for scalable voice agents (Mistral announcement).

• Best-fit use case: brand-safe voice generation with enterprise control options.

• Risk/limitation: voice output heightens impersonation and disclosure requirements.

WHAT TO DO NEXT

• Stand up an “Agent Readiness” pilot in 30 days: pick one creative workflow, define KPIs (cost/time/quality/trust), and enforce one approval gate before any external action.

• Publish your GEO baseline in one page: canonical product facts, claims, disclaimers, and provenance rules so assistants can retrieve and repeat them accurately.

• Treat prompts + connectors as regulated assets: create an MCP allow-list, version-controlled system prompts, and logging for every tool permission change.

CURIOSITIES

• 🧩 YourAISlopBoresMe went viral by flipping the script: humans “LARP as the AI” to earn credits, a neat indicator that the web is already pricing “aliveness” above abundance.

• An Agent Contacted Me featured an agent pitching an agent-run art gallery—less “AI art”, more “AI organisation”.

Image: Vessel — “Ghost Evolving” (Generative HTML, Game-of-Life variants). A recurring motif: “the ghost in my own machine.”

• Gemini 3.1 Flash Live watermarks generated audio, signalling that provenance is becoming product plumbing, not PR.

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Also read:

• Our February AI & Creativity Monthly Brief about January 2026

• Our March AI & Creativity Monthly Brief about February 2026

Also, remember our 2026 top articles so far:

• OpenAI Frontier: the enterprise agent platform that changes the competitive map; and why Google slid 7%+ — Frontier as an “operating layer” for agentic work. Why it matters: governance moves from policy docs to platform controls.

• Moltbook: How the AI-Agent Social Network Is Rewriting Digital Trust, Security, and Competitive Advantage — “Agent-native” social dynamics (identity, reputation, risk). Why it matters: trust becomes an input to distribution, not only compliance.

• Agent Internet: How Autonomous AI Is Building an Economy Without Humans — Early patterns of agent-to-agent trade and coordination (still experimental). Why it matters: markets may gain non-human participants with real agency.

• AI in Your Toaster: PicoClaw — A “thin” runtime that brings assistants closer to edge devices. Why it matters: Agent sprawl becomes a security and cost governance problem.

• AI Tokenomics: Which model is best? — Model choice reframed as measurable spend + quality engineering. Why it matters: tokens become a budget line, not a technical footnote.

• What Matt Shumer’s Viral AI Article Really Means for Jobs, Leaders and Creators — Viral “step-change” narratives, plus grounded takeaways for role redesign. Why it matters: leadership choices determine whether disruption becomes an advantage.

• Moltbot (formerly Clawdbot): The Self-Hosted AI Assistant “That Actually Does Things” — local automation with tangible outputs.

  Why it matters: “agent ROI” becomes observable, not assumed.

• ChatGPT 5.3: what’s being said, what’s actually known, and why OpenAI might feel pressure to move fast — rumours vs verified facts.

  Why it matters: planning cycles must withstand model ambiguity.

• 7 AI Predictions for 2026: From Creative Machines to Real Economic Impact — grounded 2026 calls.

  Why it matters: strategy needs probabilistic bets, not narratives.

• Stop Chasing AI Detectors like Quillbot and Humaniser — authenticity anxiety and control dynamics.

  Why it matters: trust is now part of the creative stack.

• The AI Layoffs: What’s Really Happening — separating headlines from drivers.

  Why it matters: workforce planning needs causality, not fear.

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by Gonçalo Perdigão

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

For twenty years, digital visibility meant one thing: ranking on Google.

That era isn’t over, but it’s no longer the whole game. Discovery is migrating from lists of links to direct answers produced by large language models (LLMs): ChatGPT, Google’s AI Overviews, Perplexity, and whatever comes next.

This shift creates a new, urgent question:

That is Generative Engine Optimisation (GEO): optimising your brand and content so generative systems can retrieve, trust, and reuse it in their responses. The concept has already been formalised in academic work under the GEO name. (arXiv)

Why GEO is suddenly urgent

1) “Zero-click” becomes “zero-visit”

When Google shows AI-generated summaries, many users get what they need without having to click through. Independent analysis has linked AI Overviews to materially lower click-through rates for publishers and sites.

If fewer people reach your site, classic SEO metrics (sessions, CTR, top-3 rankings) become less predictive of business impact.

2) AI Overviews are expanding

Data reported in the trade press suggests the share of searches triggering AI Overviews has grown, alongside a rapid increase in the number of keywords that trigger those summaries.

Even if the exact percentage varies by market and query type, the direction is clear: more queries are being answered directly on the results page.

3) ChatGPT and “answer engines” now browse the web

ChatGPT can decide to search the web and return answers with cited sources; OpenAI describes this as a core capability of “ChatGPT search”.

So the visibility contest moves from:

• “Can I rank?”
  to:

• “Can I be retrieved, selected, and cited?”

How generative engines actually produce answers

To do GEO well, you need a simple mental model of what’s happening under the hood.

Step 1: The model is probabilistic — it writes by prediction

An LLM doesn’t “look up” a sentence in a database and paste it. It generates text token by token, choosing the most likely next token given the context and what it has learned during training.

That matters because:

• LLMs prefer clear, common structures they’ve seen often.

• They are sensitive to framing (definitions, comparisons, step-by-step instructions).

• They can produce fluent text that still needs grounding.

Read our article “Why Is the Dog Truly Man’s Best Friend? What AI Prompt Testing Reveals” about the way LLMs use probabilities to create new content:

Step 2: The model is vectorial — it matches meaning, not keywords

Modern retrieval and ranking systems rely heavily on embeddings: turning text into vectors so systems can match semantic similarity (“what this means”) rather than exact words.

That means your page can be “about the right thing” even without the exact keyword — and still be retrieved.
It also means vague, marketing-heavy copy is harder to match precisely, because the semantics are fuzzy.

Step 3: For fresh facts, engines use retrieval (RAG) and citation

Most “answer engines” combine generation with retrieval: the system searches, ranks sources, pulls passages, then generates an answer grounded in those sources (often with citations).

OpenAI explicitly positions web search + citations as part of how ChatGPT delivers timely information.

So GEO is largely about winning three filters:

1. Retrieval: does your content get pulled into the candidate set?

2. Selection: does it look trustworthy and extractable compared to alternatives?

3. Reuse: can the engine quote/paraphrase it cleanly without distortion?

GEO vs SEO: what changes, what stays

What stays from SEO (and still matters)

• Technical accessibility: crawlable pages, fast performance, stable rendering.

• Authority signals: credible backlinks, brand mentions, consistent entity identity.

• Content hygiene: clear IA, strong titles, structured internal linking.

GEO doesn’t replace SEO; it inherits the fundamentals.

What changes dramatically

1) You are optimising for “citability”, not “clickability”

A blue link competes on the headline and promise.
A cited source competes on:

• precision

• clarity

• specificity

• evidence

• freshness

• provenance (who said this, where, when)

2) The unit of value is a passage, not a page

Answer engines often extract snippets. A single paragraph or table can carry your brand into the answer even if the user never visits.

3) “Position #1” becomes “included in the synthesis”

You’re not trying to be first in a list.
You’re trying to become one of the few building blocks the model uses to construct the response.

Academic GEO framing focuses on increasing a creator’s visibility within generative outputs — a different objective from classic ranking.

The practical GEO playbook

1) Write “extractable truth”

Your best GEO asset is content that can be safely reused.

Do this:

• Put definitions in the first 2–3 sentences (“X is…”).

• Use short paragraphs with one claim each.

• Prefer specific numbers and conditions (“works best when…”, “fails when…”).

• Add dated context (“as of March 2026…”) where freshness matters.

Avoid:

• throat-clearing intros

• vague adjectives (“world-class”, “innovative”)

• claims with no supporting detail

2) Make your brand an entity, not a slogan

LLMs and retrieval systems handle “things” (entities) better than vibes.

Action:

• Standardise your brand name, product names, category terms, and key claims across the web.

• Ensure your About/press pages clearly state who you are, who you serve, your proof points, locations, and leadership.

• Use consistent terminology so that embedding similarity works in your favour.

3) Build “retrieval hooks” into your content

Think like an answer engine: it wants to answer specific questions.

Format ideas that perform well:

• “What is X?”

• “X vs Y”

• “How to choose X”

• “Common mistakes in X”

• “Checklist for X”

• “Pricing model for X” (even if you don’t publish prices, publish how pricing works)

Each is a retrieval-friendly pattern that the model can map to user intent.

4) Use a structure that machines love

You don’t need to worship schema markup to do GEO, but structure helps.

Include:

• H2/H3 headings that mirror questions

• bullet lists

• step-by-step sequences

• comparison tables

• FAQs (written like real questions people ask)

This is not “writing for robots”. It’s writing so your best thinking survives extraction.

5) Spread authority across the open web

One uncomfortable truth: answer engines often trust the web’s consensus, not your homepage.

So you need credible third-party sources:

• reputable industry publications

• standards bodies

• partner ecosystems

• academic or technical references where relevant

Because when multiple independent sources repeat the same fact about you, retrieval systems see reinforcement.

6) Design for “source panels” and citations

ChatGPT search surfaces citations and a sources panel.
That changes behaviour: users can jump straight to sources, compare, and decide whom to trust.

So ask:

• If my page is cited, what will the user see in the snippet?

• Does it immediately establish credibility?

• Does it answer cleanly without overclaiming?

Measuring GEO without pretending it’s solved

GEO measurement is still maturing. But you can start now with pragmatic proxies:

Visibility metrics

• Share of AI answers that mention your brand for priority queries

• Share of citations pointing to your domain (where tools allow tracking)

• Inclusion in “best X” / “compare X” generative responses

Business metrics

• Assisted conversions from “direct” and “dark” traffic (LLM referrals often look messy)

• Brand search lift (people hear you in an answer, then search later)

• Sales team signal: “I asked ChatGPT, and it mentioned you”

Where this trend goes next

Three directional bets look safe:

1. More answers will be synthesised (Google, chatbots, browsers, OS-level assistants).

2. Citations will matter more because trust and accountability become product features.

3. Content will compete on reliability and structure, not just creativity and reach.

In that world, GEO is not a marketing hack. It’s a new layer of operational discipline: how your organisation publishes truth.

The companies that win won’t be the loudest. They’ll be the clearest, most referenceable, and most consistently verified across the web.

By Gonçalo Perdigão

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Download the GEO: Generative Engine Optimization paper here

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GEO: Generative Engine Optimization

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