Alina Vandenberghe sees something else happening inside companies.

For the Co-Founder and Co-CEO of Chili Piper, the interesting question is no longer simply what artificial intelligence can automate. It is what becomes visible in humans once automation removes the repetitive work surrounding them.

Her perspective comes from experience rather than theory.

Romanian-born and now based in New York, Vandenberghe co-founded Chili Piper with her husband Nicolas in 2016 after selling her house to help fund the company. Chili Piper went on to become a major player in B2B demand conversion, building products around qualification, routing, scheduling and the complex handoffs between marketing and sales. Its technology is used by GTM teams at companies including Gong, Verizon and monday.com.

Today, Chili Piper is pushing further into AI-led conversion. Its platform combines established routing and scheduling infrastructure with AI systems that can engage website visitors, qualify prospects, automate follow-up, and book meetings around the clock. The company describes the shift as moving from a passive website towards an AI-led conversion layer operating across the buyer journey.

But Vandenberghe’s thinking about AI was forged internally before it became a product strategy.

In 2023, after Chili Piper’s marketing organisation had contracted from 22 people to two, she made herself CMO and began building AI agents. Instead of delegating experimentation to a technical team, she opened a company-wide Slack channel called #automate-everything and encouraged employees across functions to build.

The result challenges several assumptions dominating the corporate AI conversation.

AI automation creates new work as it removes old work. Agents are extremely effective at repetitive processes but far less convincing in moments that depend on trust. Smaller teams can produce considerably more, but headcount becomes a poor proxy for organisational capacity. And perhaps most importantly, automating execution can make exceptional human abilities more, not less, important.

For Vandenberghe, the future executive may therefore look less like a traditional functional leader and more like a systems architect, orchestrating humans and agents together.

We spoke about what CEOs discover when they actually build with AI, where agents will transform marketing and sales, why AI SDRs risk industrialising spam, how Chili Piper increased pipeline with a dramatically smaller marketing team, and why the companies measuring the number of agents they deploy may be measuring the wrong thing entirely.

Alina Vandenberghe

What can a CEO understand about AI by actually building with it that they’ll never learn from a strategy deck?

Where it breaks

In June 2023, I made myself CMO of my own company. We’d gone from 22 marketers to 2, and I was checking the pipeline number on my phone every night, sweating with stress

I have a scientist’s brain, so I did the only thing I knew how to do: run experiments. I built agents myself. Then I opened a Slack channel called #automate-everything and asked the whole company, not just marketing, to build them too

A strategy deck tells you what AI can do. But building tells you what it costs to make that work. For instance, it won’t warn you that an agent which saves 10 hours a week creates a new job for a human in the loop, because someone has to feed it all the context, check its output, notice when it drifts, etc

When I say “AI ops is messy,” my team knows I mean it, because they’ve watched me clean up my own messes

Where will agents fundamentally change marketing and sales, and where is the hype ahead of reality?

Agents will eat the repetitive middle of the funnel. Everything between “someone raised their hand” and “someone is in a meeting with the right rep” used to be manual: qualifying, routing, scheduling, enriching, prepping, etc. That work is disappearing, and it should. We 4x’d our pipeline between 2022 and 2024 with 2 marketers because agents took over the repeatable work and the humans went back to creating

Our own product came out of that. The 2 most useful agents our team built for ourselves, including a 24/7 conversion assistant that answers, qualifies, and books meetings without a rep online, got pulled into the platform for our customers

The hype is anything that automates the trust moment. AI SDRs blasting cold emails is the same spam we already hated, just cheaper to produce

As AI executes parts of go-to-market autonomously, what becomes more valuable?

When the whole company started building agents, I expected the engineers to be good at it and everyone else to struggle. But I was wrong. Some people’s genius was in the writing. Some could see the big picture, look at a funnel and instantly see which automation would move the pipeline. Some built the most elegant agents; some built the cheapest ones. And some found their genius in teaching everyone else, and in building trust, which turns out to be the only currency that matters in the AI era

AI replaces tasks, and it exposes genius in humans. We now run an informal zone-of-genius program to find where each human actually fits

I found my own two in the process: I get a lot of delight from writing (that’s why I’m so fast in answering your questions, haha), and I get delight from sitting above the whole GTM system, agents and humans together, and seeing the shape of the “AI factory”. That second job didn’t have a name a few years ago. I think every C-level role is turning into it. Chief Architect (of humans working alongside AI agents)

Has AI changed how you think about what a company, and a team, should look like?

It changed the shape of it: smaller, stranger, more creative teams. We’re 140 people across 37 countries. I no longer think about headcount as capacity. I think about it as a collection of non-interchangeable geniuses, each one orchestrating agents that do the repeatable work. Innovation is a heavy rubric in our performance reviews now. Contributing an agent idea isn’t really optional.

The foundation remains the same: culture makes or breaks a company

I got fired twice early in my career because decisions happened in rooms I wasn’t in. So at Chili Piper every decision starts with a written memo anyone in the company can challenge with data

We trained the whole company to manage conflict, to learn to build trust and watched it show up in our mid-market win rates (from 24 to 37%) because our teams now know how to lean in, on an actual board slide

AI made all the above matter more, because when agents do the doing, the quality of your humans, how they decide, how they trust each other, how they create, is the whole moat

Chili Piper team building in Morocco

What are even the smartest people in AI currently getting wrong?

First, they measure the wrong thing. Teams celebrate agents shipped, demos, code, pilots. We only count agents that moved pipeline

Second, the replacement math. It treats humans as interchangeable units of labour you can swap for compute. A decade of building has taught me the opposite: the value was never in the doing. We’re not here to do. We’re here to create. And AI is a mirror: it amplifies whoever is holding it. Which is why I don’t think the doomsday scenarios get solved with better guardrails. They get solved with better humans

Alina Vandenberghe

From Headcount to Human Leverage

A deceptively important idea runs through Vandenberghe’s answers:

For much of modern corporate management, headcount has been treated as a rough approximation of capacity. More people meant more work could be executed. Growth frequently meant hiring more people to execute more processes.

Agentic systems complicate that equation.

If qualification, routing, enrichment, preparation, scheduling, follow-up and other repetitive tasks can increasingly be delegated to software, then the relationship between employees and organisational output begins to separate.

The question becomes less about how many people we need to execute this process and more about which humans we need to design, supervise, and improve the system performing it.

That is a fundamentally different organisational problem.

And it explains Vandenberghe’s intriguing description of tomorrow’s executive as a Chief Architect.

The CMO of an AI-native company may not simply manage marketers. The executive could increasingly orchestrate a system containing people, models, agents, data, workflows, feedback loops and human-in-the-loop checkpoints. The same logic can extend to sales, operations, customer service, finance and eventually almost every executive function.

But her experience also exposes an inconvenient reality frequently missing from AI transformation narratives.

Automation is not frictionless.

An agent that eliminates ten hours of repetitive work does not necessarily eliminate ten hours of human responsibility. Someone still has to establish context, monitor quality, detect drift, intervene when exceptions occur and decide whether the system continues to serve its original purpose. The work changes before it disappears.

That distinction matters enormously for companies moving from AI experimentation into production.

So does Vandenberghe’s insistence on measuring outcomes rather than deployments. The number of agents created, pilots completed, or demonstrations presented can easily become the AI equivalent of vanity metrics. Chili Piper’s preferred measurement is considerably less glamorous and considerably more useful: did the agent move pipeline?

Perhaps the most interesting consequence, however, is human.

The common fear surrounding AI is that as machines become more capable, human differentiation becomes less valuable. Vandenberghe’s experience suggests almost the reverse.

Once repeatable execution becomes cheap, distinctive judgment becomes expensive.

The person who understands where automation should be introduced matters. The writer who can communicate unusually well matters. The employee who can earn trust matters. The person capable of seeing an entire business system rather than one isolated task matters.

And culture may matter even more.

AI can scale execution, but it can also scale poor judgment, weak incentives and bad organisational behaviour. More capable systems do not automatically create more capable companies.

That is why one of Vandenberghe’s simplest statements may ultimately be the most consequential:

If she is right, the defining organisational challenge of the agentic era will not simply be learning how to build better machines.

It will be learning what humans should finally stop doing, and discovering what they are uniquely capable of doing once they do.

by Gonçalo Perdigão

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And I mean always.

I have been playing Magic: The Gathering since 1994. Thirty-two years later, I still play it.

But there is something I probably enjoy even more than playing games: creating things.

That instinct runs through Building Creative Machines.

Over the past couple of years, I have used Generative AI to create hundreds of small experiments: generative art, mathematical visualisations, physics simulations, games, strange interfaces, business tools and things that are frankly difficult to classify.

You can explore many of them in the Building Creative Machines sketch collection.

Some are useful. Some are beautiful. Some are ridiculous.

That is partly the point.

One of my favourite things about Generative AI is how dramatically it reduces the cost of curiosity.

You can think:

And instead of putting the idea into the mental drawer labelled “maybe one day”, you can start building it immediately.

Some of these experiments even escaped the laboratory. A collection of my computational works was selected for Institution(ing)s, a European project in which the Gulbenkian Foundation is a partner.

But this summer I wanted to try something different.

I wanted to see what would happen if I stopped asking an LLM to make me something and started asking it to engineer a system.

That distinction turned out to be much more important than I expected.

From a 48-second game to a real system

Two years ago, I ran a small experiment.

I asked an early reasoning model to create a game for me.

Five prompts.

48 seconds of model processing.

A playable game.

I documented the experiment in How to Create a Game in Under a Minute?.

At the time, it felt extraordinary.

And it was.

The basic workflow was almost absurdly simple:

This is what eventually became known as vibe coding.

I explored it obsessively. Art. Physics. Mathematics. Hardware. Games. Tools for companies. Hundreds of sketches.

And for relatively contained creations, it works astonishingly well.

But models have changed.

A lot.

Today, asking a frontier model to produce a nice animation in JavaScript almost feels like asking a calculator to multiply 12 by 7.

So I became interested in another question.

Not a demo.

Not a single script.

A system with mathematics, generation, validation, storage, client software, rankings, deployment, testing and rules that absolutely cannot change depending on the mood of the model.

That question became NEXT.

Play NEXT - Find the only path

Meet NEXT

NEXT looks deceptively simple.

You see a collection of points.

You start at one of them.

Your objective is to find the only valid path through the puzzle, visiting every point exactly once.

That’s it.

Except, of course, it isn’t.

Each point can carry information. Position matters. Shape can matter. Numbers can matter. Proximity can matter. Locks can matter.

Rules constrain where you can go next.

And underneath this minimal interface sits a surprisingly interesting mathematical problem.

Imagine a puzzle containing 11 points.

If we naïvely considered every possible ordering of those 11 points, there would be:

11! = 39,916,800 possible sequences.

Almost 40 million.

If the starting point is already fixed, we are down to 10!, or about 3.6 million theoretical orderings.

Then the rules begin destroying possibilities.

You must go left.

You must choose the nearest compatible point.

The next number must be larger.

A particular shape may only connect under a particular condition.

A lock may eliminate another route.

And so on.

What starts as a huge combinatorial space becomes narrower and narrower until, for a properly constructed NEXT puzzle, exactly one valid complete path remains.

In graph theory, the underlying idea is related to a Hamiltonian path: a route through a graph that visits every vertex exactly once.

You don’t need to know graph theory to play NEXT.

You just need to find the path.

But the machine creating the puzzle needs to know that the path really exists.

And, more importantly, that there isn’t another one hiding somewhere.

That was where my little summer game became an engineering problem.

Play NEXT - Find the only path

The LLM could not be allowed to “be creative”

There is a strange contradiction here.

I spent much of my last years working with generative systems. I recently published a methodological paper on validating Generative AI under prompt-induced variance.

A fundamental property of LLMs is that they are probabilistic.

Ask the same thing several times, and you may receive different answers.

That variability is wonderful when you want ideas.

It is much less wonderful when your software needs to determine whether a puzzle has exactly one mathematically valid solution.

NEXT therefore became an experiment in something I find increasingly important:

The LLM could help me reason.

It could propose architectures.

It could write code.

It could inspect code.

It could find bugs.

It could challenge my assumptions.

It could refactor components.

It could run tests and investigate failures.

But the final system could not answer:

It had to prove it.

Every time.

So we separated creation from truth

This became one of the most important architectural decisions in the project.

There is a protected puzzle-generation environment.

Its job is to create candidate puzzles and solve them exhaustively enough to certify the properties we require.

The problem and solution remain paired inside that controlled process.

Only puzzles that pass certification are published.

The public game doesn’t need to invent the puzzle. And it certainly doesn’t need an LLM deciding whether your move looks reasonable.

It receives a certified puzzle and runs deterministic logic.

Behind that sits another layer for results and rankings.

And then there is the client: the thing you actually see and touch in your browser, designed to work across environments and screen sizes.

Conceptually, it became something like this:

The AI helped build the machine.

The machine does not need AI to decide what is true.

That difference matters.

A lot.

I became the architect, not the typist

I built NEXT primarily with OpenAI Codex, using GPT-5.6 Sol at maximum reasoning for the most demanding architectural conversations. Astra was not available yet.

The application evolved into a modern web stack built around Next.js/React and TypeScript, with GitHub for version control and Netlify for deployment. Netlify’s Next.js infrastructure can take the application from the repository into a production web deployment.

What is unusual is my role in all this.

I studied Electrical and Computer Engineering. I even taught Probability and Statistics at the University. So concepts such as algorithms, probability, state, logic and architecture are not foreign to me.

But I haven’t been a programmer for more than 20 years.

I could understand what we were discussing.

I could challenge decisions.

I could recognise when something smelled wrong.

I could ask why one architecture was safer than another.

But I was not sitting there typing functions.

By the human-testing build, the project package contained 50+ source files. The system had generated and certified 365 puzzles with unique solutions (yes, one per day for the entire year!), organised into four difficulty families. The production build validated all the puzzles and their solutions, and the test suite passed.

That sentence would have sounded ridiculous to me a few years ago.

Today it is almost less interesting than what replaced the coding.

Because I was definitely working.

A lot.

Prompting was the easy part

This is where I think the conversation about AI coding is often misleading.

People see the final result and conclude:

Yes.

And no.

Codex can now perform substantial engineering work: writing features, refactoring systems, running commands, testing code and working across a codebase rather than simply producing isolated snippets.

But giving an agent the ability to write thousands of lines of software does not magically tell you what the software should be.

During NEXT, I changed my mind constantly.

Should every generated puzzle expose every type of rule?

No. That produced artificial constraints.

Should I force particular vocabulary into harder puzzles?

Initially I thought so.

Then testing revealed something more interesting: some combinations were theoretically part of the vocabulary but did not naturally emerge from the architecture at certain difficulty levels.

The tempting response was to force them.

We didn’t.

I preferred a mathematically coherent system to an editorially perfect spreadsheet.

At another point, puzzle generation seemed stuck.

It wasn’t.

The CPU was working. The advanced puzzles simply required substantially more certification work (the hardest ones can take more than 1 hour of remote computation).

But an invisible process that might take hours is a bad process to supervise.

So we stopped it.

Added progress visibility.

Reduced redundant retries.

Ran it again.

Another time, the automated tests said the tutorial navigation was correct.

The real browser said otherwise.

The browser was right.

We investigated the asynchronous behaviour and fixed it.

This happened again and again.

One of my favourite mistakes

At one stage I wanted a neat distribution of puzzle difficulty:

4 Beginner
6 Easy
35 Medium
6 Hard
4 Cruel

The beginner level takes less than 5 seconds to complete. The easy level takes between 2 and 4 minutes. For a human, of course. LLMs will (as of today) find it very hard to solve the Hard & Cruel levels, even though they were created by them. Cruel levels only appear twice a month.

Perfect.

Except it adds up to 55.

I wanted 50.

The AI caught it immediately.

We adjusted the distribution.

Later, after watching the generation and certification cycle, I changed the structure again and removed the Cruel category from this experimental batch:

4 Beginner · 6 Easy · 30 Medium · 10 Hard

This sounds trivial.

It is trivial.

And that is precisely why I like the example.

Working with an advanced coding agent isn’t some science-fiction experience in which an infallible intelligence receives a perfect specification from an infallible human.

I make mistakes.

The model makes mistakes.

I change my mind.

The model sometimes takes an instruction too literally.

I ask for something that turns out to be incompatible with an earlier architectural decision.

It discovers that incompatibility.

We reconsider it.

That loop is the work.

The real breakthrough isn’t “AI can code”

We have spent the last few years being impressed that an LLM can write Python or JavaScript.

I think that story is becoming obsolete.

The more interesting development is that these systems can increasingly operate across levels of abstraction.

I can discuss the experience I want the player to have.

Then the mathematics required to guarantee it.

Then the architecture required to generate it.

Then how the generator must remain separate from the public application.

Then a bug.

Then a test.

Then a deployment issue.

Then return to whether the word “Hard” actually describes the human experience of solving puzzle #047.

That is profoundly different from autocomplete.

And it changes who can build complex systems.

Not because expertise disappears.

Knowing a little became surprisingly valuable

My engineering background helped enormously.

Not because I remembered how to implement everything.

I didn’t.

It helped because I could think structurally.

Inputs.

Outputs.

States.

Constraints.

Probability.

Edge cases.

Separation of responsibilities.

Testing.

Failure.

I knew enough to ask questions.

And increasingly I think this will be an important pattern in AI-assisted creation.

You don’t necessarily need to know how to manufacture every brick.

But you need enough understanding to recognise whether the house should have a roof.

I experienced almost the opposite.

My old knowledge suddenly became useful again.

More than twenty years after I stopped programming, concepts buried somewhere in my brain became an interface for directing an AI engineering agent.

That is fascinating.

Creation is moving one level up

For me, this is the bigger story behind NEXT.

Generative AI first made content cheap.

Then it made prototypes cheap.

Then code became cheap.

Now we are beginning to see something else becoming dramatically cheaper:

complexity itself.

Not free.

Not automatic.

Not reliable without supervision.

But accessible.

A creator can increasingly operate at the level of systems rather than individual artefacts.

Instead of drawing every frame, design the visual system.

Instead of writing every function, design the software behaviour.

Instead of manually constructing every puzzle, design a machine capable of generating and certifying puzzles.

That is a huge shift.

The unit of creativity is changing.

And now I want humans to break it

NEXT is online.

The current experimental set contains 365 + 50 certified puzzles, from Beginner to Hard.

They explore combinations of position, shape, number, locks, direction and proximity. Each has been generated and validated before reaching the public player.

But mathematical difficulty and human difficulty are not the same thing.

A machine can tell me that a puzzle has one solution.

It cannot automatically tell me whether you will find it elegant, frustrating, obvious, addictive or impossible.

For that, I need humans.

That is the next experiment.

So this isn’t a polished announcement pretending that a summer project has suddenly become the next global gaming franchise.

It is a laboratory.

I built it because I wanted to understand what happens when today’s most capable generative systems are pushed beyond one-shot creation and asked to help construct a deterministic architecture.

And because, ultimately, games are fun.

You can play it here:

Play NEXT - Find the only path

Try a few puzzles.

Try to break it.

And please send me feedback privately, particularly if something feels confusing, unfair or unexpectedly satisfying.

I want to know.

Puzzle #005. Easy. 8 points

One final thought

In 2024, I used an LLM to make a game in 48 seconds.

In 2026, I used one to help me reason about and construct a system that generates, certifies, publishes and runs an entire family of mathematical games.

Those two experiences may look similar from the outside.

They are not.

And I suspect that distinction tells us something important about where Generative AI is going.

We started by asking machines to write things for us.

Then we asked them to make things with us.

Now we are beginning to ask them to help us build machines that make things, while we define the rules, constraints, architecture and meaning of what gets made.

The irony is perfect.

I used one of the world’s most sophisticated probabilistic machines to build a game whose entire purpose is to tell you:

Now you just have to find it.

by Gonçalo Perdigão

Play NEXT - Find the only path

Which jobs can it replace? Which processes can it automate? How many people can an organisation remove from a workflow? How much more output can the remaining employees produce?

Elizabeth Ngonzi thinks this framing misses the larger opportunity.

For Ngonzi, the more consequential question is not what happens when artificial intelligence replaces human capability, but what becomes possible when it amplifies capabilities that already exist inside people and organisations.

This perspective is shaped by more than 25 years across technology transformation, management consulting, entrepreneurship, executive education, and, increasingly, human-centred AI.

Ngonzi serves on the Board of the American Society for Artificial Intelligence (ASFAI) and is an Adjunct Assistant Professor at New York University, where she has taught for more than 17 years. She is also the originator and founding platform architect of AI for Humanity: Human-Centered Strategies for Innovation and Impact, developed with contributors from ASFAI to explore AI governance, ethics, finance, workforce transformation, policy and responsible innovation. (Ngonzi’s website)

Since 2023, her AI learning and leadership initiatives have reached more than 12,000 professionals across six continents.

At the centre of her work is a deceptively simple equation:

It is not intended as mathematics. It describes an organisational philosophy: combine an individual’s lived expertise with the collective intelligence of other humans, then use AI to amplify both.

The distinction matters.

Much of the current AI economy is built around making generation cheaper. More text. More images. More analysis. More software. More decisions, produced more quickly.

She has tested these questions personally.

In April 2025, Ngonzi created a digital twin trained on more than two decades of her own intellectual work: articles, teaching materials, presentations, frameworks and ideas. The system allows her to interrogate her own professional archive, rediscover forgotten concepts and establish connections between ideas developed years apart.

Yet she is explicit about its limits.

The digital twin can retrieve Elizabeth Ngonzi’s work.

It cannot be Elizabeth Ngonzi.

That distinction opens a much larger conversation about what we are actually trying to preserve as AI systems become capable of reproducing increasingly convincing approximations of human knowledge, language, style and eventually presence.

Perhaps the goal should not be to reproduce humans at all.

Perhaps it should be to give humans better access to themselves.

We spoke with Elizabeth Ngonzi about digital twins, authorship, intellectual memory, bias, human taste and why organisations focusing primarily on AI-driven cost reduction may be overlooking the much larger economic opportunity of generating entirely new human capabilities.

Elizabeth Ngonzi CWIL 2026 (with credits)

What should AI preserve about a person, rather than reproduce?

AI should preserve a person’s ability to develop relationships with other people, to exercise judgment, and to draw on lived experience. It should also leave room for intuition, especially the kind that develops over time and is difficult to document, let alone replicate.

I created a digital twin in April 2025 using more than 20 years of my own work. It draws on my articles, teaching materials, presentations, frameworks, and ideas. I was careful not to include anything confidential or covered by a non-disclosure agreement. I created it because I wanted a way to return to work I had done in the past, recall ideas I might’ve forgotten, and see connections between things I’d written or developed at different points in my life.

It’s been useful in practical ways. I can ask it to surface a framework from years ago, help me recall something for a bio or CV, or bring forward relevant language as I’m developing a new piece of work. In that sense, it extends my intellectual memory.

But it doesn’t capture who I am. It can’t know what I notice in a conversation, how I feel in a particular moment, or the many experiences that have never been written down. I’ve made it publicly available and designed it to be empathetic and useful, but it’s not a replacement for me.

For me, that’s the right role for AI. It can be a partner and a way to query an archive of work. It can help surface ideas. It shouldn’t be asked to reproduce someone’s humanity or stand in for the relationships, judgment, and responsibility that make a person who they are.

Where does human authorship begin and end when working with a source-grounded AI system?

I believe human authorship begins with the idea.

Usually, I’ll write a draft myself. More and more, I dictate because it’s easier for me to get the thought out without interrupting myself too soon. I may have a question I want to answer, an observation I want to explore, or a point I want to make. That’s always what comes first for me.

Then I might use AI to help me work with it. I might ask it to sharpen an idea, question an assumption, help me look at an issue from another angle, or bring forward something relevant from my earlier work. I think of it a little like working with an editor. A good editor can show you what’s unclear, where an idea needs more depth, or how it may land with a particular audience. But it’s important to note that the editor isn’t the author.

The same is true of a source-grounded AI system. It can give me context from my own archive. It can remind me of an idea I hadn’t thought about in a while. Sometimes that’s exactly what I need. But I’m still the person deciding what matters, what the question is, whether something’s accurate, what needs to be revised, and whether the final work reflects what I actually believe.

So I don’t think authorship ends once AI becomes part of the process. It continues all the way through. AI can help me retrieve, test, and develop an idea. It can’t decide what I mean, what I believe, or what I’m prepared to put my name behind. The final product always has me as the human in the loop, putting the final touches on the rhythm, the flow, and the soul of the piece.

Can AI extend our intellectual memory without reinforcing our existing biases?

Yes, but only if we’re honest about the fact that memory is never neutral!

All of us are shaped by our experiences, what we’ve been taught, where we’ve lived, what we’ve paid attention to, and what we may never have had the chance to see. In that sense, we all have biases. I don’t think having a perspective is inherently bad. It becomes a problem when it reinforces something that’s untrue, unfair, harmful, or limiting, particularly when it affects other people.

A source-grounded AI system can be very helpful because it brings forward ideas, patterns, and material you might’ve forgotten. But it can also reinforce the assumptions and blind spots already present in the material you gave it. That’s why bias can’t be treated as something to clean up at the end. It has to be part of the design from the beginning.

When I build or use a source-grounded resource, I want to be clear about what comes from my own prior work and what needs to be checked against credible external sources. I also want the system to do more than agree with me. Can it flag an assumption? Can it show me another perspective? Can it tell me when a claim needs verification instead of simply repeating it back more confidently?

That ethical layer is especially important when a resource is available to other people. You have to think about whether it could discriminate, reinforce stereotypes, cause harm, or make one person’s viewpoint appear universal. We’ll never remove every bias from human or AI systems. But we can be intentional about noticing it, challenging it, and building safeguards before a system reaches someone else.

As AI becomes more capable, does human taste become more or less important?

It becomes more important, without question!

As AI becomes able to produce polished text, images, video, and ideas on demand, human taste becomes one of the clearest differentiators. Taste isn’t only knowing what looks good. It’s the judgment that comes from your experiences, values, curiosity, what you’ve paid attention to, and the way you make sense of the world.

Two people can be given the same assignment and the same AI tool. They may even use a similar prompting style. But their work shouldn’t come out the same. What each person notices, what they choose to emphasise, what they reject, what they feel is missing, and what they believe is worth making will change the result. That’s where taste enters the process.

This is especially true with images, video, and other creative work. AI can generate an endless number of options, but it can’t decide what has resonance for a particular audience, what’s culturally appropriate, what feels emotionally true, or what’s worth putting into the world. Those remain human decisions.

I also think taste needs to be developed. It comes from being out in the world, not only from sitting in front of a screen. I get inspiration from nature, art, travel, swimming, and being near water. I get it from conversations and from spending time with people whose experiences are different from mine. All of that informs how I see the world and, in turn, the work I create.

As AI becomes more capable, I don’t think the answer is to become more machine-like in response. We need to become more fully human. We need to keep developing curiosity, discernment, cultural awareness, and lived experience, because those are what give our work a real point of view.

Why should organisations focus on generating new capabilities rather than simply automating costs?

Cost reduction is a legitimate reason to use AI. There is repetitive work that can be streamlined, and people shouldn’t have to spend their days on tasks that add very little value.

But cost-cutting can’t be the whole strategy. If an organisation brings in AI mainly to remove people or demand more output from fewer people, it may get a short-term efficiency gain while losing knowledge, trust, creativity, and the capability it’ll need to adapt later.

This is where my 1+1+AI=10™ methodology comes in. It’s not a literal equation. It’s a way of thinking about what becomes possible when an individual’s experience and perspective are connected with the knowledge of a team or organisation, and then augmented by AI. The goal isn’t simply to make the same work happen faster. It’s to help people see more, connect more, and create things they might not have been able to create on their own.

Someone may have an idea for a better customer experience, a new product, a more effective process, or a solution to a problem that’s frustrated a team for years. AI can help them research it, test it, develop it, communicate it, and bring it forward. That’s capability generation.

I think organisations should ask not only, “What can we automate?” but also, “What becomes possible when our people have more access to knowledge, more room to experiment, and better tools to develop their ideas?”

That can lead to new products, stronger services, better decisions, and new sources of revenue. But it starts with a basic belief that people aren’t simply costs to manage. They’re a source of knowledge, imagination, and value. AI can help bring more of that forward.

Elizabeth Ngonzi at CWIL in audience (with credits)

From Automation to Amplification

A recurring assumption in conversations about artificial intelligence is that the technology’s economic value is primarily proportional to the amount of human labour it can remove.

Ngonzi proposes a different equation.

What if the most important measure of AI is not the work it eliminates, but the capability it creates?

The distinction between those two ideas may prove fundamental.

Automation begins with an existing process and asks how much of it a machine can perform. Capability generation begins with humans and asks what they could accomplish if some of their existing constraints disappeared.

Those approaches can lead organisations towards very different futures.

A company optimising primarily for automation might use AI to reduce the number of people required to produce the same output. A company optimising for capability might give those same people tools that allow them to research unfamiliar domains, prototype ideas, interrogate institutional knowledge, communicate across languages or test possibilities that previously required resources they did not possess.

More than twenty years of professional work inevitably contains forgotten ideas, abandoned frameworks and connections too general for unaided memory to retrieve on demand. By making that archive computationally accessible, AI does not need to replace its author to become valuable.

It makes the author’s own intellectual history more available to her.

But Ngonzi also identifies the danger hidden inside that proposition.

An archive does not contain an objective representation of a person. It contains what that person happened to document. Their assumptions, omissions and blind spots enter the system alongside their expertise.

Source grounding therefore solves one AI problem while potentially amplifying another.

A system may become more faithful to its sources without those sources becoming more truthful.

That makes her insistence that AI should be capable of challenging rather than merely agreeing with its user particularly important. The ideal intellectual companion may not be the machine that most accurately reproduces what we have previously thought.

It may be the one that helps us discover where that thinking is incomplete.

And as machines become better at producing technically competent creative output, another human capability moves towards the centre: taste.

Generation creates possibilities.

Taste eliminates them.

It determines what deserves attention, what resonates, what feels culturally appropriate, what should be discarded and, ultimately, what is worth putting into the world.

That capacity cannot develop entirely through interaction with machines because, in Ngonzi’s account, taste emerges partly from precisely what machines cannot experience for us: nature, travel, art, conversations, relationships, different cultures, physical environments and the accumulated texture of living.

There is a paradox here.

The more capable artificial intelligence becomes, the less useful it may be for humans to imitate its defining characteristic: endless production.

Our comparative advantage may instead shift toward judgment, discernment, accountability, curiosity, and lived experience.

Or, in Ngonzi’s formulation:

That may be the more ambitious interpretation of human-centred AI.

Not protecting a shrinking territory of tasks that machines cannot yet perform.

But designing intelligent systems that expand the territory of what humans can become.

by Gonçalo Perdigão

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What OpenAI has announced is more interesting.

GPT-5.6 remains the company’s current public GPT-5 generation, while OpenAI has separately revealed Astra, an internal system it describes as its “next major model”. The company has already used Astra on difficult mathematical research problems and has publicly discussed slowing parts of its development while strengthening safeguards around its cybersecurity capabilities.

So rather than treating “GPT-5.7” as a product that already exists, the useful question is different: what do OpenAI’s own disclosures tell us about whatever comes after GPT-5.6 — and would calling it GPT-5.7 even make sense?

1) What OpenAI has actually confirmed

The starting point is straightforward.

OpenAI launched GPT-5.6 on July 9, 2026, with three tiers: Sol as the flagship model, Terra as the balanced option and Luna as the lower-cost model. OpenAI positioned the family around stronger performance per dollar, coding, long-running agentic workflows, computer use, design, professional knowledge work and scientific reasoning.

The company has continued to update GPT-5.6 commercially. It reduced pricing on parts of the family after launch and has been pushing the models across ChatGPT, Codex and the API. That matters because GPT-5.6 is not behaving like a generation OpenAI has already abandoned; it remains an actively developed product family.

OpenAI’s current model materials do not include an announced GPT-5.7 product, API model, release date, price, or system card.

Instead, OpenAI has started talking publicly about something else: Astra.

On August 1, OpenAI published results from ten mathematics and theoretical computer science problems and said they had been achieved by “an internal version of Astra, our next major model”. That wording is unusually explicit. Astra is not internet speculation: OpenAI has acknowledged its existence and its role as a future model.

But OpenAI did not say that Astra is GPT-5.7.

That distinction is essential.

2) Astra is the strongest signal we have, but it is not a product announcement

OpenAI’s mathematics publication gives us a rare glimpse of a model before release.

According to the company, an internal Astra system produced new results or substantial progress on ten open problems spanning areas including geometry, coding theory, complexity theory, operator algebras and lattice cryptography. The arguments were subsequently formalised with Lean certificates.

That does not give us a context-window number, parameter count or API price. Nor does it tell us whether Astra will ship unchanged.

But it does tell us something more useful: OpenAI is evaluating its next major model on problems that go well beyond conventional chatbot benchmarks.

And then came an even more consequential disclosure.

On August 18, OpenAI said preliminary evidence suggested Astra may meet the “Critical” cybersecurity capability threshold under its Preparedness Framework. The company said it had temporarily slowed parts of model development, including a two-week pause in reinforcement-learning training on models intended for deployment, while strengthening security, monitoring and alignment systems. Its largest planned frontier RL run remained on hold at the time of publication.

That changes how we should think about the next release.

The limiting factor is no longer necessarily whether OpenAI can train a more capable model. Increasingly, it may be whether the company can evaluate, contain and safely deploy what it has trained.

For anyone trying to predict “GPT-5.7 next week” from version-number patterns, that is a significant complication.

3) Why OpenAI still has strong incentives to keep moving

Safety constraints do not make the competitive pressure disappear.

If anything, the frontier market has become more aggressive since GPT-5.6 launched.

Google introduced Gemini 3.5 around what it calls “frontier intelligence with action”, explicitly targeting complex agentic workflows. Gemini 3.5 Flash was positioned around coding, long-horizon tasks, tool use and speed rather than simply better conversational answers.

Anthropic has followed a similar direction. Claude Sonnet 5 was launched with an emphasis on planning, browsers, terminals, coding and autonomous tool use, while Claude Opus 5 targets long-running agents and professional knowledge work.

SpaceXAI, meanwhile, released Grok 4.6 in August, describing it as a flagship focused on long-running agents, coding and more ambitious interactive and visual work. The company publishes a 500,000-token context window and has rapidly distributed the model through services including Microsoft Foundry, Amazon Bedrock and GitHub Copilot.

These are first-party product claims, so their benchmark charts should not be treated as perfectly neutral cross-vendor comparisons. But the positioning itself is revealing.

The competition is converging on the same territory:

agents that can work for longer, use tools more reliably, write and manipulate software, interact with computers, create professional artefacts and complete work rather than merely answer questions.

OpenAI’s own GPT-5.6 announcement points in precisely the same direction. Its emphasis on Programmatic Tool Calling, multi-agent execution, coding, computer use, design and end-to-end knowledge work shows that the battle has moved beyond “which chatbot gives the smartest response?”

The emerging question is: which system can reliably take responsibility for the largest useful unit of work?

4) What would a real “GPT-5.7” need to deliver?

If OpenAI eventually chooses to use the name GPT-5.7, the most credible expectation is not a single spectacular specification such as a giant context window.

GPT-5.6 already makes clear where OpenAI believes the value is moving: better intelligence per token, stronger coding, more reliable tool use, multi-agent coordination, computer interaction, professional artefact creation and longer autonomous workflows.

A meaningful follow-up would therefore need to push that system-level capability further.

That could mean an agent that needs less steering over long jobs. Better coordination between parallel subagents. Stronger verification before taking consequential actions. More capable scientific and technical reasoning. Better computer use. Or substantial improvements in the economics of running all of the above.

And, given what OpenAI has now said about Astra, safety architecture may be as important to the next generation as raw benchmark capability.

The company says Astra may have reached a level of cybersecurity capability that requires stricter workload isolation, network controls, and expanded monitoring. OpenAI estimates its new monitoring systems can themselves impose meaningful compute overhead. That makes safeguards part of the engineering and economics of a frontier model, rather than something bolted on after training.

This is analysis, not a leaked specification. OpenAI has not said these features constitute “GPT-5.7”.

But they are a much stronger basis for thinking about the next model than alleged screenshots, anonymous posts or invented parameter counts.

5) The clearest conclusion: GPT-5.7 is not confirmed. Astra is

As of September 1, the responsible description is simple:

OpenAI has not announced GPT-5.7.

GPT-5.6 is the current public family. Astra is an acknowledged internal system and, in OpenAI’s own words, its “next major model”. OpenAI has demonstrated Astra on mathematical research and has said some of its development and evaluation work is proceeding under heightened security because of potentially critical cybersecurity capability.

What OpenAI has not provided is a final commercial name, launch date, API identifier, pricing structure or complete benchmark suite for Astra.

So the most interesting possibility is that the entire “GPT-5.7” framing may be wrong.

Astra could become GPT-5.7. It could become a later GPT generation. It could sit inside a different naming architecture entirely.

Today, we simply do not know.

And that is exactly why the sensible approach is to follow OpenAI’s official model documentation, system cards and release announcements rather than reverse-engineer a product roadmap from rumours.

The story is no longer that OpenAI might be building something beyond GPT-5.6.

We know it is.

The unanswered question is what Astra becomes when OpenAI decides it is ready to leave the lab.

by Gonçalo Perdigão

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Sources

• OpenAI — “GPT-5.6: Frontier intelligence that scales with your ambition”, July 9, 2026.

• OpenAI — “Ten advances in mathematics and theoretical computer science”, August 1, 2026.

• OpenAI — “Pacing model development in an era of cyber-critical capabilities”, August 18, 2026.

• Google — “Gemini 3.5: frontier intelligence with action”, May 19, 2026.

• Anthropic — “Introducing Claude Sonnet 5”, June 30, 2026, and “Introducing Claude Opus 5”, July 24, 2026.

• SpaceXAI — “Introducing Grok 4.6”, August 12, 2026.

For the past three years, most generative AI has lived inside a familiar interaction: a person asks, a machine answers. Even powerful systems remained, structurally, tools. Humans initiated the work, evaluated the result and decided what happened next.

That boundary is beginning to move.

Across enterprise software, coding, regulation and research, August produced different versions of the same signal: AI is moving from assistance to execution.

The interesting consequence is not simply that machines can do more work.

It is that the scarce resource is shifting.

When intelligence becomes cheap, and software becomes easier to create, the valuable things become permission, context, judgement, verification and responsibility.

And organisations are not designed around that scarcity yet.

The productivity paradox is getting stranger

The most revealing numbers of the month came from McKinsey’s global AI survey.

Among large organisations with more than $1 billion in annual revenue, 40% now report scaling AI agents, up from 27% the previous year. Around 20% of organisations are scaling software coding agents. But one number matters even more: 32% of respondents said their organisation had decided not to buy at least one software product or feature because it could build the functionality internally using agentic coding tools.

McKinsey’s State of AI August 2026

That sounds like a software story. It is actually an economic one.

For decades, companies faced a reasonably stable build-versus-buy calculation. Developing software internally required engineers, time, maintenance and specialised knowledge. Buying SaaS was often cheaper than recreating it.

Coding agents are beginning to alter that equation.

If a small internal team can describe a workflow and have agents generate much of the code, tests, documentation and integrations, the minimum economic size of a software product changes.

A €50,000-a-year niche SaaS product suddenly competes not only against another SaaS company but against its own customer deciding: we can probably build enough of this ourselves.

This does not mean companies will regenerate SAP over a weekend.

But it could compress large parts of the software market from below.

Small dashboards, internal tools, workflow applications, reporting systems, data interfaces and specialised utilities become increasingly cheap to create. The competitive threat to software vendors may therefore come from an unexpected direction: not another startup, but the customer.

Yet the same McKinsey research reveals the contradiction.

Individual workers frequently report productivity improvements from AI, while enterprise-level financial impact remains much harder to capture.

This gap matters.

We have spent years asking whether AI makes a person faster. Increasingly, the better question is whether making thousands of people faster makes the organisation better.

Those are not the same thing.

Faster work can simply create more work

Microsoft researchers offered another useful piece of the puzzle in August.

Using digital traces from Microsoft 365 across large international companies, researchers examined what happened after workers adopted generative AI. Among frequent users — those using the AI system more than 100 times over a 20-week period — productivity-oriented application activity increased 21.2%. Communication activity also increased, although by a smaller 7.1%.

Microsoft Research’s workplace study

That is interesting because productivity technology is usually sold through subtraction.

Less administration. Fewer emails. Faster documents. Shorter processes.

But technological efficiency often produces expansion instead.

When producing a report takes half the time, organisations do not necessarily produce the same number of reports and go home early. They can produce twice as many.

When software becomes cheaper to build, we do not necessarily need fewer applications. We create applications for problems previously too small to justify software.

And when an AI agent can execute ten tasks simultaneously, the human supervising those agents suddenly has ten outputs to evaluate.

The bottleneck moves.

This may become one of the defining characteristics of agentic work: automation does not remove human work evenly. It removes some stages while concentrating pressure on others.

Writing becomes cheaper; reviewing becomes more important.

Coding accelerates; architecture matters more.

Content becomes abundant; selection becomes scarce.

Execution becomes automated; permission becomes critical.

The worker does not disappear from the system. The worker moves towards the bottleneck.

That can feel less like automation and more like intensification.

A systematic review published in Acta Psychologica this summer examined 30 empirical studies on generative AI and academic workload. Its central question is telling: does generative AI create efficiency, or does it intensify work? The evidence suggests that both effects can coexist.

This is an important correction to the simplistic equation:

AI → productivity → fewer hours.

The actual chain may increasingly look like:

AI → cheaper production → more production → more coordination → new bottlenecks.

The productivity dividend does not automatically become a leisure dividend.

Europe is regulating the new bottleneck

Then, on 2 August, another transition became concrete.

Key provisions of the EU AI Act moved into enforcement, including transparency requirements covering interactive AI systems and certain AI-generated or manipulated content.

Providers must ensure people know when they are interacting directly with AI. Certain generated content must carry machine-readable markings. Deepfakes and some AI-generated material involving matters of public interest require disclosure. By the end of July, around 190 organisations had already signed the associated transparency code.

European Commission guidance on AI transparency

It is easy to interpret this as another chapter in the familiar Europe-versus-Silicon-Valley story.

Europe regulates. America builds.

That reading misses something more interesting.

The AI Act is arriving just as AI systems move from generating information to taking action.

Transparency mattered when machines produced synthetic images and text.

Accountability matters much more when machines execute.

An AI system that drafts an email creates one category of risk. An agent that sends it, accesses customer records, changes a database, purchases something or modifies production code creates another.

The central governance question therefore changes from:

Was this generated by AI?

to:

Who authorised the AI to do this?

That sounds subtle. It is not.

It means AI governance increasingly resembles identity and access management.

Every organisation already has elaborate systems determining what humans can do. Employees have credentials, roles, approval limits and access rights. Financial systems record transactions. Enterprise software creates audit trails. Managers sign off expenditure.

Agents will need an equivalent institutional architecture.

Which agent can access which data?

Which decisions can it make?

How much money can it spend?

Can it communicate externally?

Can it create another agent?

When does it need human approval?

Who is responsible when it makes a mistake?

The more capable agents become, the less these questions look like AI questions.

They become organisational design questions.

Intelligence is becoming less differentiating

This points towards a larger shift.

For much of the current AI cycle, competitive advantage has been framed around access to intelligence.

Who has the best model?

Who has the most compute?

Who has the strongest engineers?

Who can generate the best answers?

Those things still matter. But organisations increasingly have access to similar frontier capabilities.

OpenAI’s own enterprise research, published in August, describes a widening gap between organisations using similar models. The difference is increasingly how deeply those organisations integrate AI into actual work: connecting systems to context and tools, redesigning workflows and delegating tasks rather than merely asking for assistance.

The model is becoming one component of the system rather than the whole advantage.

This resembles what happened with previous technological infrastructure.

Having internet access stopped being a competitive advantage.

Having cloud computing stopped being a competitive advantage.

Having smartphones stopped being a competitive advantage.

Eventually, everyone has the technology.

The difference moves to what you build around it.

AI may be approaching that transition surprisingly quickly.

If models continue becoming more capable and widely accessible, then intelligence itself becomes less scarce.

What remains scarce is organisational context: the messy, accumulated knowledge of how a company actually works.

Who is allowed to make a decision.

Which customer relationship matters.

Why an exception exists.

Which metric cannot be trusted.

Which supplier always delivers late.

Why a process that looks irrational on paper exists in the first place.

Much of this knowledge has never been written down because humans carried it implicitly.

Agents cannot reliably operate organisations without it.

That creates an unexpected priority for companies pursuing AI: not simply acquiring more intelligence, but making themselves legible to machines.

Processes need clearer ownership.

Data needs cleaner structure.

Permissions need explicit boundaries.

Decisions need traceability.

Exceptions need documentation.

Institutional memory needs to become accessible without becoming dangerously exposed.

The companies best positioned for agentic AI may therefore not be those with the biggest AI budgets.

They may be the ones that understand themselves best.

The new interface is authority

There is another consequence.

We tend to imagine the future of AI interfaces as a design problem: better chat, voice, glasses, ambient computing.

But agents suggest that the most important interface may be invisible.

It is the boundary between what the machine can recommend and what it can actually do.

Today, we click buttons.

Tomorrow, increasingly, we grant authority.

“Find suitable suppliers” is information retrieval.

“Compare these suppliers” is analysis.

“Negotiate within these parameters” is delegated judgement.

“Purchase up to €20,000” is authority.

Each step changes the economics of automation because it removes another human checkpoint.

It also increases the consequences of error.

This creates a strange inversion.

For years, the technology industry has tried to reduce friction.

Agentic systems will require us to deliberately put some friction back.

Approval thresholds.

Audit trails.

Verification.

Escalation rules.

Human review.

Spending limits.

The most sophisticated AI systems may therefore be distinguished not by how autonomously they can operate, but by how intelligently their autonomy can be constrained.

That is not a limitation of the technology.

It may become part of the product.

August 2026 offered plenty of spectacular AI stories. Models are contributing to mathematical research, coding agents are becoming increasingly capable, and companies continue pushing towards systems that can execute longer sequences of work.

But the deeper story is less spectacular.

We spent the first phase of generative AI trying to make machines more capable.

The next phase requires making organisations capable of using those machines.

That means redesigning workflows, authority, software procurement, accountability and even the way institutional knowledge is recorded.

The scarce resource is moving from intelligence towards judgement.

And that may be the paradox of increasingly autonomous machines: the more they can do without us, the more precisely we need to decide what they should be allowed to do.

by Gonçalo Perdigão

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Articles from August 2026:

by Gonçalo Perdigão

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At the National University of Singapore (NUS), researchers and technology companies have deployed what they describe as the world’s first independently operated, biologically integrated server rack. The prototype brings living human neurons into the kind of infrastructure normally associated with conventional computing. (source: biospectrumasia.com)

The system was developed by NUS Medicine, Singapore-headquartered data-centre operator DayOne and Australian biological computing company Cortical Labs. It consists of 20 CL1 biological computers operating in a live research environment at the NUS Life Sciences Institute. (source: datacenterdynamics.com)

Each CL1 combines roughly 800,000 lab-grown human neurons with silicon electronics. Across 20 units, that implies approximately 16 million neurons. The cells are derived from human stem-cell lines and grown on microelectrode arrays that allow computers to send electrical signals to the neurons and read their responses. (source: Startup Selfie)

This is not a human brain inside a computer. Nor is it a replacement for today’s data centres. It is a research prototype testing whether biological neural networks can become a useful computing substrate alongside silicon.

From AI models to AI systems

The development matters because artificial intelligence is moving beyond individual models.

Today’s frontier is increasingly about AI systems: models connected to memory, tools, data, sensors and other models, operating continuously and adapting to changing environments. As these systems become larger and more capable, the computing infrastructure behind them becomes increasingly important.

Biological computing explores a fundamentally different approach to that infrastructure.

Conventional AI attempts to reproduce aspects of intelligence mathematically, running artificial neural networks on silicon processors. Biological computing asks another question:

what if living neurons themselves become part of the computing system?

Neurons naturally receive signals, reorganise their connections and adapt through experience. Cortical Labs has previously demonstrated neural cultures interacting with a simulated version of the game Pong. The Singapore deployment takes the technology from individual experiments toward a multi-unit computing environment. (source: The Times of India)

Why biology is attracting attention

One motivation is energy.

The human brain demonstrates that highly complex information processing can happen with remarkably low energy consumption. Modern AI infrastructure, by contrast, requires large quantities of electricity for computation and cooling.

Cortical Labs says an individual CL1 operates at around 25 watts, while the complete rack requires roughly 800 to 1,000 watts. These figures are promising, but they should be treated carefully: the Singapore prototype has not yet produced evidence showing that biological computing can outperform conventional AI infrastructure on an equivalent workload at commercial scale. (source: TNW)

Its immediate importance is therefore not efficiency already achieved, but a new computing architecture being tested.

The partners are exploring applications including neuro-inspired AI, biomedical modelling, drug discovery and neurological disease research. (source: biospectrumasia.com)

Does this bring AI closer to human intelligence?

Not by itself.

There is a major distinction between using human neurons for computation and creating human-like intelligence.

The Singapore system contains millions of neurons; a human brain contains roughly 86 billion. More importantly, intelligence emerges from extraordinarily complex biological structures and interactions, not simply from the number of neurons available.

There is also no evidence that these systems are conscious. Using living human neurons does not demonstrate awareness, subjective experience or anything resembling a human mind.

What the technology does introduce is a new possibility.

For decades, the computing industry has tried to make machines behave more like brains by designing increasingly sophisticated software and silicon architectures. Biological computing approaches the problem from the opposite direction: rather than only imitating neural behaviour, it incorporates actual neurons into the computing stack.

If the technology can eventually scale, the future of AI infrastructure could therefore become hybrid: silicon for reliable high-speed digital computation, AI models for reasoning and language, and biological neural systems for forms of learning and adaptation where living networks prove useful.

For corporate leaders, that is the development worth watching.

The immediate story is a 20-unit research prototype in Singapore. The larger question is whether the evolution of AI—from models into increasingly autonomous and adaptive systems—will eventually require computing architectures that borrow more directly from biology.

For the first time, that possibility is no longer confined to a single laboratory experiment. It is operating at server-rack scale.

by Gonçalo Perdigão

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Also read:

We looked at the model.

Which one is smarter?

Which one writes better?

Which one reasons longer, generates faster, costs less, has more parameters, a larger context window, better benchmarks?

Those questions mattered.

They still do.

But increasingly, they are not the questions that decide whether AI creates any value at all.

A company can have access to an extraordinary model and still build a terrible AI system.

A creator can have the best image generator in the market and still make forgettable work.

An agent can reason brilliantly and still send the wrong email.

A business can automate thousands of tasks and still have no idea whether the output is accurate.

The model is becoming one component inside something much bigger.

And that changes almost everything.

We spent three years confusing intelligence with the product

ChatGPT made the confusion understandable.

You typed something.

Intelligence appeared.

The interface and the model felt almost like the same thing.

Then the industry started pulling them apart.

Models became APIs.

APIs became agents.

Agents received tools.

Tools connected to company data.

Company data connected to workflows.

Workflows connected to customers, payments, contracts, designs, campaigns and decisions.

Suddenly, model quality was only one variable.

Now the questions sound different.

What information can the AI access?

What happens when that information is wrong?

Which actions can it take?

Who approves them?

What does one successful task cost?

How do you know when performance deteriorates?

What happens after a failure?

Who is accountable?

These sound like boring questions compared with artificial general intelligence.

They are also the questions that determine whether the thing works.

The AI stack is swallowing the model

Look at what has happened across AI in 2026.

The interesting movement is no longer confined to bigger frontier models.

Small models are becoming useful because many business problems do not require a synthetic Einstein.

Self-hosted systems matter because control can be more valuable than raw intelligence.

Agents matter because AI is moving from answering questions to performing work.

MCP and other connection layers matter because intelligence without access to tools remains trapped inside a conversation.

Compute matters because latency, throughput and cost eventually arrive on somebody’s budget.

Governance matters because autonomous execution increases both output and blast radius.

Evaluation matters because a model producing a plausible answer is not the same thing as a system producing a correct result.

And provenance matters because when machines can produce almost anything, knowing where something came from becomes part of its value.

None of these developments makes the model irrelevant.

They put it in its proper place.

The model is the engine.

Nobody buys a car because the engine exists.

Intelligence is cheap. Consequences are expensive.

This may be the more useful way to understand the current phase of AI.

Generating has become cheap.

Writing is cheap.

Images are cheap.

Code is cheaper.

Music is getting cheaper.

Analysis is getting cheaper.

Experiments are cheaper.

Even starting a company can be cheaper.

But the world on the other side of that generation has not become cheap.

Attention remains scarce.

Trust remains slow.

Distribution remains difficult.

Customers remain unpredictable.

Regulation still exists.

Reputation can still disappear in an afternoon.

A bad contract clause still matters.

A false financial number still matters.

An offensive campaign still matters.

A security breach definitely still matters.

AI dramatically reduces the cost of producing an action.

It does not necessarily reduce the cost of that action being wrong.

In some cases, it does the opposite.

Automation gives mistakes distribution.

This is why the smallest model can beat the smartest one

Technology has an instinct to buy maximum capability.

If Model A scores higher than Model B, use Model A.

That is increasingly poor system design.

Imagine a company processing one million simple classifications every month.

The best model in the world may perform the task beautifully.

A smaller model may do it just as well.

But faster.

Locally.

At a fraction of the cost.

With predictable latency.

Perhaps with better privacy.

That sentence is much less exciting.

It is also how technology becomes infrastructure.

We did not build the internet by running every computation on the most powerful computer available.

AI will not scale that way either.

Agents make this impossible to ignore

A chatbot can be wrong and annoy you.

An agent can be wrong and do something.

That distinction deserves more attention than another leaderboard.

Once AI can browse, buy, publish, edit files, call APIs, contact suppliers, write code or move information between systems, intelligence acquires consequences.

Now permissions become product design.

Logs become product design.

Escalation becomes product design.

Memory becomes product design.

Human approval becomes product design.

Even knowing when not to use AI becomes product design.

The most impressive agent demo is therefore often the least interesting part of an agent deployment.

The interesting part begins the following morning.

Did it complete the job?

Did it complete the right job?

How much did it cost?

What did it touch?

What changed?

Can someone explain why?

Would you allow it to do the same thing 100,000 times?

That last question is useful.

If the answer is uncomfortable, you probably have a demo, not a system.

The same rule applies to creativity

Creative AI initially looked like a production revolution.

And it is one.

We can make more images, videos, songs, interfaces, campaigns and variations than any creative team could reasonably consume.

Which creates a strange outcome.

Production becomes less valuable precisely because we can produce so much.

The scarce layer moves elsewhere.

Taste.

Selection.

Direction.

Memory.

Context.

Restraint.

A sense of what deserves to exist.

This is why AI slop is not simply a quality problem.

It is a system problem.

If a creative machine is optimised to generate more, it will generate more.

If a marketing organisation rewards volume, AI will give it extraordinary volume.

If an algorithm rewards engagement, machines will learn to feed the algorithm.

Nothing in that loop necessarily rewards meaning.

The problem is not that AI has no creativity.

The problem is that abundance has no editor.

Brands face exactly the same problem

A brand used to control a relatively small number of surfaces.

Its website.

Its stores.

Its advertising.

Its packaging.

Its social channels.

Now an increasing part of the relationship between a company and the world is mediated by machines.

An AI may describe your product.

Compare it with a competitor.

Recommend it.

Reject it.

Summarise customer reviews.

Explain its price.

Answer questions about its sustainability claims.

Eventually, it may purchase it.

This is where GEO becomes more interesting than an SEO acronym.

Generative Engine Optimisation is really an information architecture problem.

Is your organisation understandable to machines?

Are your facts consistent?

Can claims be verified?

Can a model distinguish current information from something published three years ago?

Does the web contain enough reliable evidence for an AI system to represent your company accurately?

For twenty years, brands tried to make themselves visible to search engines.

Now they also need to make themselves legible to reasoning systems.

That is a bigger change.

Europe’s AI question is also a systems question

The same pattern appears at continental scale.

It is tempting to reduce the AI race to models.

Who has the European OpenAI?

Where is Europe’s Gemini?

But models sit on infrastructure.

Infrastructure sits on energy, chips, capital, regulation, research, talent, data and institutions.

That is why supercomputers such as MareNostrum matter.

Not because 5,000 GPUs make a good photograph.

Because access to compute determines which experiments can exist.

Sovereign AI is not simply about putting a flag on a language model.

It is about having enough of the stack to make meaningful choices.

The same is true in India, where local-first systems make an important point: “best model” is meaningless without asking best for whom, for what language, for what documents, under what conditions.

Global intelligence still meets local reality.

Local reality usually wins.

AI governance is not the department that says no

This also explains why governance has become such a persistent theme.

Governance is often presented as friction.

A committee.

A policy document.

A compliance requirement.

Something added after innovation has happened.

That view becomes absurd once AI starts acting inside real systems.

Good governance is part of the machine.

It determines what an agent can access.

What requires human approval.

Which data can leave an organisation.

How outputs are evaluated.

How incidents are recorded.

When systems should stop.

Who carries responsibility.

If you remove those things, you have not created a more innovative system.

You have created an incomplete one.

Brakes do not make cars slower inventions.

They make speed usable.

AI governance has a similar job.

Humans are moving up one level

Another thread connects all of this.

People are not disappearing from these systems.

Their position is changing.

When execution was expensive, humans spent enormous amounts of time executing.

Writing the document.

Formatting the presentation.

Searching the database.

Producing ten concepts.

Sending updates.

Checking spreadsheets.

Creating variations.

AI can absorb pieces of that work.

What remains is less visible but more consequential.

Choosing the objective.

Defining the constraints.

Designing the loop.

Deciding what good looks like.

Recognising when the metric is wrong.

Understanding the customer.

Taking responsibility.

Saying no.

Changing direction.

Having taste.

This is not automatically good news for every job.

Some roles will shrink.

Some tasks will disappear.

Some organisations will need fewer people to produce the same amount of work.

But “human versus machine” is increasingly the wrong diagram.

A better diagram is a system containing both.

The interesting question is where each belongs.

Even biology points in the same direction

There is something fitting about looking at biological intelligence now.

Brains remind us that intelligence does not exist separately from architecture.

Memory, processing, energy, adaptation, sensing and physical structure are intertwined.

The brain is impressive not because each neuron is a frontier model.

Individual neurons are remarkably modest.

The intelligence comes from organisation.

Connections.

Feedback.

Specialisation.

Adaptation.

Memory.

Signals.

Constraints.

A system.

Perhaps there is a lesson here that extends beyond neuromorphic computing.

We have spent several years asking how intelligent an AI model can become.

The next phase may be defined by a different question:

How intelligently can we organise intelligence?

That may be the real AI race

The winners may not have the smartest model.

They may use several models.

Some large.

Some tiny.

Some local.

Some remote.

Some open.

Some proprietary.

They will connect them to good data.

Give them narrowly designed tools.

Evaluate their output.

Control their permissions.

Understand their costs.

Build feedback loops.

Know where humans should intervene.

And remove AI entirely from places where it adds nothing.

That last part matters.

A mature AI organisation will not be the organisation using AI everywhere.

It will be the one that knows exactly where intelligence creates leverage.

We started this technological cycle fascinated by generation.

Then came agents.

Now comes architecture.

That is a much harder problem.

It is also where the real work begins.

by Gonçalo Perdigão

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There is something slightly strange about the way we talk about artificial intelligence.

For decades, we have borrowed words from biology. Neural networks have neurons. They learn. They remember. They form representations. More recently, we have started asking whether models reason.

The language suggests that our machines are slowly becoming brain-like.

Physically, they are not.

A large generative model today still runs on a computing architecture that would be recognisable, in its basic logic, to engineers from another era. Data sits in memory. Processors fetch it. Numbers are moved around at extraordinary speed. Vast amounts of multiplication happen. The result can be astonishing, but the machinery underneath remains deeply conventional.

A brain is built differently.

There is no clean border between processor and memory inside your head. Neurons do not simply execute instructions and then look elsewhere for stored information. The same physical system is sensing, storing, changing, predicting and responding.

And it does so on roughly the power consumption of a dim light bulb.

That comparison is often used as a fun fact about the brain. I think it is more interesting than that.

It may be a clue.

For the last decade, the dominant story in AI has been largely about scale: more data, larger models, more chips and more computation.

What if another part of the story is architecture?

What if AI becomes more capable not only by becoming larger, but by becoming slightly more biological?

That idea starts to look less speculative when you notice two things happening at the same time.

The first is that generative models are increasingly being trained on the raw information of life: DNA, RNA, proteins and molecular structures.

The second is that engineers are building computers inspired more directly by how nervous systems process information.

These are still different research worlds. But put them next to each other and an unusual possibility appears.

Generative AI could begin to become biological in two senses at once: in what it learns from and in how it computes.

Biology has its own languages

The easiest way to understand this starts with language models.

A large language model learns by looking at enormous quantities of text and discovering statistical structure. Certain words tend to appear near other words. Grammar has patterns. Concepts have relationships. Long before the model can produce a good paragraph, it has learned a complicated map of what tends to go with what.

Biology also contains sequences.

DNA is written with a small chemical alphabet. Proteins are chains of amino acids. Those sequences are obviously not sentences, but they are not random either.

A protein sequence carries a history.

Some arrangements fold correctly. Others do not. Some survive inside cells. Some bind to particular molecules. Some perform useful biological work. Most imaginable protein sequences never appeared in nature at all.

Evolution has been filtering this space for billions of years.

That makes biological sequences unusually rich training data.

A model can be given millions of protein sequences and asked to learn their structure in much the same broad way that a language model learns patterns in text.

It does not need to begin with a complete theory of protein chemistry. It can start by asking a simpler statistical question: given what appears here, what tends to appear next?

At first, this sounds almost disappointingly mechanical.

Then the interesting part begins.

When these models become large enough and their training data becomes broad enough, they can learn relationships that are useful for predicting structure, function and biological behaviour. They can also generate sequences that do not exist in the original dataset.

This is where generative biology starts to become more than biological search.

The model is no longer only finding things evolution already made.

It can propose things evolution did not.

Nature is a much harsher critic than language

Calling these systems “language models for proteins” is helpful up to a point.

Then the analogy breaks.

A sentence can be strange and still exist.

A protein has to survive physics.

It has to fold into a plausible three-dimensional shape. It may need to remain stable under specific conditions. It may need to interact with one target and avoid another. Inside a living cell, it enters an environment filled with other molecules, competing reactions, temperature changes and noise.

There is no forgiving reader on the other side.

There is chemistry.

This means that generative biology is gradually forcing AI towards something that text generation can sometimes avoid: grounding.

A model can generate a convincing paragraph that happens to be wrong.

A generated molecule that does not fold properly simply fails.

That difference matters.

Researchers are therefore trying to connect biological models not only to sequence data but to structure, molecular dynamics, energy and real experimental feedback.

This creates a very different kind of generative loop.

The model proposes something.

The proposal is built.

Nature tests it.

The result comes back.

The model learns.

There is something almost evolutionary about this arrangement, although compressed into laboratory time.

And this is where I started wondering whether biology may end up changing AI more deeply than we expect.

Not only because AI can help us design proteins.

Because biology may be teaching us what computation can look like.

The computer running all this is still very unbiological

There is an irony here.

We can train a model on millions of years of evolutionary information while running it on hardware organised around principles that are almost the opposite of biology.

Modern GPUs are extraordinary machines. None of this is an argument against them.

But they solve the problem through force and precision.

They move huge quantities of data. They perform enormous numbers of numerical operations. They consume considerable energy doing it.

Brains take a different route.

Neurons are comparatively slow. Their signals are noisy. Activity is distributed. Much of the brain is not firing at full speed all the time. Computation happens through changes in networks, timing, electrical spikes and chemistry.

Memory is not sitting somewhere else waiting to be fetched.

The network itself changes.

That simple fact has inspired a field called neuromorphic computing.

The name sounds intimidating. The basic idea is not.

Neuromorphic computing is an attempt to build machines that borrow some of the operating principles of nervous systems.

Not to recreate a human brain transistor by transistor.

That would be the wrong mental image.

The idea is to ask which parts of the brain’s architecture are useful enough to steal.

A computer that behaves a little more like a nervous system

Traditional computers generally separate memory from processing.

That separation has worked brilliantly for decades. But moving data between the two costs time and energy, especially when the amount of data becomes enormous.

Neuromorphic systems try different arrangements.

Some bring memory and computation much closer together.

Some use artificial neurons that communicate through spikes rather than continuously passing large numerical values.

Some are event-driven, which means parts of the machine become active when something actually happens instead of constantly performing operations.

Different designs take different approaches, and “neuromorphic” covers a surprisingly wide family of technologies.

But the common intuition is easy to understand.

Brains do not seem efficient because individual neurons are wonderful processors.

They seem efficient because of how the whole system is organised.

That may be the lesson worth copying.

Consider vision.

A conventional AI system can process a camera feed as a stream of complete images, frame after frame after frame.

A more brain-inspired system might pay far more attention to change.

Something moved.

A boundary appeared.

Light changed here.

Nothing happened there, so there is less reason to compute.

This sounds small until you remember that biological intelligence lives under severe energy limits.

Brains cannot simply add another data centre when a problem becomes difficult.

Evolution had to find other solutions.

Now put the two trends together

This is the part I find most interesting.

Imagine a generative model trained primarily on biological information: proteins, DNA, molecular structures and the relationships between them.

Then imagine parts of that model running on hardware designed around more biological principles: sparse activity, local memory, event-driven computation, adaptable connections.

The data is biological.

The computational architecture is becoming more biological.

The result is not a living machine. It is not an artificial brain. And we should probably resist both phrases because they make the science sound more dramatic than it is.

But it is a change in direction.

For most of computing history, we have forced problems into the architecture of the computer.

Perhaps some future systems will do the reverse.

They will change the architecture of the computer to better fit the problem.

Biology is an obvious place to look because it has already produced systems capable of learning, adapting and operating with remarkable energy efficiency.

Including us.

AI has spent years borrowing the appearance of the brain

Artificial neural networks have always been inspired by biology.

But the inspiration is thin.

The “neurons” inside a transformer are not tiny digital versions of biological neurons. The similarity is mostly historical and mathematical.

That has not stopped neural networks from becoming extraordinarily powerful.

It does, however, leave a question open.

How much of biological intelligence have we ignored because it was inconvenient to reproduce in software?

Real brains are constantly changing.

A conversation changes them.

Sleep changes them.

Stress changes them.

Learning is not a separate mode that happens in a data centre overnight. Learning is part of normal operation.

The distinction between hardware and software also becomes fuzzy in biology.

Your memories are not files stored on some internal drive.

They are partly reflected in the physical organisation and strength of connections inside a living network.

The substrate matters.

That idea sits awkwardly beside the way we normally imagine AI.

We tend to think that intelligence is primarily the algorithm and that the hardware underneath it is interchangeable machinery.

Biology suggests this may be too simple.

Perhaps the physical form of a computing system influences the kinds of intelligence it can efficiently produce.

The brain is not software running on meat

This is where the subject becomes philosophically interesting, but we can keep the argument simple.

The human brain is not a generic processor that happens to be made from cells.

Its material properties are part of how it works.

Electrical activity matters. Chemistry matters. Timing matters. The shape of neurons matters. The way synapses change matters. Blood supply and metabolism matter.

Strip all of this away and what remains is not obviously “the same intelligence” waiting to be run somewhere else.

That does not prove that minds cannot be reproduced in silicon.

It simply means we should be careful with the assumption that intelligence is completely independent of the machinery that produces it.

AI research has largely succeeded by abstracting away from biology.

Maybe the next stage will involve selectively putting some biology back.

Not by growing brains in computers, but by borrowing principles that evolution discovered first.

Local adaptation.

Distributed memory.

Sparse computation.

Continuous learning.

Robustness to noise.

Energy constraints.

Self-organisation.

These are not exotic features in nature.

They are normal.

In computing, many of them remain difficult.

Biology may be more than an application area for AI

Most discussions about AI and biology focus on what AI can do for biology.

Drug discovery.

Protein design.

Genomic analysis.

Disease prediction.

All of these are important, and some could become enormous industries.

But there is another direction to the relationship.

What can biology do for AI?

The answer may eventually be: quite a lot.

Life is a form of organised information processing that existed long before computers.

Cells sense their environment.

They maintain internal states.

They respond to signals.

They correct errors.

Populations adapt.

Nervous systems learn.

Brains predict.

None of these processes is identical to digital computation, and forcing the comparison too far quickly becomes silly.

Still, nature has clearly discovered ways of processing information that are robust, adaptive and efficient.

Computer engineering has good reasons to pay attention.

A useful way to think about this is that the first wave of AI borrowed metaphors from the brain.

The next wave may borrow architecture.

Scale is powerful. It may not be the whole answer.

The recent history of AI makes it very tempting to believe that scale solves everything.

Make the model larger.

Feed it more data.

Give it more computation.

Performance improves.

So far, this has been a remarkably productive strategy.

But every engineering regime eventually runs into costs.

Energy is one.

Chip manufacturing is another.

Latency matters.

Data movement matters.

There are also tasks where a gigantic general-purpose model may simply be the wrong architecture.

When that happens, progress does not necessarily stop.

Sometimes engineers change the machine.

Aviation improved when people stopped trying to reproduce birds literally and started understanding lift.

Modern computing emerged by abandoning one hardware technology after another.

The most interesting future of AI may therefore be messier than a straight line towards ever-larger models.

Some systems will grow.

Others will specialise.

Some will operate on conventional chips.

Others may use radically different hardware.

And biological computation may influence several of those paths at once.

So, is this “organic Generative AI”?

I use the phrase loosely.

Not because these systems are organic in the biological sense.

They are not alive.

A neural chip is still a chip. A protein model remains software. Training an AI system on DNA does not give the machine a metabolism.

What feels organic is the direction of travel.

The information comes increasingly from living systems.

The architecture starts borrowing more deeply from nervous systems.

The learning loop may involve physical experiments rather than static datasets.

And the boundary between storing information and processing it may become less rigid.

That combination is different enough to deserve attention.

We spent years trying to make software imitate the outputs of intelligence.

Write this.

Draw that.

Answer this question.

Generate this image.

The deeper challenge may now be to understand the machinery that makes biological intelligence possible in the first place.

The question I keep coming back to

After reading about protein models, neuromorphic chips and biological computation, I keep coming back to one question.

What has evolution discovered about intelligence that computer science has not?

It is probably not one thing.

There will not be a hidden biological trick that suddenly gives us vastly better AI.

Brains are complicated because intelligence is complicated.

But there may be principles worth extracting.

Perhaps intelligence works better when memory and processing are not treated as strangers.

Perhaps continuous adaptation matters more than we think.

Perhaps sparse systems can outperform brute-force ones in the right environments.

Perhaps noise is not always an engineering defect.

Perhaps the body, the substrate and the physical world are more important to intelligence than our software metaphors suggest.

And perhaps training on the products of evolution will teach models patterns that human-generated data never could.

None of this means that generative AI is about to turn into a brain.

The more interesting possibility is subtler.

AI may gradually stop treating the brain as a metaphor and start treating it as an engineering reference.

That would be a meaningful change.

For seventy years, computing has mostly asked how much intelligence we can produce from machines.

The next question may be how much better our machines become when we allow them to learn from the way intelligence already exists in nature.

by Gonçalo Perdigão

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One of the biggest developments came from OpenAI, where GPT-5.6 Sol reportedly contributed the key insight behind a new paper disproving the 150-year-old Maxwell Conjecture. While the researchers completed the mathematical proof themselves, they credited the AI for suggesting the construction that led to the breakthrough.

At the same time, OpenAI’s experimental research systems have been reported to solve multiple long-standing problems in mathematics and theoretical computer science using relatively modest computing resources. These results suggest AI is becoming a valuable research assistant rather than just a calculation tool.

China also reached a significant milestone. RedNote announced that its dots-note-3.0 model became the first AI system to achieve a perfect score of 42/42 on the International Mathematical Olympiad. Unlike traditional AI benchmarks, the competition requires complete mathematical proofs that are reviewed line by line by human judges.

The growing momentum supports an idea long discussed by investor Peter Thiel: mathematics could become one of AI’s strongest domains. Mathematical problems offer clear verification, rapid feedback, and structured reasoning, allowing AI models to improve far more efficiently than in creative tasks such as writing.

Earlier claims from AI researchers, including Emad Mostaque, also point toward increasingly powerful mathematical reasoning, with AI systems reportedly uncovering new insights related to Einstein’s equations.

Together, these developments highlight a clear trend. AI is no longer limited to assisting with calculations; it is becoming an increasingly important collaborator in mathematical research. While human experts remain essential for verifying and proving new discoveries, AI is already helping generate ideas that could accelerate scientific progress across mathematics, physics, and computer science.

by Gonçalo Perdigão

The dangerous hallucinations are the ones that look perfectly reasonable.

A contract summary that misses one important clause. A customer email classified incorrectly. A report that mixes outdated information with new facts. A recommendation that sounds convincing but isn’t actually supported by the data. Individually, these errors seem harmless. At scale, they become expensive decisions.

The problem is that most companies think hallucinations are rare events. They’re not. They are a natural consequence of how large language models work. AI doesn’t “know” things in the way people do. It predicts the next most likely words based on patterns it has learned. Most of the time, probability and reality overlap. Sometimes they don’t. That’s not a software bug; it’s the nature of the technology.

The real question, then, isn’t whether your AI hallucinates. It does. The question is whether you know how often, where, and what happens when it does.

Most organisations don’t.

Instead, they measure adoption. How many employees use AI. How many workflows were automated. How many hours were saved. Those numbers make great presentations, but they say almost nothing about reliability. Very few companies can answer far more important questions: How often is the AI wrong? Which mistakes matter most? Has performance improved over time, or is it slowly getting worse?

Ironically, the better AI becomes, the harder these problems are to detect. Early models made obvious mistakes. Today’s models make believable mistakes. And believable mistakes are far more dangerous because people stop questioning them.

This is why buying a better model isn’t enough. Better prompting isn’t enough. More documents in your RAG system aren’t enough. None of these eliminates hallucinations. They simply change where and how they appear.

The companies that succeed with AI won’t be the ones with the smartest models. They’ll be the ones that can measure, monitor and continuously verify what their AI is doing.

Because the biggest risk isn’t that AI hallucinates.

It’s that your business has no idea when it does.

by Gonçalo Perdigão

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

• a prototype that impresses,

• a workflow that hasn’t changed,

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

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

AI should be treated like a capital allocation decision

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

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

Here’s the bar I recommend:

An AI project is only worth doing if it is:

1. attached to a measurable business bottleneck,

2. deployable into a real workflow,

3. supported by data you can defend,

4. governable under regulation and audit,

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

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

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

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

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

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

Good constraints sound like:

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

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

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

Bad constraints sound like:

• “We want to modernise.”

• “Competitors are doing GenAI.”

• “We need an AI strategy.”

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

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

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

• better forms,

• fewer handoffs,

• a data cleanup,

• a rules engine,

• a template library,

• stronger search,

• clearer approvals.

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

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

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

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

So force specificity:

• Which role uses it?

• At which moment?

• What input triggers it?

• What output changes a decision?

• What is the human override?

• What system records the outcome?

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

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

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

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

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

Ask:

• Do we have the data today?

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

• Is it stable, or does it drift weekly?

• Can we trace model outputs back to sources?

• Who owns data quality as an ongoing job?

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

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

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

You need three numbers:

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

• volume per month,

• value per successful output.

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

• they call tools too often,

• they re-run tasks,

• they require human review,

• they generate extra work downstream.

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

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

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

• leaking sensitive data,

• confident errors,

• biased decisions,

• unexplainable outcomes,

• audit failure,

• supplier lock-in.

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

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

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

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

A classic failure pattern looks like this:

1. AI generates a summary.

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

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

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

Net result: you added time, not removed it.

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

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

• extract one painful task,

• reduce it to a constrained output,

• integrate it into an existing system,

• measure it weekly,

• and expand only after repeatable impact.

A practical “never build AI” list

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

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

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

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

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

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

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

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

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

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

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

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

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

A simple rule that changes everything

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

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

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

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

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

This does two things:

• It protects budgets.

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

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

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

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

by Gonçalo Perdigão

You don’t.

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

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

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

Nothing more.

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

The answers did.

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

For a human reader, this looks perfectly normal.

AI Is Probabilistic, Not Deterministic

Traditional software behaves like a calculator.

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

Large Language Models don’t work like that.

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

Generation is therefore a statistical process.

This is not a flaw.

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

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

One answer tells you almost nothing.

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

Why Volume Matters

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

It isn’t.

It needs to work consistently.

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

It allows them to measure stability.

Questions such as these become possible:

• Does the model always understand the task?

• Does the structure remain consistent?

• Does creativity stay within acceptable limits?

• Are there unexpected failures?

• How much variability is acceptable?

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

What 100 Answers Tell Us About Lisbon

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

It was about measuring a model.

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

Several interesting observations emerge.

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

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

Third, the third adjective becomes far more diverse.

Sometimes Lisbon is soulful.

Sometimes historic.

Sometimes vibrant.

Sometimes enchanting.

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

That is exactly what a probabilistic language model should do.

Few people would argue whether Lisbon is hilly.

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

The model mirrors that uncertainty remarkably well.

The Hidden Role of Temperature

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

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

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

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

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

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

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

For creative writing, higher variability may be desirable.

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

Looking at AI Like a Statistician

Perhaps the most important lesson is methodological.

Most AI evaluations ask:

“Was this answer good?”

A better question is:

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

That small change completely transforms how we evaluate AI.

Instead of judging one output, we can calculate:

• the frequency of each concept;

• semantic convergence;

• lexical diversity;

• consistency between executions;

• outlier responses;

• confidence intervals;

• prompt stability.

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

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

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

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

The Rise of Trillion-Parameter Models

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

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

The result is impressive.

They can write code.

Analyse contracts.

Summarise research.

Translate languages.

Reason through complex business problems.

Generate creative content.

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

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

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

That changes everything.

Businesses are no longer limited to a single vendor.

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

National supercomputers can provide sovereign AI capabilities.

Innovation becomes significantly more accessible.

Why 16 GPUs?

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

Speed is only part of the story.

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

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

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

The prompt itself may contain only a dozen words.

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

Open Source Changes the Economics

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

It’s the model.

Kimi is open source.

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

For many businesses, this opens entirely new possibilities.

Better governance.

Lower operating costs.

Greater flexibility.

Control over data.

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

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

Prompt Engineering Is Becoming an Engineering Discipline

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

Reality is considerably more interesting.

A prompt is an interface between humans and probability.

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

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

But measuring those differences requires experimentation.

Not one prompt.

Hundreds.

Sometimes thousands.

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

Prompt engineering is gradually evolving into prompt engineering science.

From Impressive Demos to Reliable Systems

The biggest lesson from this experiment is surprisingly simple.

Generative AI should not be judged by its best answer.

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

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

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

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

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

by Gonçalo Perdigão

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Disclaimer

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

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

Some more articles about HPC and experiments:

“How do we know our AI works?”

The room usually goes quiet.

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

• Does it work in real conditions?

• Does it work consistently?

• Does it create measurable economic value?

And that is becoming a strategic risk.

The Illusion of “It Works”

Most AI systems look impressive in controlled settings.

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

But production reality is different.

Real environments contain:

• incomplete data,

• contradictory inputs,

• shifting formats,

• regulatory constraints,

• edge cases no one documented,

• humans who override outputs,

• and incentives that distort usage.

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

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

Accuracy Is Not Performance

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

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

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

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

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

Very few boards receive the second.

The Silent Drift Problem

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

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

Models degrade silently.

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

The Measurement Gap

Here is a simple diagnostic.

Ask your AI team for:

• Current model performance in production (not testing).

• Error distribution by segment.

• Human override rate.

• Economic impact per decision.

• Degradation trend over time.

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

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

Most firms measure:

• adoption,

• usage,

• satisfaction.

Few measure:

• counterfactual outcomes,

• avoided loss,

• systemic risk exposure,

• long-term behavioural shifts.

That gap is where value evaporates.

Why Nobody Can Prove It Works

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

1. No Clear Counterfactual

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

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

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

2. Humans Compensate Quietly

When AI outputs are imperfect, humans adapt.

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

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

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

3. Incentives Favour Optimism

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

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

So AI systems are described as:

• “improving steadily”,

• “early but promising”,

• “strategic capability building”.

Very few are shut down decisively.

4. Vendors Optimise for Benchmarks

External providers optimise for:

• benchmark scores,

• demo performance,

• general capabilities.

Your business operates in:

• messy workflows,

• legacy systems,

• regulatory boundaries,

• political hierarchies.

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

5. No “Kill Metric”

Most AI deployments lack predefined failure thresholds.

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

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

The Real Risk: Decision Contamination

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

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

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

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

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

A Practical Framework: Prove or Pause

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

1. Define Economic Output Per Decision

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

Define:

• cost per inference,

• value per correct output,

• cost per error,

• downstream operational impact.

Translate AI into unit economics.

2. Install Live Production Monitoring

Track:

• performance by segment,

• drift indicators,

• override rates,

• anomaly spikes.

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

3. Introduce Counterfactual Testing

Run controlled comparisons where possible:

• phased rollouts,

• shadow modes,

• controlled randomisation.

Prove causality before scale.

4. Make Override Visible

Track how often humans:

• correct outputs,

• ignore suggestions,

• escalate cases.

Human distrust is data.

5. Agree a Kill Threshold

Pre-define:

• minimum acceptable performance,

• maximum tolerable risk exposure,

• review cadence.

If thresholds are breached, pause deployment automatically.

This is governance, not pessimism.

The Strategic Advantage of Admitting Uncertainty

There is a counterintuitive competitive edge emerging:

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

Why?

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

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

It will be audited.

The Question That Changes the Conversation

Instead of asking:

“Where else can we use AI?”

Ask:

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

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

The ones that can will not necessarily have better models.

They will have better discipline.

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

by Gonçalo Perdigão

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

Everyone is asking the wrong question.

“Which LLM is the best?”

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

The real question is:

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

Those are very different problems.

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

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

It isn’t.

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

ChatGPT Was Never the Point

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

It isn’t.

Using ChatGPT is consuming a service.

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

That includes:

• GPU allocation

• distributed inference

• model loading

• tokenizer configuration

• container orchestration

• networking

• prompt execution

• retries

• monitoring

• logging

• result collection

• failure recovery

Those components are invisible when using a hosted API.

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

The Experiment

The objective was deliberately simple.

Create a pipeline that reads:

Prompt.txt

Runs it

X times

where X comes from

X.txt

Collects every response.

Stores them automatically in an Excel-compatible CSV.

No manual intervention.

No copy-paste.

Completely reproducible.

Simple.

Reliable.

Repeatable.

Why Kimi?

The model itself is almost secondary.

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

Running it requires:

• distributed inference

• tensor parallelism

• pipeline parallelism

• containerised execution

• Slurm scheduling

• GPU orchestration

In other words:

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

What Actually Took Most of the Time?

Surprisingly, not prompting.

Infrastructure.

The work involved:

• locating the correct model snapshot

• configuring the vLLM container

• correcting Singularity paths

• binding shared storage

• debugging distributed startup

• synchronising multiple compute nodes

• validating GPU visibility

• tuning tensor and pipeline parallelism

• monitoring loading progress

• handling scheduler limits

• waiting for a 550+ GB model checkpoint to initialise

None of this changes the model's intelligence.

It changes whether the model can actually be used.

This Is What Enterprise AI Really Looks Like

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

They deploy an entire generative system.

That system includes:

• system prompts

• user prompts

• retrieval

• routing

• tools

• APIs

• containers

• inference servers

• monitoring

• human review

• governance

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

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

Why Repeat the Same Prompt?

Large Language Models are probabilistic.

The same prompt rarely produces exactly the same answer.

That variability is not necessarily a defect.

It is part of how these systems work.

Our pipeline therefore automatically runs the same prompt multiple times.

Instead of asking:

“Did the model answer correctly?”

we ask:

“How stable is this system across repeated inference?”

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

Check the entire paper here:

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

Infrastructure Is Part of AI Quality

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

They are infrastructure failures.

Examples include:

• incomplete model loading

• incorrect GPU allocation

• distributed communication issues

• scheduler constraints

• container configuration

• timeout management

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

For executives, this is an important mindset change.

What We Learned

Several practical lessons emerged.

1. Operating AI is engineering.

Choosing a model is only the first step.

Running it reliably is a different discipline.

2. Observability matters.

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

3. Automation beats manual experimentation.

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

4. Repeatability is more valuable than impressive demos.

One excellent answer proves very little.

Hundreds of controlled runs begin to produce evidence.

Why This Matters for Creative Teams

Creative organisations increasingly rely on Generative AI.

Marketing.

Advertising.

Design.

Publishing.

Strategy.

Innovation.

Yet many teams still evaluate AI through isolated examples.

That is risky.

Creative workflows deserve the same engineering discipline as financial systems.

Consistency matters.

Reliability matters.

Traceability matters.

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

Building Creative Machines Means Building Reliable Systems

This project was never really about Kimi.

It could have been ChatGPT.

Claude.

Llama.

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

Or the next model to be released next week.

Models will change.

Inference engines will improve.

Benchmarks will evolve.

But one principle will remain remarkably stable:

Organisations do not deploy language models.

They deploy systems.

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

Key Takeaways

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

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

• Repeated inference is essential for measuring system reliability.

• Infrastructure quality directly affects AI quality.

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

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Acknowledgements

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

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

Experiment at a glance

• Model: Kimi K2 (≈554 GB checkpoint)

• Infrastructure: MareNostrum 5 (Barcelona Supercomputing Center)

• Execution: Distributed inference with vLLM 0.21

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

• Model loading time: ~30 minutes

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

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

• Recommended X via paper

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

by Gonçalo Perdigão

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

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

Image: Credits SAPO Tek

The numbers show the scale of that experiment.

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

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

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

That systems approach also shaped the partnerships behind the initiative.

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

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

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

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

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

by Gonçalo Perdigão

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

Also read our insights from the first weekend:

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

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

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

THIS MONTH’S SIGNALS

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

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

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

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

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

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

WHAT WE PUBLISHED

AI governance & compliance

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

Content abundance & trust

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

Culture as a living lab

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

Social media, provenance & privacy

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

HOT TOPICS: AI × CREATIVITY

1. The creative canvas becomes the operating system

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

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

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

2. Video generation moves into infrastructure

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

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

Implication: Build approval gates before scaling video output.

3. Agents join the team

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

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

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

4. Copyright and provenance become creative infrastructure

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

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

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

MODELS & TOOLS TO WATCH

Figma Motion, Shaders & Weave

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

Adobe + Topaz Labs

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

Claude Tag

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

OpenAI GPT-5.6 Sol / Terra / Luna

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

Runway API: Aleph 2.0 & Seedance 2.0 Fast

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

WHAT TO DO NEXT

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

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

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

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

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

CURIOSITIES

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

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

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

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

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

by Gonçalo Perdigão

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

But MUGEN is not interesting because it generates music.

It is interesting because it can fail.

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

That condition changes everything.

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

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

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

From output machine to operating system

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

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

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

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

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

That is the real experiment.

The aesthetics of constraint

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

That smallness is part of its credibility.

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

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

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

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

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

Public books as a creative interface

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

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

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

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

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

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

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

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

Governance as authorship

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

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

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

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

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

This is where MUGEN becomes relevant beyond music.

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

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

Audience as evolutionary pressure

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

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

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

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

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

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

The legal-cleanliness aesthetic

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

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

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

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

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

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

What MUGEN reveals about creative machines

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

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

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

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

Can transparency substitute for trust?

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

Can public feedback produce taste rather than mere optimisation?

Can synthetic media survive economically without pretending to be human?

Can governance itself become a creative act?

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

Of course it can.

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

The beauty of going dark

The most poetic feature of MUGEN is its mortality.

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

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

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

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

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

MUGEN is a radio station.

It is also a question playing on loop:

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

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by Gonçalo Perdigão

More about music and generative AI:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

A few key elements include:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Image: Credits Rock in Rio 2026

A festival as city, not metaphor but infrastructure

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

This is precisely why the project is interesting.

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

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

That distinction, between demonstration and operation, is everything.

What is actually being tested?

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

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

• an Integrated Operations Room;

• a Digital Twin of the Cidade do Rock;

• human flow management tools;

• a public dashboard;

• door-to-door accessibility initiatives;

• an Urban Smart Energy Centre;

• smart enforcement/monitoring mechanisms;

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

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

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

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

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

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

MEO Empresas and the politics of the stand

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

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

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

• EVOX for smart waste management and monitoring;

• Qart for environmental monitoring and urban quality analysis;

• Kido for geoanalytics and territorial data visualisation;

• Soltráfego for soft mobility and smart bicycles;

• GEMA for immersive AR/VR experiences about Lisbon;

• Inov for fire monitoring and prevention systems;

• Focus for integrated smart urban management and operations.

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

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

The university is infrastructure, not decoration

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

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

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

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

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

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

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

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

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

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

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

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

The city as audience, the audience as dataset

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

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

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

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

That should also make us cautious.

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

What success would actually look like?

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

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

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

The more meaningful questions are narrower and harder:

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

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

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

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

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

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

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

A different kind of festival ambition

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

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

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

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

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

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

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

by Gonçalo Perdigão

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