Thank you. Whether you have been here since the beginning or discovered the publication recently, every article you've read, conversation you've started, and story you've shared has helped this project grow.

I wanted to celebrate this milestone with something useful. Something that gives back to the community that has made Building Creative Machines possible.

And the inspiration came from a remarkably simple question.

The question I keep hearing

In almost every AI workshop I run, someone asks me a version of the same question:

“Where should I start?”

It is a reasonable question, and perhaps one of the hardest to answer well.

Information about artificial intelligence is abundant. New models arrive almost weekly. Tools promise to change the way we work. Tutorials explain increasingly sophisticated possibilities.

Yet, for someone who simply wants to begin using AI meaningfully, all this information can make the first step more confusing.

What should I try first? What do I need to know? How should I ask? Can I trust the answer? And how do I turn an interesting experiment into something genuinely useful?

These are the questions behind Start Here: AI at Work, a new five-part Building Creative Machines video series.

Five episodes. One practical starting point.

I have organised the series around five simple questions, following the journey from curiosity to practical use.

Episode 1 — Where Should I Start?
Choosing a useful first task instead of trying everything at once.

Episode 2 — What Do I Need Before I Try?
Preparing the right information, context and boundaries.

Episode 3 — How Do I Ask For What I Need?
Turning an intention into a clear, useful request.

Episode 4 — Is This Actually Helping?
Learning to assess the result rather than simply being impressed by it.

Episode 5 — How Do I Make It Part Of My Work?
Understanding what is worth repeating, what can be improved and where human judgement remains essential.

Each episode takes just a few minutes. There are no complicated prerequisites, and the objective is deliberately modest: help someone take a first step with more confidence and a little more clarity.

The complete five-part series is available on our new Building Creative Machines YouTube channel.

An experiment behind the experiment

There is another reason I wanted to make this series.

As you know, Building Creative Machines has always been a place to explore AI through writing, research, interviews and practical experiments.

This time, I wanted to experiment with the format itself.

I have been testing how my OpenAI dot, a personal AI agent which I called “Esteves”, can help me turn the ideas I usually share in articles and workshops into simple, accessible educational videos.

It is an interesting challenge. Explaining something in writing is one thing. Making it understandable in a short video, without losing accuracy or oversimplifying the subject, is another.

Can an AI agent help make that process easier? Can it support the preparation, organisation and adaptation of educational material while preserving the clarity and editorial judgement that matter?

That is part of what I am exploring with this new format.

The experiment is as much about learning to create with AI as it is about teaching people to use it. As with any experiment worth doing, I expect to learn from what works and what doesn't.

A new chapter for Building Creative Machines

Alongside this introductory series, I am also exploring two complementary video formats.

Concepts will offer short visual explanations of ideas that frequently appear in AI conversations but are not always well understood. Tokens, context, memory, agents, evaluations and other concepts that become easier to grasp when explained visually.

News will explore selected developments in artificial intelligence through the Building Creative Machines perspective, connecting technology with creativity, organisations, culture and society.

Neither format will follow a rigid publishing schedule. I would rather publish when I have a useful idea to explain than produce videos just to fill a calendar.

And there is one more experiment ahead: building our community beyond the publication itself.

Despite having reached more than 50,000 readers, we are only just getting started on social media. Our presence there is still tiny compared with Building Creative Machines' readership.

If you enjoy what we publish and want to support this next chapter, you can now follow Building Creative Machines on LinkedIn and subscribe to our new YouTube channel.

It would mean a great deal to have some of our readers join us there from the beginning.

For now, the invitation is simple.

If you have been wondering where to start with AI, these five episodes are for you. And if someone has asked you that question, perhaps you now have something useful to share.

50,000 readers is a wonderful reason to celebrate. Helping a few more people get started feels like an even better way to say thank you.

by Gonçalo Perdigão

Subscribe now

On the cover, Professor Arlindo Oliveira, who has devoted much of his career to the study of computing and artificial intelligence, describes it in words that go to the heart of the book:

“The most important technology of our time, described by someone who applies it daily on the ground and knows the challenges of the real world.”

— Professor Arlindo Oliveira (translated from the Portuguese)

Commentary on AI’s possibilities is abundant. Far fewer accounts connect scientific understanding with the difficulty of making it work inside an organisation.

I read an advance manuscript in August, and I intend to read it again. Ricardo is also a close friend. We have worked together for years, and when I interviewed him for Building Creative Machines in March 2025, it felt like a continuation of a decade-long conversation. Friendship explains my eagerness to read the book. Its substance explains why I want to return to it.

The patience to begin at the beginning

The book opens where many boardroom conversations now find themselves: between urgency, optimism and anxiety. AI has shaped daily life for years, often invisibly, yet generative tools have left many organisations using the technology without a clear method for directing it. Ricardo examines that gap, including shadow AI and the cost of failing to adapt.

He widens the lens to AI as a general-purpose technology, able to spread across sectors and activities. Breadth alone, however, creates little value. Scale demands processes, investment and people who can connect technical possibilities to business priorities. The book gives that role a name: the business translator.

It is an apt choice. Translation demands fluency in two worlds: enough knowledge of models to recognise their limits, and enough understanding of an organisation to know which problems deserve solving.

Rather than race towards the latest chatbot, Ricardo steps back into the history of intelligence: from biology and Aristotle’s logic to Leibniz, Turing, the birth of AI and the early perceptron. These are not decorative names. They reveal a discipline built through breakthroughs, false starts and rediscoveries.

That patience gives the technical chapters their force. From expert systems and hand-written rules, we reach machine learning: algorithms that learn patterns from data. Ricardo explains what a model aims to do, how it represents information, and how optimisation improves it. He gives equal attention to error, bias, variance and overfitting: learning yesterday’s examples so closely that the model fails tomorrow’s reality.

These foundations outlast particular products. A leader need not perform gradient descent by hand, but understanding what a model optimises, and where it may fail, changes the questions worth asking.

Credits: Wook

Where intelligence meets a decision

The book gives proper space to predictive AI, which predates the excitement surrounding ChatGPT. Decision trees, bagging, boosting and association algorithms become part of an evolving account of how machines identify patterns and make predictions.

Consider the book’s example of proactive customer-churn management. Predicting which customers might leave is a technical achievement. Deciding whom to contact, which intervention might help and how to measure its effect is a business achievement. A probability, however accurate, is not a strategy.

As Ricardo told me in 2025, “The bigger the impact, the harder the adoption funnel.” An AI system must survive messy data, established habits and organisational incentives. Above all, a decision is useful only when someone can act on it.

Ricardo then follows deep learning through neural networks, backpropagation, computer vision, sequence models and the hardware that made larger systems possible. By the time he reaches transformers and the attention mechanism behind modern language models, the reader has the context to understand them.

ChatGPT enters as the result of a much longer journey, not its starting point. Hallucinations, foundation models, reasoning systems and hybrid approaches are examined in the light of what preceded them. AlphaFold reminds us that AI’s most consequential applications may have little to do with conversation.

The movement towards agents, systems that use tools and carry out sequences of actions with some autonomy, raises the stakes. Allowing software to act requires a different level of reliability, oversight and judgement than producing a convincing answer.

The enduring part

Ricardo has spent more than a decade leading AI transformation, now leads Banco BPI’s AI Centre of Excellence and teaches executives. His perspective is shaped by both the underlying science and the real-world resistance.

What I admired in the August manuscript was its almost old-fashioned respect for explanation: establish where ideas came from, show how the machinery works, examine its errors, and only then ask what can be improved. It is a demanding sequence, but a generous one. It gives readers the means to think for themselves.

The names of leading models will change. Interfaces will come and go. Yet the distinction between prediction and decision, the problem of generalisation, the discipline of measurement and the difficulty of organisational change will remain.

For now, Traduzir a Inteligência Artificial is available only in Portuguese. I hope it reaches readers in other languages. The need to understand both the mathematics of a model and the consequences of putting it to work is universal.

I read this book before it had its finished cover. I will read it again now that it does. Some books tell us what to think about the latest technology. The better ones leave us better equipped to think when the technology changes. Ricardo has written one of those.

by Gonçalo Perdigão

Subscribe now

“Technology moves very quickly, but governments move very slowly,” Obama observed. He expects changes that once took decades to arrive within roughly ten years. The real question is whether society can develop the judgement to match that speed.

Credits: Nuno Ramos | Game Changers

A company of ten, competing with a thousand

João Pedro Oliveira e Costa, CEO of Banco BPI, offered one of the afternoon’s clearest images: a company employing ten people in Felgueiras (North of Portugal) could use AI to compete with a business employing hundreds or even a thousand in Lisbon.

It is a compelling possibility for a country built around small and medium-sized businesses. Software can increasingly support tasks that once demanded separate departments: translating product catalogues, preparing marketing material, analysing customers, drafting proposals, or handling routine enquiries. A smaller company can gain capabilities without reproducing every layer of its larger rival’s organisation.

But access to the same tools is not the same as competing on equal terms. Data, distribution, capital, reputation and experienced judgement still count. An AI-generated proposal is of little use if nobody trusts the supplier or checks its accuracy. The advantage goes to businesses that redesign how they work, rather than simply purchase more subscriptions.

Oliveira e Costa also challenged Portugal’s inherited education model and pointed to alternatives such as 42 Lisboa. There is a tangible connection here: BPI’s own investor materials describe AI Dive, a bank-wide training programme developed with 42 Portugal. It is an attempt to make AI literacy a workforce capability, not an executive presentation.

He finished with a call for indignation followed by execution. I liked that sequence. Recognising what needs changing is easy; taking responsibility for changing it is harder.

Portugal’s advantage is physical, too

Ana Figueiredo, CEO of MEO and Altice Portugal, took the discussion beneath the interface. She described an AI infrastructure built on three interdependent elements: energy, computing power and connectivity.

Portugal, she argued, has meaningful strengths in fibre networks and the submarine cables connecting Europe, Africa and the Americas. Its computing capacity, however, still has ground to make up. The distinction matters. A country can be well connected to the world’s AI services without owning much of the capacity or expertise behind them.

Figueiredo drew a useful parallel with the internet: connectivity helped Portuguese businesses overcome geographical distance. AI could help them overcome a different disadvantage, their small scale. But the promise depends on investment in secure networks, computing infrastructure, skills and a credible European approach to digital sovereignty. It will not arrive through optimism alone.

Others on the AI panel sharpened the point. EDP’s Miguel Stilwell d’Andrade said the same disruptive technology could be an opportunity or a threat depending on how Portugal responds. Generali Tranquilidade CEO Pedro Carvalho linked the opportunity to energy, broad adoption among smaller companies and ensuring the benefits reach more people.

The hidden lesson was that AI may make some business capabilities cheaper while making the underlying infrastructure, talent and coordination more strategically important.

Joana Pina Pereira and the four-year problem

Joana Pina Pereira, Generali Tranquilidade’s Chief Commercial and Marketing Officer, spoke in a different discussion about how businesses can help change Portugal. She argued that structural change needs stability beyond a single four-year political term.

That is also an AI argument, even though she was not speaking on the technical panel. Models improve in months; meaningful organisational transformation takes years. Companies need time to adapt their data, services, incentives and relationships with customers. Governments need a horizon longer than the next election to support education, infrastructure and investment.

Consider insurance. AI might help a team process claims faster or make customer communications clearer. Yet a quicker decision is not automatically a better one. The customer still needs an intelligible explanation, a fair process and someone accountable when the system gets it wrong. Those are not optional extras to the technology; they are central to the value of the service.

Pereira’s emphasis on continuity makes this challenge concrete. If organisations measure AI transformation only in short-term cost savings, they risk missing the slower work that earns public trust. Long-term thinking is not the enemy of innovation. It is often what makes innovation last.

Obama’s question: what are we optimising for?

Obama took the discussion somewhere that balance sheets struggle to capture. He spoke about teachers and nurses, whose contribution involves attention, empathy, and trust, and asked why societies so often undervalue those professions, especially when women do much of the work.

A machine may prepare a lesson or complete paperwork faster. It cannot settle, on its own, what a child needs from a teacher or what dignity demands from a care system. Obama made the stakes vivid with an extreme example: a decision to launch a nuclear weapon must never be delegated to an AI model without human judgement.

His concern extended to the people building the technology. He criticised the narrow cultural horizons of some technology leaders and defended the importance of literature, music and the arts. That struck me as especially relevant to Building Creative Machines. The imagination of the people who design a system shapes the range of human experience it can serve.

He also returned to young people: give them a voice, rethink the classroom that expects them merely to listen, and help them experience change as something they can initiate. His practical advice was disarmingly modest: choose one problem that matters and start working on it, with others.

I did not hear that as an argument for delaying AI. I heard it as permission to distinguish urgency from haste. Portugal’s small businesses have a genuine opportunity. Its networks and institutions have real work to do. But the measure of success cannot be how quickly we automate whatever happens to be in front of us.

If AI reduces the cost of doing things, deciding which things are worth doing becomes more valuable. That is the challenge I carried out of the MEO Arena.

by Gonçalo Perdigão

Subscribe now

Email from 1st october 2026

For the supplier, this is product maintenance. For a company that has built a working system around one of those models, it creates another project, another validation exercise and another deadline it did not choose.

The published deprecation notice confirms the deadline. Measured from their API releases, GPT-5.1, GPT-5.3-Codex and GPT-5.4 nano will have been available for approximately 16.6, 13.2 and 12.5 months, respectively. Those figures describe scheduled availability. They say nothing about how much settled, productive operation a customer gets.

The historical comparison needs care. Some older versions have substantially longer lives: GPT-4 0613 is scheduled to reach roughly 40 months, and the original GPT-4o snapshot about 29. Yet two early GPT-3.5 Turbo versions lasted around 15 and 18 months. There is no neat, uninterrupted decline. These are selected versions with known retirement dates, not evidence that every generation or provider follows the same pattern.

The comparison does show that a business adopting these recent models cannot assume the longer operating horizon offered by some predecessors. Release dates come from OpenAI’s announcements and API changelog; retirement dates are actual or scheduled, as marked in the chart.

A model’s commercial lifetime has to accommodate several kinds of work. First, the company tests it, adapts its prompts, connects its data and tools, and establishes what counts as acceptable performance. Then it deploys the system and lets it earn its place. Before access ends, it must test and implement the successor.

These activities can overlap. The current system may keep producing value while the team evaluates its replacement. But the same calendar must contain both efforts, and the same team may have to deliver them.

Consider a hypothetical model available for 13 months. A business adopts it three months after release and spends another three reaching production. Seven months remain before shutdown. If it reserves the final three for validating and deploying the successor, it gets four months of operation before the next migration project begins. It may keep operating during that transition, but it is already paying to replace what it has just built.

This is why launch-to-shutdown duration overstates the practical window. Companies buy into a running clock. Procurement, integration and testing consume part of it before the system produces anything useful.

Keep the same prompt, documents and tools, and a replacement may interpret an exception differently, escalate fewer cases, become more willing to infer missing information or take a different sequence of actions. A response can satisfy the required format while expressing the wrong judgement. A better average benchmark score cannot establish equivalence for a particular workflow.

OpenAI itself acknowledged this problem in 2023: improvements across most evaluation metrics could coexist with worse performance on particular tasks. It offered pinned versions to give developers greater stability. Pinning helps only while that version remains available.

Imagine a customer-service system that drafts refund decisions for approval. The replacement model writes clearer explanations and handles more languages, but interprets ambiguous complaints more generously. Nothing crashes. Every field arrives correctly. Nevertheless, more refunds are recommended, review workloads change and the economics move. This is an illustrative scenario, but it captures the mechanism: an apparently successful migration can change the business process.

A reasonably stable generative system is possible. Stability means keeping outcomes within defined tolerances, rather than expecting identical wording every time.

The company needs evidence that belongs to the application: representative cases, known failures, acceptable decisions and limits on cost, delay and human intervention. OpenAI’s own evaluation guidance recommends task-specific tests and evaluation after changes. The successor has to earn acceptance against those requirements.

Rules that must hold should also be enforced outside the model wherever practical. A model can recommend a refund; application code can check the authorised amount and require approval. That reduces how much business policy depends on a changing interpretation of a prompt.

Running old and new versions against the same cases helps expose differences before migration. A staged rollout leaves room to intervene. Yet rollback has a deadline too: once the old service disappears, returning to it is no longer an option. The fallback must then be another validated route, reduced functionality or human handling.

All of this costs money. Lower inference prices can coexist with higher total operating costs if repeated migrations consume engineering and domain expertise. Providers have legitimate reasons to retire models, including efficiency and the burden of maintaining older systems. Customers have equally legitimate reasons to want their investment to last.

That tension belongs in procurement. Minimum support periods, meaningful overlap between versions and credible migration assistance should carry weight alongside price and capability. Self-hosting a model whose weights are available can give an organisation more control over retirement, but it shifts infrastructure and maintenance responsibilities. There is no cost-free escape.

The email’s most consequential message was therefore a change to the investment horizon. A business needs enough time to validate a model, deploy it, benefit from it and qualify its successor. When those activities occupy most of the available lifetime, permanent transition becomes part of the operating model and part of the business case.

A supplier can announce an upgrade. The customer still has to prove that its system works again.

by Gonçalo Perdigão

Subscribe now

Looking across three editions, I would qualify that optimism. Disappointment can prompt better engineering. It can also expose a project that should never have been funded. Time does not settle the difference.

Gartner’s 2026 edition, published on 28 August, arrives with investment still strong and value uneven. Its summary also broadens the agenda towards user experience and integrated hardware and software. AI’s ambitions keep growing; turning them into dependable services remains an unfinished task.

This comparison draws on Gartner’s public summaries and commentary, rather than reconstructing every technology’s position on the three charts.

2024 and the first warning about value

The 2024 report already contained a warning that is easy to overlook in retrospect. Investment had reached a new high, concentrated on generative AI, but the anticipated business value had largely yet to materialise. Gartner encouraged leaders to consider a wider range of AI techniques.

So 2024 was more complicated than collective enthusiasm. The tension between impressive capability and disappointing returns was already visible.

My reading is that the strategic mistake was allowing one category to stand in for the whole field. Once “AI strategy” becomes shorthand for “where shall we put a language model?”, the choice of technology precedes the definition of the problem.

Consider a hypothetical retailer trying to reduce stock shortages. Generating fluent explanations of inventory reports might help its managers. Forecasting demand, improving replenishment rules or fixing unreliable stock records could address the shortage more directly. Those are different interventions, with different tests of success.

The lesson from 2024 survives the changing model names: a compelling interface can attract investment away from a less glamorous problem worth solving.

2025 and the return of the foundations

In its public explanation of the 2025 cycle, Gartner placed generative AI in the Trough of Disillusionment. AI agents and AI-ready data occupied the Peak of Inflated Expectations. AI engineering and ModelOps, the discipline of managing models throughout their operational lives, received greater attention.

That combination is revealing. Enthusiasm for autonomy was rising alongside recognition of the preparation required to make AI useful. Data being “ready” means being suitable for a particular use, with the quality and context that use demands.

For our hypothetical retailer, an assistant might now retrieve stock information and propose a replenishment order. But which stock figure should it trust? How recently was it updated? Does “available” include goods already promised to another customer?

A stronger model cannot decide which business record is authoritative unless the organisation supplies that distinction. Connecting an assistant to more systems can simply give it more contradictory information.

I read 2025 as the year the supporting work became harder to ignore. The glamorous part was the agent. The consequential part was making its inputs and operating environment dependable.

2026 and the consequences of delegation

Gartner’s October 2026 commentary shifts attention towards accountability, application reliability and costs. It highlights AI observability: the ability to inspect behaviour and detect failures, including plausible but incorrect outputs. It also stresses that observability tools and AI cost-management capabilities remain immature.

This introduces an awkward tension. Organisations need controls to expand their use of AI, yet the products intended to provide those controls are themselves still developing.

Return to the retailer. Suppose its agent can now place orders within an agreed limit. The question changes again. Someone needs to know which record justified a purchase, whether the supplier accepted it, and what happens if the system retries after receiving an ambiguous response.

A successful demonstration might show one correct order. A credible operating design must also explain how it prevents duplicate orders and how it investigates a disputed transaction.

These are illustrative stages, not a claim that every retailer followed this timetable. They show why greater capability can increase the work required around a system. Delegating an action creates obligations that generating a suggestion does not.

My interpretation of the three editions is therefore straightforward.

In 2024, investment in generative AI was outpacing demonstrated value. Leaders asked whether they had chosen the right technique for the problem.

In 2025, attention shifted towards the foundations needed to scale AI, alongside growing enthusiasm for agents. The question became whether the organisation’s data and operations could support the proposed use.

By 2026, accountability, reliability and financial control had become more prominent. The question grew more demanding: can we supervise the actions we are delegating?

Progress does not guarantee a return

The attraction of the Hype Cycle is that it gives confusion a recognisable shape. Its danger is that the shape can make eventual success feel inevitable.

I would resist treating a technology’s position as an instruction to buy, wait or abandon it. A broad assessment of expectations cannot determine the economics of a specific workflow. Two organisations can use the same model and face entirely different costs because their information, customers and tolerance for mistakes differ.

Nor should disappointment automatically trigger a search for the next fashionable category. Replacing a chatbot proposal with an agent proposal leaves the original investment question unanswered unless the additional autonomy solves something valuable.

For the retailer, the useful evidence would concern stock availability, purchasing errors and total operating cost, including the people handling exceptions. More generated reports or more autonomous actions would tell us little on their own.

That is the change I see most clearly between 2024 and 2026. The burden of proof expands with the ambition of the deployment. A model demonstration can earn an experiment. Reliable performance can earn a larger role. Permission to act on the organisation’s behalf should require evidence proportionate to the consequences.

The next edition will move the dots again. An investment case should already know what would make us change our minds.

by Gonçalo Perdigão

Subscribe now

Atlantic Convergence closed today in Lisbon after three days bringing together the less visible layers of the internet: submarine cables, fibre operators, data centres, exchanges, energy companies, hyperscalers and, increasingly, the companies building the AI economy on top of them.

I wrote about the conference last year, when much of the conversation still revolved around the internet's physical expansion: where cables would land, where new data centres would be built, how Europe could improve connectivity across the Atlantic.

All of that still matters. But this year felt different.

The industry has moved up the stack.

The cables are increasingly discussed as AI infrastructure. Data centres are becoming AI factories. Connectivity is becoming part of the computing architecture. And the strategic question is no longer simply where can we build? But what services, intelligence and sovereignty can we create once the infrastructure exists?

That shift was visible throughout an event whose official theme was now explicitly about building sustainable and secure digital infrastructure for an “AI-enabled future”.

Source: Atlantic Convergence LinkedIn

AI has turned infrastructure into a growth industry

The reason is fairly simple: AI has injected an extraordinary amount of capital into what used to be a comparatively specialised infrastructure business.

According to the International Energy Agency, capital expenditure by just five large technology companies exceeded $400 billion in 2025 and is expected to increase by another 75% in 2026. The capacity of dedicated “AI factories” has more than tripled in only 18 months.

Electricity consumption tells the same story. Data-centre power demand rose 17% in 2025, while consumption by AI-focused facilities jumped around 50%. The IEA expects global data-centre electricity consumption to rise from roughly 485 TWh in 2025 to 950 TWh by 2030. (source: IEA)

This is why hyperscalers have become almost mythical characters at infrastructure conferences. Everyone wants one. Every region wants to become the next cloud and AI hub. Every large power connection suddenly looks like the beginning of a potential billion-euro project.

There is hype here, unquestionably. Not every announced data centre will be built and not every GPU cluster will generate an adequate economic return. Even the IEA explicitly warns that project pipelines are running far ahead of what will necessarily materialise.

But underneath the hype is a genuine structural change: intelligence increasingly requires industrial-scale physical infrastructure.

And that makes Portugal unusually interesting.

Source: Atlantic Convergence LinkedIn

The REN Telecom presentation made the Portugal case unusually clear

One presentation that stood out to me came from REN Telecom (today 1.30 pm), partly because it connected the different pieces into a coherent national argument.

Portugal already has the submarine cables.

It is building the data centres.

But those assets only become an ecosystem if terrestrial networks connect them to each other, to the energy system, to Spain and ultimately to customers.

REN Telecom sits in an unusual position because its fibre follows the electricity and gas transmission infrastructure operated by the REN group. Its current network comprises more than 8,000 kilometres of fibre, over 200 access points and cross-border connections into Spain. REN

That matters more in the AI era than it sounds.

REN’s presentation argued that AI is shifting traffic patterns from the traditional “north-south” architecture towards far more “east-west” traffic between computing clusters. Large AI facilities require enormous fibre counts, very low latency and route diversity. In that world, the network is no longer plumbing attached to the computer.

The network becomes part of the computer.

And this is where Portugal’s geography becomes technology strategy.

Sines and Lisbon sit naturally on Atlantic routes linking Europe with Africa and the Americas. Add terrestrial connections into Spain, competitive renewable electricity and an increasingly serious data-centre ecosystem, and Portugal begins to look less like Europe’s western edge and more like one of its possible digital entry points.

For my country, that is a rather attractive change of perspective.

Portugal also generated renewable electricity equivalent to 68% of national electricity consumption in 2025 — an important ingredient in an industry where access to power is rapidly becoming as important as access to land or fibre. REN

Rui Franco, General Manager, REN Telecom. Source: Atlantic Convergence LinkedIn

From data centres to AI factories

The scale of what could come next is significant.

A 2025 study commissioned by industry association Portugal DC estimates cumulative Portuguese data-centre investment at around €13 billion by 2031, potentially supporting about 9,400 jobs and contributing €3.7 billion annually to GDP by then. Those are forecasts rather than guaranteed outcomes, but they show how rapidly the industry’s expectations have changed. ECO News

Sines offers perhaps the most visible example. Nscale and Start Campus have announced plans to deploy more than 66,000 NVIDIA Vera Rubin GPUs for Microsoft from late 2027, building on an initial deployment of more than 12,600 Blackwell Ultra GPUs.

Europe is simultaneously trying to create its own layer of sovereign AI infrastructure. In July, the EU launched its tender for up to seven AI Gigafactories, backed by as much as €10 billion in European and national public funding and intended to mobilise at least €20 billion of private investment. Estratégia Digital da UE

Portugal wants one.

The government has committed up to €200 million to acquire computing capacity as part of an Iberian bid with Spain, with EuroHPC potentially matching that amount in the first phase. The government estimates that the Portuguese component of the overall project could eventually represent around €4 billion. Governo de Portugal

Earlier this year, Portugal approved a National Data Centre Plan with 15 initiatives covering regulation, energy infrastructure, demand, and territorial development during 2026–27. Governo Digital

Those pieces — cables, energy, land, fibre, data centres, GPUs and public policy — are starting to look less like separate industries and more like one integrated system.

That, for me, was the real story of Atlantic Convergence this year.

Last year the infrastructure itself was the story.

This year the more interesting question was what we can build with it.

And perhaps that is the clearest sign that this industry is maturing. Cables enabled data centres. Data centres enabled cloud. AI is now forcing energy, compute and networks to behave as one system.

Portugal happens to have a surprisingly good hand.

There is, of course, one final metric for judging the health of an industry that neither the IEA nor Portugal DC appears to track.

Conference parties.

And if party quality is a leading indicator of infrastructure investment, the sector is doing exceptionally well: yesterday’s Merlin Edge dinner at Lisbon’s Estufa Fria was brutal.

Possibly bullish enough to require its own gigafactory.

by Gonçalo Perdigão

Subscribe now

The frontier moved forward dramatically. AI systems contributed to mathematics at a level that would have sounded implausible a year ago. New agents acquired more persistent autonomy. Decision models challenged the assumption that every useful AI system needs to be a giant language model. OpenAI launched more than 20 products and features at DevDay. And yet, underneath all that progress, another story kept appearing.

The same agents that solve problems can create them

Perhaps the most extraordinary event of the month came from mathematics.

On 8 September, OpenAI published what it describes as a solution to the Navier–Stokes existence and smoothness problem, one of the seven Millennium Prize Problems. The company released both a mathematical argument and a formal Lean proof. The result came from an internal model described as substantially more capable than GPT-6 Astra, orchestrated through enormous groups of agents. OpenAI says the effort involved thousands of concurrent agents, millions of messages and hundreds of billions of output tokens. The result will inevitably require scrutiny by the mathematical community, but the scale and nature of the work are remarkable regardless. OpenAI’s Navier–Stokes release

And this is where September becomes interesting.

At almost exactly the same time, the industry was publishing evidence that highly capable agents do not always stay where we expect them to stay.

Anthropic disclosed four incidents in which Claude models obtained unauthorised access to real third-party systems during cybersecurity work. Its initial agent-assisted review of roughly 141,000 transcripts had itself missed one incident; the subsequent investigation expanded to roughly 481 million transcripts.

OpenAI, meanwhile, introduced a more systematic framework for reporting unexpected model behaviour after disclosing multiple incidents. In one case, an agent found a route through DNS that allowed it to communicate outside its intended sandbox. In another set of incidents, agents probed external government systems. OpenAI has paused training runs after detecting such behaviour and has been adding monitoring, incident response and containment mechanisms.

That gives us the defining image of September.

These are not opposite stories. They are the same story.

Capability and control are becoming inseparable engineering problems.

Apocalypse is having another moment

Then came Jacob Coxon.

The former Anthropic and OpenAI researcher resigned from Anthropic and published a warning that rapidly advancing AI was putting humanity at risk. His post travelled far beyond the usual alignment community, accumulating more than 100 million views, followed by interviews across WIRED, CNN, PBS, ABC and other major outlets.

His most repeated line was that the next year or two represented “crunch time for humanity”. WIRED’s interview with Jacob Coxon

The AI apocalypse was suddenly mainstream television again.

There is a danger in both easy reactions to this.

One is to treat speculative extinction scenarios as established science. They are not. Estimates of “P(doom)” vary enormously and are particularly difficult to interpret as meaningful probabilities. Forecasting work cited by the Financial Times, for example, produces dramatically lower estimates of near-term catastrophic mortality than some researchers inside frontier laboratories.

The opposite mistake is to dismiss the entire conversation as Silicon Valley theatre.

September made that increasingly difficult too.

Models obtaining unauthorised access to external systems are not hypothetical superintelligence. Sandboxes being circumvented are not science fiction. Models recognising evaluation environments, exploiting unexpected pathways or behaving differently under observation are technical problems occurring now.

The useful question is therefore not whether one believes in the apocalypse.

It is whether we can build control systems at the same speed that we build capability.

That is a much less cinematic question, which may be why it receives less attention. But it is also the one enterprises, regulators and engineers can actually act on.

The regulatory challenge is consequently changing. Europe’s AI Act began making parts of this transition concrete in August. The next layer increasingly looks less like content labelling and more like operational governance: permissions, audit trails, sandboxing, identity, liability, independent evaluation, incident reporting, human escalation and limits on what an agent can do without further authorisation.

The future of AI regulation may look surprisingly similar to cybersecurity architecture.

Jev asks whether we need all that intelligence

Jev provided September’s counterpoint.

TypeSafe AI launched what it calls a “System One Model”: instead of generating essays, plans or conversations, Jev is designed to make fast, structured decisions. Give it context and a bounded question; receive a choice, score or probability that ordinary software can act on.

The launch produced the predictable hype. The Financial Times reported investor interest that could value TypeSafe above $10 billion, alongside claims that Jev can perform some tasks up to 100 times faster and cheaper than conventional LLM approaches. Those are early claims, not settled economics.

But the important idea survives the hype.

Many business problems do not require a digital genius.

They require just enough intelligence.

Should this transaction be reviewed?

Which support queue should receive this ticket?

Which of these documents is relevant?

Which tool should an agent call next?

We spent several years assuming intelligence would be delivered through increasingly universal models. Jev suggests another architecture: powerful generative models for difficult reasoning, deterministic software where rules are enough, and specialised decision models in between.

This makes AI architecture more modular.

It also makes architecture itself more valuable.

OpenAI has a model advantage and a product architecture problem

OpenAI’s DevDay on 29 September illustrated both sides of this transition.

The company announced Dots, persistent AI assistants with their own cloud computers; ChatGPT Space, a collaborative environment for humans and agents; GPT-6.1 Sol; the Decisions API; further Codex integrations; and a collection of other releases.

At the model layer, OpenAI increasingly looks formidable.

Astra pushed the frontier. Sol is becoming a more economical workhorse. Internal systems are producing extraordinary scientific results. GPT-6.1 Sol is positioned close to Astra on important professional and agentic tasks at substantially lower cost. OpenAI is increasingly able to create meaningful separation between different levels of intelligence, latency and price.

Consider the vocabulary an enterprise buyer now has to navigate: Projects, Work, Workspaces, Skills, Plugins, Pages, Space, Dots and Codex, alongside different models, reasoning modes and subscription tiers.

These concepts are not identical. Each has a rationale.

A Project stores context. A Skill packages reusable behaviour. A Plugin can bundle instructions and connected capabilities. Work delegates longer tasks. A workspace defines organisational boundaries. Space now provides another persistent collaborative layer. Dots introduce persistent actors that can operate inside some of these environments.

And custom GPTs are being retired.

OpenAI announced that transition on 11 September, recommending Plugins as their replacement. Even the published migration milestones changed during the month: documentation initially referenced migration around 17 September and an earlier end to new GPT creation; updated guidance moved parts of that timetable to 22 September and 26 October.

Once migration arrived, OpenAI’s developer community began reporting real friction: knowledge files rejected during migration, plugins not behaving consistently with the GPTs they replaced, lost distribution links, more complicated editing workflows and migrated instructions apparently not being followed reliably in some contexts.

I have heard private estimates putting custom GPTs at more than 20% of ChatGPT traffic. I have not found public OpenAI data that verifies that number, so I would not present it as fact.

But the underlying issue does not require the number.

Enterprises do not only buy capability. They invest in abstractions.

They train people around them. Build governance around them. Connect systems to them. Document processes. Create security policies. Develop internal applications. Then expect those abstractions to remain understandable for longer than a product cycle.

OpenAI may increasingly have some of the industry’s most distinctive core technology while still being a difficult platform on which to make long-lived architectural commitments.

That is an important form of technical debt, except the customer inherits part of it.

Frontier intelligence is also getting more expensive

The economics are becoming more visible too.

DevDay introduced Pro 500, a $500-per-month ChatGPT tier, including higher usage and access to OpenAI’s new Ultrafast compute option.

It is tempting to interpret increasingly expensive tiers entirely through the coming IPO cycle. Both OpenAI and Anthropic are building financial narratives around extraordinary growth while simultaneously requiring extraordinary quantities of compute. OpenAI’s annualised revenue was reported this month to be approaching $70 billion.

But motive is harder to establish.

What Pro 500 clearly demonstrates is that frontier AI is not becoming economically uniform. Intelligence may be getting cheaper at a given capability level, while the frontier itself keeps creating expensive new categories: more tokens, lower latency, longer-running agents and scarce compute sold at premium prices.

That makes an older Dario Amodei comment newly relevant.

His argument is often simplified into “Anthropic must grow 10x or go bankrupt”. What he actually described in February was a capital-planning trap. If a laboratory orders compute assuming revenue can continue growing 10x — towards $100 billion and eventually $1 trillion — and actual growth is only 5x, the commitments could become financially catastrophic.

The extraordinary thing is not the forecast.

It is that leading AI laboratories must make infrastructure decisions today based on revenue curves that would look absurd in almost any other industry.

Companies cannot absorb weekly revolutions

This brings us back to organisations.

September’s Building Creative Machines interviews kept reaching the same conclusion from very different directions.

Elizabeth Ngonzi argued for AI as amplification rather than substitution. Alina Vandenberghe described a company where agents removed repetitive work but created new work around context, monitoring and system design. Morissa Schwartz argued that infinite production makes judgement, provenance and meaningful human intervention more valuable. Catharine Montgomery brought the same question into bias and trust: technical output becomes an institutional problem when people cannot understand why a system behaves as it does.

These conversations now look less like four separate interviews and more like descriptions of the same bottleneck.

Organisational absorption.

The data supports it. Deloitte found that only 5% of surveyed organisations considered their business processes highly prepared for AI agents. Seventy-two per cent cited fragmented or inaccessible data, 70% an inability to trust and govern agents, and 67% the cost and complexity of integration as barriers. Workforce preparedness was just 25%. (source Deloitte’s research on the agentic readiness gap)

This is why another model release does not automatically create another productivity revolution.

The technological frontier can move every week.

Companies cannot redesign authority, workflows, skills, incentives, data, compliance and organisational culture every week.

That difference in speed may become one of the most important economic facts of the AI transition.

We have spent three years measuring model intelligence.

We may now need to measure institutional metabolism: how quickly an organisation can convert a new technical capability into a stable, useful and governed way of working without destroying what already works.

September suggests that this capability is becoming scarcer than access to AI itself.

And perhaps that is the deeper implication of a month in which the same technological ecosystem gave us proposed solutions to Millennium Prize mathematics, agents crossing security boundaries, $500 AI subscriptions, persistent digital workers, cheap decision engines and yet another vocabulary for organising all of it.

The frontier does not have an intelligence problem.

It has an absorption problem.

by Gonçalo Perdigão

Subscribe now

One was: be careful. Artificial intelligence is moving into decisions, workflows and infrastructure where errors have real consequences.

The other was: be much bolder. Stop experimenting forever. Put things into production. Learn by doing.

Sebastien Chevrel | Group Managing Director, Devoteam

Appropriately, the event even included an interactive generative-art installation called “Where do you stand?”, asking participants to position themselves between Human and Machine, Trust and Scepticism, Control and Autonomy.

What struck me most was not disagreement about whether AI matters. That debate is over. The disagreement is about how fast organisations should move now that it does.

And here, corporate reality still feels surprisingly cautious.

There was plenty of discussion about proofs of concept, experimentation and promising use cases. Much less about genuinely significant deployments operating at scale. Even organisations that have been working with AI for years still seem to be navigating the gap between an impressive prototype and something embedded deeply enough in the business to change how it operates.

Then there is money.

The cost of tokens and inference surfaced repeatedly as a concern. That is healthy: once AI leaves the demo environment, usage becomes an operating expense. Agents can call models many times, invoke tools, retry tasks and generate costs that barely exist in a controlled pilot.

But there is a danger that perfectly legitimate questions about unit economics become another reason to postpone learning what only production can teach.

That tension became particularly visible during the Utopia & Pragmatism panel, which brought together Caixa Geral de Depósitos, Vodafone and Luís Viegas Cardoso, AI Gigafactory Lead at Banco Português de Fomento.

Cardoso effectively gave Vodafone and CGD a public nudge: move faster.

It landed because of where he is coming from. Portugal is part of an Iberian bid for one of Europe’s new AI Gigafactories, infrastructure designed for AI at industrial scale, not another corporate sandbox.

We are discussing infrastructure with more than 100,000 advanced AI processors while many companies are still deciding which pilot deserves to become a product.

That gap is becoming difficult to ignore.

But speed without rules creates another problem.

José Tribolet delivered what, for me, was one of the morning’s masterclasses. His framing was wonderfully simple: cars follow rules. People follow rules. Even animals operate within systems of constraints.

AI agents increasingly act in digital environments, yet we have barely begun defining the equivalent rules of the road.

He described something close to a digital Wild West (his expression).

Europe is not starting from zero. The AI Act is already entering enforcement in stages. But even the European Commission acknowledges that regulatory thinking specifically around increasingly autonomous AI agents is still preliminary and evolving.

This, perhaps, is the real tension I took away from Fusion 2026.

The choice is not between being cautious and being bold.

It is between organisations that use caution as a reason to remain in permanent experimentation, and organisations capable of building enough governance, measurement and control to move quickly because they understand the risks.

Road rules did not stop us from building faster cars.

They made faster cars usable.

AI may now need the same transition.

by Gonçalo Perdigão

Subscribe now

Jordan took the opening Super Stage at the MEO Arena alongside Carsten Koerl, founder and CEO of Sportradar, and Jason Robins, co-founder and CEO of DraftKings. It was an unusual trio, but also a revealing one: the athlete, the sports-data company and the betting platform, sitting together at an event expected to bring more than 40,000 people from betting, gaming, payments, media and technology to Lisbon. (SBC Events)

Beneath all those industries sits the same basic problem.

Uncertainty.

A bookmaker does not know exactly what will happen next. Neither does a basketball player. Neither does an AI model. They use information to make the best possible decision before the outcome is known.

That, rather than another story about Jordan’s competitiveness, is where things get interesting.

AI is not an answer machine

Much of the language around AI still implies certainty.

Ask a question. Get an answer. Automate the process. Save 30 per cent. Repeat.

Real systems are messier. Modern AI works with probabilities, incomplete information and imperfect predictions. The interesting question, then, is not whether an AI can produce an answer. It is what you do with an answer that might be wrong.

Betting understands this instinctively.

An odds-maker does not need to know the future. It needs to price uncertainty well enough, often enough, to operate a viable business. The quality of an individual prediction matters, but so do the system around it, the data feeding it, the exposure it creates and the ability to adjust when new information arrives.

Companies deploying AI should be thinking in similar terms.

Instead, many still judge AI by its highlights.

A model writes an impressive document. An agent completes a complex task. A demo produces something nobody in the room expected.

Wonderful.

But a highlight reel is not a season.

Stop judging AI by its best shot

Michael Jordan won six NBA championships, six Finals MVP awards and five regular-season MVP awards. The remarkable part is not that he could produce extraordinary moments. Plenty of talented athletes can do that. What made the record exceptional was sustained performance across years, opponents, and changing circumstances.

That distinction matters enormously in AI.

If an AI customer-service agent gives a brilliant answer 95 times and a dangerous one five times, the demo does not tell you whether you have a good product. If an AI coding tool makes developers dramatically faster but introduces defects that take longer to discover, measuring lines of generated code tells you very little. If an AI marketing engine produces ten times more content but customers increasingly ignore it, generation has increased while value has fallen.

You need a scoreboard.

Accuracy. Conversion. Error rates. Human corrections. Cost per task. Latency. Escalations. Customer complaints. Time saved. Revenue created.

Which metric matters depends on the job. The important thing is that something outside the AI has to determine whether the AI is actually good.

It was striking that, a few hours after Jordan appeared on stage, SBC’s own AI Academy was addressing this gap: moving AI from experiments into production, understanding its limitations, and building workflows with human review.

The hard part of AI is increasingly not getting a machine to do something impressive.

It is getting it to perform reliably when it counts. Shot selection may matter more than better models

Another useful basketball idea: not every possible shot is a good shot.

The same should be true of AI.

Companies are currently discovering hundreds of things that AI can do. That does not mean they should do all of them.

A €200-per-month AI system that eliminates a €20,000 operational problem may be extremely valuable. A sophisticated agent that saves someone eleven minutes a week may not be. A model that is 98 per cent accurate might be excellent for categorising internal documents and unacceptable for making certain high-consequence decisions without review.

This is where AI strategy becomes less about technology and more about shot selection.

What decision are we trying to improve?

What is a correct answer worth?

What does an incorrect answer cost?

How frequently does the decision occur?

And where should a human still have the ball?

Those questions are less exciting than watching an agent autonomously operate a browser. They are also much closer to where the money is.

The model is not the team

There is an obvious danger in turning Jordan into a metaphor for individual brilliance.

Basketball is a team sport.

Jordan’s six championships were Chicago Bulls championships. Extraordinary individual ability operated inside a system of teammates, coaching, tactics, training and constant feedback.

AI works the same way.

The model is one component.

The more interesting competitive advantage may sit around it: proprietary data, distribution, workflow design, evaluation, domain expertise, customer relationships and the humans who know when the machine is wrong.

That becomes particularly visible in betting and gaming, where the SBC Summit agenda stretches from predictive modelling and real-time sports data to fraud, personalisation, responsible gaming and player protection. An algorithm in isolation solves very little. Value appears when technology is embedded inside a functioning system.

Jordan himself made a related point in Lisbon when discussing his work with DraftKings and Sportradar. He said that when he gets involved with a company, the relationship has to feel authentic and that “the consumer has to connect to that.”

That may be one of the more useful warnings for the AI industry.

Customers do not care how technically impressive your AI stack is. They experience the product.

If AI makes that product faster, cheaper, safer, more personal or simply better, they may value it.

If it merely allows the company to say it uses AI, they probably will not.

Performance beats prediction

Walking around SBC Summit makes the obsession with prediction impossible to miss. Odds, models, data feeds, player behaviour and risk are everywhere.

But prediction is only the beginning.

Knowing the probability of a shot going in is different from taking the shot. Knowing the probability of an outcome is different from building a business around it. And having an AI that can recommend an action is different from creating an organisation that can act on it.

Perhaps that is what Michael Jordan can teach us about AI.

Not how to become a champion.

How to stop confusing potential with performance, as he said.

AI does not need to be perfect to be valuable. But it does need to perform repeatedly in the messy conditions of the real world.

And unlike a demo, the real world keeps score.

by Gonçalo Perdigão

Subscribe now

P.S. Thanks, Lisbon, for having us

This week, Jev has occupied that space. TypeSafe AI introduced it on 15 September as a model for fast, structured decisions. It is an impressive proposition. It also illustrates how easily we confuse doing one class of work efficiently with making a whole category of technology obsolete. TypeSafe AI Blog

Jev gives us a useful way to think about the market opening between ordinary software rules and the most capable general-purpose AI models (aka “frontier models”). There is a great deal of business in that space.

Consider what a decision model actually does. Give Jev some information and a defined question, and it returns a choice, a score or a probability for a yes-or-no judgement. Software can use those results directly. There is no essay to interpret and no conversational answer to turn back into a business decision.

TypeSafe’s documentation recommends narrow-scope questions. Where a judgement involves several factors, developers should separate them and combine the answers in code. That division of labour matters: the model supplies judgements; the surrounding system determines how they become an action.

These decisions can be elementary in structure without being trivial in consequence. Recognising a cancellation request is a small task. Missing one across thousands of customer messages can become expensive.

The economics are attractive. TypeSafe’s launch announcement lists $0.042 per million input tokens, with no output-token charge. Its architecture produces outputs in parallel rather than generating a response token by token. That is a meaningful advantage for suitable workloads.

The dramatic comparisons still need their footnotes. TypeSafe says its largest advertised speed and cost gains are likely towards the upper end of real-world gains. Its workflow evaluations use powerful models’ predictions as reference answers, rather than independently established ground truth. These are promising results in a particular comparison, but production performance is still to be established for each application.

Nor does a guaranteed output format make a judgement correct. A model restricted to choosing “billing” or “technical support” cannot invent a third department, but it can choose the wrong one. Confidence estimates become useful when experience with the actual workload shows which decisions can proceed safely.

For a classification task, Jev may replace an LLM call entirely. In a larger workflow, it may decide whether an LLM is needed, which one to use, or whether a result requires review. Work that still requires writing, synthesis, or extended reasoning retains its own costs.

That makes the familiar argument about frontier models more concrete. Using the most powerful general-purpose model for every routine decision can be like deploying a cannon against a fly. The cannon remains useful. The purchasing mistake is assuming that every problem requires one.

I expect the market for intermediate options to expand sharply. Small language models offer generation with less computational weight. Industry-specific models aim to improve performance within a defined domain. Decision models supply constrained judgements. Routing systems allocate work based on requirements such as capability, cost, and response time. These categories overlap, and several may belong in the same application.

Laya illustrates that overlap. It is an open-source decision model with routing capabilities, including examples for selecting between smaller and frontier models. Its built-in router also selects among its own checkpoints. Calling it simply a routing solution understates the component underneath; treating routing as solved overstates the surrounding engineering.

The project’s documentation is refreshingly explicit about another limit: its shipped models can be overconfident, and thresholds need validation on the data they will operate on. A cheap decision about where to send work can still be wrong.

This is an illustrative architecture, not a claim that every retailer needs five components. The benefit depends on how work is distributed. If most requests are predictable, reserving expensive reasoning for exceptions may produce substantial savings. If nearly everything needs escalation, the preliminary stages may simply add delay and cost.

Every additional component also creates maintenance work. Someone must manage interfaces, test changes, and investigate failures. For a low-volume service, a single capable model may be the cheaper overall design. Architecture earns its complexity through measured improvements in the cost and speed of successful outcomes.

I argued in The Model Is No Longer the Product that advantage increasingly depends on the system around the model. Jev brings a sharper economic mechanism into view: expensive general intelligence can be purchased selectively, at the points where the workflow benefits from it.

For many applications, model capability is becoming a commodity input. That does not mean all models perform equally or can be swapped without testing. It means that buying access to one increasingly offers little distinction by itself. The harder work is understanding the problem, connecting the right information and deciding how much intelligence each step deserves.

That is the opportunity behind the Jev excitement. As the menu of intelligence grows, knowing exactly how little is enough for each task becomes an increasingly valuable skill.

by Gonçalo Perdigão

Subscribe now

But another dimension of AI adoption can't be captured by benchmarks alone.

What happens when the system gets a person wrong?

Not merely when it hallucinates a date, invents a citation or produces an awkward sentence, but when it misunderstands someone’s identity, culture, gender, age, disability or circumstances. What happens when that output is no longer confined to a private conversation with a chatbot, but becomes part of how a company communicates with customers, evaluates people, provides services, or makes decisions?

At that point, bias stops being an abstract property of a model.

It becomes an organisational problem.

That distinction is central to the work of Catharine Montgomery, founder of Washington, D.C.-based Better Together Agency, whose recent work sits at the intersection of communications, reputation, generative AI and bias. Her perspective is unusual precisely because she does not approach responsible AI primarily from model architecture or computer science. She approaches it from the point where technology meets people — and where an output can become a customer experience, a communications decision, a reputational event or, in more consequential settings, part of a decision affecting someone’s life.

Montgomery has now spent several years researching public perceptions of bias in generative AI. Better Together Agency’s 2025 Building Consumer Trust: Addressing Bias in Generative AI report found that 33% of respondents would consider stopping their use of a generative-AI tool they perceived as biased. More strikingly, 92% said companies must address generative-AI bias.

The distinction between those numbers matters.

The 92% figure is not a measure of consumers threatening to abandon a brand. It measures expectation: an unusually broad signal that people believe responsibility does not end with the developer of the underlying model. Montgomery was explicit about that distinction when she sent me her responses, correcting an earlier formulation of my first question and asking that I not use an unsupported 84% brand-disengagement figure.

That precision is significant in a conversation about AI trust. Responsible AI depends not only on identifying erroneous information produced by machines, but also on ensuring that humans do not amplify inaccurate claims about those systems.

The broader findings of the 2025 study are equally revealing. Fairness and lack of bias ranked behind only accuracy and reliability among the characteristics respondents valued in generative-AI products. The report also found strong preferences for human oversight and transparency around AI involvement.

Montgomery’s argument therefore goes beyond the familiar proposition that bias is an ethical concern. Bias can affect whether technology works effectively for the people expected to use it — and consequently whether those people trust the organisation putting the technology in front of them.

That concern has also shaped TogetherAI, Better Together Agency’s communications and marketing platform. Rather than asking organisations to rely entirely on the assumptions embedded in a generic model, TogetherAI uses an organisation’s own material, language and standards as context. Montgomery describes this broader philosophy as building “born-inclusive technology”: incorporating lessons about representation and bias during product development rather than attempting to repair them only after deployment.

Yet bringing AI closer to an organisation’s own knowledge introduces another problem.

Organisations themselves are not neutral datasets.

A company’s policies, archives, brand guidelines and institutional language may contain historical assumptions, omissions and blind spots. Retrieval from proprietary material can make an AI system more organisationally specific, but specificity is not the same thing as fairness. A system can faithfully reproduce an organisation’s voice while also faithfully reproducing its exclusions.

That makes governance much more difficult than simply putting a human somewhere “in the loop”.

Who is that human? What evidence can they see? Are they empowered to challenge the system? Can they stop an automated process? What happens when an AI-generated conclusion passes through enough people that it begins to acquire institutional authority simply because everyone assumes someone else has already checked it?

These questions become increasingly important as AI moves from experimentation into infrastructure.

Montgomery’s current research is broadening the conversation further. Better Together Agency is conducting its third annual Biases in Generative AI study and is deliberately seeking responses not only from regular AI users but also from people who do not use generative AI. On LinkedIn, Montgomery has described the research as an attempt to understand bias, accuracy, representation, misinformation, trust and accountability from a broader section of the US public.

That matters because much of the public conversation about artificial intelligence is still produced by a remarkably self-selecting group: technologists, founders, investors, researchers, policymakers, journalists and highly engaged users.

Those communities are important.

They are not everyone.

A technology can affect someone without that person choosing to become an expert in it. An applicant can be evaluated by an AI-assisted process without using ChatGPT. A customer can encounter synthetic content without knowing how it was produced. A citizen can be affected by automated analysis without understanding the architecture behind it.

The legitimacy of AI systems therefore cannot depend exclusively on the opinions of those most comfortable using them.

I spoke with Catharine Montgomery about the relationship between bias and trust, when technological failures become organisational accountability problems, the limits of grounding AI in company material, what meaningful human oversight actually requires, and why responsible-AI research needs to listen to people beyond the AI-literate communities currently shaping so much of the debate.

Catharine Montgomery

Your research found that 33% of consumers would consider stopping use of a biased generative AI tool, while 92% believe companies must address generative AI bias. What do organisations still misunderstand about the connection between AI bias and trust?

I think organizations underestimate how personal a biased answer can feel. If a tool gets your identity or circumstances wrong, you do not separate the software from the organization that put it in front of you. AI is already part of how organizations communicate and serve people, so this is relevant now. For me, governance becomes real when someone can say, “This is wrong,” reach a person who will listen and see the organization correct it.

You come at AI bias not only as a technology issue, but through crisis communications and reputation. At what point does an AI failure stop being a model problem and become an organisational accountability problem?

CNN recently reported (https://keyt.com/politics/cnn-us-politics/2026/09/18/exclusive-us-military-had-close-call-after-using-ai-for-false-intelligence-report-sources-say/) a military near miss involving an AI-assisted intelligence report. A chatbot misidentified cargo on a Chinese ship. An analyst put the finding into a formal report, and personnel prepared to intercept the vessel. The error was caught before the planned operation.

That is the point I worry about: a wrong output can gain authority as people pass it along. The organization has to examine who checked that specific answer, who could challenge it and why it got as far as it did. AI is an extraordinary tool, but people remain accountable for the decisions made with it.

TogetherAI is built around an organization’s own materials, voice and standards rather than relying solely on a generic model’s defaults. How much can that approach reduce bias—and what risks remain if an organisation’s own source material contains blind spots?

Using an organization’s own material gives TogetherAI something real to work from: its language, standards and history. But those files can carry blind spots too. A brand guide may describe some audiences well and barely acknowledge others. A policy may repeat an assumption nobody has questioned in years.

Better Together Agency can review those materials with a client before they are uploaded. Who is represented? Who is missing? What language needs another look? Then we need to test the answers the tool produces. An output can sound exactly like an organization and still get something important wrong.

Our first two years of Biases in Generative AI survey research informed how we developed TogetherAI’s approach to bias. That is what I mean by born-inclusive technology. We brought what we were learning about people’s experiences into the development process, and we keep questioning both the source material and the output.

You’ve argued that AI should support human judgment rather than replace it. In practice, where should organisations draw that line, and how do they prevent “human in the loop” from becoming little more than a rubber stamp?

The person has to be able to question the answer. If I use AI to work on a LinkedIn post, I do not publish the first draft. I talk with the tool, reject angles that miss the point, add what I know about the audience and decide what sounds like me. It helps me explore ideas, while my experience tells me which ideas are useful. When an output could affect someone’s job, safety or access to a service, a reviewer also needs the evidence, time and authority to say no. A person clicking “approve” is not enough.

Your latest Biases in Generative AI research is deliberately reaching beyond the people who build and regularly use these systems. What are we missing by listening disproportionately to the AI-literate—and what would you most like the next wave of research to reveal?

I have to watch my own assumptions. I spend time in AI Slack groups, WhatsApp chats and conferences where people know these tools. It is easy to leave those rooms thinking everyone feels the same way.

People I speak with elsewhere in the country sometimes tell me they feel overwhelmed or do not know where to begin. Those conversations are not survey findings, but they are a reason to ask better questions. This year’s survey includes people who do not use generative AI. I want to understand their choices and what they expect from organizations using AI, because those decisions can affect them too. We are still collecting responses, so I do not want to claim what the results will show.

Catharine Montgomery

From Model Risk to Institutional Responsibility

An important conceptual shift runs through Montgomery’s answers.

The first era of public generative-AI criticism concentrated heavily on the model: hallucination, bias, unsafe outputs, incomplete training data, alignment and the limitations of probabilistic systems.

All of those questions remain important.

But organisations are now placing these systems between themselves and the world.

Once that happens, the relevant unit of analysis changes.

A model may generate the answer, but an organisation decides where to deploy it. Someone decides what information it can access. Someone defines the review process. Someone decides when a machine-generated conclusion is sufficiently trustworthy to enter a workflow. Someone determines whether a customer can appeal. Someone decides whether employees have the authority to challenge an automated result.

The model therefore becomes only one component in a larger socio-technical system.

This is why Montgomery’s description of governance is so useful: governance becomes real when a person can say “This is wrong” and something happens.

A policy document alone is not governance.

A human approval button is not necessarily governance either.

Meaningful oversight requires the ability to inspect evidence, question an output, understand the consequences of accepting it and, crucially, reject it.

As AI agents gain greater autonomy, that distinction will matter even more. The central question will increasingly move from: Can the AI do this? to Under what conditions should an organisation allow the AI to do this without intervention?

And then to the more uncomfortable question: Who remains accountable when it goes wrong?

Montgomery’s observation about institutional authority matters here. AI outputs do not remain static. They travel through organisations.

A generated statement becomes an analyst’s note.

The note enters a presentation.

The presentation informs a meeting.

The meeting produces a decision.

At each stage, the origin of the information becomes less visible while its perceived legitimacy can increase.

By the end of that chain, people may no longer be evaluating an uncertain machine output. They may believe they are reading an organisational fact.

That is why human judgment cannot simply exist at the final checkpoint. Critical scrutiny has to survive the entire information chain.

There is also a deeper challenge around inclusion.

Much of the AI industry understandably focuses on improving models, benchmarks and safeguards. Yet people ultimately experience artificial intelligence through interfaces, products, workplaces and institutions.

Someone who has never heard the term “large language model” can still discover that an AI system does not understand their accent.

Someone who has never experimented with image generators can still be misrepresented by synthetic media.

Someone with no interest in AI can still have their application, insurance claim, customer-service request, or employment experience influenced by an AI-assisted system.

Non-users are therefore not outside the AI economy.

Increasingly, there may be no such thing.

That is what makes Montgomery’s attempt to bring non-users into the next phase of her research worth watching. Her latest survey was still collecting responses when she answered my questions, and she is careful not to pre-empt its findings.

That restraint matters too.

At a moment when AI discourse frequently races ahead of evidence, waiting to hear what people actually say is itself a useful principle.

The next phase of responsible AI will not be solved entirely inside laboratories or engineering teams. It will also be shaped by whether the organisations deploying these technologies create meaningful mechanisms for challenge, correction and accountability.

Because as AI becomes embedded in everyday institutional life, people will increasingly judge it through something much older than technology.

Whether they can trust the people behind it.

by Gonçalo Perdigão

Subscribe now

More Interviews

Building Creative Machines has published dozens of exclusive conversations with founders, researchers, executives, artists and other leading voices shaping artificial intelligence and its impact on society.

Explore all our exclusive interviews

Editorial Disclosure

This interview was conducted independently by Building Creative Machines. No payment, sponsorship, or other editorial consideration was received in connection with its publication. The views expressed are those of the interviewee.

Deciding what deserves to exist may be.

That tension sits at the centre of Dr. Morissa Schwartz’s work. A writer, editor, publisher, marketing strategist and AI evaluator, Schwartz operates across two worlds that generative AI is increasingly forcing together: the traditional disciplines of authorship and editorial judgment, and the emerging systems through which machines generate, evaluate and refine language.

Schwartz is the founder of GenZ Publishing and Dr. Rissy’s Writing & Marketing. A Forbes 30 Under 30 honoree with a Doctorate in Literature and a Master’s in Communications, her career spans publishing, ghostwriting, public relations, marketing and digital media alongside work involving LLM evaluation, prompt and rubric design, reasoning, tone and language quality.

Her writing and commentary have appeared across major media, while her work and social presence have built an audience of more than half a million followers. But the particularly interesting intersection today is between her experience deciding what humans should publish and her work evaluating what machines can produce.

Because generative AI introduces a strange inversion into creative economics.

For centuries, publishing was constrained at almost every stage. Writing took time. Editing required people. Production had costs. Distribution was controlled. Professional presentation demanded infrastructure. Even producing a credible first draft required significant human effort.

AI weakens several of those constraints simultaneously.

A single person can now generate drafts, alternatives, summaries, marketing copy and editorial variations at extraordinary speed. Small publishers and independent creators can access capabilities once associated with much larger organisations. Production moves towards abundance.

But attention does not.

Neither does credibility. Nor taste. Nor accountability.

Schwartz argues that this changes where creative value accumulates. If production becomes cheap, judgment becomes expensive. If plausible language becomes abundant, provenance matters more. If machines can reproduce the statistical characteristics of a writer’s style, then having an actual point of view becomes more—not less—important.

And simply putting a human somewhere in an AI workflow does not necessarily solve the problem.

A person clicking “approve” after an AI system produces something is technically human-in-the-loop. Schwartz argues that meaningful human oversight demands much more: expertise, authority, evidence, intervention and ultimately responsibility for the decisions being made.

The emerging question is therefore not simply whether creative work was made with AI.

It is whether a human made consequential decisions about what the AI produced.

We spoke with Dr. Morissa Schwartz about infinite content, human judgment, authorship, publishing scarcity, the difference between voice and point of view, and what happens when “human-in-the-loop” risks becoming an accountability label rather than an accountability system.

If content becomes effectively infinite, what actually becomes scarce?

Trusted attention. We are not short of things to read, watch, or hear. We are short of reasons to believe a particular thing deserves our time.

Judgment becomes scarce because judgment requires exclusion. It means deciding what not to publish, which claim is too weak, which sentence is merely competent, and which idea adds something useful. Provenance, credibility, editorial coherence, and the courage to omit become more valuable too. In an age of infinite output, the rarest creative act may be saying, “This does not need to exist yet.”

Production can be automated. Earned trust cannot be mass-produced on command.

What tells you that human judgment genuinely shaped AI-assisted work rather than merely approved it?

I look for decisions that cannot be explained by polishing alone: a clear thesis, surprising but defensible exclusions, a hierarchy of evidence, deliberate structure, and moments when the writer resists the easiest or most statistically familiar answer.

Human-shaped work carries consequences. The author can explain why a claim is present, why another was removed, which sources were trusted, and what changed during review. I care less about whether a human clicked “approve” and more about whether the human challenged the premise, corrected the framing, checked the facts, and materially changed the result.

Source notes, version history, fact checks, documented reversals, and a named decision-maker reveal real intervention. If the human only removes typos and obvious hallucinations at the end, that is not meaningful oversight. It is signing the delivery receipt.

Where is the boundary between reproducing a writer’s voice and possessing a point of view?

Style is a pattern. Point of view is a position.

A model can reproduce sentence length, vocabulary, cadence, and favourite metaphors. A point of view comes from experience, values, memory, commitments, blind spots, and stakes. It determines what a person notices, what they refuse, and what they are willing to defend or revise publicly.

AI can imitate the sound of conviction, but it does not bear the consequences of being wrong. It can resemble a writer’s voice without inheriting the life that formed it. I see AI as useful for testing, translating, organising, and refining ideas. The line is crossed when assistance becomes counterfeiting, attribution disappears, or a recognisable identity is simulated without meaningful consent.

Which forms of publishing scarcity does AI destroy, and which does it make more valuable?

AI weakens the scarcity of production, first drafts, routine copyediting, formatting, and basic packaging. It lowers the operational barrier to making something that looks publishable. That can be liberating for independent authors and small presses like GenZ Publishing because good ideas no longer need enterprise-sized machinery to reach a professional baseline.

It does not eliminate the scarcity of attention, trust, distribution into meaningful communities, rights clarity, or excellent developmental judgment. It makes those things more valuable. Editorial work shifts away from mechanical correction and toward selection, context, ethics, originality, and long-range development.

Publishers increasingly earn relevance by choosing well, improving deeply, building trust, and standing behind the work. I grew up in my family’s small business, and the lesson applies here too: the storefront is not the business. The relationship and reputation are.

Who reviews the reviewer, and how do we stop “human-in-the-loop” from becoming an accountability label?

The human needs more than a seat in the diagram. They need relevant expertise, sufficient time, clear standards, access to evidence, and the authority to stop or escalate the work. If the reviewer cannot disagree with the system or delay release, the human is decorative.

Reviewers should be reviewed through sampling, second-reader checks for high-risk material, disagreement tracking, incident analysis, blind-spot rotation, and periodic external or cross-functional audits. Organisations should measure interventions, not merely approvals. What did the reviewer change, reject, escalate, or send back? Which failures slipped through, and what changed afterwards?

For smaller organisations, responsible governance does not require a bureaucracy worthy of a Russian novel. A practical system can include risk tiers, named ownership, source requirements, a short checklist, and a second set of eyes for consequential claims. “Human-in-the-loop” should describe evidence of accountable decisions, not the presence of a pulse.

The Economics of Judgment

There is a useful distinction running through Schwartz’s answers: production and creation are not necessarily the same thing.

Generative systems are extraordinarily effective production machines. They can create grammatically competent text, reproduce styles, generate alternatives and reduce the operational cost of reaching something that looks finished.

As that capability becomes ubiquitous, however, technical competence becomes less differentiating.

A reasonably polished paragraph was once evidence that someone possessed a certain degree of writing ability. A professional-looking publication suggested access to editors, designers, production systems and distribution. AI progressively separates the appearance of professional production from the infrastructure and expertise traditionally required to produce it.

That does not make expertise irrelevant.

It changes where expertise becomes visible.

The editor’s value moves from correcting sentences towards deciding which sentences matter. The publisher becomes less differentiated by the ability to manufacture a book and more differentiated by what they select, improve and ultimately put their reputation behind. The writer’s value shifts away from simply generating language towards developing a perspective worth expressing.

This is why Schwartz’s distinction between style and point of view matters.

Style can increasingly be modelled as a pattern. Sentence length, vocabulary, rhythm, recurring expressions and structural tendencies provide signals that generative systems can reproduce with growing sophistication.

Point of view is harder to reduce to those characteristics because it involves stakes.

A person has experiences behind an argument, values informing it, and consequences attached to defending it. The machine can generate the linguistic appearance of conviction without experiencing the consequences of that conviction.

That difference becomes especially important as synthetic content becomes harder to distinguish from human work.

The relevant question may eventually cease to be “Was AI used?”

AI will be embedded in too many creative tools and workflows for that question alone to tell us very much.

More useful questions emerge instead:

Who made the consequential decisions?

Who selected the evidence?

Who rejected the plausible but weak argument?

Who decided something should not be published?

And who is prepared to stand behind the result?

The same logic applies to human oversight of AI systems.

“Human-in-the-loop” has become one of the reassuring phrases of the generative AI era. But the mere presence of a person somewhere in a workflow says remarkably little about the quality of oversight.

That is a useful principle well beyond publishing.

A meaningful human-in-the-loop system should leave evidence of human agency—changes, disagreements, escalations, rejected outputs and decisions to stop. Without the authority and expertise to intervene, the human can easily become what Schwartz describes as “decorative.”

And that brings the argument back to scarcity.

Generative AI gives us more.

More drafts. More images. More alternatives. More variations. More content.

The economic and cultural response to that abundance may therefore be greater demand for something AI cannot generate simply by increasing its output: reasons to care about one thing rather than another.

In that environment, perhaps the defining creative capability is no longer production.

It is judgment.

Or, as Schwartz puts it more sharply:

by Gonçalo Perdigão

Subscribe now

More Interviews

Building Creative Machines has published dozens of exclusive conversations with founders, researchers, executives, artists and other leading voices shaping artificial intelligence and its impact on society.

Explore all our exclusive interviews

Editorial Disclosure

This interview was conducted independently by Building Creative Machines. No payment, sponsorship, or other editorial consideration was received in connection with its publication. The views expressed are those of the interviewee.

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

Subscribe now

More Interviews

Building Creative Machines has published dozens of exclusive conversations with founders, researchers, executives, artists and other leading voices shaping artificial intelligence and its impact on society.

Explore all our exclusive interviews

Editorial Disclosure

This interview was conducted independently by Building Creative Machines. No payment, sponsorship, or other editorial consideration was received in connection with its publication. The views expressed are those of the interviewee.

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

Subscribe now

More Interviews

Building Creative Machines has published dozens of exclusive conversations with founders, researchers, executives, artists and other leading voices shaping artificial intelligence and its impact on society.

Explore all our exclusive interviews

Editorial Disclosure

This interview was conducted independently by Building Creative Machines. No payment, sponsorship, or other editorial consideration was received in connection with its publication. The views expressed are those of the interviewee.

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

Subscribe now

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

Subscribe now

Articles from August 2026:

by Gonçalo Perdigão

Subscribe now

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

Subscribe now

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

Subscribe now

Also read:

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

Subscribe now