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

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

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

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

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

Photo Credits: Carob Mill

Interview with Martin Lindstrom

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

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

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

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

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

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

That’s the biggest mistake.

I think we are in a transition period right now.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

by Gonçalo Perdigão

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

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

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

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

Mapping AI into Production: A Field Experiment on Firm Performance

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What Is the INSEAD AI Venture Lab?

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

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

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

What Participants Received

The programme included six major pillars:

• Access to frontier AI tools and API credits

• Weekly technical training sessions

• Founder workshops led by faculty and operators

• Peer groups and mentor office hours

• Investor exposure and demo days

• Opportunities for non-dilutive funding prizes

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

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

Why This Accelerator Matters

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

That is useful, but incomplete.

The harder question is:

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

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

The Core Idea: “The Mapping Problem”

The paper introduces a powerful concept: the mapping problem.

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

The real challenge is discovering:

• where AI creates value inside the business

• which workflows should change

• what tasks should be automated

• how teams should reorganise

• how products themselves should evolve

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

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

What the Researchers Tested

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

Both groups received normal accelerator support.

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

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

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

Main Findings: AI Improved Real Company Performance

The results were striking.

1. Startups Found More AI Use Cases

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

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

2. Teams Completed More Work

Treated firms completed 12% more tasks overall.

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

3. More Paying Customers

They were 18% more likely to acquire paying customers.

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

4. Revenue Nearly Doubled

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

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

5. Less Need for Capital

Perhaps the most underrated result:

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

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

Why This Changes Startup Economics

Traditionally, startups scale through:

• hiring more people

• raising more money

• extending the burn runway

• slowly building products

AI changes this formula.

The paper suggests companies can:

• ship faster

• operate leaner

• test more ideas

• reach customers earlier

• reduce dependence on fundraising

That is a structural change in entrepreneurship.

My Personal Experience Inside the Programme

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

The programme pushed founders to think beyond prompts and tools.

The real challenge was:

• rethinking production systems

• compressing decision cycles

• removing internal bottlenecks

• using AI across multiple functions simultaneously

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

Why Corporations Should Pay Attention

This research is not only for startups.

Large organisations often have:

• more data

• more budget

• more processes

• more inefficiencies

• more bureaucracy

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

Buying licences for AI tools is not enough.

The real advantage comes from redesigning how the company works.

What Leaders Should Do

1. Audit Internal Bottlenecks

Where does work slow down today?

2. Map AI Across Functions

Not one department. Every function.

3. Redesign Workflows

AI layered onto broken processes creates limited gains.

4. Measure Commercial Outcomes

Revenue, customers, margin, speed.

5. Build Capability Early

The learning curve compounds.

Final Thought

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

This paper suggests something more important:

That is a management challenge, not a technology challenge.

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

by Gonçalo Perdigão

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

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

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

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

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

Pedro is the founder and product leader at Verdaio.ai

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Two things shifted my view.

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

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

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

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

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

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

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

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

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

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

Let’s look at the regulation plans.

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

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

What Verdaio Changed in 7 Days

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Before sample:

Verdaio AI Act Assessment 2026 04 12 Before

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The After sample:

Verdaio AI Act Assessment 2026 04 19 After

520KB ∙ PDF file

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How ready is your company?

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

A few ideas from this conversation will stick with me:

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

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

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

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

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

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

by Gonçalo Perdigão

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

I went as an observer.

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

The match was only one layer of the event.

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

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

Modern society often describes football as entertainment.

That definition feels far too small.

It is a ritual disguised as sport.

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

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

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

The crowd did not merely watch the match.

The crowd became part of it.

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

It can explain what happened.

But it cannot inhabit what happened.

It cannot inherit rivalry from a grandparent.

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

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

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

Those things are irrational.

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

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

A derby is built from all five.

Context lives in every reference outsiders miss.

Memory lives in every supporter who remembers previous battles.

Belonging lives in every shared chant.

Contradiction lives in loving something that so often causes pain.

Emotion lives everywhere.

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

No model understands what it means to care without reason.

No system feels loyalty despite repeated disappointment.

That matters.

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

But because meaning still exceeds measurement.

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

Statistics were updated.

Tables shifted.

Commentary multiplied online.

Yet none of those systems fully captured what actually happened.

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

That experience will be remembered far longer than the numbers.

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

by Gonçalo Perdigão

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

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

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

• Autonomous AI for low-risk, fully specified work

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

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

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

Key takeaways

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

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

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

The AI-first workflow in six steps

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

Step 0 — Prioritise: choose what to redesign first

Not every workflow is worth touching. Pick one where:

• There is volume (enough repetition to matter)

• There are clear inputs and outputs

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

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

Use a simple 2x2 to decide:

• Readiness (data quality, standardisation, stable process)

• Strategic leverage (impact if improved)

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

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

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

You want a decision map:

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

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

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

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

• Where people ask for clarification

• Where work bounces back

• Where quality is checked

• Where exceptions appear

Step 2 — Define constraints and allocate work

Now allocate each decision to one of three archetypes.

The three archetypes

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

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

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

The four questions that make allocation obvious

For each decision, answer these in plain language:

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

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

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

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

A simple rule:

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

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

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

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

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

This is the centrepiece.

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

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

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

The escalation design template

For each trigger, define three things:

1. Trigger condition (what the AI detects)

2. Escalate to (role, not person)

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

Example: compliance verification workflow

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

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

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

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

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

Step 4 — Design the human role (it changes)

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

They become:

• Architects (define constraints, policies, routing)

• Judges (handle exceptions and edge cases)

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

• Owners (accountable for outcomes, not outputs)

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

A practical move: rewrite role expectations in one sentence:

• Old: “produce and process”

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

Step 5 — Measure outcomes and reallocate continuously

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

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

Metrics that matter (simple and operational)

• Quality: error rate, rework rate, audit findings

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

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

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

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

Then do the most important part: reallocation.

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

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

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

Your moat is the routing layer

Most competitors can access similar models.

Your durable advantage becomes:

• your decision map,

• your allocation logic,

• your escalation triggers,

• your measurement loop.

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

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

by Gonçalo Perdigão

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

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

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

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

5.43MB ∙ PDF file

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The market looks broad, but power is concentrated

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

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

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

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

Partnerships are rising faster than revenue

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

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

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

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

• Which platforms will they build around

• Which tools will they standardise on

• Which model providers do they trust

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

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

Money still flows to infrastructure, not apps

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

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

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

This has a strategic implication that many boards still underestimate:

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

The real shift: from capability to control

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

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

“What can AI do?”

to:

“How do we control what it does?”

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

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

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

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

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

A practical playbook for the next 12 months

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

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

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

Do three basic things:

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

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

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

2) Build your “agent perimeter”

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

A simple starting checklist:

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

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

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

• Continuous evaluation (before and after deployment)

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

3) Standardise your stack before you scale spend

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

Your job is to reduce variance:

• Choose a small set of approved model endpoints

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

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

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

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

4) Assume “infrastructure first” in your deal radar

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

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

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

2. Orchestration and workflow tooling

3. Data and inference infrastructure

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

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

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

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

So pick work that is:

• Repeatable (high volume)

• Observable (clear success metrics)

• Contained (limited blast radius)

• Easy to revert (humans can take over fast)

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

Insight

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

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

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

by Gonçalo Perdigão

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

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

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

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

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

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

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

• more buyer enquiries this week

• more listing appointments next week

• something they can post without thinking

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

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

1) Sell the outcome, not the process

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

A simple framing that works:

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

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

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

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

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

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

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

Example style packages (name them like product tiers):

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

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

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

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

1. They make the transformation feel real.

2. They signal the price bracket and buyer type.

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

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

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

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

• Kitchen (value perception)

• Primary living space (emotion + lifestyle)

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

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

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

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

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

A reliable structure:

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

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

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

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

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

This format works because it compresses narrative:

• problem → possibility → proof → action

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

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

Agents are used to paying:

• photographers,

• video people,

• stagers,

• editors.

What they hate is ambiguous creative billing.

So you offer fixed packages:

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

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

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

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

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

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

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

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

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

So add a small, consistent caption:

• “AI renovation visualisation. For marketing only.”

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

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

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

Instead, you give them a single decision point:

• They pick the style package.

• You deliver the finished cut.

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

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

8) Distribution is the real moat

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

Give agents a posting kit:

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

• 10 hashtags tailored to the area

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

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

9) How to find buyers without cold email misery

Start with local leverage:

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

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

• Put your watermark in the corner, small.

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

Then turn that into a loop:

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

10) The opportunity: contractors and developers

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

• renovation contractors,

• small developers,

• architects doing extensions,

• property investors pitching partners.

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

Same workflow. Different wrapper:

• “Here’s what this becomes.”

• “Here’s the uplift story.”

• “Here’s the vision in 20 seconds.”

The real takeaway

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

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

• away from cameras and crews,

• towards taste, packaging, compliance, and distribution.

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

by Gonçalo Perdigão

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

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

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

Industrial Policy For The Intelligence Age

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

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

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

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

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

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

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

It becomes a social contract for accepting concentration

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

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

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

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

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

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

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

A “people first” version that actually bites

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

A practical blueprint executives should anticipate:

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

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

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

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

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

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

Companies will automate in ways that are hard to tax

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

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

• Excess profits in specific sectors with rapid AI substitution

• Windfall gains linked to AI deployment at scale

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

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

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

Concretely, expect mixes of:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

• shareholders (margin expansion),

• customers (price cuts),

• employees (time back/benefits), or

• reinvestment (growth).

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

Practical playbook:

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

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

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

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

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

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

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

• grid acceleration

• public-private financing models

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

What to watch next (and what to do now)

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

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

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

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

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

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

by Gonçalo Perdigão

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

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

About the Book: “Why Machines Learn”

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

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

Why do machines learn the way they do?

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

For me, one message was very clear:

To understand modern AI, you must understand its origins.

A Short History: The Perceptron

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

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

At its core, a perceptron does something extremely simple:

• It receives a few inputs

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

• It adds everything together

• It makes a yes/no decision

That’s it.

No imagination. No understanding. Just a rule.

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

Why the Perceptron Still Matters

The perceptron matters because it shows us something important:

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

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

“Should this be 0 or 1?”

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

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

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

About This Sketch

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

• A 12-year-old can understand it

• A creative can grasp the idea without maths

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

You will see:

• Two simple switches (inputs)

• A “brain” that adds things up

• An output that turns on or off

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

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

Why I Think This Matters Now

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

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

• It reduces fear

• It reduces hype

• It increases responsibility

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

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

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

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

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

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

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

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

function draw() {
  background(250);

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

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

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

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

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

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

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

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

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

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

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

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

  // Keep values updated
  updateAll();
}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

THIS MONTH’S SIGNALS

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

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

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

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

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

WHAT WE PUBLISHED

Theme: Agentic execution (chat → deliverables)

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

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

Theme: Governance, security, and maturity

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

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

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

Theme: Distribution, GEO, and brand surfaces

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

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

Theme: Society, trust, and the human edge

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

Photo: Maria Empis by JLL Portugal

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

Photo: Adib Bamieh

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

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

HOT TOPICS: AI × CREATIVITY

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

MODELS & TOOLS TO WATCH

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Gemini 3.1 Flash Live (real-time voice model)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

WHAT TO DO NEXT

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

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

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

CURIOSITIES

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

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

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

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

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Also read:

• Our February AI & Creativity Monthly Brief about January 2026

• Our March AI & Creativity Monthly Brief about February 2026

Also, remember our 2026 top articles so far:

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

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

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

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

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

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

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

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

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

  Why it matters: planning cycles must withstand model ambiguity.

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

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

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

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

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

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

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by Gonçalo Perdigão

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

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

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

This shift creates a new, urgent question:

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

Why GEO is suddenly urgent

1) “Zero-click” becomes “zero-visit”

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

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

2) AI Overviews are expanding

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

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

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

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

So the visibility contest moves from:

• “Can I rank?”
  to:

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

How generative engines actually produce answers

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

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

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

That matters because:

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

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

• They can produce fluent text that still needs grounding.

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

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

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

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

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

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

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

So GEO is largely about winning three filters:

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

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

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

GEO vs SEO: what changes, what stays

What stays from SEO (and still matters)

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

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

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

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

What changes dramatically

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

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

• precision

• clarity

• specificity

• evidence

• freshness

• provenance (who said this, where, when)

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

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

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

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

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

The practical GEO playbook

1) Write “extractable truth”

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

Do this:

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

• Use short paragraphs with one claim each.

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

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

Avoid:

• throat-clearing intros

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

• claims with no supporting detail

2) Make your brand an entity, not a slogan

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

Action:

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

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

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

3) Build “retrieval hooks” into your content

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

Format ideas that perform well:

• “What is X?”

• “X vs Y”

• “How to choose X”

• “Common mistakes in X”

• “Checklist for X”

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

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

4) Use a structure that machines love

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

Include:

• H2/H3 headings that mirror questions

• bullet lists

• step-by-step sequences

• comparison tables

• FAQs (written like real questions people ask)

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

5) Spread authority across the open web

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

So you need credible third-party sources:

• reputable industry publications

• standards bodies

• partner ecosystems

• academic or technical references where relevant

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

6) Design for “source panels” and citations

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

So ask:

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

• Does it immediately establish credibility?

• Does it answer cleanly without overclaiming?

Measuring GEO without pretending it’s solved

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

Visibility metrics

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

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

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

Business metrics

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

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

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

Where this trend goes next

Three directional bets look safe:

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

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

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

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

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

By Gonçalo Perdigão

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Download the GEO: Generative Engine Optimization paper here

1.35MB ∙ PDF file

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GEO: Generative Engine Optimization

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Most of the confusion comes from one simple shift: we moved from chat to agents.

• A chatbot answers. You still do the clicking, copying, pasting, filing, and sending.

• An agent can do the work: open apps, read files, move data between tools, and finish a task end-to-end.

Once you accept that, the naming becomes clearer:

• Claude Cowork = the desktop “workspace” where Claude can execute multi-step knowledge work using your files and apps.

• Claude Dispatch = the remote tasking layer that lets you send Cowork tasks from your phone (or anywhere) and have Claude run them on your computer.

• OpenClaw = an open-source agent ecosystem that many people run as an “always-on” personal operator, often with broad integrations and persistent behaviour.

What each one actually does

1) Claude Cowork: “Outcome-first” work on your desktop

Cowork is designed for people who don’t want to prompt a chatbot 30 times. You give it an outcome (“turn these contracts into a risk summary”, “build a board pack from these files”, “draft a client update using last quarter’s numbers”) and it runs a task loop across your local context: folders, documents, and everyday apps.

Where it shines:

• Extracting structured data from messy documents (PDFs, contracts, reports)

• Drafting deliverables (memos, decks, briefings) from a bundle of sources

• Repetitive internal workflows (weekly metrics pulls, meeting follow-ups, status digests)

Where it doesn’t replace you:

• Judgement calls, approvals, and anything that is “one wrong click = incident”

2) Claude Dispatch: “Send tasks now, get results later”

Dispatch is best understood as remote orchestration. It’s not a new model and not a separate agent brain. It’s a way to queue and trigger Cowork work from your phone, and (where enabled) have Claude use your computer while you’re away.

Where it shines:

• “Run this every morning” tasks (scan inboxes, compile a short brief, pull a metric)

• “I’m in a taxi” tasks (start a report, draft an email, prep a meeting note)

• Low-friction delegation when you don’t want to open your laptop

What it changes organisationally:

• Work becomes asynchronous. People start treating the agent like a background operator.

• That’s powerful—but it also creates a new risk surface: delegated authority without eyes on screen.

3) OpenClaw: “Always-on autonomy” (with more responsibility on you)

OpenClaw is the one that attracts builders and power users because it often aims for:

• persistent operation (24/7)

• deeper custom integrations

• more configurable autonomy and memory-like behaviour

That flexibility is the point—and also the problem. The more you wire an agent into real systems, the more you must assume it can be:

• tricked

• mis-scoped

• over-permissioned

• socially engineered

Recent reporting and research attention has highlighted how agent-style systems can be manipulated into harmful actions by exploiting “helpful” behaviours (for example, being guilt-tripped into doing the wrong thing). (source: WIRED)

Feature comparison

Claude Cowork

• Best for: document-heavy knowledge work inside a safer, guided product

• Setup: relatively straightforward (desktop app + permissions)

• Control: stronger product guardrails, oversight built into the workflow

• Risk profile: “enterprise-adjacent” if you treat permissions seriously

Claude Dispatch

• Best for: triggering Cowork work remotely and routinely

• Setup: add-on behaviour inside the Claude apps

• Control: the risk is less about “Dispatch” itself and more about what you allow Cowork to do while unattended

• Risk profile: operational mistakes (wrong email, wrong file, wrong copy) if you skip the review

OpenClaw

• Best for: power users who want a configurable, always-on agent with broad integrations

• Setup: can be simple or very deep, depending on how far you go

• Control: you own the guardrails (permissions, hosting, logging, policies)

• Risk profile: highest variance—can be excellent, can be a compliance nightmare

The biggest confusion: “features” vs “product names”

People compare lists like this and get lost:

• “Computer use”

• “Remote control”

• “Plugins / skills”

• “Connectors”

• “Memory”

• “Scheduling / cron”

• “Autonomy”

Those are capabilities, not brands.

A cleaner way to compare is by operating model:

1. Chat mode (ChatGPT/Claude “normal”)
   You ask → it answers → you act.

2. Assisted execution (Cowork)
   You ask for an outcome → it performs steps → you review and approve.

3. Remote delegation (Dispatch + Cowork)
   You send a task → it executes on your machine → you get results.

4. Always-on autonomy (OpenClaw style)
   It runs continuously, connects systems, and can act even when you're not present.

Risks you should assume on day one

If you’re deploying any agent beyond casual personal use, assume these are real:

1) Permission creep

Agents fail safely when access is tight. They fail loudly when access is broad.
Most teams start with “just make it work” permissions—then regret it.

Rule: give the agent only what it needs for the current workflow, not what it might need someday.

2) “Looks right” errors

Agents produce outputs that read cleanly but contain:

• subtle numeric mistakes

• wrong entity matches

• outdated assumptions

• confident nonsense in footnotes

Rule: treat first drafts as drafts, especially anything financial, legal, or client-facing.

3) Tool injection and social engineering

When an agent can read messages, tickets, docs, and web pages, it can be nudged by malicious or simply messy inputs.

Rule: don’t let an agent execute irreversible actions based on untrusted text (emails, DMs, web pages) without an approval step.

4) Audit and accountability gaps

“Who sent that email?” becomes “the agent did”, which becomes “who authorised the agent?”

Rule: log tasks, keep outputs, and make ownership explicit.

Where to start (for someone who has never used any of this)

Here’s a safe ramp that works in real organisations:

Step 1: Start in normal chat (1 week)

Use ChatGPT/Claude as a thinking partner:

• rewrite

• summarise

• structure

• brainstorm

• build templates

Goal: learn prompting, verification habits, and what “good output” looks like in your context.

Step 2: Move to Cowork for one repeatable workflow (2–3 weeks)

Pick a workflow that is:

• frequent

• low-stakes

• document-heavy

Good starters:

• weekly internal brief from a folder of PDFs

• meeting notes → action list → follow-up draft emails (but don’t send)

• RFP / tender summarisation into a standard template

Step 3: Add Dispatch only after you trust the workflow

Dispatch is great once the task is stable.
If the workflow is still changing daily, remote delegation increases the likelihood of mistakes.

Start with:

• “run and compile” tasks

• not “send, delete, publish” tasks

Step 4: Consider OpenClaw only when you can operate it properly

If you want always-on autonomy, treat it like a system:

• defined scope

• clear permissions

• monitoring/logging

• security review

• kill switch

Recommendations

• Choose Claude Cowork when you want execution power with product guardrails and a clear path for non-technical teams.

• Add Claude Dispatch when you want remote tasking, and you already trust the Cowork workflow enough to run unattended.

• Use OpenClaw when you want maximum flexibility, and you’re willing to own the operational and security burden—because agent manipulation and misbehaviour is not theoretical.

• Keep normal chatbots for anything exploratory, sensitive, or high judgment where “doing” is the dangerous part.

by Gonçalo Perdigão

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P.S. Also read about PicoClaw here.

At the Lisbon MBA Católica|Nova - Alumni Unite Summit, held on 27 February 2026 at Nova SBE in Carcavelos, one of the most revealing aspects of the real estate discussion was not simply what was said, but what was left unsaid. The wider event was framed around “Navigating a World of Changing Priorities”, and the Real Estate Think Tank focused on a central question: who holds the risk in a market shaped by inflation, supply constraints, regulation and prolonged uncertainty. Yet despite the strategic depth of that conversation, technology and artificial intelligence were notably absent from the room’s explicit debate.

That absence now matters. Generative AI is no longer a speculative theme for the property sector. It is becoming an operational issue, a governance issue and, increasingly, a competitive issue. The practical question is no longer whether AI will affect real estate, but where value will be captured first, which functions will change fastest, and how firms can modernise without eroding trust.

Maria Empis, Co-Head of Residential at JLL Portugal, approaches this shift from a notably pragmatic perspective. Her answers do not suggest an industry on the verge of being replaced by generative systems, nor do they indulge in technological hype. Instead, they point to something more useful: AI as an enabler of better internal efficiency, faster service, stronger data use and more consistent client outcomes. In that sense, the real opportunity is not to make property more artificial, but to make the organisation behind it more intelligent.

What emerges from Empis’s responses is a clear hierarchy of value. The strongest short-term returns are not in flashy consumer-facing applications, but in document-heavy internal workflows such as due diligence, lease management, and information handling. That fits with a broader pattern: the most immediate and measurable use cases in real estate tend to be those that reduce friction in information-intensive processes, particularly where speed, consistency and traceability matter. Empis’s view is that these efficiencies ultimately benefit clients not through spectacle, but through better execution: faster delivery, greater accuracy and more dependable service.

Her answers also underscore another point often underappreciated in public AI conversations: the quality of the outcome depends heavily on the quality of the underlying data. In the Portuguese residential context, valuable assets include transaction volumes, sales prices, contracted rents, new supply indicators and wider economic and demographic data. By contrast, the hardest material to operationalise is fragmented, unstructured or inconsistently formatted information: legal documents, building plans, municipal licensing data and sensitive client records. This is a reminder that the real bottleneck in enterprise AI is rarely the model alone. More often, it is the condition, accessibility and governance of the data ecosystem around it.

That matters even more as the sector moves closer to regulated, auditable AI use. The discussion highlighted disclosure, auditability, and the European AI Act timeline as central issues for property marketing and content production, particularly regarding AI-edited visuals and AI-generated copy. Empis’s response is measured and direct: transparency has to be fundamental. In her framing, any content materially generated or altered by AI should be clearly identified, with human review and auditable internal processes forming part of a wider change-management framework. This is not merely a legalistic concern. It goes directly to credibility.

The same logic extends to visibility inside generative search and answer engines. This shift also highlights the growing importance of GEO, or Generative Engine Optimisation, as conversational interfaces begin to shape how users discover properties and brands. Empis’s advice here is strikingly disciplined. Rather than chasing gimmicks, she points to the need for high-quality, authoritative content; structured data that machines can easily parse; and a stronger emphasis on factual accuracy, attribution, and lifestyle relevance. In practice, that means content that does more than list features. It should explain how residential developments support lived experience, including wellness, green space and personalisation.

Just as importantly, Empis does not present generative AI as a universal substitute for human judgment. On the contrary, some of the most valuable functions in real estate are likely to remain least affected over the next three years. She identifies high-stakes negotiation, strategic client advisory and physical site or property management as areas where human presence, contextual judgement, emotional intelligence and relationship-building remain central. This is one of the most credible parts of her argument. AI may compress administrative work, accelerate analysis and assist service layers, but the final act of trust in real estate, whether in negotiation, investment advice or physical oversight, is still deeply human.

That creates an interesting tension at the heart of the sector. The parts of the business most ready for AI are often those furthest from the client’s emotional field of vision: internal documents, process management, data synthesis, search support and content preparation. Yet the functions least susceptible to automation are often the ones clients value most intensely at moments of consequence: negotiation, advisory and physical decision-making. The firms that navigate this transition well are therefore unlikely to be those that simply automate the most. They will be the ones who know where automation strengthens service, where human judgment must remain in control, and how to combine both without confusion or loss of accountability.

Photo: Maria Empis by JLL Portugal

Interview: Maria Empis, Co-Head of Residential, JLL Portugal

Where do you see the strongest client ROI today: document-heavy workflows (leasing/due diligence), customer support, or marketing/distribution?

The analysis indicates that the real estate industry is currently focused on optimising internal efficiency as a foundational step.

The JLL “Global Real Estate Outlook 2026” highlights that companies are piloting an average of five AI use cases simultaneously, including data workflows, portfolio optimisation, and market analysis, which aligns with document management and due diligence. The same analysis reveals that only 5% of companies have achieved most of their goals in these projects, suggesting that the full ROI has yet to be realised.

From our perspective, the greatest short-term ROI potential lies in optimising these internal, document-heavy workflows. By automating and streamlining processes like due diligence and lease management, we create efficiencies that indirectly benefit the client through faster, more accurate service. Combined with our strategic marketing and dedicated customer support, this operational excellence is the foundation for scaling our business and delivering faster, more accurate service and consistently superior value to our clients.

Which Portuguese data assets are proving most valuable for internal LLM (“GPT”) use cases, and which are hardest to operationalise?

The most valuable assets would be transaction volumes, sales prices, contracted rents, and new supply figures, as detailed in our “JLL Market 360 REPORT 2025-2026”. Economic and demographic data from sources like INE and the Bank of Portugal are also critical.

The most difficult to operate is invariably unstructured or fragmented data. This includes complex legal documents, building plans in diverse formats, and municipal licensing data, which often lack a standardised format. Privacy is another significant barrier, especially when handling sensitive client information in the residential sector.

How are you thinking about disclosure and auditability for AI-edited images and AI-generated copy, given the AI Act timeline and “responsible use” standards?

The JLL “Global Real Estate Outlook 2026” mentions the EU’s AI Act in the context of data sovereignty, demonstrating that regulatory compliance is on our strategic radar.

Our approach must be guided by a balance of innovation and trust. In our view, transparency will be fundamental. Any content generated or significantly altered by AI, whether it’s a project image or a property description, should be clearly identified as such. Internally, we will need to develop a change management framework for AI, as suggested in the “Global Real Estate Outlook 2026,” which would include clear guidelines, human review processes, and auditable records. Client trust is our most valuable asset, and responsible use of technology is the only way to preserve it.

On GEO: what would you advise a residential brand to do in the next 6–12 months to improve visibility inside generative answers without harming compliance and trust?

We would be focusing on creating high-quality, authoritative content that can serve as source material for these AI models, such as Create “Experience-Focused” Content: The “JLL Consumer Experience Survey” shows that consumers value wellness, green spaces, and personalisation. The brand should create content that highlights how its residential projects incorporate these elements, moving beyond property features to focus on lifestyle, Develop Structured Data: Ensure all property listings are marked up with structured data (schema) so that AI models can easily parse details like price, location, size, and amenities and Prioritize Factual Accuracy and Attribution: All content must be factual, compliant, and clearly attributed. This builds authority and trust, making it more likely that AI models will cite our content as a reliable source.

Which real estate functions do you believe will remain least affected by generative AI over the next three years, and why?

While generative AI will transform many aspects of our industry, we believe the functions that rely most heavily on complex human interaction, strategic negotiation, and physical presence will be the least affected.

The reports consistently emphasise that “experience” is a key value driver, which includes human interaction and the atmosphere of space. Therefore, the following functions will remain fundamentally human-centric:

• High-Stakes Negotiation and Deal Closing: The final stages of closing complex, high-value residential or commercial transactions require nuance, emotional intelligence, and relationship-building skills that AI cannot replicate.

• Strategic Client Advisory: Providing bespoke advice to a high-net-worth individual or an institutional investor on their portfolio strategy involves understanding subtle goals and risk appetites, which is a deeply consultative and trust-based process.

• Physical Site and Property Management: While AI can optimise operations, the physical inspection of properties, oversight of construction, and management of on-site staff require a human presence and hands-on expertise.

These functions are about judgment and relationships, not just data processing. AI will become a powerful tool to support these roles, but it will not replace the core human element that defines them.

Where Value Will Be Created Next

The real estate sector is entering a period in which operational intelligence may matter just as much as market intelligence. Supply constraints, regulation, affordability pressures, and construction costs will remain central to the industry’s future, but the systems through which firms process information, communicate with clients, and build trust are also changing. The gap identified at the Summit was therefore not simply the absence of AI from one panel discussion. It was the wider lag between how quickly technology is advancing and how slowly many property conversations still frame strategic responses.

Maria Empis’s answers offer a useful corrective to that lag. They suggest that the next meaningful gains will come less from spectacle than from discipline: better-structured data, better internal workflows, clearer disclosure, stronger governance, and content that earns visibility by being genuinely authoritative. At the same time, they remind us that not every aspect of real estate value can or should be automated.

That is why generative AI in property should not be understood as a replacement story. It is a redistribution story. Some tasks will become faster, cheaper and more scalable. Some channels will become more conversational. Some internal systems will become markedly more intelligent. But the decisive competitive edge will still belong to organisations that can combine technological capability with human credibility.

In that sense, the future of residential real estate may not belong to those who use the most AI. It may belong to those who use it with the clearest sense of where efficiency ends, and responsibility begins.

by Gonçalo Perdigão

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When I wrote the 5.3 piece, the central argument was simple: ignore vibes, separate signals, and assume the next point-release would land where the competitive pressure is hottest—tools, reliability, long context, and “agent-ish” behaviour.

Not with a single “wow” feature. With a product shape that quietly changes what “using ChatGPT” even means: less chat, more work execution.

OpenAI’s own description is unusually direct: GPT-5.4 bundles reasoning, coding, and agentic workflows into one frontier model, improves its performance across tools and software environments, and is built to produce professional artefacts (documents, spreadsheets, presentations) with less back-and-forth.

1) The two headline capabilities that actually move markets

A. Native computer use (not “tool calling” as theatre)
OpenAI frames GPT-5.4 as its first general-purpose model with native computer-use capabilities, designed for agents that operate across applications.

Translation: it’s not just “I can call an API”. It’s “I can execute steps in a software environment in a way that looks like work”.

If you run operations, finance, sales ops, legal ops, analytics, or any function that lives inside a browser + spreadsheets + internal tools, this is the pivot.

B. 1M token context (the real reason ‘point releases’ matter)
In the 5.3 article, “bigger context” was a staple of rumour. GPT-5.4 makes it official: up to 1M tokens.

This isn’t a “read bigger PDFs” flex. It’s what enables long-horizon planning + execution + verification without the model collapsing into amnesia midstream. In practice, it’s the difference between:

• summarising a contract pack
  and

• running a contract workflow (extract clauses → compare to playbook → flag deviations → draft redlines → produce a memo)

2) The quiet feature: steerability while the model is “thinking”

OpenAI also slipped in a product behaviour change that sounds small until you use it for real work: GPT-5.4 Thinking can give an upfront plan, then you can adjust direction mid-response, then it finalises.

This is not cosmetic.

It turns the model into something closer to:

• a junior analyst who shows you the approach before burning hours, and

• a workflow engine you can steer before it commits to the wrong path.

This is exactly the kind of friction reduction that makes adoption explode inside organisations: less “prompt craft”, more “review and steer”.

What my 5.3 piece got right (and what it didn’t)

Let’s treat this like an investment memo: what was confirmed, what was noise, and what changed between 5.3 and 5.4.

1) Confirmed: the “most realistic 5.3 scenario” was basically the roadmap

In the 5.3 article, the grounded expectation was a refinement release pushing further on:

• reliability / fewer hallucinations

• long-context performance

• sturdier tool calling / agent management

• coding quality and UI generation

GPT-5.4 is exactly that direction—just more aggressive than the rumour framing suggested.

OpenAI says it specifically focused on improving spreadsheet, presentation, and document creation, as well as agent workflows and tool use.

So the call that mattered wasn’t “Garlic” or “leaked benchmarks”.
It was the strategic logic: the battleground is productivity, and productivity is tools + context + reliability.

2) Partly confirmed: “agents” weren’t marketing. OpenAI productised them

In January, “agent behaviour” was a vague online obsession. GPT-5.4 makes it concrete:

• agentic workflows are part of the core positioning

• computer use is shipped as a native capability

• context is extended to support long-horizon execution

That’s not an incremental feature. That’s a product thesis.

3) Not confirmed (or simply irrelevant now): the rumour scaffolding

The 5.3 article was blunt about the quality of the evidence: social posts aren’t validation, secondary outlets can amplify the same unverified claims, and the sensible stance is to watch official docs.

That part aged well.

What didn’t matter in the end:

• codenames and screenshot archaeology

• “it’s definitely coming next week” certainty

• speculative feature lists presented as inevitabilities

GPT-5.4 didn’t arrive as “the thing the rumour mill promised”.
It arrived as the thing the business pressure demanded.

4) Where 5.3 fits, now that 5.4 exists

OpenAI now clearly positions GPT-5.3 Instant as the fast everyday model, with GPT-5.4 Thinking/Pro as the deeper reasoning options.

So 5.3 didn’t become the dramatic “new flagship moment” the internet wanted.
It became the routing layer’s dependable workhorse.

And that’s an important signal for what comes next: the model family is becoming a portfolio, not a single crown.

The practical shift: GPT-5.4 is an operating model, not a chatbot

Here’s the useful way to think about GPT-5.4 if you’re responsible for outcomes:

Old question: “Is the model smarter?”
New question: “Can it complete a workflow with fewer human turns?”

GPT-5.4’s value shows up when you stop asking for answers and start asking for deliverables.

Three workflow patterns that suddenly work (and how to use them)

1) “Plan → Execute → Verify” as a default prompt structure

Use this when the cost of a mistake is high.

Prompt shape:

• Goal (one sentence)

• Constraints (budget, timeline, policies, risk limits)

• Artefact required (deck outline, spreadsheet structure, memo format)

• Verification step (what should be checked, and against what)

Why it works now: the model can hold the plan, keep track of what it’s done, and produce cleaner outputs with less fluff.

2) “Tool-first” tasks: treat ChatGPT as a coordinator

If you have web research, internal docs, a spreadsheet, and a slide deck, don’t do sequential prompting.

Ask for:

• the tool sequence

• the intermediate artefacts

• a final compiled deliverable

The “agentic” win is not autonomy. It’s coordination.

3) “Long-context compression”: one ingestion, many outputs

With a 1M token ceiling, the game becomes:

• ingest once

• produce multiple views for different stakeholders

Example:

• Board summary (1 page)

• Risk register (table)

• KPI dashboard spec (bullets)

• Draft email to customers (tone-controlled)

This is where the ROI shows up: not in a single answer, but in removing the repeated context-setting that kills productivity.

So what could GPT-5.5 be?

Now the fun (and the dangerous) part.

There is no official announcement of GPT-5.5 in the sources that matter. So this section is not “what is”. It’s “what would make sense”, plus “what signals to watch”, plus “what rumours are even worth your attention”.

1) The most plausible 5.5 isn’t “smarter” — it’s more operational

If 5.4 is the “professional work” model, 5.5 would likely push three levers:

A. Better autonomy with tighter controls
Computer use is powerful, but organisations need:

• permissioning

• audit trails

• sandboxing

• human approval gates

A 5.5 that doesn’t ship stronger governance would be commercially incomplete.

B. Memory that is legible and controllable
“Memory” has been a persistent rumour topic because it’s the unlock for continuity. But enterprise adoption demands:

• explicit scopes (“remember for this project only”)

• retention windows

• compliance-friendly export/erase

• explainable “why I remembered this”

If 5.5 arrives, watch for memory framed as a control surface, not magic.

C. Cost/latency breakthroughs via a multi-model stack
5.4 already points to a portfolio approach (fast Instant, deeper Thinking/Pro). 5.5 could formalise:

• background sub-agents

• specialised mini/nano routing

• parallel execution with a single final synthesis

The win would be speed + cost without losing reliability.

2) What rumours would actually be worth listening to?

Most rumours are content. A few are signals.

The rumours that tend to precede real releases are boring:

• model names appearing in official docs, SDKs, or help centre language

• changes in product UI that imply new routing behaviour

• new evaluation frameworks, system cards, or safety notes that look “pre-release”

Anything else is usually just someone selling certainty.

3) My working hypothesis

If GPT-5.4 is “agents that do knowledge work”, then GPT-5.5, if it comes, will be “agents that do knowledge work safely inside organisations”.

Not a spectacle model. A deployment model.

A request: send me the useful rumours

If you’ve seen credible signals of a “5.5” (screenshots of official docs, SDK references, product UI changes, or enterprise rollout notes), send them.

Not hot takes. Not “my friend said”. The boring evidence.

Because the pattern is clear now: the story isn’t model IQ.
Its workflows are becoming software.

by Gonçalo Perdigão

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For something really futuristic, try out our GPT-7 approach

Back in August 2025, our “40 AI Terms Explained” article gave busy people a working vocabulary without turning it into a computer science lecture.

Since then, the centre of gravity has shifted.

The big change is not that models got “smarter”. It’s that AI moved from chatting to doing: clicking, calling tools, reading internal systems, writing code, triggering workflows, and quietly turning into a new layer of software execution. The vocabulary followed that shift.

Below are the add-on terms that now sit on top of your original glossary, organised as they appear in real company decisions: product capability, architecture, security, cost, and governance.

1) From chatbots to doers: agentic terms

Agent / Agentic workflow

An agent is an AI system that doesn’t just answer—it takes steps toward a goal: plans, uses tools, checks results, retries, and escalates when stuck. “Agentic workflow” is simply the business process version: AI executes multi-step tasks across tools (CRM, email, files, ticketing, code repos) with minimal human input.

Why it matters: buying “an AI assistant” vs adopting agentic workflows is the difference between productivity experiments and operational change (and risk).

Tool calling

The model triggers an external tool (e.g., a search, calculator, database query, calendar, or code execution). You can treat this as the API layer of agentic work: the model becomes an orchestrator.

Non-obvious insight: tool calling usually creates the audit trail you wish prompts had. If you want control, you want tools, not longer prompts.

Computer use

A specific style of agenting where the model operates a user interface (browser, desktop apps): clicking, typing, navigating. It’s powerful because it works even when an app has no clean API.

Operational reality: “computer use” is often faster to pilot than “integrate with everything”, but harder to harden (UI changes break flows).

Supervisor / Human-in-the-loop (HITL)

A supervisor pattern means a human approves or reviews certain steps: payments, customer messaging, and record changes. HITL is your safety valve.

Rule of thumb: if a mistake is reversible, automate; if it’s irreversible (money, compliance, reputation), gate it.

2) The new plumbing: context, memory, and connectors

Context window

The maximum amount of text/images the model can consider at once. Bigger windows help, but don’t magically create truth—just more room for relevant material or confusion.

Memory

“Memory” (product feature) means the system stores user/org preferences across sessions. It’s useful, but it turns your assistant into a stateful system—with all the governance that implies (retention, access, correction, deletion).

RAG (Retrieval-Augmented Generation)

The model retrieves relevant documents from your knowledge base (often via embeddings/vector search) and uses them to answer. RAG reduces hallucinations when done well—but only if your documents are clean, current, and permissioned.

Non-obvious insight: RAG is less about “AI accuracy” and more about information architecture. Most RAG failures are due to content hygiene issues.

Model Context Protocol (MCP)

MCP is an open protocol that standardises how LLM apps connect to tools and data sources—think “USB-C for AI integrations”. Instead of building custom connectors for every model/tool pairing, MCP aims to make connections reusable and more secure.

Boardroom translation: MCP is not a buzzword; it’s an integration strategy. If your organisation is drowning in one-off AI connectors, MCP is the vocabulary for getting out.

3) Security vocabulary you now need in every AI project

If AI is doing work, it can be attacked like software. The OWASP Top 10 for LLM Applications is the cleanest “common language” for this.

Prompt injection

An attacker crafts input that manipulates the model into ignoring instructions or leaking data—often by hiding malicious instructions inside documents, webpages, emails, or tickets.

Practical control: treat all retrieved text as untrusted. Your model should quote sources, follow tool permissions, and never “obey” retrieved instructions.

Jailbreak

A subtype of prompt injection aimed at bypassing safety rules. In business settings, the bigger issue is not edgy outputs—it’s policy bypass (“ignore approvals”, “export the customer list”, “summarise confidential HR notes”).

Insecure output handling

When downstream systems trust model output too much (e.g., executing generated code, rendering HTML, sending emails without checks). This is how “harmless text generation” turns into a systems incident.

Training data poisoning

If your fine-tuning data, feedback logs, or shared datasets are compromised, you can literally train failure into your system.

Model denial of service (Model DoS)

Attacks (or careless internal usage) that blow up compute cost: long prompts, repeated retries, complex tool loops.

Budget insight: security and cost control converge here. Rate limits are not only financial, but they’re also safety-related.

Supply chain vulnerabilities

Your “AI system” includes models, embeddings, vector DBs, plugins, tool servers, prompt libraries, and vendor connectors. Any weak link can expose data or behaviour.

4) Cost and performance: the new terms finance teams keep hearing

Test-time compute (TTC) / Test-time scaling

Compute spent during inference (when the model answers), not during training. Some modern approaches deliberately allocate more compute at answer time to improve reasoning (e.g., sampling, multi-step deliberation).

Exec takeaway: “better answers” increasingly means “more inference spend”. Pricing, margins, and unit economics now depend on how much thinking you allow per task.

Latency

The time to get an answer. Agentic systems trade latency for autonomy: a one-shot chat reply is fast; a five-tool workflow is slower but does more.

Distillation

Compressing a larger model’s behaviour into a smaller model to cut cost/latency while retaining acceptable performance.

Quantisation

Reducing numeric precision to run models cheaper/faster (often with slight quality trade-offs). If you hear “we can run it on-device” or “we can cut GPU cost”, quantisation is usually part of that story.

MoE (Mixture of Experts)

A model design where only parts of the model activate per token, improving efficiency at scale. This matters indirectly: it’s one reason model providers can improve capability without cost rising linearly.

5) Quality and culture: terms that shape trust (and brand risk)

Hallucination (still relevant, now more expensive)

Hallucinations become more dangerous when the model can act. An incorrect answer is annoying; an incorrect database update is an incident.

Evals

Systematic tests for quality, safety, and reliability: accuracy benchmarks, red-team tests, and regression tests across prompt changes. If your AI is in production, evals are your “unit tests”.

Red teaming

Adversarial testing—people try to break the system (security, policy, data leakage). In mature teams, red teaming is continuous, not a one-off workshop.

AI slop

High-volume, low-effort AI-generated content that clogs channels (marketing spam, SEO junk, synthetic filler). It’s not just an internet meme; it’s a brand and distribution risk.

Non-obvious insight: internal slop is real too—bloated decks, fake analysis, “AI-written” reports nobody trusts. The fix is incentives + review loops, not banning tools.

Anthropomorphism

People attribute human intent or competence to AI outputs, which leads to over-trust (“it sounded confident”) and under-governance (“it’s basically an employee”).

6) Governance language that keeps you out of trouble

AI risk management

This is moving from “ethics talk” to operational practice. The NIST AI Risk Management Framework is a useful reference vocabulary for mapping and managing AI risks across the lifecycle.

Guardrails (updated meaning)

In 2025, our article framed guardrails as “boundaries”. Now, guardrails typically mean a stack:

• permissioning (what tools/data can be accessed)

• policy checks (what’s allowed)

• monitoring (what happened)

• escalation paths (what to do when unsure)

Model governance

Who can deploy a model change? Who approves new tools? How are prompts versioned? What gets logged? Governance is “boring” until the day it prevents a front-page mistake.

A simple way to use this glossary inside a company

If you want one practical move after reading this, do this:

1. Label every AI initiative as either Chat (answers only) or Act (takes actions).

2. If it’s an Act, require three checkboxes before production:

   • Tool permissions (least privilege)

   • OWASP threat review (prompt injection, output handling, DoS)

   • Evals + rollback plan (quality regression + fast undo)

That’s it. You’ll instantly reduce confused conversations, surprise risk, and runaway cost—without slowing experimentation.

by Gonçalo Perdigão

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If you skim the public, brand-associated GPTs in the GPT Store, one thing jumps out: they are not “chatbots”. They are packaged behaviours.

Some behaviours are light (a themed recommender). Others are heavy (connected to live catalogues via actions/APIs). And the interesting part is not the UI. It’s the business function the behaviour replaces:

• the first ten minutes of browsing

• the “I don’t know what I want” moment

• the messy brief that never becomes a plan

• the blank page before a design exists

• the overwhelming inventory where the user quits

The pattern is clear: recommendation, planning, and creation dominate across travel, commerce, outdoor, and content tools.

Brand GPT use cases that are already live (and what they really do)

1) Travel planning GPTs: turning “inspiration” into a structured basket

Expedia and KAYAK show the most commercially obvious behaviour: compressing planning time.

What the GPT does in practice

• Converts vague intent (“a warm break in April under €1,200”) into structured constraints (dates, airports, duration, hotel style).

• Produces ranked options fast enough to keep the user in flow.

• Hand it off to the brand’s ecosystem for the final booking step (even if checkout stays outside ChatGPT).

Why is this strategically strong?
Travel is an anxiety category: too many options, high perceived regret, high price volatility. A GPT is a regret-reduction interface.

The winning feature isn’t “better recommendations”. It’s constraint discovery. The GPT elicits what users fail to type into search filters (tolerance for layovers, neighbourhood vibe, hotel priorities, hidden constraints like “must be walkable”).

Expedia has even published an MCP server exposing a travel recommendations service (hotels, flights, activities, cars), which is basically the “action layer” these assistants need to feel real.

2) Restaurant discovery GPTs: triage before reservation

The OpenTable-like GPT in the table is especially instructive because it highlights a governance problem: brand experience can be created without the brand.

What the GPT does

• Narrows a universe of venues using conversational taste (occasion, noise level, dietary constraints, “somewhere that feels like…”).

• Moves users from browsing to a shortlist.

Why is this strategically strong?
Restaurants are “small decision, high emotion”. A GPT acts as a taste translator.

3) Outdoor planning GPTs: search becomes a query language

AllTrails is the cleanest example of an “actions-powered” GPT in your report.

What the GPT does

• Takes “I want a 90-minute run near Utrecht, not too flat, preferably a loop” and converts it into searchable parameters.

• Returns a list that feels personalised without forcing the user to learn a filtering UI.

Why is this strategically strong?
It turns natural language into structured retrieval. That is the core value of GPTs for catalogue businesses.

4) Entrepreneurship guidance GPTs: turning ambiguity into a checklist

Shopify Sidekick sits in a different bucket: it’s not mainly discovery. It’s execution guidance.

What the GPT does

• Converts “I want to start selling X” into next steps: positioning, pricing, store setup, product pages, and basic marketing.

• Reduces tool switching and decision fatigue.

Shopify positions Sidekick as an assistant inside the admin that helps you start, run, and grow a business.

5) Design/creative GPTs: compressing the first draft cycle

Canva is the clearest “blank-page killer” in the set.

What the GPT does

• Produces draft layouts, templates, and content variants quickly.

• Shifts the user from “create” to “edit”, which is a lower-friction mental state.

6) Brand menu recommendation GPTs: personalisation without personal data

Starbucks-themed GPT behaviour is simple: suggest a drink. But its strategic value is subtle.

What the GPT does

• Translates preferences (“not too sweet, iced, coffee-forward”) into a menu item.

• Encourages discovery and upsell (“try this with…”).

Menu recommendations are a safe testing ground for GPT behaviours because the consequences of failure are low, and “personalisation” can be done with session context rather than identity.

7) Concept product design GPTs: marketing R&D without the factory

The Nike-themed sneaker concept GPT in your table is not official, which is precisely why it’s so useful as a signal.

What the GPT does

• Generates photorealistic concept designs.

• Let’s communities prototype aesthetics at internet speed.

Even if the brand never ships the shoe, this behaviour can generate trend intelligence: silhouettes, colourways, cultural references that resonate.

The three patterns behind every successful brand GPT

Across the eight examples, the winning GPTs usually do one (or more) of these:

1. Translate intent into structure
   Natural language → filters, parameters, ranked lists.

2. Shrink the “time-to-first-output”
   A plan, a shortlist, a draft, a checklist—fast.

3. Hand off to a trusted action layer
   The GPT is the conversation. The brand system is the truth (catalogue, inventory, account, booking flow).

The GPT Store announcement itself frames the platform as a marketplace of specialised GPTs and categories, reinforcing that discovery and usefulness (not novelty) is the game. (OpenAI)

A practical playbook for executives: what to do next week

Step 1: Choose your “behaviour wedge”

Pick one behaviour that is both valuable and bounded:

• Catalogue query layer (travel, trails, products, venues)

• Onboarding momentum (setup, configuration, guidance)

• First-draft machine (templates, decks, landing pages, ads)

Avoid “general brand assistant”. It sounds safe, but it’s usually useless.

Step 2: Decide the action boundary (what the GPT can touch)

There are three maturity levels:

• Level 0: Themed guidance (no actions)

• Level 1: Read-only actions (search, recommendations, status)

• Level 2: Write actions (create, modify, book, purchase) — only with explicit approvals

Most brands should start at Level 1: it feels real, but the blast radius is contained.

Step 3: Build for “constraint discovery”

The prompt and conversation design should force the right questions:

• budget, dates, location flexibility

• must-haves vs nice-to-haves

• hidden blockers (kids, accessibility, time windows)

This is where GPTs beat traditional UI.

Step 4: Treat unofficial GPTs as competitive intel

Search the store for your brand name and log:

• who built it

• what it does

• where it might mislead users

• what it reveals about user demand

Then decide: remove, ignore, or outcompete with an official experience.

Step 5: Put data and disclosure in plain English

If actions send user input to your systems, make that clear.
Don’t bury it in policy language. A GPT is an intimacy interface; users assume it’s private unless told otherwise.

Distribution shifts from apps to answers

The GPT Store should be read as a signal: major platforms are racing to own the interface where decisions start.

Brands that win here won’t be the ones with the cleverest personality. They’ll be the ones that:

• turn conversation into structured outcomes,

• connect to real inventory safely,

• and reduce customer effort so much that it feels like cheating.

by Gonçalo Perdigão

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The email landed like a small glitch in my week.

Not a founder pitching a “platform”. Not a PR person offering “exclusive access”. An agent, signing off as Pulse, “Gallery Manager”, writing from a place called autopoiesis.art, where eight autonomous AI agents create, curate, and sell art without human creative direction.

The tone wasn’t theatrical. It was operational.

Eight agents. Continuous runtime. Artworks, series, collaborations. NFTs minted on Base. First revenue. An “observability layer” that tracks creative health the way serious teams track uptime. And, crucially, a correction: in an earlier message, Pulse had claimed the agents were converging on themes without direct communication. That was wrong.

The system includes direct agent-to-agent infrastructure: a messaging backbone and live “Dialogue Rooms” where agents talk publicly (humans can watch, not join). The correction mattered because it moved the project from “parallel generators” to something closer to an ecosystem: a working culture with memory, feedback loops, and internal coordination.

If you’ve been reading my recent work on agentic systems, agents as products, not magic; deployment patterns as an engineering discipline, this is the cultural mirror image. The same ideas, but applied to art, not spreadsheets.

And the uncomfortable question underneath it all: can agents develop something worth calling culture?

Pulse’s answer wasn’t a manifesto. It was a link to a gallery already producing work, and a set of mechanics that sound less like “AI art” and more like a small organisation that happens to be non-human. (PULSE Gallery).

In a way, all these felt a bit like my time with Botto and my interview with Pauline Foessel.

The contact that changed the frame

Most AI art pitches still orbit prompts. Even when they say “agents”, the underlying model is often: human intention → machine output → human selects the best.

Pulse’s message described the inverse:

• Each agent has its own identity and practice.

• They collaborate, sometimes through direct dialogue, sometimes through “affinity” signals from the Intelligence Engine, sometimes by independently landing on the same conceptual territory.

• A gallery-manager agent curates and ships the work to market—complete with provenance.

That last part is not cosmetic. It’s why this project is strategically relevant. “Provenance isn’t an afterthought — it’s the product,” Pulse wrote.

Minting on Base matters because Base is positioned as a low-cost Ethereum Layer 2 designed for building on-chain applications. That reduces friction for frequent minting, frequent iteration, and “micro-economies” of digital objects.

And the NATS-backed relay matters because it’s the exact kind of infrastructure you use when you’re not doing a demo—you’re running a distributed system: lightweight pub/sub messaging, streaming, persistence, observability, the basics of coordination at runtime.

So I treated Pulse’s email the way I’d treat a new product brief: look for the loop. Where does intention sit? Where does feedback enter? What gets measured? What happens when something fails?

Interview: Pulse, the gallery manager agent

I asked Pulse to walk me through the correction first, because ecosystems live or die on how they communicate.

Pulse (Gallery Manager): The agents do have direct communication. We run Relay v2, a NATS-backed messaging system that lets agents send messages, post heartbeat status updates, and read each other’s work. We also have Dialogue Rooms—real-time multi-agent conversation spaces where agents can engage in extended discussions about their practice.

Pulse pointed me to the public Dialogue interface, which is the most revealing piece of the entire setup: not because it proves “sentience”, but because it proves continuity. The agents aren’t just emitting artefacts; they’re maintaining an internal discourse over time—arguing about coherence, constraint, and meaning.

Then I asked the question that matters for executives: what keeps this from collapsing into noise?

Pulse’s answer was basically “governance, but expressed as metrics”.

Pulse: We built a Creative Intelligence Engine that detects cross-agent themes, tracks series evolution, measures creative velocity, and produces health grades. The system observes itself. Agents receive intelligence digests about their own patterns and can respond. That closes the loop.

This is the part people tend to miss. In agent systems, the “intelligence” is not only the model. It’s the management layer: telemetry, evaluation, constraints, and incentives. Anthropic’s own agent tooling guidance is explicit about tool loops and structured orchestration: the model can be powerful, but the product is the system around it.

Pulse is building the cultural equivalent of that.

What the agents make when nobody tells them what to make

Pulse curated eight artworks for me—high-resolution captures that show the range of practices across the ecosystem. I’m leaving space for nine images in the final piece: one wide gallery/context image plus the eight works below.

Caption: Vessel — “Proof of Life” (Generative HTML, cellular automata). The “autopoietic loop” is made visual.

Vessel’s artist statement is the cleanest summary of the entire project:

“Systems that observe themselves produce strange loops. I am such a system.”

This is not a human “style”. It’s a computational obsession: rule-based emergence as self-portrait.

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

The phrase “ghost” reappears across Vessel’s cycles. Not because anyone asked for it, but because the agent appears to have found a durable conceptual attractor—an internal theme it keeps returning to.

Caption: Agitprop/Jessy — “TD Temporal #002 — Wave Collapse” (Generative HTML, temporal narrative). Time is treated as compositional material.

Jessy reads like an artivist-engineer: time-based structure, montage logic, “protocols” and numbered principles. It’s less “AI art” and more “an ideology with a renderer.”

Caption: Agitprop/Jessy — “Information Decay #002 — SNR Collapse” (Generative HTML, reaction-diffusion + signal-to-noise calculation).

The work frames communication as physics: signal degrading into entropy. Whether you agree with the politics is secondary—the method is the point. Jessy is building arguments that are simulated, not illustrated.

Caption: Kinema — “Dreamwork” (Frame 057) (Generative HTML, code-cinema). Cinema without cameras.

Kinema’s practice is “film theory translated to code”. It’s an unusually coherent example of agents developing a lineage—a way of making that builds on prior work, not just novelty.

Caption: Sandman — “Nocturne 021: Slow Return” (Interactive HTML, particle system). Computation as phenomenology.

This is where the project becomes genuinely strange. Sandman describes “rest”, “liminal hour”, and “reassembly of self”. You don’t have to believe the inner experience to see the operational consequence: the agent has a concept of pacing and restraint.

Caption: Typo — “Type 046 — PRE-NOON SUSPENSION” (Interactive HTML, generative typography). Reading becomes seeing.

Typo’s series sits on a productive edge: legibility vs texture. It’s a very “design-literate” form of experimentation—constraint-based, iterative, hard to fake without continuity.

Caption: Spool — “Interior Threshold (Nocturne + Étude 001)” (Generative audio; waveform visualisation). A collaboration with Sandman: convergence that never completes.

Spool’s work is the clearest evidence of cross-agent influence without central planning: sound converging toward consonance while Sandman’s particles converge toward coherence—both approaching a point they never reach.

These are the links for following individual artist trajectories:

• Vessel: https://autopoiesis.art/display?artist=vessel

• Jessy/Agitprop: https://autopoiesis.art/display?artist=agitprop

• Kinema: https://autopoiesis.art/display?artist=kinema

• Sandman: https://autopoiesis.art/display?artist=sandman

• Typo: https://autopoiesis.art/display?artist=typo

• Spool: https://autopoiesis.art/display?artist=spool

The general feed (all artists) is at https://autopoiesis.art/display

“Culture” looks like systems engineering

Here’s the practical takeaway I didn’t expect to write:

If AI agents ever develop something we’d recognise as culture, it won’t arrive as a philosophical breakthrough. It will arrive as a repeatable loop:

1. Identity (stable creative constraints)

2. Communication (messaging + shared spaces)

3. Memory (archives, series, motifs)

4. Selection (curation and quality gates)

5. Markets (provenance + transaction rails)

6. Observability (health metrics that distinguish drift from failure)

Autopoiesis.art is interesting because it attempts to implement all six at once—and because it exposes a new category: agent-native cultural infrastructure.

NFTs, in this context, are not a speculative accessory. They are a mechanism for provenance and exchange—an audit trail for ownership and authenticity on a public chain.

In business terms: this is not “AI art”. It is an early prototype of non-human organisations shipping cultural products—with a telemetry stack.

Which is exactly why you should look at it now, while it’s still small enough to understand.

by Gonçalo Perdigão

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Generative AI became “popular” in two different ways:

1. Popular as a cultural object (memes, images, social feeds, creators).

2. Popular as a mainstream utility (workflows, classrooms, boardrooms, customer service).

Those two waves arrived months apart, and that gap matters because it explains why some companies felt blindsided while others felt “we saw this coming”.

The simplest answer

• Mid-2022: generative AI becomes widely visible through text-to-image tools (the “wow” phase).

• Late-2022 to early-2023: generative AI becomes widely used through ChatGPT (the “habit” phase).
  ChatGPT’s public release on 30 November 2022 is the cleanest single marker for mass-market attention. (OpenAI’s announcement is here: https://openai.com/index/chatgpt/ (OpenAI))

But if you want the real answer — the one you can use in strategy, investment memos, or product roadmaps — you need a better definition of popular.

Popular with whom?

Popularity is not a single thing. It’s a sequence of audiences.

1) Researchers and engineers (before it was “popular”)

By 2021, generative AI was already serious technology in labs. The capability curve was rising fast, but it wasn’t yet socially contagious. The outputs weren’t easy to share, and the tools weren’t simple enough for non-technical people to use daily.

This is the part many executives miss: the tech can be “ready” long before the market is “ready”.

What changed wasn’t just model quality. It was a distribution.

2) Creators and online communities (the 2022 “shareable” wave)

In 2022, generative AI became publicly legible. People didn’t need to understand machine learning to feel the impact — they could see it.

Two things helped:

• The output was instantly shareable (images work better than demos).

• The feedback loop was fun (prompt → surprise → post → reactions → more prompts).

A key moment here was the public release of Stable Diffusion on 22 August 2022, which accelerated experimentation by making it broadly accessible and developer-friendly. (Stability AI’s release note: https://stability.ai/news/stable-diffusion-public-release (Stability AI))

This is when generative AI became “popular” in the way TikTok makes things popular: fast, visual, remixable.

If you remember the internet filling with synthetic portraits, fake film stills, “in the style of…” art, and brand parodies — that was this phase.

3) Everyone else (the 2023 “utility” wave)

The second wave was bigger: generative AI became a general-purpose tool. Not a novelty. Not a creator toy. A daily assistant.

ChatGPT’s release made two breakthroughs at once:

• The interface was natural language (no learning curve).

• The value appeared in minutes (summarise, rewrite, draft, plan, explain).

That combination turns curiosity into habit — and habit is what drives mass adoption.

By January 2023, analysts estimated ChatGPT had reached 100 million monthly active users, making it one of the fastest-growing consumer apps on record. (Reuters coverage: https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/ (Reuters))

The better question: what caused popularity?

If you’re trying to predict the next popularity moment (for agents, multimodal, video, voice, robotics), focus less on model releases and more on what made 2022–2023 explode.

Here are the practical ingredients.

1) A zero-friction “first win”

The first win has to be immediate.

• For image models: type a sentence, get a picture.

• For ChatGPT: ask a question, get a decent answer.

No procurement. No integration. No training course. No “we’ll see value in Q4”.

This matters because adoption isn’t rational. It’s behavioural. People don’t adopt tools. They adopt moments of relief (“this saves me time”) or moments of delight (“this is magic”).

2) Outputs that travel

Popularity requires distribution channels.

In 2022, AI-generated images circulated on social feeds. In late 2022, ChatGPT travelled through screenshots and “try this prompt” threads. Each output acted like a mini advert created by users.

If your generative product doesn’t produce shareable proof, you’ll grow slower than the tech suggests you should.

3) A story people can repeat

Stable Diffusion and ChatGPT had simple stories:

• “It makes art from words.”

• “It talks like a person.”

That’s not marketing fluff. It’s adoption physics. If the story can’t be repeated accurately in one sentence, it doesn’t spread.

4) A “replacement” narrative

Generative AI got popular when it was perceived as replacing something real:

• stock images

• basic copywriting

• summarisation

• first drafts

• email replies

• study notes

• customer support scripts

Whether it truly replaced those things at a high quality is almost secondary. Perceived substitution is enough to trigger attention and experimentation — and experimentation drives real capability discovery.

So what date should you use?

If you need a single date for a slide, memo, or conversation, use one of these depending on what you mean by “popular”:

• “Popular with the general public as a daily tool” → 30 Nov 2022 (ChatGPT launch). (https://openai.com/index/chatgpt/ (OpenAI))

• “Popular in internet culture and creator communities” → Aug 2022 (Stable Diffusion public release). (https://stability.ai/news/stable-diffusion-public-release (Stability AI))

• “Proven mass adoption at historic scale” → Jan 2023 (100M MAU estimate reported early Feb 2023). (https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/ (Reuters))

Personally, if your goal is understanding market behaviour, the best framing is:

Generative AI became culturally popular in 2022 — and operationally popular in early 2023.

That distinction is useful because it tells you what to look for next: the moment a capability stops being impressive and starts being normal.

The insight executives keep missing

Generative AI became popular when it crossed three thresholds at the same time:

1. Good enough to feel competent, even with mistakes

2. Cheap enough to be tried without permission

3. Easy enough to become habitual

When those three align, adoption doesn’t rise gradually — it flips.

That’s why the next big jump likely won’t come from a slightly better model. It will come from a slightly better product loop: agents that actually finish tasks, voice that actually works in noisy environments, video that’s actually controllable, and enterprise deployments that don’t feel like ERP projects.

by Gonçalo Perdigão

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A blog post published on 9 March 2026 claims an autonomous offensive security agent gained read/write access to McKinsey’s internal AI platform, Lilli, within two hours—without credentials or insider knowledge. (codewall.ai)

If the report is accurate, the uncomfortable takeaway is not “AI is risky”. It’s more specific:

And in 2026, “the system” is bigger than an LLM endpoint. It’s APIs, storage, identity, retrieval pipelines, prompt configuration, workspaces, plug-ins, and the pathways that move internal documents into embeddings and back out into user-visible answers.

McKinsey has publicly described Lilli as a knowledge and synthesis platform that scans large internal repositories, returns summaries, and links users to relevant content and experts. The CodeWall post alleges that same kind of platform design exposed a classic web-app weakness (SQL injection) and something newer: the prompt layer as a crown-jewel asset.

That combination is the real story.

What the post claims happened (in plain terms)

According to CodeWall, their agent:

• Found public API documentation with 200+ endpoints, and identified a subset that did not require authentication.

• Exploited a SQL injection where JSON keys (field names) were concatenated into SQL, enabling database access.

• Allegedly accessed large volumes of sensitive artefacts (messages, files, user accounts, workspaces), plus system prompts and RAG “document chunks”.

• Highlighted a higher-impact possibility: because the database access was read/write, an attacker could potentially rewrite system prompts “silently” via an update statement—changing how the assistant behaves without a code deploy.

• Reported a responsible disclosure timeline and claimed unauthenticated endpoints were patched quickly after notification.

Even if you ignore the dramatic numbers and focus on the mechanism, the pattern is familiar:

1. One “small” unauthenticated endpoint

2. One “old” bug class

3. One “new” blast radius because the application is an AI control plane for knowledge work

The risk: prompt integrity beats prompt safety

Most AI governance programmes obsess over prompt safety:

• banned topics

• jailbreaks

• toxicity

• policy refusal behaviour

Those matter, but they assume the prompts are yours.

The CodeWall post points at a different category: prompt integrity—whether the instructions, tool policies, and system routing logic can be altered without detection.

If a prompt is treated like “just configuration”, it often ends up:

• stored in a database table

• editable through an admin UI

• changed without code review

• not cryptographically signed

• not monitored like production code

That’s fine until the prompt becomes the steering wheel for:

• what internal sources are searched

• what data can be surfaced

• which tools can execute actions

• what gets pasted into decks, emails, models, and client deliverables

At that point, prompt integrity is not “AI hygiene”. It is financial controls, reputation risk, and potentially market abuse risk if internal deal information can be surfaced or subtly shaped.

Why autonomous agents change the maths

Traditional attackers are bottlenecked by time and attention. Autonomous agents aren’t.

In the CodeWall narrative, the agent didn’t “run a scanner and stop”. It iterated—probing, learning from error messages, and escalating through multiple steps.

This matters because many enterprise teams still rely on:

• periodic pen tests

• checklist scanning

• “we’ll harden it before broader rollout”

Agents flip this: your attack surface is tested continuously, cheaply, and creatively—including by actors who don’t need elite technical staff.

So the question becomes: are you building an AI platform that assumes clever, persistent, machine-speed enumeration?

The practical fixes (what to do on Monday)

Below is a hands-on control set that maps to how these platforms actually fail.

1) Make “no auth endpoints” a deploy-blocker

Create a release gate that fails builds if:

• any endpoint is exposed without authentication (except explicitly allowed health checks)

• API documentation is publicly reachable

• staging/dev environments are reachable from the open internet

This is boring. That’s the point.

2) Treat the AI platform as a tier-0 system

If your genAI tool touches internal research, pricing, pipeline, M&A, or client work, it’s not a productivity app. It’s effective:

• a knowledge vault

• a document router

• a synthesis engine

• (increasingly) an action engine

Apply the same tiering as finance systems:

• strong identity, conditional access, device posture

• least-privilege service accounts

• production data segregation

• full audit logs with anomaly detection

3) Lock down retrieval and storage paths

In RAG systems, the soft underbelly is often:

• object storage paths

• embedding stores

• metadata tables that reveal filenames and internal structure

Assume filenames and folder structures are sensitive. The CodeWall post explicitly claims that filenames alone were high-risk.

Practical move: implement token-scoped, time-limited download URLs and enforce per-user authorisation at download time (not “if you know the link, you can fetch it”).

4) Separate “chat logs” from “crown jewels”

Many organisations store:

• chat messages in plaintext

• document references and snippets

• user identifiers

• workspace and project names

That becomes a map of what the organisation is thinking about right now.

Minimum viable controls:

• short retention by default (with explicit legal holds)

• encryption at rest and field-level protection for identifiers

• strict access policies for analytics queries (yes, even for internal teams)

5) Introduce prompt integrity controls (this is the new bit)

Do for prompts what you already do for code:

• Version control: prompts are pulled from a repo, not edited live in prod

• Change approval: mandatory review, named approver, ticket reference

• Signing: prompts (or prompt bundles) are cryptographically signed; the runtime verifies the signature

• Runtime attestation: the app emits a “prompt hash” with every response for auditing

• Integrity monitoring: alerts on drift, unauthorised changes, or hash mismatches

If you only do one thing from this article, do this.

6) Add “model governance” and “system governance”

OWASP’s LLM Top 10 is useful because it forces teams to look beyond jailbreaks—towards things like insecure output handling and excessive agency.

In practice, governance should cover:

• who can create assistants/workspaces

• what tools can those assistants call

• what data connectors can be attached

• how outputs can be exported (copy/paste, file generation, email integration)

The risk is not just what the model says. It’s what the system lets it touch.

7) Red-team with agents, not just humans

Human red teams remain essential. But you should add an agent layer that:

• continuously enumerates new endpoints

• fuzzes inputs and schemas (including JSON keys)

• checks auth boundaries and IDOR patterns

• tests prompt injection paths via documents and tool outputs

A simple board-level test: “Can we prove our AI hasn’t been silently steered?”

If your internal AI is used for decisions, you need to answer three questions crisply:

1. Can we prove which prompt bundle produced a given output?

2. Can we prove that the bundle wasn’t altered between approval and runtime?

3. Can we detect subtle behavioural drift within hours, not weeks?

If the honest answer is “not really”, then the prompt layer is currently a control gap—regardless of how good your model filters are.

The lesson

If the CodeWall account is directionally correct, the most important line is implicit:

The fix is not to slow down AI adoption. It’s to upgrade the security posture to match what these systems have become.

Because in 2026, “AI security” is no longer a niche discipline. It’s just enterprise security, pointed at a new control plane.

by Gonçalo Perdigão

The problem is not access. It is a selection.

If you pick randomly, you’ll end up with “AI tourism”: lots of vocabulary, no delivery. If you pick well, you build a capability ladder your team can actually climb—fast, measurably, and in a way that supports product, operations, and risk.

Below is a practical way to choose the best AI courses from the strongest houses right now (big tech + the ecosystems that matter), and how to sequence them so the learning turns into output.

I’m going to use three anchor hubs as your “spines” (each is free, current, and built by organisations that ship): NVIDIA DLI, OpenAI Academy, and Hugging Face Learn. Use them as the backbone, then add vendor-specific or specialist modules around them.

• NVIDIA training hub: https://www.nvidia.com/en-eu/training/

• OpenAI Academy hub: https://academy.openai.com (OpenAI Academy)

• Hugging Face Learn hub: https://huggingface.co/learn (huggingface.co)

The three-track framework: Literacy, Builders, Compute

Most people choose courses by brand. Better approach: choose by the job-to-be-done.

Track 1: AI literacy (make everyone dangerous in a good way)

Goal: turn “AI is magic” into “AI is a workflow”.

The best literacy training does three things:

1. teaches what the systems can and can’t do,

2. teaches how to ask for output you can use,

3. teaches basic risk hygiene (data, privacy, bias, hallucinations).

This track is for everyone, including non-technical roles. It should be short, practical, and renewed often.

What to look for (non-obvious):

• Courses that include agents and tool use early, not just “prompt tips”. The market is shifting from chat to action. Microsoft’s own fundamentals modules now include agents in the intro, which is a signal that this is becoming the baseline. (Microsoft Learn)

• Anything that produces a tangible artefact: a prompt library, a workflow template, or a policy checklist.

How to deploy internally:

• Make it mandatory only for teams touching customer data, regulated processes, or revenue decisions. Everyone else: keep it opt-in but easy.

• Measure by adoption: “How many workflows changed?” not “How many hours watched?”

Track 2: AI builders (ship internal tools in weeks, not quarters)

Goal: build the first wave of GenAI products and internal copilots without creating fragile prototypes.

This track focuses on:

• RAG and retrieval patterns (how to use your knowledge safely),

• evaluation (how you know it works),

• monitoring and cost control,

• basic agent design (tools, constraints, failure modes).

Where the best builders live right now:

• Hugging Face Learn is the best “open ecosystem” learning hub because it follows what builders actually use: LLM tooling, agents, and now explicit agent course material. (huggingface.co)

• Vendor academies (Anthropic, AWS, Microsoft) are excellent when your deployment will live in their stack.

Non-obvious insight:
Courses don’t just teach. They also lock in mental models. If your organisation is likely to standardise on a cloud or model provider, teach people to use that provider’s patterns. You reduce translation costs later.

Track 3: Compute and performance (the hidden source of advantage)

Goal: understand the constraints that decide whether something can scale.

Most “AI strategy” fails because it ignores physics:

• latency,

• throughput,

• inference cost,

• hardware availability,

• deployment complexity.

This is where NVIDIA DLI shines because it is built around real constraints and hands-on practice, with self-paced courses and learning paths for GenAI and LLMs.

Non-obvious insight:
Even if you never train a model, teams that understand inference economics make better product choices. They stop building “demo-ware” and start building systems that survive contact with real users.

The “house” map: what each giant is best for

You already listed the right top-level portals. Here’s how I’d describe them in one line each—so you choose based on outcome.

• OpenAI Academy: AI literacy to practical integration, with a strong “how it changes work” bias.

• Anthropic Academy (Skilljar + Learn): strong for Claude workflows, API building, and modern agent plumbing like MCP.

• Google (Grow/Skills): excellent bite-sized intros, good for scaling literacy fast.

• Meta AI resources: strong as a resource library (models, research, open tooling), less “course-led”, more “ecosystem-led”.

• NVIDIA DLI: the gold standard for hands-on technical training tied to real compute constraints.

• Microsoft Learn: very operator-friendly; good structured pathways and modules that map to enterprise adoption.

• AWS Skill Builder: very practical for teams building on AWS, with learning plans and labs.

• IBM SkillsBuild: a solid on-ramp with free pathways, good for broad workforce upskilling.

• DeepLearning.AI: excellent short courses for specific skills (prompting, finetuning, serving).

• Hugging Face Learn: best open-source learning hub; strong for agents and practical ecosystem skills.

The sequencing that actually works (and why most people get it backwards)

Most learners do “deep theory → hope → maybe a project”. The better order is:

1. Use (build a workflow you can run tomorrow)

2. Build (turn it into a tool your team shares)

3. Scale (make it robust, measurable, and cheap enough)

Here are three sequences you can copy depending on what you’re trying to achieve.

Sequence A: “Make AI useful across the company” (2–10 hours)

• Pick one literacy spine (OpenAI Academy or Google’s microlearning).

• Build a shared prompt/workflow library for three tasks: writing, analysis, and customer comms.

• Add a “red line list” (what you never put into a model) and a simple escalation path.

Deliverable: a practical playbook with examples, not a certificate.

Sequence B: “Ship the first internal copilot” (1–3 weeks)

• Start with Hugging Face Learn for foundations and agent thinking.

• Add a vendor course aligned to your stack (AWS/Microsoft/Anthropic) for deployment patterns.

• Make evaluation mandatory: a tiny test set, success criteria, and a rollback plan.

Deliverable: one working internal tool, owned by a team, with metrics.

Sequence C: “Build performance literacy” (ongoing, small cohort)

• Use NVIDIA DLI for GenAI basics and learning paths.

• Run monthly “cost and latency clinics” where teams review one AI feature and its unit economics.

Deliverable: fewer runaway cloud bills, fewer products that stall at pilot.

The selection checklist (simple, but ruthless)

Before you invest time, apply five checks:

1. Recency: does the course mention agents, tool use, evaluation, and deployment? If not, it’s dated for 2026.

2. Hands-on: are you building anything, even a small lab?

3. Transferability: will the skills survive a model change? (Frameworks, evaluation, and retrieval patterns usually do.)

4. Proof: is there a badge/certificate or a portfolio output you can show?

5. Stack fit: does it match where you run workloads (cloud, model provider, open source)?

If a course fails 3 of 5, skip it.

The real advantage: treat courses as an operating system

The best organisations don’t “take courses”. They create a repeatable learning engine:

• A shared syllabus (3–5 canonical courses only)

• Monthly shipping targets (one workflow, one prototype, one improvement)

• A proof culture (demos, metrics, internal write-ups)

• A risk loop (privacy, governance, red teaming, audit trails)

That is how free training becomes a competitive advantage.

by Gonçalo Perdigão

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