Olivier Toubia is a Columbia Business School professor working at the frontier of AI, marketing, consumer behaviour, and digital twins. This was recorded late last year but his arguments are relevant and clear: LLMs drive towards the average which means its hard to mimic the diversity of human existence.

• Digital twins are promising — but not ready. Synthetic consumers for research purposes (e.g. virtual focus groups) are fast and cheap, but Toubia’s research suggests they are still too biased and inconsistent to trust too far. Learn what happened when they compared digital and human twins' responses to the same questions.

• Politics is the worst domain to deploy synthetic focus groups: The base model exerts a powerful pull. Twins often reflect the worldview of the model as much as the individual they are meant to represent, especially in politics, where performance is weakest. AI is too sensible - compared to humans it tends to be more moderate: in one study 45% of humans wanted to deport illegal immigrants and only 5% of digital twins agreed.

• Synthetic personalities reflect a clear pro-tech bias. Twins tend to be more comfortable with algorithms, less concerned about privacy, and more trusting of technology than humans actually are. AI twins thinks AI is smarter than humans....

• Generative AI has entered its monetisation phase with advertising as the likely endgame Subscriptions and enterprise sales matter, but the bigger prize is becoming the interface for search, discovery, decision-making, and purchase.

• Business adoption remains uneven but labour anxiety is rational Many pilots are failing to deliver meaningful returns; senior leaders are often enthusiastic, while middle managers and employees are more sceptical.

• AI can be creative, but not at the edges. AI tools can recombine ideas - "its a fallacy to think that AI will never be able create new things". However, it still misses the diversity of lived experience that gives humans their edge. The push is towards the average.

We usually worry about AI driving polarisation - Olivier's work shows that current LLM tools trend towards the opposite. Humans can provide the spikiness and AI the padding.

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Cristina Caffarra is an eminent economist and veteran antitrust practitioner who has become a leading voice behind Eurostack — a grassroots, industry-led push to rebuild Europe’s digital infrastructure as a sovereignty and competitiveness play. She has a strong argument to make:

• Europe is a “digital colony” — and it’s self-inflicted. US hyperscalers are excellent; Europe vacated the field through fragmentation, weak risk capital, and complacency.

• The "kill switch" is a distraction; dependency is the disease. The real risk is gradual denial: deprecated features, constrained access, and strategic leverage — not a Hollywood blockbuster blackout.

• Productivity is the core argument. Europe’s gap is investment per worker, especially into high-growth tech that diffuses across the whole economy.

• Regulation can’t create an industry. Antitrust and platform rules “nibble at the corners”, take years, and leave the giants stronger — while absorbing all the political oxygen.

• Europe chose theatre over building. “Taming Big Tech” became a substitute for the only question that mattered: where are Europe’s builders, customers, and scale-ups?

• Demand is the lever, not more grants. Without customers, no stack survives — procurement and enterprise buying decisions are the flywheel.

• Procurement should be the no-brainer. Every other major power has local preference norms; Europe’s non-discrimination logic is now being weaponised against European options. She notes that even the European Commission's own CIOs focuses on performance and efficiency alone.

• Private enterprise is the real swing voter. Public sector is ~20% of demand; the other 80% sits with CEOs and CIOs who complain about European weakness — and then buy American.

• European tech can compete cost — but not ease of use. European components exist; what’s missing is end-to-end “peace of mind” and the glue between parts.

• Mercedes is the case study. They want sensitive AI loads (e.g. autonomous driving) not wholly dependent on US infrastructure — but need suppliers and buyers to co-design workable, integrated alternatives.

• She argues that no one wants to “decouple” form the US — that’s a straw man. The practical goal is share: move European supply from <20% to something like 30–40% in a growing market.

• This is wartime logic, not business-as-usual. Europe has surged before under pressure; Caffarra argues that its time to stop waiting for Brussels and to start acting like a superpower.

She leaves us with a blunt challenge: will Europe keep buying convenience — or invest, through demand, in a tech stack that keeps its innovation future in its own hands?

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Sangeet Choudary is the best-selling co-author of Platform Revolution and the author of the new book Reshuffle. He has advised CEOs at more than 40 Fortune 500 companies and is currently a Senior Fellow at the University of California, Berkeley.

• The SaaS crash isn’t cyclical — it’s structural. AI is eroding seat-based pricing, collapsing product boundaries, and destroying the old logic of defensible SaaS moats.
• AI doesn’t just change tasks — it rewrites value. Focusing on “automation vs augmentation” misses the point; AI reshapes whole systems of work, competition, and advantage.
• Translation is the real superpower of AI. By collapsing the cost of translating across silos, AI enables coordination without standards, APIs, or shared workflows.
• Moats built on customer understanding are dissolving. When users can no-code, extend, or bypass tools — and adjacent platforms can invade workflows — retention logic breaks.
• The decisive divide is above vs below the algorithm. Those who design and own learning systems capture capital-like returns; those whose knowledge is absorbed become commoditised labour.
• Platforms don’t just intermediate — they absorb learning. Campaign managers, drivers, and operators trained the systems that ultimately priced them out.
• Firms must be re-designed, not AI-enabled. Automating existing workflows locks in obsolete constraints; the real prize is questioning why the workflow exists at all.
• The future firm is modular — but selectively integrated. AI makes context exportable, pushing work outside the firm, while pulling learning-critical activities tightly inside.
• In physical AI, learning beats scale. The advantage isn’t owning assets — it’s owning the feedback loops that reveal how complex systems actually behave (the Tesla lesson).
• Nations compete on where learning compounds. The US bets on intelligence concentration, China on model commoditisation plus execution, India on open standards — while Europe risks playing defence in a power game.

He leaves us with a lingering question: are you above or below the algorithm?

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