Ray Eitel-Porter led Accenture’s global responsible AI practice and co-authored Governing the Machine. This episode asks what happens when AI moves from giving answers to taking actions — and why AI Governance may yet become the cornerstone of management in the Age of Intelligence.

Chatbots make mistakes; agents do things. Today’s AI can hallucinate, but Agentic AI spends money, updates systems, contacts third parties and triggers workflows. When it fails, it will fail at speed with potentially huge consequences.

A company-destroying AI accident is plausible. Ray does not dismiss the risk of a mid-sized business being badly damaged by an AI agent running loose — deleting records, spending cash, exposing data or creating operational chaos before anyone notices.

“Human in the loop” is not a magic shield. Humans only help if they understand the system, stay alert and have authority to intervene. The more accurate AI becomes, the easier it is to over-trust it.

If AI is right 99% of the time who stays awake for the 1%? The smarter the AI, the more dangerous complacency becomes. Ray highlights the example of “cognitive speed bumps” — deliberate pauses that force people to question the machine rather than simply approve its output.

Big firms are waking up; smaller firms may be exposed. Regulation, reputational risk and financial losses are pushing large corporates towards better AI governance. Mid-sized companies may adopt powerful tools faster than they build controls.

AI regulation will probably be sector by sector. Healthcare, finance, recruitment and public services need different rules because AI risk depends on context. One global rulebook is unlikely.

The best governance uses existing business systems. Do not bolt on a new AI bureaucracy. Review procurement, compliance, risk, legal and operational controls with an AI lens.

Accountability should sit with the business owner. Not just IT, legal or the vendor. The executive who wants the AI system and signs off the business case should own both the upside and the risk.

Boards need to ask three questions. Who is accountable? Do we know where AI is being used? Have people been trained well enough to use it safely and productively?

Training is where many rollouts fail. Generic Copilot training is not enough. The value comes when teams redesign real work around AI, not when they learn a few prompts.

Governance may become a source of value. In a world where machines produce more of the work, customers will pay for assurance: that outputs have been checked, systems are controlled and someone trustworthy stands behind them.

The more we worry about the technology of AI, the more we come back to people. Ray’s message is cautiously optimistic: AI governance is possible. But the weak link may yet be the humans.

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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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Helen Toner, whose decade of work on AI Safety came in to prominence when she was the OpenAI Board member who led the revolt against Sam Altman, and Emelia Probasco, who covers the national securty angles of AI both now work at the Centre for Security and Emerging Technology (CSET).

They join the podcast to discuss the security issues around AI with the conversation ranging from their take on the China / US race, the role of allies, alternative paths for the technology and the "AI Adulting Problem".

We discuss the challenges around AI as a dual use technology. As a general purpose technology it is impossible to control what happens next. This can cut both ways - drones optimised for warfare can deliver humanitarian aid with great precision.

The key is to keep talking - and we discuss the current state of diplomacy around AI, why the US needs allies and how the worriers need to better articulate their concerns if we want to solve them.

We also touched on the alternative - how AI is being deployed in the military, why the existing rules of war matter, the challenges of deploying AI in legacy organisations (and with legacy weapons systems).

Helen and Emelia bring real insight from working on some of the hardest problems from where national security meets the transformational power of AI.

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In this episode, we sit down with Karen Hao—award-winning journalist, MIT-trained engineer, TIME 100 AI honoree, and author of the best-seller Empire of AI—to unpack the power structures shaping today’s AI, business, and competition.

She has a clear and punchy point of view. In our conversation we ask:

- Is the Silicon Valley–Wall Street model the only way forward, or others can chart a better path?

- Do paradigm shifts like AI inevitably create harmful externalities—hidden data workers, energy and water shortages—just as the Industrial Revolution created pollution and child labor?

- Are today’s billion-dollar AI investments solving big problems—or just fuelling hype?

- Are we consciously shaping AI—or sleepwalking in “a dream made in the Valley”?

Karen argues that today’s AI giants act as modern empires: claiming resources that aren’t theirs, exploiting vulnerable labour, monopolizing knowledge, and sustaining themselves with a narrative of existential competition. She shows how many tools we use were born from subjective and often ad-hoc decisions—like scraping even the murkiest corners of the internet—despite harmful downstream effects.

She is critical of current regulatory efforts, weakened by Big Tech lobbying and points to real alternatives. She warns of an inevitable financial bust—investments wildly outpacing business value. But her message is ultimately hopeful: beyond the Big Labs, smaller teams are building AI for healthcare and the environment, showing that another path is possible.

Her call to action is clear: we must not sleepwalk into a future shaped by a self-serviing AI elite. We need to collectively imagine alternatives, and build a more balanced future. As Karen claims, something better is achievable: "The shape of a technology is never inevitable: there are many, many human choices."

Note: This podcasst was recorded as part of an INSEAD alumni webinar. Live questions were submitted by the audience.

Empire of AI book link.

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Saif Al Salman is Microsoft's National Technology Officer in the United Arab Emirates where "AI is a national bet for the country for the next 50 years."

Saif has a front row seat to their sustained push: from appointing the first AI MInister to the recent promise to invest $1.4 trillion in US-aligned AI technology and infrastucture. The country has invested across multiple fronts ranging from talent to infrastructure to new local language models. AI strategy has become national strategy as they deploy their huge legacy capital stock (and solar power) to pivot in to the business of selling compute (or tokens). This national transformation is projected to grow the economy by over 20% in the coming years.

And their strength means that they are now positioned to deploy AI as "soft power". Saif outlines how UAE is offering Africa support with local language models, infrastructure and "blueprint for how tech can drive inclusive growth."

Saif argues that nations must "dream big" if they want to prosper in the Age of Intelligence. Technology is moving fast and new powers are rising - are you ready for this new world?

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"I want to give people the map ... what you do with it is up to you." Demba Ba has a 5-year ambition to cut open the AI Black Box and explain what makes LLMs work.

Demba Ba, the Harvard Professor described as an "AI Neurosurgeon", shows why explainability lies at the heart of humans taking control of AI's alien intelligence. And if we get this right then we will also gain huge insight - "a neurosurgeon's dream" - in to not only how AI but also humans think.

The conversartion ranges from the immediate opportunities from healthcare roll-out and AI audit (why did the AI make that decision?) to the potential power this could give the state to manipulate people. And if we can understand and predict AI behaviour, and do the same with people, what does this tell us about free will, or are we all just products of our training data? If we can create individual human knowledge graphs - building the human model - then does this mean we can predict an individual's actions? What are the implications for AI tweaking - does lobotomy (or AI dissection and editing) have a wider impact?

We also discussed the huge AI opportunity for Africa - potentially one of the biggest winners in the coming Age of Intelligence.

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