Brett Schklar is a technology expert known for helping organisations move beyond AI hype and focus on measurable business value. As CEO of AI-First Leadership and author of AI Without the BS, he works with senior leaders to build practical frameworks that turn experimentation into execution. His approach centres on governance, culture and return on AI rather than surface-level adoption.

As a technology speaker addressing global audiences, Brett challenges the assumption that deploying generative AI guarantees competitive advantage. Instead, he urges businesses to scrutinise pilots, empower employees from the ground up and treat AI literacy as a strategic priority. His work focuses on sustainable transformation, incremental performance gains and leadership accountability.

In this exclusive interview with the London Keynote Speakers Agency, Brett discusses the myths that continue to shape AI decision-making, the structural changes organisations must put in place to drive growth and why confidence, not fear, will determine who achieves a genuine return on AI.

Where Are Leaders Most Often Misjudging Risk and Return on AI?

Brett Schklar: “There are a lot of AI myths that I talk about in the speeches, but one of the most important points is not actually a myth, it is a fact, that 95% of all new generative AI pilots fail. That is not a myth.

“There are other myths. AI is going to take us over. AI is replacing humans. There is some truth and some fiction to that.

“But the most important myth that needs to be overcome is that deploying AI is an automatic formula for success, and that if you are not doing it, you are missing out.

“Companies that are looking at technologies, evaluating them, and looking for that return on AI before they jump in will help reduce that 95% of AI initiatives that are failing.”

What Practical Strategies Should Organisations Adopt to Drive Growth Through AI?

Brett Schklar: “There are a couple of practical AI strategies for business growth that I help companies put together and organise. One is that this transformation needs to happen top down and bottom up at the same time.

“Employees need to feel empowered and autonomous to explore new technologies and capabilities that will help them in their role. At the same time, leadership, the CEOs and business leaders, need to give employees the freedom to look at innovation and technologies that can really help.

“This is not about getting 40% gains overnight or 50% gains overnight. It is about allowing every employee to get 1% better. Those small, incremental gains will continue to add up.

“The second big thing we must have in place as a strategy for leveraging AI for business growth is to build either a centre of excellence or a steering committee within the company, made up of cross-organisational functions and people who are most passionate about what AI can do for the business.”

How Is AI Reshaping Workplace Innovation in Practice?

Brett Schklar: “AI is doing a lot to drive workplace innovation. It is the essence of workplace innovation, but it is doing it in a way that is different from what we expect.

“A lot of companies think AI is going to create huge gains in a very short amount of time, 20% gains, 30% gains, more efficiency, better targeting, more growth.

“The reality is that AI helps drive businesses forward by empowering each employee to look at what AI can do in their job to get them 1%, 2% or 3% better.

“These small gains across an entire organisation are better than a large initiative forced from the top down, which can get stalled, slowed down and face resistance.”

What Core Message Do You Want Audiences to Take Away?

Brett Schklar: “My hope is that when people are in my sessions or in my keynote, they take away a couple of things.

“First, it is possible to overcome the fear of AI that has been ingrained in our brains since the early 1920s through Hollywood and the broader fear of AI. We can overcome that.

“Second, as employees build more confidence and comfort in these generative AI tools, the AI IQ, or AIQ, elevates across the organisation.

“If you remove the fear of AI and empower employees to ramp up their AIQ, then you are headed towards a really good formula for a return on AI.”

This exclusive interview with Brett Schklar was conducted by Tabish Ali of the Motivational Speakers Agency.

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European Business Magazine caught up with  Ruben Miessen, Co-Founder and CEO of legal tech startup, LegalFly to discuss company LEGALFLY positions itself as “agentic AI” rather than just a legal copilot.

In practical terms, what does that mean for an in-house legal team using the platform day-to-day — and why does that distinction matter commercially?

The distinction between legal co-pilot and agentic AI is important. The way we see it, a ‘legal co-pilot’ is something that has been trained to speak ‘legalese’ and is therefore limited to a very conversational interface. For example, these enable users to undertake simple Q&A-like use cases, which are relatively simple — therefore a legal co-pilot cannot be used to execute the legal work required by an in-house team.

It is our ambition to integrate LEGALFLY within the existing suite of tools used by internal legal teams, and to mimic their current workflows as precisely as we possibly can – something that a co-pilot cannot do.

We therefore position ourselves as ‘agentic AI’. We build workspaces within a setup of more than a dozen agents, which have each been developed in a unique way to mimic very specific legal tasks. We have an agent that is conversational and used for onboarding; agents that you can drag and drop a contract on and it will output a version with risks flagged on individual cards; a red lines / redraft class that you can then insert into a Word document to redraft any contract in a matter of seconds. This broad service is something that cannot be achieved with a co-pilot in a solely conversational interface. We’re doing this because we want to empower our in-house clients. LEGALFLY executes all the core legal work we can (about 80% of an in-house legal team’s work), enabling them to refocus their time on more strategic tasks.

In addition, we’re about to take this one step further. The Discovery agent (which is our conversational interface) acts as the intake of legal work, and whatever question you pose in Discovery, it will route it to all the other agents. Therefore, when you ask a question and insert a contract, Discovery will activate all agents and convert the contract into a red line document and a workflow which is no longer conversational – like Microsoft Co-pilot. We have also integrated with email: customers can send any email to LEGALFLY, for example forward a contract, and one minute later LEGALFLY will reply with a red line rewritten contract, based on your internal company policies.

Building features is handy, but ultimately this has a huge impact on company ROI by speeding up internal processes. A legal copilot is limited in its capabilities. For example, it might be able to offer first line legal support from an advisory perspective, but we didn’t want to build a solution which only advises. We wanted a product that gets things done, which is only possible through an agentic solution. We’re aiming for an ROI of at least 10x on the licence cost for the client, versus what they are saving in legal spend. More broadly, we’ve already witnessed a significant macro-shift in the market, where legal spend is being pulled away from law firms and ASLPs, and invested in legal tech solutions to make the internal team more efficient and enable them to handle a broader range of legal tasks, versus simply outsourcing it to a service provider.

In legal tech, data security and confidentiality are arguably more important than raw AI capability. How did you architect LegalFly to win trust from enterprise legal teams, and how does that differ from the way big US foundation-model vendors approach legal workflows?

There are two elements to my answer here: accuracy and legal depth, and data security.

First, on accuracy and legal depth: generalist solutions are good, but they have a high error rate – currently sat at 31% with solutions like Co-pilot or ChatGPT. So, whilst AI might be useful to carry out more creative work, you simply cannot take the risk in legal tasks.

The legal depth of the solution is extremely important. LEGALFLY is legally trained in 35 jurisdictions, with live access to over 250 government portals which feed the platform directly at the source, to track and incorporate any changes across the legal landscape which have been published by public and government agencies.

Whenever LEGALFLY provides advice, every answer is grounded in a reputable, authoritative source. This means that the legal professional using the tool can very easily verify sources within seconds by clicking on the link. On top of that we run a confidence check in the background, offering a secondary back-up from our internal knowledge base. If LEGALFLY is not confident, it will not offer any answer, or if unsure, will answer with a disclaimer which tells the user to seek further legal counsel. This is a crucial distinction from LLMs which currently may hallucinate answers to achieve a goal.

Second, on data security: one of our principal design cores is an anonymisation model that can even be deployed on premises. This model redacts all sensitive data from every single document or contract before an AI agent is connected – we’re the only provider globally that does this. This is one aspect which is highly sought after by sensitive industries and as a result, I’m very proud to say we’re working with several governments, including the government of Luxembourg. These are the type of client very concerned about security when deploying AI on their most sensitive documents. But thanks to our unique approach, we can help them feel comfortable around using AI in legal tasks.

We also care deeply about data sovereignty, so we offer a wide range of deployment options, including single tenancy which we host in every region, whether that’s mainland Europe, the UK, Middle East, or US.

You’ve built LegalFly in Belgium, with operations in London and Dubai, rather than San Francisco. What advantages — and constraints — come with building a legal-AI company in Europe, especially around regulation, data sovereignty, and enterprise sales?

Europe has a tendency toward hyper-regulation. This means it’s tougher to bring legal AI solutions to market in Europe, compared to the US. But the benefit is that as soon as we get it right here, then we can get it right anywhere, because Europe operates one of the strictest legal frameworks in the world.

As a Belgian company, we benefit from understanding complexity. We have six jurisdictions in one small country, and three official languages, which all need incorporating into training. An additional plus is that the European Commission is headquartered in Belgium, which gives us greater access to the legal system and legal policymakers in the region too.

From a commercial perspective, it can be tough. There are 27 member states of the European Union, all with different cultures, languages, jurisdictions in which we need to train LEGALFLY. Development comes with its own challenges, which in the short term slows us down versus a US company. But in the longer term, it puts us in a unique position. If you look at the clients we’re already selling to, they’re working across all those jurisdictions we already know by heart, so we’re building with that knowledge and those requirements taken into consideration. In terms of data sovereignty, we host data in the UK, Germany, UAE, and US, with the capability to do further deployment should that be required.

However, one key constraint is on fundraising, which is certainly tougher when building in the EU than the US. Thankfully, this hasn’t affected LEGALFLY. For us, VC was inbound for both our Seed and Series A rounds and continues to arrive.

When companies like Agristo or Duvel Moortgat deploy LegalFly, where do they see the fastest and biggest return on investment — cost reduction, risk management, deal velocity, or something else?

This depends on which team you are looking at: to expand, Legafly is indeed solely focused on working with in-house teams (96% of clients are in-house or public sector), but we’re not just selling to their legal teams. LEGALFLY works with Legal, Procurement, Compliance, and depending on the client, the Claims team. In most cases, at present, we’re working with Legal and Procurement teams, and in most cases, we work with both. But each team has its own requirements.

In Legal, the incentives are a little different: it’s usually purely about cost prediction, with significant reduction of reliance on the costly third-party legal counsel. If you’re asking the Procurement team, it’s mostly about deliverables; with other important considerations being deal velocity, cost reduction and reducing risk.

When we talk about cost reductions, it’s important to distinguish that it’s not about reducing the size of the legal team, because even in large organisations, internal legal teams are already rather small, operating with an intense amount of pressure. Instead, it’s about giving the in-house team independence from their legal counsel and shifting that legal budget spend from the law-firm to only spending a fraction on the AI. That’s not to say the law firm will be cut out entirely, as there will remain some specialised tasks, like litigation, but many other functions can be brought in-house.

With Microsoft, Google, and OpenAI all moving aggressively into legal and compliance workflows, what is LegalFly’s long-term moat — and how do you avoid being commoditised as “just another AI layer” inside Word and Outlook?

We have a strategic partnership with Microsoft, where clients can buy a LEGALFLY licence through Microsoft.

But why did Microsoft become interested in this partnership? We have all seen multinationals, such as Microsoft, add AI into every existing solution and product. They have achieved their success by being world-leading generalists. However, the one area where it would be difficult to sell a generalist solution is in legal. Microsoft’s existing product suite is not legally trained; plus, a legal co-pilot is not an agentic legal operating system, so it has zero capability to actually ‘do’ any legal work, besides advice — and even then, it may even hallucinate.

LEGALFLY’s ability to anonymise documents — especially sensitive documents — is also key to our success. Despite current behaviour, it remains unsafe to upload documents to ChatGPT or Co-pilot, particularly in a legal environment.

That is exactly why Microsoft has decided to partner with us, specifically for those Legal and Procurement teams. We’re definitely not just another AI layer. Inside Outlook we’re a legally verified solution to undertake legitimate legal tasks.

Do you see LegalFly remaining a best-in-class legal automation platform, or evolving into something closer to an AI operating system for corporate legal and compliance functions across Europe?

Last week, we launched V3 of LEGALFLY as an operating system. The more time we spent working with our clients’ amazing Procurement teams, the more we learned specifically about the processes and tasks we want to build an agent for.

We’ve now reached a point whereby we have built an agent for any task an in-house legal team is undertaking. As a result, our clients can spend an entire day within the LEGALFLY platform, so it essentially became a de facto legal operating system.

We have also ensured LEGALFLY is easy to use across platforms: it can be used as an agent, as a web platform, in Microsoft Word, to email, to Slack, through Teams. Therefore, we’re deeply integrated through any system on which our clients prefer to work.

Looking ahead, how do you think AI will change the structure of legal teams in large European companies — fewer lawyers, different skill sets, or simply much higher leverage per lawyer? And where does LegalFly fit into that future?

I don’t foresee a huge structural change for in-house, corporate legal teams. This is largely because they are already rather small, so there’s not a huge amount to change! To speak from experience — we meet with the world’s largest public institutions, airlines, construction companies, banks, insurance firms, and so on — the amount of legal work, and the number of risks that these small-and-mighty internal teams are defending the company against, is surprising!

So, whilst I don’t foresee a change for internal teams, I am certain that there will be a big change in the structure of law firms or ASLPs, where we expect junior hiring freezes, and slimmer law firms overall. This is because in-house teams will be powered by AI, putting a lot more work in-house, and therefore less money into the pockets of the law firms, which will significantly increase over time. Secondly, those law firms are becoming much more efficient, so you won’t need a full army of lawyers, even in the prestigious magic circle firms. It won’t make sense anymore.

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Quick Answer: Cristiano Ronaldo has invested $7.5 million for a 10% equity stake in HBL Pro2col Software, a Herbalife subsidiary that operates an AI-driven personalised wellness platform. The deal deepens a partnership that began in 2013 and marks Ronaldo’s second significant technology investment in three months, following his stake in AI search company Perplexity. Pro2col is currently in beta across the US, Canada, and Puerto Rico, with European expansion planned for later in 2026.

Cristiano Ronaldo has been Herbalife’s global nutrition partner for over a decade. Now he is a part-owner. The 41-year-old announced on 18 February that he had acquired a 10% equity stake in HBL Pro2col Software, an indirect subsidiary of Herbalife (NYSE: HLF) that houses the company’s next-generation digital wellness technology. The investment was worth $7.5 million — valuing the subsidiary at $75 million — and came alongside commitments to provide services and sponsorship rights to the platform.

Pro2col is Herbalife’s attempt to reinvent itself for an era in which nutrition advice is expected to be personalised, data-driven, and delivered through a screen. The platform uses individual health data — from user inputs, wearable devices, and DNA analysis — to build tailored wellness plans with daily habits and smart nutrition tracking. At its core is Pro2Score, a proprietary wellness scoring system that tracks progress across key metrics.

The technology did not originate inside Herbalife. The company acquired the underlying assets from Pro2col Health LLC and Pruvit Ventures in April 2025, paying milestone-linked contingency fees as the platform hit development targets. By December 2025, Pro2col Beta 2.0 had launched in the US, Canada, and Puerto Rico. Herbalife has confirmed plans to expand beta access to select European markets later this year.

Why Ronaldo, and Why Now

For Ronaldo, the deal extends a relationship that already includes the Herbalife24 CR7 Drive sports drink and years of brand ambassadorship. But it also fits a pattern that has accelerated sharply. In December 2025, he invested in Perplexity AI, the search engine startup valued at $20 billion and backed by Nvidia and Jeff Bezos. Before that, his portfolio had been largely confined to Portuguese ventures — hotels, gyms, a media business, and the 2024 acquisition of the Lisboa Racket Centre.

The Herbalife investment is different in character. It is not a brand deal repackaged as equity. At $7.5 million for 10%, Ronaldo is buying a meaningful stake in a specific technology at a specific valuation, with financial upside tied directly to Pro2col’s commercial success. If the platform scales across Herbalife’s network of two million distributors in more than 90 countries, that $75 million valuation could look conservative. If it stalls in beta, the investment remains a relatively modest outlay for a man whose net worth sits at approximately $1.4 billion.

Herbalife CEO Stephan Gratziani framed the deal as a milestone, describing Ronaldo’s decision to take an ownership stake as reflecting “a shared belief in the power of nutrition, data, AI, and personalised insights to drive better health outcomes.” Ronaldo, for his part, called the investment “a natural evolution” of a relationship built on trust and shared ambition.

Herbalife’s Bigger Bet

The Ronaldo announcement was timed to coincide with Herbalife’s fourth-quarter earnings, which beat guidance across key metrics. Revenue rose 6.3% year-on-year to $1.3 billion, exceeding the top end of the company’s own projections. Adjusted EBITDA came in at $156 million, also above expectations. The stock surged roughly 18% in the immediate aftermath, though analysts noted that much of the rally had been building throughout a 2025 in which shares gained over 90%.

The results revealed a company in transition. North America delivered its second consecutive quarter of double-digit new distributor growth, up 19% year-on-year. Latin America posted its seventh straight quarter of growth. But China — historically a critical market — declined 6% in local currency terms, with volume down 11%. Management said no recovery is expected before 2027.

Pro2col sits at the centre of Herbalife’s strategy to navigate this uneven landscape. The company is positioning itself as what Gratziani called “a more connected, data-driven health and wellness platform” — integrating products, community, AI, and digital tools to support its distributor model rather than replace it. The platform equips distributors with insights and engagement tools that Herbalife hopes will improve retention and attract a younger, more digitally native customer base.

Management guidance for 2026 is cautiously optimistic: revenue growth of 1% to 6%, with full-year EBITDA of $690 million at the midpoint. Notably, current projections include minimal revenue contribution from Pro2col — the company is treating the platform as upside rather than baking it into baseline expectations.

The Wider Trend

Ronaldo is not the only elite athlete moving capital into personalised health technology. But what distinguishes his approach is scale. With over one billion social media followers, his endorsement carries distribution power that most venture capital firms cannot replicate — and Herbalife, with its distributor-led model, is uniquely positioned to convert that visibility into platform adoption.

Whether Pro2col delivers on its promise remains to be seen. The personalised wellness space is crowded, and Herbalife’s direct-selling model carries baggage that some consumers will never look past. But with Ronaldo’s name, money, and image now directly tied to the platform’s success, the incentives are aligned in ways that a standard sponsorship deal never could achieve.

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With the deadline for mandatory electronic invoicing in France fast approaching, European Business spoke with Jason Kurtz, CEO of Basware during his visit to the country, including his vision for the use of Agentic AI and its impact on the Accounts Payable (AP) function.

Basware, which works with over 6,500 customers globally, including Mercedes and Heineken says it reported 20% year-over-year increase in sales in 2025, primarily driven by a surge in interest in AI-empowered products.

Q: Mandatory electronic invoicing will begin in France as of 1st September 2026. What challenges will businesses face in preparing for this deadline, and what are the consequences of failing to do so?

The deadline puts pressure on businesses to update their accounts payable and ERP processes, systems, and data to align with the new compliance rules to make sure every invoice meets the new e-invoicing standards.

Failing to comply will carry hefty costs – French authorities estimate they could recoup up to €3 billion annually from invoices previously subject to VAT fraud, late or incomplete payments and errors in reporting – and fines

For CFOs and finance teams, overhauling invoicing processing to meet the new standards isn’t just about avoiding penalties. It’s about protecting future revenue, reducing risk, and maintaining trust with regulators and stakeholders.

Q: Your recent report on AI to ROI suggests that finance leaders are under pressure ‘to do something’ with AI, but many are struggling to do so effectively. What role will AI play in helping French businesses meet the deadline?

Many leaders feel pressure to “do something” with AI, and the ones that get technology adoption right will be the ones who succeed. AI isn’t a magic wand and requires a clear strategy and governance to make a meaningful impact, anchored in areas where value can be proven and scaled – like AP. AI’s role is AP is threefold. First, it accelerates automation across invoice processing, procurement, and financial workflows, reducing manual intervention and enabling teams to scale without adding cost. Second, it improves decision quality by turning fragmented financial data into real-time insights, helping CFOs manage cash flow, risk, and supplier relationships more proactively. Third, and most important for deadline-driven initiatives, AI shortens implementation timelines by identifying process gaps, recommending optimizations, and continuously learning from transaction data to improve accuracy over time.

Q: The research also says that 72% of finance leaders see accounts payable (AP) as the most obvious starting point for agentic AI. How can Basware help in this area, and what might the future look like?

At the end of the day. AP is a data problem, and Basware is solving it with AI. Over the last 40 years, we’ve built the industry’s largest set of structured, high-quality AP data of over 2.5 billion invoices. And we’re applying AI trained on this data to deliver context-aware predictions and recommendations to finance teams so they can spend less time analyzing and more time deciding and acting. The future of finance involves Agentic Finance, where AI entities transact on behalf of the enterprise to drive faster, smarter decisions and real business outcomes. We accomplish through designing trust into the processes and AI capabilities – this is the future we are creating at Basware and preparing our customers for today.

Q: Should finance leaders fear an “AI Armageddon,” and what impact will artificial intelligence have on their jobs?

The “AI is coming for your jobs” narrative makes for good headlines, but we’ve heard this story before. Every major technological shift has sparked anxiety about widespread job loss. But the doomsday scenarios didn’t really play out. AI is one of the most disruptive technologies we’ve ever seen. And it’s certainly going to change the nature of work. But it isn’t going result in the wholesale replacement of people. What it will do is free us to do the work we want to do and make us better at it.

There’s historical precedent for the argument. The assembly line was going to kill manufacturing jobs. Instead, it turned skilled artisans into specialized machine operators. And it actually boosted overall factory employment.

The same scenario is playing out today in AP – an area that would seem ripe for AI to wipe out an entire category of jobs.  AI isn’t replacing AP teams, it’s replacing the manual tasks that bog them down so they can do work that is more strategic and meaningful. When they don’t need to spend hours chasing invoices and routing them for approval, AP teams can focus on things like cash flow optimization that impact the bottom line.  We’re designing a world where AI Agents will be trusted teammates that today’s AP clerks and managers will oversee.

At the end of the day. AI isn’t about getting rid of people. It’s about getting more out of the people you have by empowering them to be and do their best. The reality is, most companies that are eliminating jobs are not doing it because of AI. They either over hired or don’t have the right people in the right roles and are using AI as an excuse to right the ship.”

Jason Kurtz is CEO of Basware

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The fourth global AI summit convened this week at Bharat Mandapam in New Delhi — the largest gathering yet, the first hosted in the Global South, and by several measures the most politically charged since the series began at Bletchley Park in 2023. Over five days, more than 20 heads of state, 60 ministers, and the chief executives of virtually every major AI company on earth descended on India’s capital to talk about the future of a technology that is simultaneously generating record profits and record anxiety.

Sundar Pichai was there. So was Sam Altman. Dario Amodei of Anthropic. Mukesh Ambani. Rishi Sunak. Emmanuel Macron shared a stage with Narendra Modi. António Guterres addressed the hall. Bill Gates was supposed to speak but pulled out hours before his keynote, the Gates Foundation citing a desire to keep the focus on the summit’s priorities — though the timing, amid renewed scrutiny of his ties to Jeffrey Epstein, was noted by everyone in attendance.

It was, by any measure, a spectacle. More than 250,000 visitors. Over 300 exhibitors across a 70,000-square-metre expo. Delhi hotel suites that normally run at $2,200 a night were listed at $33,000. The Supreme Court issued a circular allowing advocates to appear by video link because of anticipated traffic gridlock. And India set a Guinness World Record for the most pledges received for an AI responsibility campaign in 24 hours — 250,946 of them.

But spectacle is not the same as substance. And for all the diplomatic language, carefully worded voluntary commitments and carefully staged photo opportunities, the most important words spoken at the India AI Impact Summit may have come not from a head of state or a Silicon Valley chief executive, but from the 32-year-old founder of a French AI company that most people outside the industry have never heard of.

Mensch’s Warning

Arthur Mensch, co-founder and chief executive of Mistral AI, took the stage on Thursday and said what many in the room were thinking but few were willing to say out loud.

“We are at risk today,” he told delegates. “We are facing too much concentration of power in artificial intelligence. We don’t want to be in a world where three or four enormous companies actually own the deployment and the making of AI — actually own access to information.”

It is not, on the surface, a novel observation. The dominance of a small number of American firms in frontier AI — OpenAI, Google DeepMind, Anthropic, Meta — has been a recurring theme at every global AI gathering since Bletchley. But Mensch was making a more specific and more uncomfortable point: that despite three years of summits, declarations and voluntary commitments, the concentration has only deepened.

Mistral, valued at nearly €12 billion, is Europe’s leading independent AI model builder. It is also a fraction of the size of its American competitors. OpenAI was last reported to be valued at over $850 billion. The US-based cloud providers — AWS, Google, Microsoft — are building out most of the infrastructure needed to power and run AI models globally. The asymmetry is not shrinking. It is accelerating.

Mensch’s argument went beyond market share. He warned that concentrated ownership of AI creates excessive geopolitical leverage — that countries and institutions which rely on a handful of foreign providers for their AI infrastructure are ceding something more fundamental than a technology contract. They are ceding sovereignty. “Everyone who runs AI workloads must have access to the turn-on and turn-off button,” he said. “They must not be dependent on external providers who can turn off the button.”

He called for a different path: decentralised AI, built on open-source models, owned and operated by the countries and institutions that use it. It was, unmistakably, a pitch for Mistral’s own approach. But it was also a challenge to every government in the room — and to the American companies sitting in the front rows.

The Gap Between Words and Action

The AI summit series was born in November 2023 at Bletchley Park, where the UK convened an urgent conversation about AI safety following the explosive growth of ChatGPT. That gathering produced the Bletchley Declaration — a statement signed by 28 countries acknowledging the risks of frontier AI. Seoul in 2024 followed with further voluntary commitments. Paris in 2025 was billed as an “Action Summit” that would move from promises to outcomes.

New Delhi was supposed to go further still, shifting focus from safety and governance to real-world impact — particularly for the developing world. Modi’s keynote introduced the MANAV vision (the Hindi word for “human”), a five-pillar framework covering ethics, accountability, sovereignty, accessibility and legitimacy. Macron praised India’s digital infrastructure as something “no other country in the world has built.” Guterres called on tech companies to support a $3 billion global fund to make computing power more affordable and AI skills more accessible, warning that the technology’s future “cannot be decided by a handful of countries — or left to the whims of a few billionaires.”

But the structural critique published by TechPolicy.Press cut to the heart of the problem. The summit’s architecture, it argued, granted multinational corporations parity with sovereign governments — through the CEO Roundtable and the Leaders’ Plenary — while providing no equivalent platform for civil society, labour leaders, or human rights defenders. The people most likely to be affected by AI’s disruption of work, privacy and public services had the least voice in shaping its governance.

And the US delegation, according to the same analysis, arrived with an agenda centred not on cooperation but on dominance — framing AI as a geopolitical race against China rather than a shared challenge requiring collective governance.

The Ethics Problem Nobody Solved

Four summits in, the fundamental ethical questions around AI remain largely unresolved. Who is responsible when an AI system causes harm? How should the economic value generated by AI be distributed? What rights do workers have as their roles are automated? How do you govern a technology that is developing faster than any regulatory framework can keep pace with?

The voluntary commitments that emerged from Delhi — the “New Delhi Frontier AI Impact Commitments” — are, like their predecessors from Bletchley, Seoul and Paris, non-binding. They rely on the goodwill of companies whose primary obligation is to their shareholders and whose competitive incentive is to move as fast as possible.

OpenAI’s Altman told the summit that regulation is needed “urgently.” But urgently by whose timeline? Altman has also argued that overly tight regulation could hold the US back in the AI race — a tension that captures the central contradiction of every global AI gathering: the companies calling for governance are the same companies whose market position depends on moving faster than governance can follow.

The deeper ethical challenge is structural. Less than one per cent of ChatGPT usage comes from low-income countries. AI adoption is overwhelmingly concentrated in wealthy nations and within those nations, in wealthy firms. The promise that AI will democratise access to knowledge and capability is, for now, running well behind the reality that it is entrenching existing advantages.

India’s bet — that it can lead through deployment rather than development, using AI to improve public services, agriculture and healthcare for 1.4 billion people — is the most ambitious attempt to challenge that pattern. Whether it succeeds will depend on whether the technology can be adapted to local languages, local needs and local infrastructure at a scale that justifies the rhetoric.

What Mensch Got Right

Mensch’s speech was self-interested. Mistral benefits directly from a world that values open-source AI and digital sovereignty. But self-interest does not make an argument wrong.

The concentration of AI power in a handful of American companies is not a theoretical risk. It is the current reality. And three years of summits have not altered it. The voluntary commitments have not slowed the consolidation. The declarations have not redistributed the compute. The speeches about inclusion have not changed who controls the models, the data or the infrastructure.

What Delhi demonstrated, perhaps more clearly than any previous summit, is the gap between the conversation the world is having about AI and the decisions that are actually shaping its trajectory. The conversation is about ethics, inclusion and shared prosperity. The decisions are being made in boardrooms in San Francisco, driven by competitive pressure, investor expectations and the logic of scale.

Mensch’s warning was not new. That is precisely what made it so damning. We have heard it before — at Bletchley, at Seoul, at Paris. And still, nothing has changed.

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Quick Answer: David Silver, the British AI researcher who led the creation of AlphaGo at Google DeepMind, is raising $1 billion for his London-based startup Ineffable Intelligence in what would be Europe’s largest seed round ever. Led by Sequoia Capital at a $4 billion pre-money valuation, the round has also attracted interest from Nvidia, Google and Microsoft. Silver believes large language models cannot achieve superintelligence and is betting on reinforcement learning — AI that teaches itself from scratch rather than learning from human data.

David Silver built the system that beat the best Go player on Earth. Now he wants to build the system that outthinks every human on every task. And he has persuaded some of the world’s most influential investors to fund the attempt.

Silver, one of Britain’s most celebrated AI researchers, is raising $1 billion for Ineffable Intelligence, a London-based startup he founded after leaving Google DeepMind late last year. The seed round, led by Sequoia Capital, would value the company at approximately $4 billion before the new investment — making it the largest first-round fundraise by a European startup in history, according to PitchBook.

Sequoia partners Alfred Lin and Sonya Huang flew to London to meet Silver personally. Nvidia, Google and Microsoft are also in talks to invest, though negotiations remain live and final terms could change.

The company has no product, no revenue and no public roadmap. What it has is a thesis — and a founder with a track record that makes investors willing to write billion-dollar cheques on conviction alone.

The Thesis: LLMs Are a Dead End

Silver’s core argument is that large language models — the architecture behind ChatGPT, Claude, Gemini and every major AI system in commercial use today — are fundamentally limited. They learn from human-generated data. They can synthesise, summarise and extend what humans have already written or thought. But they cannot, in Silver’s view, discover genuinely new knowledge.

This is not a marginal critique. It strikes at the foundation of the current AI industry, which has invested hundreds of billions of dollars in scaling transformer-based language models on the assumption that more data and more compute will eventually produce artificial general intelligence.

Silver disagrees. He believes that to reach superintelligence, AI systems will need to discard human knowledge entirely and learn from first principles — through trial, error and self-play, the way AlphaGo learned to play Go by competing against itself millions of times. The result was a system that made moves no human had ever conceived, some of which initially looked like mistakes but turned out to be brilliant.

Ineffable Intelligence aims to build what Silver has described as “an endlessly learning superintelligence that self-discovers the foundations of all knowledge.” The approach is rooted in reinforcement learning — the branch of AI Silver has spent his entire career advancing.

The Researcher

Silver was one of DeepMind’s first employees when the company was founded in 2010. He led the reinforcement learning group that produced AlphaGo, which defeated world champion Lee Sedol in 2016 in one of the defining moments in AI history. He subsequently led AlphaZero, which mastered chess, Go and shogi from scratch without any human training data, and MuZero, which learned to play Atari games without even being told the rules.

He holds a doctorate from the University of Alberta, where he studied under Richard Sutton, widely regarded as the father of reinforcement learning. He remains a professor at University College London.

Silver had been on sabbatical from DeepMind in the months before his departure and never formally returned. Ineffable Intelligence was incorporated in November 2025, and Silver was appointed director in January 2026. The company is actively recruiting AI researchers.

The Pattern

Silver is not alone in leaving Big Tech to pursue superintelligence independently. Ilya Sutskever, former chief scientist at OpenAI, founded Safe Superintelligence in 2024 and has raised $3 billion to date at a valuation that reached $32 billion by April 2025 — despite having no product. Jerry Tworek, who helped develop OpenAI’s reasoning models, recently left to found Core Automation.

The pattern is consistent: elite researchers who believe the current paradigm has limits are leaving to explore alternatives, and capital is following them at extraordinary speed and scale. Investors are effectively pricing in the possibility that the next breakthrough in AI will not come from making GPT-5 bigger, but from rethinking the approach entirely.

What It Means for Europe

If the round closes at $1 billion, Ineffable Intelligence would instantly become one of the most valuable AI startups in Europe — and a powerful signal that London remains capable of producing world-class AI companies, not just world-class AI researchers who leave for San Francisco.

The deal also underscores a broader shift in how deep-tech companies are funded. A decade ago, a $1 billion seed round would have been inconceivable. Today, in the race to superintelligence, it reflects the market’s belief that the right founder with the right thesis is worth more than a finished product.

Silver built the machine that changed how the world thought about AI. Now he is betting his career — and $1 billion of other people’s money — on the idea that the industry’s dominant approach will not be enough. If he is right, the implications extend far beyond London.

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Google’s parent company debuts century bond alongside massive $20 billion dollar issuance, signaling unprecedented capital needs across Silicon Valley’s AI race.

QUICK ANSWER

What’s happening? Alphabet has lined up banks to sell a rare 100-year bond as part of its debut sterling debt issuance, while simultaneously raising $20 billion in dollar bonds—upsized from $15 billion due to overwhelming demand. The century bond represents Big Tech’s increasingly desperate search for long-term capital to fund massive AI infrastructure investments that are outpacing their cash flows. This multi-currency borrowing blitz includes planned Swiss franc bonds, highlighting how even cash-rich tech giants are struggling to self-fund their AI ambitions without accessing global debt markets.

The Century Bond Gambit: Betting on AI’s 100-Year Future

Alphabet’s decision to issue a 100-year bond represents one of the most audacious financing moves in corporate history, effectively asking investors to bet on Google’s survival and profitability through the next century. Century bonds, also known as “secular bonds,” are financial instruments typically reserved for sovereign governments with the implicit backing of entire nations.

The timing is no coincidence. Alphabet faces the same funding crunch afflicting all major technology companies as Big Tech’s $650 billion AI spending spree collides with reality. The company’s capital expenditures have surged to unprecedented levels, with data center construction, AI chip purchases, and cloud infrastructure demanding cash flows that even Google’s massive advertising revenues cannot fully sustain.

This multi-currency debt issuance—spanning sterling, dollar, and Swiss franc markets—demonstrates the global nature of the funding challenge. By tapping three major currency markets simultaneously, Alphabet is diversifying its funding sources while taking advantage of varying interest rate environments across different economies.

Unprecedented Demand Signals Market Confidence

The upsizing of Alphabet’s dollar bond offering from $15 billion to $20 billion due to “strong demand” reveals investor appetite for high-grade technology debt, even at these historically elevated borrowing costs. This $5 billion increase suggests institutional investors view Alphabet’s AI investments as strategically essential, despite the uncertain returns.

The strong reception contrasts sharply with the broader challenges facing European capital markets, where companies have struggled to access growth capital amid economic uncertainty and regulatory pressure. Alphabet’s success in multiple currency markets highlights the premium investors place on established technology platforms with diversified revenue streams.

The sterling debut is particularly significant, marking Google’s first foray into UK debt markets as a primary issuer. This expansion beyond traditional dollar funding suggests tech companies are increasingly willing to take on currency risk to access the deepest possible pools of capital.

Historical Context: When Century Bonds Made Sense

Century bonds experienced their last major surge during the post-financial crisis period of ultra-low interest rates, when governments like Austria and Argentina issued 100-year debt at historically attractive borrowing costs. The logic was compelling: lock in minimal interest rates for an entire century while betting on long-term inflation to erode the real value of repayments.

Today’s environment presents a starkly different backdrop. With central banks maintaining elevated rates to combat inflation, Alphabet is paying significantly higher borrowing costs than those enjoyed by previous century bond issuers. This makes the strategic calculation more complex—the company is betting that AI infrastructure investments will generate returns sufficient to justify premium borrowing costs over an unprecedented timeframe.

The comparison to sovereign issuers is instructive. Governments issue century bonds based on their presumed permanence and ultimate taxation authority. Alphabet’s century bond implies similar confidence in the company’s longevity and market position, essentially arguing that Google’s technological moat will persist through multiple generations of disruption.

The Broader Big Tech Funding Crisis

Alphabet’s borrowing blitz reflects industry-wide pressure as technology giants confront the reality that AI infrastructure requires capital expenditures that dwarf even their substantial cash generation. Amazon faces a similar challenge, needing approximately $200 billion for AI infrastructure while generating only $50 billion in annual free cash flow—a $150 billion gap that requires external financing.

This funding crunch comes as regulatory pressures intensify across major markets, potentially limiting these companies’ ability to generate the cash flows needed to service massive debt loads. European regulators have imposed billions in fines on major tech platforms, while the US government considers additional antitrust actions that could fragment these companies’ integrated business models.

The timing creates a perfect storm: maximum capital needs coinciding with increased regulatory risk and elevated borrowing costs. JPMorgan estimates tech companies will need to issue $337 billion in bonds throughout 2026 to bridge this funding gap, representing the largest corporate borrowing spree in modern history.

Currency Diversification Strategy

Alphabet’s multi-currency approach reflects sophisticated treasury management in an era of elevated geopolitical risk. By issuing in sterling, dollars, and Swiss francs, the company hedges against potential sanctions, trade wars, or currency volatility that could affect any single market.

The Swiss franc component is particularly intriguing, as Switzerland’s traditional neutrality and stable currency make it an attractive funding source for companies facing potential geopolitical pressures. Given ongoing tensions between the US and various trading partners, diversified currency funding provides strategic flexibility.

This approach mirrors European companies’ increasing focus on financial sovereignty, reducing dependence on any single financial ecosystem. For Alphabet, currency diversification represents both risk management and strategic positioning for an increasingly multipolar global economy.

Market Implications and Precedent Setting

The success of Alphabet’s century bond could trigger similar issuances across Big Tech, potentially creating a new asset class of ultra-long-duration technology debt. Investment managers seeking duration exposure and inflation hedging could drive significant demand for such instruments, particularly given the scarcity of corporate century bonds.

However, the broader implications extend beyond debt markets. Alphabet’s willingness to take on 100-year obligations signals management’s confidence that current AI investments will generate sustainable competitive advantages. This long-term commitment stands in contrast to the quarterly earnings pressures that typically drive corporate decision-making.

The precedent could influence how European technology companies approach their own funding challenges, particularly as the continent seeks to compete with American and Chinese AI capabilities. European tech companies may need to consider similarly aggressive financing strategies to match the scale of investment their Silicon Valley counterparts are deploying.

Looking Forward: The New Reality of Tech Financing

Alphabet’s century bond represents more than creative financing—it signals the emergence of a new era where even the most profitable technology companies require external capital to fund their strategic ambitions. The AI race has fundamentally altered the economics of technology competition, transforming it from a software-centric industry to one requiring massive physical infrastructure investments.

The success of this issuance will likely embolden other tech giants to pursue similarly ambitious financing strategies. As the industry’s capital needs continue growing, traditional corporate finance approaches may prove inadequate, pushing companies toward increasingly creative solutions.

For investors, Alphabet’s century bond offers a unique opportunity to participate in the long-term success of AI technology while earning fixed income returns. The real question remains whether any company—even one as dominant as Google—can maintain its competitive position across an entire century of technological change.

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AI and creativity are no longer separate conversations. For organisations trying to balance innovation with human value, the two are now inseparable. As AI tools move deeper into everyday work, leaders are being forced to rethink how creativity is developed, protected and scaled across teams.

Few people are better placed to speak on that shift than James Taylor. A recognised authority on creativity, innovation and artificial intelligence, Taylor has spent more than two decades working with global organisations, entrepreneurs and leadership teams to understand how creative thinking actually works in high-pressure environments. His perspective is shaped by practice, not theory, drawing on real-world experience rather than abstract futurism.

As a leading creativity speaker, Taylor focuses on how people can work with AI rather than compete against it. His work explores human–machine collaboration, cultural change and the psychological barriers that prevent teams from unlocking the full value of emerging technologies. Rather than promising quick wins, he challenges organisations to rethink habits, processes and assumptions that limit creative performance.

In this exclusive interview with the AI Speakers Agency, James Taylor shares his insights on AI as a creative partner, the mistakes businesses make when adopting new technology, and what leaders must do to ensure creativity remains a human advantage in an AI-driven world.

How can organisations help teams work productively with AI while addressing fears around job displacement and change?

James Taylor: “I think for some jobs and some people it will be a threat, and there’s no getting around that. There will be jobs that disappear because of AI. That’s already starting to happen, and sometimes it’s not the jobs you necessarily think are going to disappear or change dramatically.

“For the people who are staying within the organisation and who really want to accelerate their use of artificial intelligence, the way I usually talk to them depends on whether they are a manager or leader, or someone who is more operational. They are going to use AI in slightly different ways.

“I talk about this idea of super collaboration, using artificial intelligence as a creative collaborator to help spar on ideas, make your ideas better, make them stronger and more resilient.

“People tend to work in two main ways. The ones who use AI well, we sometimes call the cyborgs. These are people who tend to be more technical or tactical in the work they are doing. They intertwine everything they do with artificial intelligence, constantly refining and moulding its responses, like a guitar player with their guitar. It becomes part of who they are.

“A centaur, on the other hand, is usually in a managerial role. They look at a project and decide which tasks the human team will do and which tasks the artificial intelligence or agent AI will handle.

“Whether you decide to be a cyborg or a centaur doesn’t really matter. The point is that you start to integrate these technologies into the work you do so you’re working in a much more super-collaborative way.”

What does effective human–AI collaboration look like in practice across different industries and roles?

James Taylor: “It really depends on the industry, the job title, the role and the level of seniority.

“Recently, I was working with one of the largest semiconductor companies. Their environment is very procurement, engineering and manufacturing focused. In procurement, AI is dealing with almost the majority of the process, including pricing, bidding and invoicing. Humans are involved at the final stage to double-check and provide final approval.

“My wife is a lawyer, and when I speak to legal and accountancy firms, they use AI in a slightly different way. They tend to offload lower-quality, routine tasks onto AI. The first time I showed my wife how to use some AI tools, her eyes lit up. She’s not particularly technical, but what excited her was how much time it saved, allowing her to focus on higher-quality work.

“AI is also powerful when it comes to challenging thinking. I talk about building virtual, imaginary masterminds to help deal with confirmation bias and other cognitive biases. AI is very good at finding weak points in your ideas, arguments or proposals, which ultimately helps you do a better job.”

What is the most common mistake organisations make when implementing AI at scale?

James Taylor: “The biggest mistake organisations make is thinking this is a technology issue, when actually it’s a people and process playbook issue.

“There was a study by Boston Consulting Group that I often reference in my talks. They found that the majority of AI initiatives fail to deliver value. Only about 20% of that failure is down to technology. The remaining 80% comes from people, playbooks and processes.

“When I speak to organisations, whether large multinationals or smaller professional firms, I focus much more on the human side. You have to change people’s perception of what AI can do and the value it can bring.

“I also talk about something called the competency penalty. Often, if a woman or an older worker uses AI, they are judged to be less competent than their peers for doing the same work. If you don’t deal with these cultural issues, you will never achieve full value from artificial intelligence.”

Which roles are most exposed to automation through AI, and how should leaders support employees through that transition?

James Taylor: “The roles that will disappear first are rule-based, repetitive and routine jobs. AI loves those tasks. You are already seeing many customer service roles disappear.

“There was a company called Klarna that generated around $14 billion in revenue and replaced 700 customer service workers with a single AI agent. What people don’t always think about is the impact on the humans who remain. Their jobs become harder. Instead of dealing with mostly routine issues, they now handle only complex and challenging cases, which increases cognitive load.

“Leaders need to support people through that transition.

“Routine and rule-based roles will disappear quickly. In healthcare, certain types of doctors, such as radiologists, are also affected because AI performs well in those areas. By contrast, roles like nursing require empathy and physical interaction, which are far more complex than rule-based systems.”

What do you ultimately want audiences to take away from your talks on AI and the future of work?

James Taylor: “I hope audiences leave feeling inspired, energised, and equipped with actionable steps they can take to implement artificial intelligence in their organisations.

“It varies depending on the audience. For senior leadership teams, the focus may be on AI governance. For frontline workers, it’s about implementing AI quickly and at scale to save time.

“One of my assistants is three times more productive today than he was two years ago because he uses AI in almost everything he does. That productivity gain allows him to spend more time with his family. No one should be doing mundane, boring work. That’s what we should be shifting to machines.

“I don’t shy away from the dangers of AI. I talk about them openly. But ultimately, I want people leaving to feel inspired, energised, engaged, and ready to get at it.”

This exclusive interview with James Taylor was conducted by Tabish Ali of the Motivational Speakers Agency.

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