Euan Blair's Multiverse Argues For "All In" AI Adoption

When Euan Blair tells Bloomberg that AI transformation needs an "all in" approach, he's not making idle conversation—he's betting £188 million in cash reserves and a £1.4 billion valuation on being right. The CEO of upskilling platform Multiverse has committed to training 15,000 new AI apprentices over the next two years, positioning his company at the center of what he calls the greatest workforce transformation since the industrial revolution.

Blair's thesis is simple but provocative: in AI adoption, there's no middle ground. Companies either embrace comprehensive transformation or risk being left behind by competitors who do. It's a philosophy that mirrors his father's political playbook—bold, systemic change delivered through institutional reform rather than incremental adjustment.

The Numbers Game: Why Urgency Matters

The data supporting Blair's aggressive approach is compelling. Goldman Sachs predicts AI-driven workflow shifts could expose 300 million full-time jobs to automation, with two-thirds of US jobs vulnerable to some degree of AI impact. Meanwhile, the World Economic Forum estimates that 40% of core skills will change for workers, and automation will displace 85 million jobs by 2025.

But here's where Blair's "all in" philosophy becomes more than rhetoric: while 89% of business leaders in a 2024 BCG study said their workforce needs improved AI skills, only 6% had begun upskilling "in a meaningful way." The gap between recognition and action represents both the challenge Blair identifies and the opportunity Multiverse is pursuing.

Coursera's 2025 Job Skills Report shows an 866% increase in demand for generative AI content over last year, making it the fastest-growing skill people seek to acquire. Yet according to Pew Research, while half of workers received training in 2024, only 12% learned about AI. This disconnect between demand and delivery is precisely what Blair's "all in" approach aims to solve.

Multiverse's Evolution: From Alternative to Necessity

Blair's own company journey illustrates his thesis in action. Multiverse started as an alternative to university education, matching school leavers with apprenticeships. But market forces drove a strategic pivot—companies loved the educational programs and wanted to extend them to existing workforce upskilling rather than just new talent acquisition.

Today, 80% of Multiverse's revenue comes from upskilling existing employees rather than placing new apprentices. Nearly half of participants already have degrees, but they need AI-specific skills their traditional education didn't provide. This shift from "alternative to university" to "complement to careers" reflects the broader workforce reality Blair advocates addressing comprehensively.

The company's recent acquisitions support this evolution. Purchasing San Francisco startup Searchlight adds AI-powered hiring optimization, while the Eduflow acquisition enhances online learning capabilities. These aren't random additions—they're integrated components of an "all in" approach to workforce transformation.

The Risk Calculation: Bold vs. Reckless

Multiverse's financial trajectory reveals both the promise and peril of Blair's approach. Revenues grew 66% to £45.2 million, with AI-related programs now accounting for one-third of UK sales. The company's cash position has ballooned to £188 million, providing substantial runway for expansion.

However, losses nearly tripled to £40.5 million from £14.2 million, raising questions about whether "all in" means "all out" of profitability. The company has conducted layoffs, particularly in its early talent team, as it pivots toward the more lucrative upskilling market. Critics argue this represents abandoning social mission for financial returns.

Blair counters that the pivot reflects market evolution, not mission abandonment. As he told City A.M.: "Increasingly as employers said 'Hey, this is good. How can we do more of this? Can we reach people within our workforce?', we've started to deliver far, far more [programmes] to people who are in the workforce."

The All-In Implementation Framework

What Bold Looks Like in Practice

Multiverse's partnerships illustrate what "all in" AI transformation means operationally. The company's collaboration with HCLTech involves a 13-month 'AI for Business Value' program focused on business benefits and ethical AI implementation. This aligns with HCLTech's goal of upskilling 50,000 employees in generative AI by 2025.

The Multiverse AI Advisory Board, featuring Stanford's Professor Mehran Sahami and other luminaries, provides strategic guidance on using AI to identify skills gaps and deliver personalized learning at scale. This isn't casual consultation—it's systematic expertise integration designed to accelerate transformation.

Blair's approach emphasizes that capturing AI's potential gains requires more than technology deployment—it demands individuals equipped with real-world application skills. "By empowering teams with advanced AI skills and instilling confidence," he argues, companies can "unlock the transformative potential of ethical, precise and productivity-enhancing AI."

The Counterargument: Measured vs. Maximalist

Not everyone endorses Blair's "all in" philosophy. The Aspen Institute warns that current AI training efforts remain "fragmented, reactive, and in many cases, ineffective." TalentLMS research reveals that 63% of employees believe their company's training programs need improvement, with nearly half feeling AI advances faster than organizational training capacity.

PwC's research shows only 9% of employees use generative AI daily for work, with a quarter saying employers haven't provided access or training. This suggests that even companies attempting comprehensive AI transformation struggle with effective implementation.

The Conference Board found that only 7% of HR leaders focus on reskilling strategies for AI-impacted roles, while 62% prioritize piloting AI in HR management—emphasizing automation over workforce preparation. This disconnect between executive AI enthusiasm and employee preparation reality challenges Blair's assumption that bold approaches automatically succeed.

The Marketing Intelligence Angle

From a growth strategy perspective, Blair's "all in" approach represents sophisticated market positioning rather than reckless optimism. By committing to large-scale AI apprenticeship programs while competitors pilot smaller initiatives, Multiverse establishes itself as the definitive workforce transformation partner.

The timing aligns with enterprise buying patterns. Companies increasingly recognize that AI implementation without workforce preparation produces suboptimal results. Multiverse's comprehensive approach—combining skills diagnostics, personalized learning, and ethical AI training—addresses the full transformation challenge rather than isolated components.

Blair's background as Tony Blair's son provides credibility for institutional-scale change arguments. His positioning echoes New Labour's "third way" philosophy—using market mechanisms to achieve social outcomes. In this case, profitable upskilling services enable broader workforce transformation and economic opportunity.

The Verdict: Context Determines Correctness

Blair's "all in" AI transformation thesis appears most valid for organizations with sufficient resources, clear strategic vision, and commitment to comprehensive change management. For these entities, half-measures may indeed prove inadequate against competitors making systematic AI investments.

However, the approach may be less suitable for smaller organizations, those with limited change management capacity, or companies in stable industries where AI disruption remains uncertain. The high cost and complexity of comprehensive AI transformation programs require careful cost-benefit analysis.

The broader principle—that successful AI adoption requires systematic rather than piecemeal approaches—seems well-supported by available evidence. Whether that necessitates Multiverse's specific "all in" methodology depends on organizational context and risk tolerance.

Blair's bet on workforce transformation through comprehensive AI upskilling represents a logical response to documented market trends and skills gaps. The execution challenge lies not in the strategy's theoretical soundness but in its practical implementation across diverse organizational contexts.

As AI continues reshaping work fundamentals, the companies that thrive will likely be those that combine Blair's transformation ambition with execution discipline adapted to their specific circumstances. The "all in" philosophy works best when it's strategic rather than reckless, comprehensive rather than chaotic.