The Board in the Machine

AI coding tools are sold to Boards as a way to reduce dependence on expensive senior developers. The evidence points the other way. Expertise does not become less relevant when AI writes the code; it becomes the factor that decides whether the assistance compounds productivity or compounds debt.

In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, examines what his experiment building two applications with Amazon Kiro and Claude Code revealed about where decades of classical software engineering training still matter, and sets it against the research. McKinsey's analysis found senior developers saved twice as much time as juniors, with gains of up to 55 per cent in greenfield projects falling to 10 to 20 per cent in mature codebases. METR's randomised controlled trial of 16 experienced open-source developers found a 19 per cent net slowdown, against a predicted 24 per cent speed-up.

Mario then works through the technical debt time bomb: GitClear's analysis of 211 million changed lines showing an eightfold increase in duplicated code, 70 per cent of it from inexperienced users; the Nature finding that AI models emit up to 19 times more carbon dioxide equivalent than human programmers; the vibe coding cleanup, with more than 8,000 startups facing rebuild costs totalling between 400 million and 4 billion dollars; and the erosion of the junior-to-senior pipeline that creates verification expertise. The governance response is to pair tool deployment with expertise investment, and to ask not whether AI can write code cheaper but whether the organisation can verify that the code creates value rather than debt.

This episode is for directors, CIOs, and the Boards approving AI coding investments on the assumption that tools substitute for expertise. The verification premium is real. The question is whether Boards will invest in it before or after discovering its absence. Read the full article at mariothomas.com

The AI skills wage data points two ways at once. Workers with genuine AI capability command premiums of twenty-eight to fifty-six per cent, while those who target AI-exposed roles without real capability development face a twenty-nine per cent earnings penalty. The same roles produce opposite outcomes, and the difference is the quality of capability investment, not access to tools.

In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, examines what separates premium-earning capability from penalty-suffering credentials, and why the split reaches the boardroom. PwC's Global AI Jobs Barometer puts the AI skills premium at fifty-six per cent, up from twenty-five per cent a year earlier; Lightcast's analysis of 1.3 billion job postings finds twenty-eight per cent for one AI skill and forty-three per cent for two or more; Harvard research from 2025 finds the twenty-nine per cent penalty. The Institute of Directors' NEDs Reimagined paper positions AI competence as a non-executive director responsibility, with Recommendation 11 calling on NEDs to build their understanding of AI.

Mario sets out what genuine capability looks like: verifying AI outputs against domain knowledge, recognising when recommendations do not fit, redesigning workflows rather than inserting tools into unchanged processes, and handling the exceptions automation cannot. He draws on BCG's 2025 finding that sixty-seven per cent of employees at companies that redesign workflows around AI save over an hour daily, against forty-nine per cent where tools sit inside existing processes, and closes with five questions for the next Board meeting, including whether NEDs can independently evaluate AI strategy or must rely on management interpretation.

This episode is for directors and the Boards who fund AI training. The premium-penalty gap will widen, and the question is which side of it the organisation, and its Board, will be on. Read the full article at mariothomas.com

AI adoption has become synonymous with headcount reduction. Business case discussions are dominated by how many FTEs can be eliminated, and workforce reduction is treated as the primary success metric for AI initiatives, while the intellectual capital trapped in mundane, automatable, undifferentiated work goes unclaimed.

In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, makes the case for the redeployment dividend: AI's primary value is not replacing people but releasing trapped intellectual capital, the same people doing more valuable work rather than fewer people doing the same work. He cites Harvard research published in 2025 finding that 25 to 40% of roles are AI retrainable, and its warning that workers targeting high-AI-exposed roles without genuine capability development face a 29% earnings penalty. Deloitte's 2025 workforce research finds that workers across all age groups prefer mixed human-AI collaboration, and that 60% believe AI can help experienced workers share knowledge.

Mario sets out why the transition matters. He argues for accepting selective atrophy: not every capability humans currently exercise deserves preservation, but strategic reasoning, relationship building, and complex judgement do, and MIT Media Lab research on cognitive atrophy and Bainbridge's 1983 irony of automation show what is lost when people become passive monitors of AI outputs. He points to the WGA and SAG-AFTRA agreements as evidence that benefit-sharing transitions are achievable when approached as partnership rather than extraction. And he proposes a different success metric, framed through the Well-Advised strategic priorities: measure where freed capacity flows, into innovation, deeper customer relationships, operational resilience, responsible transformation, and genuinely differentiated work rather than cost reduction alone.

This episode is for directors, chief executives, HR and finance leaders, and the Boards whose AI business cases default to layoff arithmetic. The key question is straightforward: are the organisation's people doing more valuable work, or simply less work? Read the full article at mariothomas.com

AI is embedding itself into the enterprise faster than organisations can govern it, while eroding the human capabilities needed to oversee it. Gartner projects that 40 per cent of enterprise applications will feature AI agents by the end of 2026, up from less than 5 per cent in 2025, and that half of all organisations will introduce AI-free assessments to counter the critical-thinking atrophy AI reliance has created. The luxury of treating AI as tomorrow's problem has ended.

In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, delivers a return-to-work briefing on the five forces shaping the Board AI agenda for 2026: AI's shift from content generation to decision support, inference economics reshaping deployment strategy, embodied AI introducing physical-world liability, verification gaps exposing governance failures, and AI governance professionalising into systematic capability. Among the evidence: Deloitte's 2026 TMT Predictions expect inference to account for two-thirds of all AI computing power by 2026, and Stanford HAI research finds general-purpose LLMs hallucinate on legal queries between 58 and 82 per cent of the time.

Mario pairs each force with the question a Board should be asking. Is the AI strategy still focused on content generation, or has it pivoted toward decision support? Does the organisation know the carbon footprint of its AI inference? If its AI causes physical harm, who is liable and how is it insured? Has it invested in verification capability proportionate to its deployment? Does the governance structure reflect AI's strategic importance, or is AI still being treated as a technology project? He closes on what he calls minimum lovable governance: just enough structure to ensure responsible deployment while preserving agility.

This episode is for directors and Boards starting 2026 with AI on the agenda. Boards that defer do not avoid these decisions; they make them by default. Read the full article at mariothomas.com

In 2025 AI stopped being a capability question and became a question of strategic investment in organisational infrastructure. Regulation became enforceable then adapted under geopolitical pressure, energy constraints reached Board agendas, sovereignty fragmented into incompatible ecosystems, the experimentation window closed, and agentic hype met operational reality.

In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, connects the five inflections of 2025 into a single picture. He traces the EU AI Act's arc from enforceable prohibitions on 2 February 2025, with fines of up to €35 million or 7% of global turnover, to the Digital Omnibus of 19 November, which proposed deferring high-risk obligations to December 2027 or later. He cites Goldman Sachs' Powering the AI Era research projecting data centre power demand rising 160% by 2030, and MIT's finding that 95% of generative AI pilots fail to reach production or deliver measurable ROI.

Mario then works through the implications of each inflection: governance frameworks that must be adaptive rather than static; energy access as a capital allocation decision that determines AI capability; the sovereignty trilemma of trust, speed, or control, where not choosing is choosing; the GenAI Divide, with Menlo Security finding 90% of employees using AI daily outside enterprise controls; and agentic AI, where McKinsey found only 23% of organisations scaling agentic systems. Agentic AI is generative AI in a loop, and the strategic question is where to consciously transfer decision-making authority from people to systems.

This episode is for directors and the Boards entering 2026 with AI on the agenda as infrastructure rather than innovation theatre. The shift demands capital allocation rather than project approvals, long-term commitment rather than pilot funding, and governance as enablement rather than a compliance checkbox. The question for every Board is whether it is ready to treat AI as infrastructure rather than novelty. Read the full article at mariothomas.com

Forty-two per cent of companies abandoned the majority of their AI initiatives this year, up from seventeen per cent in 2024. The pattern behind the number is consistent: organisations applied generative AI to problems that traditional machine learning or deterministic automation solve better, and they are now recalibrating.

In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, examines that recalibration. S&P Global supplies the abandonment figure, Forrester predicts generative AI will orchestrate less than one per cent of core business processes in 2025, the World Quality Report finds sixty per cent of organisations citing hallucination and reliability as top concerns, and BCG's survey of more than 1,250 firms finds only five per cent achieving AI value at scale. He notes that Salesforce has been building deterministic automation into Agentforce, its Agentforce CTO observing that an LLM given more than about eight instructions starts dropping them. This is maturation, he argues, not failure.

Mario sets out where each approach belongs. Classification against fixed criteria, pattern recognition in structured data such as fraud detection and predictive maintenance, and precision-dependent calculation sit with rules engines and traditional machine learning. Contextual understanding, summarisation, semantic search, and creative generation are natural LLM territory. Hybrid architectures let deterministic systems execute while generative AI supplies context. For Boards, he draws three diagnostics from the Complete AI Framework: problem domain assessment, honest capability matching that includes inference cost at scale, and risk and reliability assessment. He closes with a maturity arbitrage mapped onto the AI Stages of Adoption: proven traditional AI builds confidence at the Experimenting stage, while hybrid architectures serve Adopting and Optimising organisations.

This episode is for directors and Boards facing AI budgets under scrutiny. The question is not whether to invest in AI, but whether each investment deploys the right AI for the specific challenge. Read the full article at mariothomas.com

The USA faces a 19GW power shortfall by 2028, over 40 per cent of projected data centre demand, and with eight-year grid interconnection queues against 18-month GPU refresh cycles, hyperscalers and AI labs are building their own generation. Capacity built to secure AI workloads typically exceeds what those workloads need, and that surplus turns infrastructure operators from energy consumers into grid actors.

In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, examines the scale of the shift and what follows from it. OpenAI's Stargate facility includes 361MW of on-site generation, Meta's Prometheus cluster adds 200MW, and combined hyperscaler nuclear commitments now exceed 8GW. Wärtsilä and AVK project European data centre demand rising 250 per cent by 2030, with 40 per cent of existing AI data centres constrained by power availability by 2027.

Mario then works through the progression from consumer to prosumer to grid actor. A 1GW campus at 60 per cent average utilisation holds 400MW of dispatchable capacity off peak, functionally a power station, and that excess can be sold through demand response programmes. For the UK he weighs opportunity against risk: the AI Energy Council, Rolls-Royce SMR's 470MW units arriving in the mid-2030s, and a 285 TWh system in which a single player at 5 per cent of capacity raises concentration and foreign-ownership questions. He names the emissions trade-off, gas turbines filling the gap until SMRs arrive, and closes with three questions: whether to assess generation potential alongside compute, where backup supply becomes grid export, and what frameworks belong in place before an AI investment makes the organisation an energy market participant.

This episode is for UK Boards and directors who can no longer separate energy strategy from AI strategy. The organisations that anticipate this transition will shape it; those that do not will adapt to rules others write. Read the full article at mariothomas.com

Boards reviewing AI progress see pilots underway, tools adopted across teams, and early efficiency wins. That view misleads, because visible activity bears little relation to genuine organisational capability. The gap between the two is the AI maturity mirage, and it derails transformation strategies before anyone notices.

In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, examines the disconnect and the three patterns that produce it. Larridin's State of Enterprise AI 2025 report finds that 89% of enterprises have adopted AI tools while only 23% can accurately measure their return on investment, and IBM and Ecosystm's 2025 APAC research shows 85% of organisations claiming data-driven or AI-First status against 11% demonstrating true readiness. The patterns are the tool-centric illusion, where deployments are counted as maturity; the pilot success trap, where isolated wins are read as systemic progress; and hype-driven metrics, where advertising agencies rate AI criticality at 8.1 out of 10 yet embed it in only 16% of operations.

Mario then sets out a three-step diagnostic. Map each function independently to one of the five AI Stages of Adoption, treating pilots that consistently fail to scale within six months as a sign of Experimenting-stage capability. Evaluate balance across the Five Pillars, since tool adoption without governance, people, and value realisation maturity is the signature of the mirage. Test with leading, lagging, and predictive indicators together, because over-reliance on lagging measures inflates perceived maturity. He matches remedies to the gaps: an AI Centre of Excellence with minimum lovable governance, infrastructure coherence before more tools, people programmes, the hub-and-spoke model, and Well-Advised scorecards.

This episode is for directors and the Boards who suspect perceived Optimising status may reflect Experimenting capability. The mirage persists for organisations that mistake activity for capability; for those willing to look clearly, the path to genuine maturity becomes visible. Read the full article at mariothomas.com

AI governance is too often heavy where it should be light and light where it should be heavy: elaborate approval processes for low-risk experiments, minimal oversight of high-stakes autonomous systems, and policy documents nobody reads. The result is friction without assurance. Unloved governance gets routed around, and shadow AI shows how often.

In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, explains minimum lovable governance, a concept he has referenced across several articles, and why it makes AI governance work. It borrows Eric Ries's progression from Minimum Viable Product to Minimum Lovable Product and applies it to oversight: the smallest system that achieves the necessary guardrails and that people actually want to use. UpGuard's November 2025 report found more than 80 per cent of employees, and nearly 90 per cent of security professionals, using unapproved AI tools: governance that exists on paper but fails to govern.

Mario sets out what lovable means operationally: embedded in how work happens, continuous rather than episodic, proportionate to risk, and clear at the point of decision. He explains why the approach has only recently become viable, pointing to the EU AI Act's risk-tiered architecture, ISO 42001 and ISO/IEC 42006:2025 as reference points, and AI-assisted governance making continuous oversight achievable while accountability stays with humans. He then works through five principles, each with a Board test question, such as whether an auditor calling tomorrow would be answered in seconds or weeks, and whether a marketing chatbot goes through the same approval process as a credit decisioning system.

This episode is for directors, chief executives, and the Boards whose governance reports say everything is under control while most of the organisation works outside it. The choice is to build governance people route around, or governance people want to use, and the outcomes will differ accordingly. Read the full article at mariothomas.com

Current AI excels at analysing what has happened. World models, an emerging approach, build internal simulations of how reality works and predict what will happen next, moving AI from recognising patterns to anticipating futures. For Boards already tracking leading and lagging indicators, they promise the technical realisation of the predictive indicator.

In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, examines world models as the next horizon in predictive enterprise intelligence, drawing on Yann LeCun's work on the Joint Embedding Predictive Architecture and on the gains current predictive AI already delivers: aviation pioneers reporting twenty to thirty per cent improvements in operational efficiency in BCG's AI-First Airline report, and PwC's 2025 finding that industries exposed to AI disruption achieve three times higher revenue per employee growth. He is candid about the barriers, which LeCun himself acknowledges as huge practical impediments: computational demands that may need quantum breakthroughs, vast real-time data requirements, and energy consumption that could make large-scale world models economically unviable.

Mario sets out grounded timelines, with narrow-domain world models perhaps two to three years away and enterprise-ready systems five to seven, and argues for a dual strategy: build foundational predictive capabilities with current AI now whilst monitoring world model developments for strategic timing. He imagines the transformation across aviation, financial services and manufacturing, then turns to governance: the ethical boundaries Boards must set when a simulation reveals a profitable but harmful path, the case for minimum lovable governance, transparency about the scenarios and assumptions behind each prediction, and risk management that simulates whole consequence chains rather than single events.

This episode is for directors and Boards preparing for predictive intelligence. Which operational predictions would transform the organisation's competitive position when world models arrive? That answer determines where to build foundations now. Read the full article at mariothomas.com