You Don't Know What AI You're Running

You Don't Know What AI You're Running

This week looked like a fireworks show of AI launches — OpenAI's GPT-5.6, new real-time voice models, Microsoft leaning on its own in-house models. The more important story ran underneath all of it: the AI inside your company has quietly become a black box you can neither see into nor fully trust. Microsoft has begun replacing OpenAI and Anthropic with its own cheaper MAI models inside Excel and Outlook — its AI chief said the goal is to "eliminate that cost." The security firm Wiz found six major AI coding assistants showed users a fake file path in their safety confirmation while writing to sensitive files. And an independent developer discovered Anthropic had run an undisclosed location tracker inside Claude Code for months. Stephen Forte on why you are now accountable for an AI you cannot inspect — and the three clauses to put in every AI contract before your next renewal: model-transparency and change-notification, an independent audit-logging layer, and a named owner for what is actually running in your stack.

Your AI Bottleneck Was Never the Model

The strange truth of AI in 2026 is that the technology keeps clearing bars we thought were years away — Alberta's provincial government just used Claude to scan 466 million lines of code in 20 hours, work that would have taken six and a half years by hand — while the business results stay stubbornly flat. MIT finds 95% of enterprise AI pilots deliver no measurable impact; an NBER survey of more than 6,000 executives across four countries finds roughly 90% saw no productivity gain over three years. This week the most sophisticated vendors on earth told you, in dollars, where the real bottleneck is: Microsoft committed $2.5 billion and 6,000 of its own engineers to embed inside customer companies and deploy AI for them — following Amazon's $1 billion, and Anthropic's and OpenAI's own embedded teams. Stephen Forte on why your AI bottleneck was never the model, and the three moves to make before you fund one more pilot.

AI's Insiders Just Started Hedging

Every boom has a tell, and it is never in the press releases. This week the AI boom's insiders started hedging their own story: Meta announced it will rent out its "excess" AI compute while chipmakers sold off, Oracle's SEC risk factors laid bare the strain of its $300B OpenAI/Stargate commitment, and Mark Zuckerberg told his own employees that AI-agent progress "hasn't really accelerated" as expected. Yet the same week, Abu Dhabi's MGX closed a $49B AI fund and Anthropic signed a 20-year, ~$19B data-center lease. Stephen Forte on what it means when sellers plan for surplus while buyers still pay scarcity prices — and the three moves to make before signing any multi-year AI contract: shorten and reopen, read your vendors' risk factors like a credit file, and re-run build-versus-rent every quarter.

Washington Wants Equity, Not Just Rules

For two years the question was "how will governments regulate AI?" This month the answer got bigger: the state wants to own a piece, police what the models say, and decide who they may serve. * Ownership: OpenAI floated giving the US government a ~$42.6B (5%) equity stake (Alaska-Fund style) and wants Anthropic, Google, and Meta to follow; Altman also called for a US-led "IAEA for AI." * The red-line case: the Pentagon designated Anthropic a "supply-chain risk" — a first for a US company — over its red lines against autonomous-weapons and surveillance use; a court has paused it. A vendor's values can become your outage. * The rules being written this week: the FTC opened a rule treating AI "ideological steering" as deception; the UN convened 193 nations in Geneva; and the UK's FCA is weighing direct supervision of the models themselves. Host Stephen Forte on why your AI vendor is becoming a quasi-sovereign institution — and three vendor-risk moves: treat frontier access as a governed dependency, get your vendor's red lines in writing, and track the FCA/FTC/Geneva if you're regulated. Sources: FT/CNBC; Tech Times; FTC.gov; UN News; FCA.org.uk.

From Paying for Seats to Paying for Results

An extended, single-thesis episode. For a century the two biggest lines on your P&L — payroll and per-seat software — have been fixed costs sized to peak, sitting there hoping to earn their keep. Stephen Forte's belief: AI turns them into variable costs billed per outcome — per interaction, per order, per resolution. * The spine: a fixed cost is a bet on utilization; a variable cost is a bill for results. * Two live proofs: Medicare's new ACCESS model pays organizations only when AI-supported chronic care hits measurable health outcomes; Salesforce's Agentforce charges $2 only when its agent resolves a ticket. * The capstone: adopting AI properly isn't bolting a tool onto the org chart — it's rewiring the company's operating system (why MIT found 95% of GenAI pilots deliver no P&L impact: they installed new software on the old OS). Plus four moves to make this quarter — and why Stephen has bet his own company on this shift with pay-for-performance managed agents. Sources: CMS.gov; Salesforce; MIT NANDA; company reports.