From Paying for Seats to Paying for Results

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.

AI Is Now on Your Power Bill

The AI stories that get headlines are about models and jobs. The one that hits your P&L first is physical: the buildout ran out of the one thing money can't instantly buy — electricity. * The bill is landing: Henrico County, Virginia saw power rates jump 25% overnight because of 37 data centers, with schools asked to conserve — a $5M budget hit. * Megawatts, not money: Brookfield 5x'd its Bloom Energy power deal to $25B and National Grid put $1.75B into a dedicated gas plant for a Microsoft AI campus — both routing around a grid with 5-year connection queues. JPMorgan pegs AI capex at $5.5T. * The squeeze: memory prices are up 700%, with high-end supply sold out into 2028. In our 100th episode, host Stephen Forte on why the constraint shifted from money to megawatts — and three moves: audit your utility contract, treat interconnection queues as your real expansion timeline, and pull hardware refreshes forward. Sources: Henrico Citizen; Bloom Energy; National Grid; JPMorgan/Fortune; Tom's Hardware.

AI Layoffs Are Outrunning the Technology

The pink slips are arriving ahead of the product. This week companies cut thousands of jobs and blamed AI — but the technology can't yet do the work those jobs involved. * The cuts: British American Tobacco is cutting 9,000 roles; Cisco is cutting while posting record $15.8B revenue; Oracle's filing blames AI for 21,000 cuts. 56% of 2026 layoffs now cite AI. * The capability gap: OpenAI's own GeneBench-Pro benchmark shows top models failing ~68% of realistic expert tasks, and AWS committed $1B to embed engineers because companies can't deploy AI on their own. * The reversal: Gartner found the heaviest AI-cutters see no financial gain and projects 50% will reverse by 2027 — and the AI industry itself just funded a $500M retraining nonprofit (RAISE US). Host Stephen Forte on why the layoffs are outrunning the technology — and three moves before you trust an AI-driven headcount projection: cut on measured productivity, fix stalled deployments before cutting teams, and keep the human judgment layer. Sources: Yahoo Finance; Forbes; OpenAI; AWS; Gartner; Fortune.

Your AI Agents Leak Data and Money

The race to deploy AI agents just outran the controls to manage them. This week three numbers proved it. * The breach: Straiker (which raised $64M) found 91% of attacks on production AI agents silently exfiltrate data, and 36% of attacks on coding agents achieve remote code execution. A separate Amazon Q Developer flaw let a booby-trapped repo steal a developer's cloud credentials with no clicks. * The bill: GitHub Copilot's first metered billing cycle closed June 30 — agentic dev teams report $750–$3,000/month per developer, up from a $29 flat rate. IDC says the largest firms will underestimate AI infrastructure costs by 30% through 2027. * The failure rate: Gartner projects 40% of agentic-AI projects canceled by 2027 on cost, unclear value, and weak controls. Host Stephen Forte on the breach, the bill, and the failure rate — and three moves before your next board meeting: run an agent inventory, set per-developer spend caps, and make audit-trail detection a required vendor question. Sources: PR Newswire; The Hacker News; Visual Studio Magazine; Gartner; TechCrunch; MIT Sloan.

Frontier AI Got Cheap, Open, and Chinese

The story of the year was supposed to be who controls AI. The real story this week: control and cost split in opposite directions, and your business lives in the gap. * The market already switched. US labs fell from 72% to 33% of model traffic on OpenRouter in a year; Chinese models now hold six of the top ten spots. One startup, Lindy, moved 100% of its traffic to DeepSeek. * The capability gap closed. Zhipu's open-weight GLM-5.2 landed within a point of Anthropic's Opus 4.8 on a key agentic benchmark, at roughly a fifth of the cost — and you can run it on your own servers. * The theft question. Anthropic alleges Alibaba ran ~25,000 fake accounts and 28.8 million Claude conversations to distill its models (Alibaba denies). Senators are now moving to attach a sanctions amendment to the NDAA. Host Stephen Forte on what model sovereignty means for your stack, your budget, and your leverage — and the two moves to make before your next budget review. Sources: CNBC; The Strategy Stack; Nate's Newsletter.