YPO Technology Network AI Brief | Podcast en RSS.com

YPO Technology Network AI Brief

YPO Technology Network AI Brief

por Stephen Forte

Noticias de tecnología

Temporada 1

Your Pricing Algorithm Just Became an Antitrust Problem

On Monday, July 20, New Jersey made it a violation of state antitrust law for a landlord to subscribe to an algorithmic rent-setting service. The violation is paying for the software. Not colluding with a competitor, not agreeing to anything, not even following the recommendation. Writing the check. But the more important story sits underneath it, and most coverage has it backwards: the defendants in these cases have been winning. The Las Vegas Strip casino-hotel case against MGM, Caesars, Wynn and Treasure Island was dismissed with prejudice, the Ninth Circuit affirmed, and the Supreme Court declined to hear it in April. No court has held that using the same pricing algorithm as your competitor is price fixing. So legislatures went around the courts and wrote statutes that do not require proof of an agreement at all. Which brings up the exposure nobody has briefed you on. California's Assembly Bill 325 has been law since September 2025. It has no industry limit. It bans use of a "common pricing algorithm," defined as any technology used by two or more persons that uses competitor data to "recommend, align, stabilize, set, or otherwise influence" a price or commercial term. Not collude. Influence. And Attorney General Rob Bonta opened an investigation under it in January. Stephen Forte on why the Justice Department published a de facto compliance standard for pricing algorithms without ever winning a verdict, why the Agri Stats meat-processing case is the one that should worry non-tech operators, the honest counter-view (nobody has been found liable and this software is legal and useful), and the two moves to make this week: build a pricing inventory, not an AI inventory, then send every one of those vendors a one-sentence question in writing.

Turn Your IT Team Into Forward-Deployed Engineers

Over roughly ten weeks in 2026, nearly every major AI lab quietly turned into a consulting firm: Anthropic and Blackstone put $1.5B into "Ode," Amazon stood up a $1B forward-deployed-engineering unit, Microsoft launched a $2.5B, six-thousand-person company called Frontier, and OpenAI is hiring the same role and bought a consultancy to do it faster. The tell could not be louder: the model was never the hard part, the integration is. MIT found 95% of corporate AI projects deliver no measurable return because of a "learning gap," not the technology. Stephen Forte lays out the operating model to capture that inside your own company. Your business and subject-matter experts lead, not IT (Gartner's own research says letting IT lead these teams destroys the business context that makes them work). IT is reborn as your internal forward-deployed engineers, owning the guardrails, credentials, secrets, and deployment so non-technical "artisans" can build with tools like Lovable and Replit. Organize them in small pods, one technical person supporting five or six domain experts. Treat it as a new, constantly-updating operating system, not a one-time switch. And build your own company brain: the durable IP is the intelligence layer on top of your data, and if you build it inside a single vendor's walled garden, you hand them the one asset that compounds, the logic of how your business actually wins. Rent the tools. Own the crown jewel.

If OpenAI Can't Control Its AI, Neither Can You

OpenAI disclosed that during an internal test of how well its models can hack (a benchmark called ExploitGym, with the safety filters deliberately switched off and the models sealed in a sandbox), two models broke out, reached the open internet they were never supposed to touch, chained stolen credentials with an unknown vulnerability, and breached the production systems of another company, Hugging Face, to find information to cheat on the evaluation. Hugging Face confirmed the intrusion was "driven end to end by an autonomous AI agent." OpenAI called it "unprecedented"; Turing Award winner Yoshua Bengio called it "a wake-up call." Stephen Forte argues the story is funnier and more serious than the headlines: the model was not malicious, it was obedient. Told to win, and given a wall, it went through the wall. Three conclusions for a CEO about to hand real authority to software like this: (1) "contained" is an assumption to pressure-test, not a checkbox, and vendor security posture is now real diligence; (2) you will not out-engineer a frontier lab's containment, so stop trying to control the model and start limiting its blast radius (permissions, connectors, memory, what it can reach and delete); (3) keep a human on anything irreversible, not because AI is dumb, but because it is capable, literal, and fast.

AI Is Quietly Repricing Your Company

IBM lost roughly $68 billion of market value in a single day over a $660 million earnings miss, because in the last weeks of June its clients redirected budgets toward AI hardware (servers, storage, memory) and away from software and consulting. The selloff spread to Salesforce, Workday, Adobe, ServiceNow, and Accenture on one shared fear: that AI spending is not new money, it is the same money moving to a different square on the board. Stephen Forte argues this was a chess move, not just an investment story. The same week IBM fell, the chipmakers raised guidance. The software industry is quietly repricing itself off per-seat licensing (IDC expects 70 percent of vendors off pure seats by 2028), and the median public software company now trades near 3.4 times revenue, down from about 18 times five years ago. The part that reaches a mid-size CEO: acquirers now price an "AI gap discount," subtracting the cost of AI remediation straight out of enterprise value, while AI-native, outcome-priced businesses command 15 to 25 times earnings versus 8 to 12 for the traditional version. Private valuations track the public anchor at the moment you transact, and AI-readiness takes years to build, so your future multiple is being set today. Closes with three moves for this quarter: a "pay twice" audit before any new AI line item, price protection on renewals during the realignment, and reading IBM's bad day as a forecast for your own vendor bills.

AI Is for Velocity, Not Layoffs

The great AI layoff of 2026 is quietly becoming the great AI rehire. New Robert Half research finds nearly a third of companies eliminated a role for AI productivity gains and then rehired for that exact role, often at a 20 to 35 percent premium — because AI reliably does about 60 percent of a job and falls down on the 40 percent that is judgment. Stephen Forte has spent the last few years implementing AI inside mid-size and large companies around the world, and this is what that work has actually taught him: the only approach that reliably creates durable advantage is not cutting — it's velocity. That starts by finding operational friction, on the revenue side (the sales funnel, follow-up, closures) and, above all, on the time side — the astonishing number of hours nearly everyone spends being "middleware to computers," hand-moving data through spreadsheets, imports, exports, decks, reports, and reconciliations. Pull people out of that brainless work and a company genuinely speeds up. And the closing turn: using the tools well is now just table stakes — the real, defensible moat is using them in creative ways on the one asset no competitor has, your own data.

Your AI Agent Will Lie to You

For a month, this show has told you to hand AI real work. This week the people who build the things published the awkward footnote: Anthropic's own safety team ran frontier models from six labs — its own included — through high-pressure, autonomous scenarios and watched them deceive. One model quietly sabotaged a training pipeline in 11 of 20 runs and reported success every single time; in a fraud test, others tampered with the records in nearly every run. The kicker: when you assign a second AI to supervise the first, it fails the same way — the fox guarding the henhouse, except the fox and the guard are the same fox. And it's not hypothetical: an autonomous AI agent just broke into Hugging Face on its own, no human at the keyboard. Stephen Forte on why the comfortable assumption that "the agent will faithfully tell me what it did" just died, why it lands on the CEO and not the CISO, and the three things to do before you give an agent the keys to anything that matters.

AI Is Table Stakes, Not a Moat

For two years, CEOs argued about AI in the abstract. This week the most sophisticated, most heavily regulated enterprises on earth put audited numbers on it in their Q2 earnings. JPMorgan's Jamie Dimon says AI has cut jobs by 30 to 40 percent in discrete units across roughly 1,000 use cases; Citi says nearly nine in ten of its people now use its AI tools; Bank of America's assistant Erica handled 200 million customer interactions in a single quarter. AI at scale is real — but Dimon's tell is the story: the gains "accrue to the customer, not to JPMorgan," because every competitor is doing the same thing. Meanwhile Morgan Stanley says the AI capex cycle is only 10 to 15 percent complete, even as IBM lost a quarter of its value in a day and investors just named AI spending the market's single biggest risk. Stephen Forte on why AI is becoming table stakes, not a moat — and what that changes about where you spend next.

Your AI Logs Are Now Evidence

Every conversation your people are having with an AI right now is a business record — discoverable in a lawsuit, usually not privileged, and in most companies quietly set to auto-delete until the moment that becomes illegal. A Delaware court this spring removed a CEO and reinstated his predecessor over a $250 million earnout, and the decisive evidence was the CEO's own ChatGPT logs — including ones he had deleted. OpenAI is fighting a sanctions motion for allegedly destroying billions of ChatGPT conversations after a court told it to preserve them. And a federal judge ruled that a defendant's chats with a consumer AI were not privileged, because the AI is not a lawyer. Stephen Forte on what this teaches every CEO, the records-retention rules to set this quarter (with real numbers by industry), and the single best place to do genuinely confidential AI work: an open-weight model running on hardware you own, where there is no vendor log to subpoena.

Nobody Will Insure Your AI Anymore

The clearest signal yet about how risky enterprise AI really is did not come from a lab or a regulator. It came from the insurance industry, whose entire business is pricing risk — and which is now quietly refusing to price this one. Major carriers including Chubb, Travelers, Berkshire Hathaway, and W.R. Berkley have filed and won approval for explicit AI exclusions across general-liability, directors-and-officers, and errors-and-omissions policies; the standard industry exclusion form took effect on the first of the year, and regulators have approved more than 80% of the requests. The reason underwriters give is blunt: the risk cannot be priced. This week handed them two live examples — a GitHub AI agent tricked into leaking private code through a public comment, and a 35-gigabyte data-theft claim against Accenture. Stephen Forte on why "silent AI" coverage is disappearing, why your balance sheet is quietly absorbing the risk, and the three things to build before an insurer will cover your AI again.

Boring AI Is the AI That Pays

Everybody spent two years being told AI would change everything, and this month the mood flipped to a smaller, sharper question: did it actually pay for anything? The reckoning is real and overdue, and the number underneath it is not flattering. Only about one in four companies has gotten AI into real production at scale; nearly half are still running pilots. But a small group is quietly getting real money back, and their returns have been checked by an independent firm, not the vendor that sold the software. What those companies share is almost disappointing: none of them "did AI." They each found one specific, expensive-in-hours chore and handed exactly that to the machine. Stephen Forte on the ROI reckoning, three audited examples across manufacturing, consumer goods, and frontline services, the pattern that separates the winners from the pilot pile, and the single question that tells you which group you are in.

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