Rent the model. Own the harness. Own the network.

In 2004, Tiger Woods was already the greatest golfer alive. Eight majors. Years as world number one. Nothing in the data said fix anything. He rebuilt his swing anyway. Working with Hank Haney, he dismantled the mechanics that had produced his greatest results and started over from the ground up. His driving accuracy fell to 182nd. The press said his game was coming apart.

Three years later he had the best season of his career.

That is the dilemma almost every company we work with is in. The business is running. The numbers are fine. Someone on the team is tweaking a workflow with a language model and calling it AI adoption. The easy move is to leave it there and renew the SaaS contracts for another year. It is also the biggest risk sitting on the balance sheet.

Becoming AI-first requires something closer to Tiger’s rebuild. Thirty or fifty specialized models, orchestrated into a recursive organization that gets smarter every quarter. I made the full architectural case in Infinite Leverage. Renewing with a legacy SaaS vendor who glued an AI wrapper on last year’s product is shuffling deck chairs on the Titanic, which I covered in The AI Shuffle. Language models are a good place to start. They are not a place to stop, and the frontier labs want you to.

I wrote in Peak Token that the frontier model makers were pricing themselves out of their own market. The capability gap between the best closed model and the best open one has nearly closed while the price gap stayed enormous. A commodity priced like a luxury does not stay a luxury for long. The frontier labs see the same data. The response is the standard reflex of a software business whose moat is eroding: hold customers on inertia when you can no longer hold them on capability. I covered the broader software collapse in Software Is Not Going Down Alone and the incumbent debt response in The SaaS Debt Trap.

Which brings me to the word I want leaders to start using. Choke points. A choke point is any place in your architecture where a single party decides what you pay, what you can use, and how fast you can move. Every stack has them. Some are wonderful. A few are worth running toward. Most will quietly cost you your freedom. The most dangerous one is the layer almost nobody in the boardroom is discussing yet.

The one that made everyone richer

History as context. The best network effect I ever built was at LinkShare. We dominated affiliate marketing, and the dominance was good for everyone who touched it.

For the affiliates sending traffic to merchants, our scale was a gift. One place to find the best merchants. One set of reports. One check at the end of the month instead of chasing fifty small payments. For the merchants, the consolidation was equally valuable. One network with the scale to hunt scammers gaming commissions and the plugins quietly hijacking commissions at checkout. A thousand fragmented programs could never have won those fights. One network could.

Nobody at LinkShare was trapped. Affiliates and merchants stayed because leaving meant giving up the thing that made their lives easier. The network and the customer’s interest pointed the same direction.

That is the shape of the best businesses ever built. Google search improves as more people use it. Waze gets more accurate with every driver. eBay is worth more to a seller because the buyers are there, and worth more to a buyer because the sellers are there. PayPal got safer the more it connected. Collective[i] gets smarter about economic behavior with every new company on the network. Intelligence.com maps professional relationships, and every new member sharpens the map. The door is unlocked the whole time. The network itself is the reason to stay.

The one that holds you hostage

Now the other kind, the second-order effect from decisions you make today. Too many leaders are not thinking about what it means to be AI first. The AI labs are trying to take advantage of this by learning from software.

SQL is a public standard. Any database runs it. Oracle invented PL/SQL as a gift. Bundle your logic into the database, run it faster, do things plain SQL could not. The other half of the deal was that every procedure got written in a language only Oracle spoke. The standard SQL stayed portable. The PL/SQL wrapped around it did not. Over years, decades of business logic ended up fluent in a dialect no other database understood. By the time a company wanted to leave Oracle, the bill was not a software license. It was a rewrite of decades of code.

Microsoft ran the same play against WordPerfect: easy to bring your old files in, impossible to take them cleanly back out. The format itself became the choke point.

What keeps you there is not the value of the product. It is the cost of leaving. When that cost exceeds the value of staying, you are no longer a customer. You are a hostage with a renewal date. The gap between the two lines is the margin the vendor took from you. I wrote about the walled version of this play in Karp and Nadella Are Selling You a Wall.

In AI, lock-in moves overnight

Software lock-in played out over years. Executives had time to spot the trap and plan the rebuild. AI does not grant that courtesy. The enterprise mood shifted off ChatGPT and onto Claude in a matter of weeks after the Department of War contract decision. Then a second wave moved to open-weight models shortly after and in rapid fashion. If the most sophisticated software businesses on earth could not control the switching when it hit them, do not assume you will control yours.

You are going to need many models in the next year, not one, and the specialized ones will not come from OpenAI or Anthropic. Locking into a single language-model vendor today locks you out of twenty-nine of the thirty most important models you will deploy. That is not a cost problem. It is an existential architecture mistake you will spend the entire next cycle paying for.

At Collective[i] we live this cycle every week. The model mix underneath Telli Assistants shifts constantly. When a new open-weight release lands, we test it against the sales workflows that matter and promote the winner within days. When a frontier release gets quietly worse at a specific job, which happens, the harness routes around it the same hour we see the drift. The customer never sees the swap, which is the point. The companies running their most important workloads on a single-vendor stack are running them on last Tuesday’s model, hoping the price does not move and the capability does not drift. Hope is not an architecture.

A moat by any other name

None of this makes the vendors villains. The founders and investors behind the frontier AI labs learned their craft in software, and they are applying the software playbook to AI with precision. Investors do not fund software on revenue alone. They fund it on defensibility. Warren Buffett built a career on the word moat. Building barriers to exit is not a deviation from the software business. It is the software business, imported directly into AI.

What changed is that the old moats stopped working. A Morningstar analysis in early 2026 found that four of the five classic moat pillars had lost most of their predictive power under AI exposure, and the companies most exposed to AI underperformed the resilient ones by close to 26 percentage points in the first months of the year.1 I covered the........

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