# Plan for the week your AI vendor goes away, before someone else picks the date. · VANDAM > Treat your AI model provider as a single-source supplier: a tested second provider, owned prompts and evals, and API terms in every renewal. URL: https://vandam.ai/notes/plan-for-your-ai-vendor-leaving Contact: hendrik@vibelab.be # Plan for the week your AI vendor goes away, before someone else picks the date. In February 2026 defense contractors started moving off Claude within days, for reasons unrelated to the model, and every provider now publishes retirement dates for the models you depend on. Why the switching cost sits in prompts, evals, traces and data access, and the supplier-risk checklist a CFO and COO can run before the next renewal. Hendrik De Winne 26 October 2026 9 min read In the last week of February 2026, an executive at a US defense technology company told employees to start switching Claude out for other models, including some open-source ones. The executive expected the work to take “a week or two,” according to [CNBC](https://www.cnbc.com/2026/03/04/pentagon-blacklist-anthropic-defense-tech-claude.html). Nothing was wrong with the model. On Friday, February 27, President Trump ordered every federal agency to “immediately cease” using Anthropic’s technology, and Defense Secretary Pete Hegseth said he was directing the Pentagon to designate the company a supply chain risk. Alexander Harstrick of J2 Ventures said ten of his portfolio companies were replacing Claude in their defense work, and that the move “in no way reflected a perceived shortcoming of Claude.” The first thing a CFO should take from that week is that a company can lose its AI supplier through no fault of its own and no fault of the supplier’s. The second is less obvious. The real switching cost sat in prompts tuned to one model, in workflows built on one vendor’s tools, and in the absence of any test that could show a replacement was good enough. Those costs can be cut in advance, cheaply, and almost nobody does it: in a mid-2025 survey by [Menlo Ventures](https://menlovc.com/perspective/2025-mid-year-llm-market-update/), an Anthropic investor, only **11%** of teams building with AI had changed model providers in the past year. So the rule I would use is plain: treat the model provider like a single-source supplier of a critical part. Keep a second source qualified, own the specifications, and write the exit into the contract before you need it. - **88%** Share of enterprise LLM API usage held by Anthropic, OpenAI and Google, per [Menlo Ventures](https://menlovc.com/perspective/2025-the-state-of-generative-ai-in-the-enterprise/), December 2025. - **11%** Teams that changed model providers in the past year, mid-2025, per [Menlo Ventures](https://menlovc.com/perspective/2025-mid-year-llm-market-update/). - **60days** Minimum notice [Anthropic](https://docs.claude.com/en/docs/about-claude/model-deprecations) gives before retiring a public model. - **<5days** Notice Windsurf says it got before losing most direct Claude capacity in June 2025, per [TechCrunch](https://techcrunch.com/2025/06/03/windsurf-says-anthropic-is-limiting-its-direct-access-to-claude-ai-models/). ## A supplier can disappear for reasons that have nothing to do with you In a [statement on February 26](https://www.anthropic.com/news/statement-department-of-war), Anthropic CEO Dario Amodei said the Department of War would only contract with AI companies that accept “any lawful use,” while Anthropic held to two exceptions: mass domestic surveillance of Americans and fully autonomous weapons. Trump’s post set a six-month phase-out for agencies such as the Defense Department. On March 4 the Pentagon sent Anthropic a letter [confirming the designation](https://www.anthropic.com/news/where-stand-department-war). By then, CNBC reported, officials at Treasury, State and Health and Human Services had also told employees to move off Claude. The facts deserve to be stated fairly. Anthropic [said](https://www.anthropic.com/news/statement-comments-secretary-war) its commercial customers kept full access, and Microsoft, Google and Amazon all [kept offering Claude](https://www.cnbc.com/2026/03/06/google-says-anthropic-remains-available-outside-of-defense-projects.html) outside defense work. On March 26, Judge Rita Lin of the Northern District of California issued a [preliminary injunction](https://www.cnbc.com/2026/03/26/anthropic-pentagon-dod-claude-court-ruling.html) against the president’s directive and the designation, calling the government’s conduct “classic illegal First Amendment retaliation.” On August 27 she [ruled the designation unlawful](https://www.cnbc.com/2026/08/28/judge-blocks-pentagon-blacklist--anthropic-.html). A parallel case in the D.C. Circuit was still open at that point. None of that helped a contractor in the first week of March. It had to assume the ban would hold and move on a timeline set by a social media post. The lesson applies to every provider. Any of them can be cut off by a government, an acquisition, a capacity squeeze or a pricing decision, and the customer has no say in which one arrives first. ## Every model you test now comes with a retirement date Politics is the rare case. Retirement is the routine one, and the providers publish it. On Anthropic’s [deprecations page](https://docs.claude.com/en/docs/about-claude/model-deprecations), Claude Sonnet 4 and Opus 4 were announced for retirement on April 14, 2026 and switched off on June 15. Opus 4.1 followed on August 5. After that date, the page says, “Requests to models past the retirement date will fail.” OpenAI’s [deprecations page](https://platform.openai.com/docs/deprecations) promises at least six months’ notice for generally available models and as little as two weeks for preview models. On October 14, 2026 it told developers that several GPT-4o and GPT-4.1 mini snapshots leave the API on January 14, 2027. It also retired a whole interface: the Assistants API, OpenAI’s earlier toolkit for building assistants, was removed on August 26, 2026, a year after notice. [Balaji Srinivasan](https://x.com/balajis/status/1931671546113994769) put the economics in one line last year: “AI doesn’t take your job. AI takes the job of the previous AI.” For a buyer, that means the model is the least durable part of any AI investment. A workflow that works today will run on a different model within a year or so, whether you choose the date or the provider does. ## The data your agents read is rented too Most of the value in an agent comes from what it can reach: the CRM, the ticket queue, the ERP, the chat history. Those systems belong to other vendors, and their terms can change. On May 29, 2025, [Slack](https://docs.slack.dev/changelog/2025/05/29/rate-limit-changes-for-non-marketplace-apps) cut the rate limit on the two API methods that read message history to one request per minute, returning at most 15 messages, for new installs of commercially distributed apps outside its Marketplace. Slack said the change was meant to stop “bulk data exfiltration by unvetted applications.” Apps a company builds for itself were exempt. Its updated [API terms](https://slack.com/terms-of-service/api) also bar third-party apps from using Slack data to “train a large language model” or bulk exporting it without a separate agreement. Slack had its reasons. The point for a COO is narrower: if you bought an AI search or assistant tool that reads your Slack, its ability to do that now depends on its standing with Salesforce, which owns Slack. The same applies to every AI tool sitting on top of a system of record you rent. Model access can move just as fast. In June 2025, Windsurf, a coding tool then reportedly being acquired by OpenAI, said Anthropic had cut off “nearly all” of its first-party capacity to Claude 3.x models “with less than five days of notice.” Windsurf had some backup capacity with other inference providers, “but not enough,” and warned its own users of availability problems. That case involved a competitor, but the mechanism carries over: one decision upstream, and a product downstream degrades the same week. ## The switching cost sits in four things you can own When a team says changing models would take months, the time goes into four things, and a company can own all of them. **Prompts and instructions.** The standing instructions an agent follows are business documents: how to code an invoice, which customers get a callback, what a refund requires. They belong in your own repository, with an owner and a review date, in plain text that any model can read. Anthropic published its [Agent Skills format](https://claude.com/resources/articles/organization-skills-and-directory) as an open standard in December 2025, saying “the same skill should work whether you’re using Claude or other AI platforms.” **Evals.** An eval set is a collection of real cases from your own work with the answer you expect: a hundred invoices with the right coding, fifty support tickets with the right next step. It is the only thing that lets you say a second model is good enough, and it turns a migration into a test run. I set out how corrections become those tests in [the note on fixing a mistake once](https://vandam.ai/notes/fix-a-mistake-once). **Traces.** The logs of what each agent read, decided and did are your audit trail and your best source of new test cases. Keep them in your own storage, in a format you can query without the vendor’s console. **Integration code.** Agent frameworks tied to one lab are the costliest thing to leave. A thin layer of your own between the workflow and the model, so the model name is one setting, costs little to build at the start and a lot to retrofit. ## Your AI services vendor should hand back what it learned The same four things matter when someone else builds your agents. A lab’s forward-deployed team, a consultancy or a small firm like ours spends months learning how your work runs, and that learning ends up in prompts, skill files, evals and traces. One builder who posts as [@dom__inic](https://x.com/dom__inic/status/2066260212772679864) argued in June that “organizations are effectively outsourcing key learning curves and domain knowledge to the AI labs.” That is his reading of the labs’ services businesses. The labs’ own terms point the other way on training: Anthropic says that [by default](https://privacy.claude.com/en/articles/7996868-is-my-data-used-for-model-training) it does not train on inputs or outputs from its commercial products, though feedback you send it can be used. Training is only half the concern. The other half is whether you could keep running the work if the vendor left. If the prompts live in the vendor’s workspace, the evals in its notebooks and the traces in its account, you will rent your own know-how back at renewal. Whichever kind of [provider you pick](https://vandam.ai/notes/who-does-the-work), the contract should say that every prompt, skill file, eval set, trace and piece of integration code is yours, delivered in open formats every quarter and in full at exit. That is the standard we hold our own work to, and a provider that will not sign it has told you something useful before the renewal. ## A supplier-risk checklist for the CFO and COO This is the checklist we use. The COO owns the first five items, the CFO the last three. 1. **List every workflow that calls a model,** with the provider, the exact model version and the business process it supports. Mark the ones where an outage would stop revenue, payroll, the close or a customer commitment. 2. **Check each model’s retirement date** on the provider’s own deprecations page and put it in the same calendar as contract renewals. Anything on a preview model in production gets moved or signed off by name. 3. **Qualify a second provider** for each critical workflow. Run the same eval set on both, record the scores, and keep the second one live at small volume so the keys, billing and access already work. 4. **Run a 72-hour switch drill** once a year on one critical workflow: route it to the second provider, rerun the evals, and write down what broke and how long the fix took. 5. **Map the data dependencies.** For each agent, list the systems it reads and writes, whose API that is, and what happens to the workflow if the rate limit drops to one call a minute. 6. **Put data access into renewals.** For every system of record, get written terms on API access for your own agents and for the third-party AI tools you approve, with notice periods for rate limit and terms changes, the way you already negotiate uptime. 7. **Put the handback into services contracts,** as above, and check the model provider’s training defaults and feedback exception. 8. **Price the switch.** Put a number on what one critical workflow would cost to move in a week: engineering hours, review hours, any rate difference and a month of lower quality. Carry it in the risk register next to other single-source suppliers. A second provider costs money and attention, so I would keep one warm only for workflows on the critical list. ## What to do before the next renewal This month, the COO should pick the one AI workflow the company would miss most, find its model version on the provider’s deprecations page, and ask the team how they would know a replacement model is good enough. If the answer is a demo, have them build a fifty-case eval set from last quarter’s real work before doing anything else. Before the next renewal of any system your agents read, the CFO should add one page to the negotiation covering API access, rate limits and notice periods, and before signing any AI services contract, ask for the handback clause in writing. Then schedule the first switch drill for a quiet week, while it is still your choice when it happens. ## More notes 29 October 2026 6 min read ## Who owns what your AI learns. Y Combinator now asks for startups that help companies own their intelligence. Before a mid-sized company owns a model, it can own the four things every model works with: context, evals, rules and connections. 26 October 2026 9 min read ## Delete the step first and give the agent whatever survives. Musk’s five-step method puts automation last, and most AI programs start there because an agent is the one step a vendor can sell. I walk accounts payable through the steps in his order and show why an agent that clears exceptions fast hides the steps that should go. ## Your agents need a critic and so does the plan they carry out. Models grade their own work too kindly, and the labs now separate the agent that makes from the agent that checks, the way finance separates maker and checker. I set out a four-layer review design with a minutes budget, and why the brief itself needs a critic with no stake in it.