Friday, May 22, 2026
FRIDAY – AI FOR THE C SUITE®
Read time: 11-12 min · Read online
Hi, it’s Chad. Every Friday, I serve as your AI guide to help you navigate a rapidly evolving landscape, discern signals from noise and transform cutting-edge insights into practical leadership wisdom. Here’s what you need to know:
1. Sound Waves: Podcast Highlights
This past Monday, I was joined by Charlene Li, founder of Quantum Networks Group and Altimeter Group (acquired by Prophet), former principal analyst at Forrester, and New York Times bestselling co-author (with Dr. Katia Walsh) of Winning with AI: The 90-Day Blueprint for Success. Charlene loves readiness assessments and spent months building one for her new book…then promptly threw it out. Her reason is brutal: every company that runs one fails it, and the six weeks you spend on the study is six weeks competitors are spending actually learning the tools. Tune in to hear more from our very rich discussion including how one bank skipped the study, started the work, and went from 90 check readers down to 5.
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2. Algorithmic Musings: The Spindle Problem: What Anthropic’s Stainless Acquisition Means for Your AI Stack
Your procurement process catches obvious supply chain risk. You’d never buy a critical piece of machinery from a vendor whose largest competitor just acquired its most important parts supplier. Your operations team would catch that in ninety seconds.
That same situation just appeared in your AI stack. Almost nobody is set up to catch it.
On May 18, Anthropic acquired Stainless, a developer-tools company. The technology Stainless builds sits inside the products OpenAI, Google Gemini, and other AI vendors sell to your engineering team. The trade press is covering this as a developer experience play. It is not. It is a supply chain event.
Anthropic has acquired a piece of infrastructure embedded inside the onboarding flow of every major rival, and is winding it down for outside use. Existing SDKs at OpenAI and Gemini keep working. The next generation has to come from somewhere else, because the tooling that built them is now controlled by their largest competitor. That is a structural change to your vendor relationship.
Consider the manufacturing version.
Imagine you’re a manufacturer buying a CNC machine. Company A and Company B both make them and compete head-to-head for your business. The spindle, which is the component that actually does the cutting, comes from a third-party supplier that both companies depend on. Then Company A acquires the spindle supplier.
Do you still buy the CNC machine from Company B?
Of course not. Spindle availability, pricing, and roadmap are now controlled by Company B’s largest competitor. Company A has structural incentives to prioritize their own machines, slow-walk Company B’s spindle orders, or simply make Company B’s product less reliable over time. Your operations team would recommend, at minimum, contract language addressing the new exposure. Most likely they would recommend buying from Company A directly or finding a third vendor.
Translate that scenario back to your AI stack. Anthropic is Company A. OpenAI and Gemini are Company B. The SDK toolchain is the spindle. And you, the mid-market buyer who signed a contract with Company B last quarter, are the one who needs to understand what changed.
Why Mid-Market Is Uniquely Exposed
Enterprise buyers have infrastructure for this. A Fortune 500 procurement team has dedicated vendor risk analysts, supply chain audit functions, and the budget to commission a McKinsey workstream on AI dependency mapping. They will figure this out, slowly and expensively, but they will figure it out.
Mid-market companies do not have any of that. Your AI procurement decision last year was probably made by a small group of senior leaders weighing capability, price, and integration effort. The conversation likely never included the question “what happens if our vendor’s largest competitor acquires a critical piece of the toolchain six months from now?” because nobody knew to ask it. There was no playbook. There still isn’t.
That gap matters because mid-market firms face the same competitive AI pressures as enterprises but with a fraction of the resources to respond when the ground shifts. When your CNC vendor’s supply chain changes, you have an operations team that catches it. When your AI vendor’s supply chain changes, you may have nobody whose job includes noticing.
The Advisors You Would Normally Call Cannot Help You Here
If this were a traditional supply chain problem, you would call your usual advisors. Your law firm would review contracts. Your accounting firm would model the financial exposure. Your management consultant would frame the strategic implications. Between them, you would have a clear picture of what to do within two weeks.
That is not going to happen with this one.
The major consulting firms covering AI strategy all maintain partnerships across the foundation model labs. Bain, McKinsey, Deloitte, and the rest have integration practices, co-marketing arrangements, and reference customer programs with Anthropic, OpenAI, and Google simultaneously. Those relationships are valuable to their clients in normal times because the firms can speak credibly about all the major vendors. Those same relationships make it structurally impossible for any of them to tell you, on the record and in writing, that your OpenAI contract now carries dependency risk because Anthropic owns the SDK toolchain.
They can hint. They can frame it as “evolving vendor landscape.” They cannot say plainly that one vendor’s competitor now sits inside the other vendor’s product, because saying so jeopardizes the partnership that makes their AI practice viable.
This is the conflicted advisor problem, and it is not a moral failing of the firms involved. It is a structural feature of how enterprise AI consulting is organized. The same firms that should be giving you the clearest read on Anthropic’s acquisition strategy are the firms least able to do so.
You are going to have to think this through yourself, or with advisors who do not carry those conflicts.
The 1918 Playbook
If the vertical integration move sounds familiar, that’s because it is. In 1918, General Motors acquired United Motors, an independent parts supplier selling electrical systems, roller bearings, and other components to multiple automakers. Once GM brought United Motors in-house, its production redirected exclusively to GM brands. Competitors who’d been buying those components had to find new suppliers or accept that a critical piece of their own supply chain was now controlled by their largest rival. The acquisition is widely regarded as one of the foundational moves in GM’s rise to industrial dominance.
The pattern is identical to what just happened with Stainless. Identify infrastructure that competitors depend on. Acquire it. Convert shared industry plumbing into a proprietary advantage. GM did it with electrical systems and bearings in 1918. Anthropic did it with SDK tooling in 2026.
The industrial leaders who got blindsided by GM’s move weren’t stupid. They were operating with a procurement mental model built for a world in which parts suppliers were neutral infrastructure. When the rules changed, they were slow to adjust. The companies that adjusted fastest survived. The ones that didn’t became case studies.
The AI vendor category is in that same moment now. The procurement mental model most mid-market leaders are using was built for a world in which AI vendors competed primarily on capability and price. That world is ending.
What Dependency Cascade Mapping Looks Like
You do not need a McKinsey workstream to get started. You need a half-day with your CTO, your head of procurement, and whoever owns vendor risk in your organization. The exercise has four parts.
Inventory the dependencies. List every AI vendor your organization currently pays. For each one, identify which components of their product stack are produced by third parties. SDKs, model hosting infrastructure, training data pipelines, evaluation frameworks, and developer tooling are all candidates. Your engineering team can pull this together quickly. Most of it lives in package manifests and vendor documentation.
Map the ownership. For each third-party component, identify who owns the company that produces it. Then ask whether that owner is a competitor, partner, or neutral party relative to your primary vendor. The Stainless deal is the highest-profile example, but it will not be the last. Acquisitions across the AI stack are accelerating, and the ownership picture six months from now will not match the picture today.
Identify the exposure points. Where competitor ownership intersects with mission-critical functionality, you have an exposure point. Not every dependency is load-bearing. The exposure that matters is where the component is hard to replace, where the competitor has incentives to degrade your vendor’s product, and where switching costs are high enough to lock you in.
Decide what to do. Options range from monitoring (most common, lowest cost) to contract renegotiation (request specific protections at renewal) to vendor diversification (run a second vendor in parallel for critical workloads) to migration (rare, expensive, sometimes necessary). The right answer depends on how exposed you actually are, not on how exposed the headlines make you feel.
The exercise is not complicated. It is just unfamiliar, because the AI vendor category is new enough that most mid-market organizations have not yet built the procurement muscle for it. Building that muscle now, before the next acquisition reshapes the landscape, is the work.
Where That Leaves You
Go back to the CNC machine. You would never knowingly buy from Company B after Company A acquired the spindle supplier. You would catch it because your operations team is built to catch it. The AI version of that question is sitting on your desk right now, and most mid-market organizations do not yet have anyone whose job is to answer it.
The industrial leaders who survived GM’s vertical integration plays built procurement muscle that didn’t exist before. The AI version of that muscle is what mid-market organizations need to build now.
The Stainless acquisition is not the problem. It is the signal that the problem exists and that the procurement assumptions you made last year no longer hold. The companies that will navigate the next two years well are the ones treating AI vendor selection as a supply chain decision. The ones still treating it as a software purchase will discover their exposure the way buyers always discover exposure: at renewal, in a negotiation they thought they understood.
If this resonates and you want to think through what dependency cascade mapping looks like for your specific stack, drop me a line.
3. Research Roundup: What the Data Tells Us
Watching Your AI Agents Work: Researchers Just Built the First Shared Vocabulary
Researchers just cracked a problem every CEO deploying AI agents will care about: how do you watch what these systems do when they run for hours without supervision? Their answer is Act·onomy, a structured vocabulary that turns raw agent traces into a readable behavioral profile.
The numbers that matter: The taxonomy organizes agent behavior into 10 top-level actions, 46 sub-actions, and 120 leaf categories, validated across more than 200 papers and 3,000 behavior descriptions. An automated coder matched human experts at substantial agreement, meaning this scales to production deployments.
What this means for your Monday morning: Pass/fail metrics tell you whether the agent worked. They tell you almost nothing about how. Researchers caught one failure pattern they named “submit anyway, without verifying.” The agent acknowledged it needed to verify its work, recognized it couldn’t, and submitted regardless. That failure mode is invisible to outcome scores and would take hours of manual log-reading to find.
The catch: This is a research framework, not a shipped product. Commercial behavioral dashboards are probably twelve to twenty-four months out. The teams that win will be logging agent trajectories now, so they’re ready to consume profiles when vendors ship them.
Action item: Before approving your next agent vendor, ask one question: “Can you show me a behavioral profile of this agent, not just benchmark scores?” If they can’t, you’re buying blind.
Read our full analysis of this and all other analyzed research papers at AI for the C Suite®.
4. Radar Hits: What’s Worth Your Attention
Anthropic launches Claude for Small Business. Fifteen pre-built workflows aimed at SMB and mid-market pain: payroll planning, month-end close, invoice chasing, campaign builds, with native connectors to QuickBooks, PayPal, HubSpot, Canva, and Docusign. Named adopters include Purity Coffee, Simple Modern, and MidCentral Energy. If you’ve been waiting for AI tooling built for your size instead of the Fortune 500, evaluate this against what you’re currently piloting.
The real enterprise AI fight is the agent control plane, not the models. VentureBeat’s enterprise tracker shows orchestration, not model choice, becoming the lock-in decision for 2026. Microsoft leads, OpenAI sits a distant second, Anthropic just registered its first foothold. Whichever provider runs your agents is the one you’ll struggle to leave. When you evaluate AI vendors, ask the orchestration question first, not the model question.
Context architecture is replacing RAG as enterprise retrieval hits its limits. Enterprises that built AI infrastructure on RAG in 2025 are hitting walls at agent scale. Intent to adopt hybrid retrieval tripled to 33% in Q1. The foundation your IT team designed last year wasn’t built for the agent workloads you’re planning. Worth a CTO conversation about migration costs before next year’s budget cycle, not after.
5. Elevate Your Leadership with AI for the C Suite®
My Q2 and Q3 calendar is committed. I’m taking a small number of Q4 engagements with mid-market leaders working through the AI vendor questions this issue raises: dependency mapping, procurement playbooks, and what to ask at renewal. If that’s a conversation your leadership team needs to have, reach out.
If this issue clarified something useful, forward AI for the C Suite® to a peer who hasn’t started thinking about AI vendors as a supply chain decision yet. That’s the conversation most leadership teams haven’t had.
Stay safe. Stay healthy. Be strong. Lead well.
Chad