Why every GTM org will need AI skill marketplace

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Why every GTM org will need AI skill marketplace<br>Your team's AI fluency gap is widening and what to do about it

Alex Lindahl<br>Jun 14, 2026

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If this was forwarded, welcome to the GTM Engineering newsletter. Hi, I’m Alex, one of the first GTM Engineers at Clay and here to share what I’m building for customers, how to build AI-native GTM, and resources for GTM Engineering. Join 7,000+ GTM operators, founders, and investors.

Most of the time - I have no idea what model to use. The subtitle on the model selection says ‘complex reasoning’. But what does that even mean?<br>Many of us default to the most powerful model for most tasks. There’s no incentive not to. It’s not problem yet, but it will be soon.<br>Think about what that means at scale. Anthropic’s flagship model costs roughly 15x more per token than its smallest one. A rep summarizing a call transcript doesn’t need the flagship. Neither does a workflow that formats CRM notes or drafts a follow-up email. But nobody told them that, there’s no system enforcing it, and nobody’s measuring the difference. Across a 200-person GTM org running AI workflows all day, that adds up to a real budget line nobody owns yet.

This is just one problem with AI adoption on GTM teams.<br>AI skill sprawl is another one. Lack of centralization is another, and the larger one is how inconsistent AI usage is and varied due to a wide spectrum of individual fluency.<br>I’ve been thinking about this problem for awhile. Then was introduced to Tessl, a company founded by Guy Podjarny. He also started Snyk (a cybersecurity unicorn where I used to work).<br>Tessl is a management layer that turns risky, sprawling, invisible skills into a governed, measurable system. They argue there’s 3 questions that every AI native team need to answer:<br>Security & governance - if a risky skill ran in your environment, would you even know?

Standardization & reuse - how much are duplicate, outdated skills costing your team?

Continuous optimization - your agents have the skills, but are they actually using them?

There’s a lot more to AI skills to unpack than I realized.

Skills are a new unit of software.<br>This has many implications for security, governance, and so on. But at a basic level, they’re building a product I believe will become a default layer of everyone’s GTM stack and approach to adopting, scaling, and using AI effectively across the org.

This is one symptom of a bigger problem: GTM teams are building AI skills with zero infrastructure underneath them. And we’ve seen this exact movie before.

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A skill marketplace will solve for more than just cost and a single source to find your organizations built skills. But let’s dive into the cost problem first.<br>A parallel to cloud computing

In the early cloud days, every engineer could spin up whatever compute they wanted. It felt like freedom. Then the bills arrived, and an entire discipline — FinOps — was invented to clean up the mess. It still hasn’t fully worked: Flexera’s 2025 State of the Cloud Report found that 27% of cloud spend is wasted, a number that’s barely moved since 2019. At a $675B global cloud market, that’s roughly $180B a year evaporating into idle instances and over-provisioned resources .<br>Flexera’s own analysts drew the line to what’s next: just as early cloud usage produced unwieldy costs, AI spend is following the same arc — and “FinOps for AI ” is already forming as a category to deal with it.<br>Most GTM leaders haven’t connected this yet: Skills, not infrastructure, decide what AI costs in your org. The prompts, instructions, and workflows your team feeds to Claude, ChatGPT, and your agents determine which model runs, how many tokens burn, and whether the output is even usable. Skills are the unit where cost, quality, and security all get decided. And right now, in almost every GTM org I see, skills are markdown files living in someone’s Slack DMs.<br>The wild west of AI exploration

The cost problem is the visible one. There are three quieter problems compounding underneath it.<br>Nobody is testing anything.

Software engineers learned long ago that untested code is broken code — you just don’t know it yet. The dev world is already applying this to AI: Tessl runs task-based evaluations on agent skills and found that evaluated, optimized context produced up to 3.3x improvement in agents using APIs correctly. Now ask yourself: who in your GTM org has ever run an eval on a prospecting skill? On an account research prompt? Anyone? In GTM, “testing” a skill means one person ran it twice and it seemed fine. We’re shipping un-evaluated skills to entire sales teams and wondering why output quality is inconsistent.

Everyone gets different outputs.

When ten reps each write their own version of “research this account before my call,” you get ten different research standards walking into ten different meetings. The whole point of GTM engineering is building systems that...