How Claude Exposes Your Hedge Fund to the Fragility of the Global Software Supply Chain

In 2015, fewer than 5–10% of hedge funds shipped proprietary code. That number held for nearly a decade. Then AI-assisted coding arrived — and in two years, from 2024 to mid-2026, that number leapt past 25%.

This is, mostly, an exciting outcome. Progress is messy, and the ability for more people to build software faster is a net positive for the industry. I have concerns, but fundamentally I am optimistic.

Here is the thing to keep in mind: writing code is a great responsibility. There is so much opportunity to do so much harm that the last 40 years have produced an entire discipline of philosophy, process, and tooling designed to mitigate risk and minimize damage.

Someone asked me two years ago whether AI would eliminate the market for developers. I said then — and I believe now — that it is the opposite. Over the next several years, everyone will become a coder in some capacity. What that means will evolve. The tools will change. But the end result is that more of us ship code than ever before.

And that means more of us are exposed to risks most of us have never thought about.

What Is the Software Supply Chain?

The software supply chain is the global network of third-party components, tools, and services that companies rely on to build and run modern software — similar to how manufacturers rely on outside suppliers for parts and raw materials.

Very few firms write every line of software themselves. Developers routinely incorporate open-source libraries, cloud services, automated build systems, and external code packages created by others. This creates enormous efficiency and innovation benefits. It also introduces dependency risk: if one trusted supplier in that chain is compromised, malicious code can be distributed downstream to thousands of organizations at once through normal software updates.

When your fund uses Claude, Copilot, or any AI coding assistant to generate code, that code almost always references external packages. The AI suggests them because they are popular, well-documented, and widely used. That is exactly what makes them high-value targets.

Why Is It So Fragile?

The scale of the problem is staggering. Let me give you this week's numbers.

A Real-World Example: The TanStack Attack

Let me walk through one incident so the mechanics are clear.

TanStack is a set of popular building blocks used by millions of developers for web application routing and state management. It is trusted, widely adopted, and maintained by respected open-source contributors.

The result: organizations including Mistral AI, UiPath, and internal tools at companies like OpenAI were affected. Developer machines and automated cloud systems were compromised. Any stolen credentials could then be used to break into broader systems, access customer data, or continue spreading.

Why This Matters to Your Hedge Fund

Until recently, most hedge funds did not need to think about this. Your code was your models, maybe some Excel macros, and a Bloomberg terminal. The operational technology was purchased from vendors with security teams of their own.

That world is gone.

With AI-assisted coding, your team is now pulling in open-source packages, building internal tools, deploying web applications, and automating workflows using the same global dependency ecosystem that everyone else uses. Every npm install or pip install command reaches into that supply chain.

Who Gets This Right?

I have had the privilege to work with some very forward-thinking people. Over the last ten years, I have learned from some of the smartest firms in the industry about how they manage this risk.

One example is Jane Street. Relative to most financial firms and most modern software companies, Jane Street is widely known for minimizing dependence on external software components and building a large portion of its technology stack internally. They use OCaml as a core language across trading, infrastructure, tooling, and even internal applications. They maintain their own foundational libraries, developer tools, and runtime systems.

That is an extreme approach — and it works for them because they have the engineering depth to sustain it. Most firms cannot do what Jane Street does. But the principle underneath it is accessible to everyone:

How to Reduce the Risk

You do not need to become Jane Street. But you do need a posture. Here is what the most responsible firms are doing right now:

If None of This Makes Sense Yet — That's Okay

If you are new to shipping code, or if your team is already shipping but none of this supply chain language feels familiar — you are not behind. You are exactly where most hedge funds are right now. The important thing is that you recognize the gap and close it before it costs you.

The team that built Audition AI has been helping hedge funds ship production code for two decades. We have seen every phase of this evolution — from the era when funds ran nothing proprietary, to today, when AI-assisted development is creating extraordinary opportunity and extraordinary exposure.

We can help you put good process in place. Process that does not slow you down. Process that does not break the bank. The kind of guardrails that let your team move fast because the foundation is solid — not in spite of it.

You do not need to become a software company overnight. You just need the right people in your corner — people who have done this before, who understand the regulatory landscape you operate in, and who know how to make governance feel like an accelerator rather than a brake.

The Bottom Line

AI-assisted coding is not the problem. It is a force multiplier. But force multipliers amplify risk as much as they amplify capability.

The hedge funds that thrive in this new landscape will be the ones that embrace AI-assisted development and invest in the governance, tooling, and expertise to manage what comes with it.

You would not buy a building without inspecting the foundation. Do not ship code without understanding the supply chain it stands on.