In May 2025, Elon Musk's xAI was running dozens of gas turbines of its own alongside Colossus, the supercomputer cluster it built in Memphis, Tennessee. The turbines were switched on before the company had secured official permits. As nearby residents prepared lawsuits over air pollution, xAI offered a single justification: the standard procurement process for power infrastructure takes at least 18 months. The logic was simple — winning the model race requires compute first, and running compute requires electricity first. Musk's reported push to bring gas-turbine manufacturing in-house follows the same logic.
That scene is a compressed snapshot of what's happening across the entire AI industry right now.
The Vertical-Integration Race Has Already Reached the Physical Infrastructure Layer
Nvidia has completed its own vertical integration, spanning chip design, networking hardware, and the software stack, while Anthropic and OpenAI have pivoted toward developing their own chips and operating their own data centers. As of the first half of 2025, Meta has been running experiments with robots inside its data centers to automate hardware inspection and replacement — a move meant to cut its reliance on outside maintenance contractors.
What these moves have in common is a shrinking of the external procurement chain. Fewer procurement steps means more speed, but it also means the responsibility for controlling each step now concentrates inside a single organization. That concentration of responsibility is creating new vulnerabilities.
As of May 2025, three separate security incidents targeting AI infrastructure surfaced within days of each other. A privilege-escalation vulnerability was discovered in Omarchy, a Linux-based AI development environment that had been spreading rapidly — a flaw that let an attacker with ordinary user privileges take over an entire system. In the EU, legislative discussions reignited around requiring backdoors for government access to encrypted communications; tech companies are pushing back, arguing it undermines core security design principles, but the debate continues. And AI safety research organizations METR and Redwood Research published a postmortem on the Hugging Face hack, with a core finding that public model hubs are a structurally weak point in AI supply-chain security.
Viewed separately, these are three unrelated events. Viewed together, they're different layers of the same problem. The more vertically integrated the AI stack becomes, the shorter the path by which a vulnerability in one layer propagates to the entire stack.
The Faster the Infrastructure Race Moves, the Further Security Audits Fall Behind
There's a familiar pattern behind how security vulnerabilities arise in software development: teams adopt an external library or platform without vetting it, for the sake of speed, and the problem surfaces later in that component. What's happening in AI infrastructure right now is that same pattern, playing out at a far larger scale.
Hugging Face is the platform where AI developers around the world upload and download pretrained models. For any individual developer, verifying the provenance of every single model is, in practice, unrealistic. Speed comes first, and vetting comes later — if at all. If an attacker embeds malicious code in a model file, it can infiltrate thousands of development environments at once. That's exactly what the METR and Redwood report flagged: the platform runs on trust, but the technical basis for that trust hasn't been solid enough.
The Omarchy vulnerability fits the same pattern. It was adopted quickly as a tool that simplified AI development-environment setup, but security auditing didn't keep pace with adoption. The EU backdoor debate reveals the same tension at the national level: governments want access to security infrastructure, and companies argue that the access itself weakens security.
Vertical integration, as a strategy for reducing outside dependency, does shorten the supply chain. But the security-auditing responsibility for each of those shortened steps gets absorbed internally. If your stack combines Nvidia chips, Anthropic's API, and models downloaded from Hugging Face, then a vulnerability in any one of those layers hits you at the same time as the others. Anyone running that kind of combination needs to know exactly how many supply chains they're actually standing on.
There's also a related data point: the Texas governor froze the budget for a rollout of Flock Safety's camera system, which combines automatic license-plate recognition with AI analysis. Texas had generally been classified as one of the more AI-regulation-friendly states in the US, which makes this financial brake on AI surveillance infrastructure a small but telling signal. While AI regulation remains gridlocked at the federal level, state and local governments are starting to move first.
What Solo Entrepreneurs and Small Teams Should Be Checking Right Now
The vertical-integration race in AI infrastructure looks like a Big Tech story, but it has real consequences for everyone building on top of that infrastructure — the developer who wires a Hugging Face model directly into a project, the product manager who installs an open-source AI dev environment as-is for the team, the solo entrepreneur who has automated a workflow by stitching together outside APIs.
Whether you're shaping a career or building out a business system, how you balance "expanding your tool stack quickly" against "understanding what each tool depends on" has become a genuinely practical question. That sense of balance matters more the closer you get to a leadership role, in any function. Understanding the supply-chain structure of the tools you use is becoming as much a part of good judgment as reading the numbers and setting strategy.
Here are a few concrete things worth checking.
Map your own AI stack. Start by writing down which models you use and where they come from, which APIs your workflow passes through, and which platforms store your data. Once that map exists, you can check whether any layer of it has had a recent security advisory.
When downloading a model from Hugging Face, it's worth asking whether you actually have the habit of checking download counts, last-update dates, and community reviews. Plugging an unverified model file straight into a production environment brings its risk along with it.
When adopting an open-source AI development tool for your team, build in a step to check when it last received a security patch. The Omarchy case showed that the faster a tool spreads, the shorter its audit cycle needs to be.
The EU backdoor debate and the Texas surveillance-infrastructure case won't directly affect a solo entrepreneur in Korea any time soon. But when you're evaluating a new AI tool, taking a moment to check where that service's data jurisdiction sits, and how that country's government-access policy is shifting, will make for better decisions.
The race over energy infrastructure feeds directly into cloud-service pricing. How fast xAI and Meta bring power infrastructure in-house, and how hard established cloud providers compete to secure power for their own data centers, will eventually show up in the cost of GPU access and API pricing. Reviewing the contract terms on the services you use now — when and how their pricing could change — will save you an unpleasant surprise later.
At this moment, when the weight of the AI power struggle is shifting from model benchmarks to power lines and security audit reports, the capability that actually matters for solo entrepreneurs and small teams isn't being first to try the newest model — it's understanding the structure of the infrastructure you're standing on, and reading the changes moving through it.
