For decades, code contributions have been how open source projects learned who to trust. People would show up, do the work, take responsibility for their changes, and stick around. Over time, trust emerged from the work itself. AI tools have changed the economics of this very quickly. We use them ourselves every day, but a pull request no longer tells us as much as it used to about the person submitting it. A substantial patch used to imply substantial effort, and that effort was a reasonable proxy for good faith. That assumption no longer holds. For a browser, this matters. A browser runs untrusted input from the entire internet on the user’s machine, and one well-disguised vulnerability is all an attacker needs. We have already seen patient, well-resourced campaigns in open source to earn maintainer trust and abuse it. What has changed is how much faster and cheaper it has become to produce work that looks like a serious contribution.

Restoring global trust in American AI is another thing entirely. No matter how long the shutdown lasts, it shined a light on how fragile access to US frontier AI models is.

You cannot trust what you cannot see.

Each of these companies recognized the cognitive burden of unbundling. They're not selling features. They're selling trust.

They want a platform they can trust for three to five years.

Each of these companies recognized the cognitive burden of unbundling. They're not selling features. They're selling trust.

Agents gain credibility by doing. The fastest way to get other people to trust and use your Plus One is to have it execute tasks in public.

Only 9% of workers trust AI for complex, business-critical decisions, compared to 61% of executives — a 52-point trust chasm.

Gemma 4 models undergo the same rigorous infrastructure security protocols as our proprietary models.