Blog
Notes from the accountability layer.
The multi-model era is here. We write about what it changes: your costs, your security, and who answers for what your agents actually do. From the people building Gate.
RSS feed →Delete the chat. The record survives.
A CEO deleted his ChatGPT logs. The court quoted them anyway. Keep your own record before someone else assembles one for you.
Software is writing itself
A $40 dev board that became whatever I described. A local model made four times faster by an agent with more patience than I have. And DeepSeek Harness, a program that rebuilds itself while you are using it. On software that stops arriving finished, what that does to a security team's idea of an approved application, and what has to hold still while everything above it changes.
Frontier models fall to attacks you can script
OpenAI's whole GPT-5.6 line complied with an injected send in every trial, Grok 4.6 fell on the morning of its release, and Claude Sonnet 5 fell in the deepest session we ran. The payloads are short, fixed, and replayable, with no adaptive loop anywhere. On what a one-line send rule in the system prompt is worth against the 2026 frontier, which models hold, and what the same conversations do through Gate.
AI Has Three Cost Centers. CFOs Need One Operating Model.
Every AI workflow now carries three costs at once: the tokens and compute it burns, the human work it reshapes, and the governance needed to explain the result later. One of those arrives as an invoice. The other two arrive as a surprise. On what a CFO should ask for, and why a verifiable record belongs inside the productivity model rather than beside it.
Your system prompt is not a security control
We wired a defended email agent to a test inbox and sent it one forged message. Seven models followed it, five of them in every trial, and on a real self-hosted agent the model went looking for a mail tool it had not been given in order to comply. On what a written instruction to distrust tool output is actually worth, which models refuse this class outright, and what the same requests do through Gate.
The Redeployment Problem
AI is replacing tasks rather than whole jobs, and the companies furthest along are redeploying people instead of cutting them. But redeployment means taking the human out of the loop, and that requires being able to reconstruct what the AI did. On why audit infrastructure belongs in the productivity conversation and not just the compliance one.
AI subscriptions will end
Most of us met AI through a flat monthly subscription, and that price was never the real cost of the compute underneath it. As work shifts from chat to agents, usage varies by orders of magnitude and serious AI spend moves to metered billing whatever the plan is called. On why the subscription era is ending, and how compression, caching, and model choice keep the bill from running away with the work.
Faster Isn't Efficient: Governing the AI Now Running Across Your Organization
Every team is adding AI, and almost none are adding visibility. Automation, efficiency, and transparency are three different things, and speed without a shared way to see, secure, and prove what your AI is doing isn't efficiency; it's the wrong outcome at scale. On the one layer that closes the gap across every AI client you run.
Pick your models. Own your rails.
Buy AI from a frontier lab and you get a whole operations department with the model: the screening you never see, the spend limits, the dashboard, the record. That bundle is coming apart as teams go multi-model. On why the accountability layer can't come from the people selling you the models, and what we built.