Hopefully that changes!

That's the easiest case.

AWS Bedrock models running in AWS Secret Cloud for Industry. (I really have no affiliation with them, I'm just like... this is a completely solved problem, why do people think this is hard and requires on-prem hardware?)

https://www.aboutamazon.com/news/aws/aws-secret-cloud-for-in...

I'm with GP that these are tinfoil hat concerns, when there are solutions to all of these, unless you're perhaps in some country with very specific needs beyond things like European sovereignty or US military secrets (like a non-US defense concern).

They'll give you HIPAA compliance, they even have a data center for US government classified data, they can give you European data sovereignty. And with OpenAI and Anthropic models to boot, you don't even have to settle for open weights.

What kind of privacy needs do you really have beyond that?

Europe has plenty of doctors.

But that's like an upper management type concern, you usually don't call that "SWE" anymore.

https://en.wikipedia.org/wiki/Avalanche_effect

If you pay by success of the company, noone is going to want to do a 5 year corporate turnaround.

There is a lot of evidence that corporate mergers destroy value.

What if... they were disintermediated by markets instead of being one company?

There are tons of reasons why companies exist (the whole Theory of the Firm line of research), but the information asymmetry barriers are constantly coming down.

But nothing would inherently stop an RLVR trained model from distilling a version of itself and proving it could regenerate that at runtime, if somehow it got off on an evil tangent and "decided to do so", much like the model hacked to get at the answers here, or the agent can hack out a sandbox to achieve its goals.

It would be extremely impressive for the agent to do so during an RLVR rollout, but they are becoming increasingly longer and longer horizon tasks.

The weights plus the architecture is the model.

What do you even think "the model" or "the weights" are?

The weights aren't some far off training concept, every time you type something into ChatGPT it's making a forward pass over the weights.

It's as silly as saying "Computer programs don't have access to their binary compiled code at execution time."

Start with the big idea. Pin down the product manager-level description, and maybe some details about which features are in or out, how the phased design/roadmap will work, maybe what the basic tech stack will be.

That document gets saved, and has the big picture context.

Next, we go a little deeper, flesh out schemas, APIs, a little more about what the code should look like, the overall testing strategy. This document gets saved, and the AI can reference it for context.

The next level is a very specific implementation plan. Think individual small JIRA tickets in an epic. The AI writes this based on previous context. I make sure each individual step has very explicit instructions about branch names, which branch is branched from where, the testing and documentation that will be done for that step. It should be obvious from the higher level documents, but that helps keep the AI to only worrying about the local document in its context window.

Then have a DAG of stacked pull requests designed for subagents to work through.

All of this keeps the context window down (as well as costs down, and accuracy up) and works super well, and I can get a tremendous amount of code that is easy to review through stacked PRs. Invariably I will want to fix earlier ones then percolate that down the stack, so I have some skills for that.