Because what is aligned, how and for whom? And who decides how that alignment should look like? There are probably many domains in which required alignment is in conflict with each other (e.g. using LLMs for warfare vs. ethically based domains). I can't imagine how this can be viable on the required scale (like one model per domain) for the already huge investments.

Anyone spotting the issue here? What did that really cost?

I am not against compute being used for scientific or other important problems. We did that before LLMs. However, the major LLM gatekeepers want to make all industries and companies dependent on their models. And, at some point, they need to charge them the actual, unsubsidized costs for the compute. In the meantime, companies restructure in the hopes that the compute costs remain cheap.

In my example, I expected it to create UI elements for a business application / expert system. And it did fine. In fact, I believe its perfect for creating average and functional designs. Its a better way to test variations of UIs for expert systems. But I want to know what the actual costs are.

This technology needs to become a commodity to destroy this aggregation of power between a few organizations with untrustworthy incentives and leadership.

Also explains perfectly why I never met an engineer who was eager to run workloads on Azure. In orgs I worked, either the use of Azure was mandated by management (probably good $$ incentives) or through Microsoft leaning into the "Multi-Cloud for resilience" selling point, to get Orgs shift workloads from competitors.

Its also huge case for open (cloud) stack(s).

Someone (with deep pockets to bear the token costs) should let Claude run for 26 months to have it optimize its Rust code base iteratively towards equal benchmarks. Would be an interesting experiment.

The article points out the general issue when discussing LLMs: audience and subject matter. We mostly discuss anecdotally about interactions and results. We really need much more data, more projects to succeed with LLMs or to fail with them - or to linger in a state of ignorance, sunk-cost fallacy and supressed resignation. I expect the latter will remain the standard case that we do not hear about - the part of the iceberg that is underwater, mostly existing within the corporate world or in private GitHubs, a case that is true with LLMs and without them.

In my experience, 'Senior Software Engineer' has NO general meaning. It's a title to be awarded for each participation in a project/product over and over again. The same goes for the claim: "Me, Senior SWE treat LLMs as Junior SWE, and I am 10x more productive." Imagine me facepalming every time.

What is included in the 'verify' step? Does it involve changing the generated code? If not, how do you ensure things like code quality, architectural constraints, efficiency and consistency? It's difficult, if not (economically) impossible, to write tests for these things. What if the LLM does not follow the guidelines outlined in your prompt? This is still happening. If this is not included, I would call it 'brute forcing'. How much do you pay for tokens?

I'd consider that a feature that would increase trust in such a platform. These platforms require trust, right?