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That’s a straw man.

You don’t know how often we use LLM calls in our workflow automation, what models we are using or, what our margins are or what a high cost is to my organization.

That aside, business processes solve for problems like this, and the business does a coat benefit analysis.

We monitor costs via LiteLLM, Langfuse and have budgets on our providers.

Similar architecture to the Open Source LLMOps Stack oss-llmops-stack.com

Also, your last note is hilarious to me. “I don’t want all the free stuff because the company might charge me more for it in the future.”

Our design is decoupled, we do comparisons across models, and the costs are currently laughable anyway. The most expensive process is data loading, but good data lifecycles help with containing costs.

Inference is cheap and LiteLLM supports caching.

It’s professional development of an emerging technology. You’d rather bury your head in the sand and say it’s not useful?

The reason not to take it seriously is to reinforce a world views instead of looking at how experts in the field are leveraging it, or having discourse regarding the pitfalls you have encountered.

The Marketing AI hype cycle did the technology an injustice, but that doesn’t mean the technology isn’t useful to accelerate determistic processes.

It depends on the methodology. If you’re trying to do a direct port. You’re probably approaching it wrong.

What matters to the business most is data, your business objects and business logic make the business money.

If you focus on those parts and port portions at a time, you can substantially lower your tech debt and improve developer experiences, by generating greenfield code which you can verify, that follows modern best practices for your organization.

One of the main reasons many users are complaining about quality of code edited my agents comes down to the current naive tooling. Most using sloppy find/replace techniques with regex and user tools. As AI tooling improves, we are seeing agents given more IDE-like tools with intimate knowledge of your codebase using things like code indexing and ASTs. Look into Serena, for example.

Accelerated delivery. We use it for intelligent verifiable code generation. It’s the same work the senior dev was going to complete anyway, but now they cut out a lot of mundane time intensive parts.

We still have design discussions that drice the backlog the developer works off with their AI.

Early adopters will be rewarded by having better methodology by the time the tooling catches up.

This is why I say some people are going to lose their jobs to engineers using AI correctly, lol.

What are you even trying to say? You have no idea what these products are, but you think they are going to fail?

Our company does market research and test pilots we customers, we aren’t just devs operating a bubble pushing AI. We are listening and responding to customer needs.

These tools are mostly determistic applications following the same methodology we’ve used for years in the industry. The development cycle has been accelerated. We are decoupled from specific LLM providers by using LiteLLM, prompt management, and abstractions in our application.

Losing a hosted LLM provider means we prox6 litellm to something out without changing contracts with our applications.