AI has become a very useful tool for me#

If you know me personally, you know that I’ve been pretty slow on the LLM/GenAI uptake. At this point, however, if you are a software developer that isn’t integrating GenAI tools into your workflow I think you are probably going to be left behind. I’ve been playing around with these tools a lot more lately, and I’ve come to a few conclusions.

  1. Models that can be run locally (I’ve used a lot of different Ollama models) are mainly just ok. The best results I’ve had is with Gemma3:27b on an m4 macbook. Haven’t tried qwen3 coder yet.
  2. The amazing metrics you see referenced require 100b+ models that are not going to be feasible to run locally for most people.
  3. Your application matters a lot. I find that these tools are extremely useful for java spring boot microservices but a lot less useful for scientific projects in Rust.
  4. I really enjoy using the expensive LLM tools provided at work and enjoy using the free version of tools less.
  5. Claude has given me the best performance so far.

I’m still debating which subscription to get for personal projects. I know Claude is my favorite, but it’s also much more expensive. I also have a newborn right now and not much time for side projects, so I’m delaying the decision.

AI has NOT become an irreplaceable tool#

I am not a vibe coder and don’t think I will ever give in to the vibes. Even when I prompt claude code to make changes, I always know exactly what I want done and how I want it done. Many times I’ll make the changes myself because it’s faster than trying to prompt Claude. LLMs have definitely improved my efficiency, but I would be fine if all the GPUs in the world suddenly melted down. I’m not a viber for a couple reasons:

  1. I enjoy coding and problem solving.
  2. I’m not convinced models are capable enough to be fully trusted.
  3. I want to keep my own skillset because the future of AI is uncertain to me

How does the economics of AI work?#

From my very limited understanding, the fact that major AI companies are still requiring funding rounds indicates that AI is currently not profitable. That makes sense to me since ChatGPT and Claude are available for free even though inference is certainly not free. I think the significant portion of cost is due to model training. It seems like model training costs won’t be stopping anytime soon as companies race towards general and then “super” intelligence. I’m not convinced either of those are a given. As a result, prices will have to increase or ads will be introduced to recuperate investor money. I’m also not sure how this applies to providers of open weight models. Those prices can seem more reasonable, but are those subsidized as well? Finally, the current datacenter power consumption for model training seems entirely unsustainable. If I recall correctly, Elon Musk is powering some of his datacenters with a constant flow of diesel fuel into generators because he can’t get enough power from the power grid. The scale of everything at the moment seems to border absurdity. I think we are in for a bumpy ride for the next few years as the impact of GenAI technology and infrastructure works itself out.

Please let me know if I’m wrong about anything or with better predictions!