Exponential vs S-Curve
GPT-5 has been out for a few days now, and apart from marketing hype, the response has been a resounding "meh". Some say it's great for their particular use case, others say it's mediocre at best.
In the endless cycle of whose company's AI model is the current best, we can get the impression that there are huge strides being made. The whole "accelerationist" movement tells us that we can expect an exponential growth of model capabilities, just because the early steps (GPT-1 to 2 to 3 and 4) were so monumental. They'd tell us that, before we'd know it, the AI would design better versions of itself and then we'd really be off to the races, towards the so-called singularity, super-human intelligence and, depending on your mood, annihilation or abundance for all.
Well, just like many other promises of endless growth, this one doesn't quite seem to pan out as well. Instead, progress levels off to incremental gains and diminishing returns. Just like medicine in the 1900s has made tremendous strides and made it look like life expectancy was on an exponential growth curve didn't mean that life expectancy would grow indefinitely (don't tell Peter Thiel or Ray Kurzweil I said that, though), there are natural limits and constraints.
So, what does that mean? It means it's crunch time for engineers. We can't just sit around with the same old systems and same old prompts and just wait for better models. Models will keep getting better, but not at a rate that excuses laziness. Now's the time to tweak the prompts, the model selection, the vector databases and the scaffolding. Now's also the time to be less forgiving of products and tools that seemed to bank too hard on vast improvements in bare LLM capabilities. If it's nowhere near useful right now, don't let them tell you to "just wait until GPT-6 hits".
It's okay for bare LLM progress to slow down. It's not like in classical software engineering we write low-performance software and then say, "oh well, we'll just wait for the next version of PostgreSQL to make our bad queries execute faster". (Though there was that glorious time in the 90s where CPU speed doubled every time you blinked...)
Long story short, GPT-5 was underwhelming but that fact itself is also underwhelming. Lets just get back to the actual work of engineering working solutions to real problems.
