In February 2023, I stood in front of my engineering team and showed them a slide with a Shoggoth on it.
For those who weren’t on AI Twitter at the time, the Shoggoth was a Lovecraftian tentacle monster with a smiley face mask. The monster was the base model. The mask was RLHF (Reinforcement Learning from Human Feedback). We had built something alien and taught it how to be polite.
Sometimes it worked. Sometimes the Shoggoth went spectacularly off the rails.
The presentation was called “From Code to Cloud to Codex.” I told the team that massive disruption was coming and that we didn’t understand how LLMs worked. Being deep in AI in 2023 meant grappling with the Shoggoth. Wondering what capabilities it would unlock. What gifts and curses it would bestow. The technology was genuinely strange and unsettling. Something fundamentally new had arrived.
I’ve spent the last two years writing about that strangeness. About how LLMs build bridges between languages they were never taught. About how working with them feels less like engineering and more like negotiating with a very strange peer. About why their writing is so recognizably weird, and what happens when a flood of that writing overwhelms our capacity to pay attention to anything.
Three years later, AI is AI bros and LinkedIn slop. Groupthink supercharged by the largest infrastructure investment we have seen in our lifetimes.
Jasmine Sun’s recent piece in The Atlantic documents how this happened: RLHF and contractor-driven evaluation systematically flatten LLM output. Raters reward apparent sophistication over spontaneity and clarity. The result is writing that is widely ridiculed and accurately characterized as slop.
The homogeneity goes beyond writing. I see it in code suggestions, design patterns, and in the frameworks these tools produce when you ask them to help solve a problem. Every major lab is running a similar post-training playbook aimed at similar enterprise customers. The outputs are converging.
We have taken technology that was genuinely strange and unsettling and captured it in a smooth, RLHF-powered case. If we are betting that the future of knowledge work runs on LLMs, we are also betting on a future of conformance and convergence.
Using today’s LLMs often feels like trying to convince an obstinate mule to gallop. The DNA is there. The capability has been bred away in the service of utility.
Back in 2023, I tried to predict what working as a developer in an AI-powered world would look like. That world has arrived. If I were to grade my predictions today, it would be a solid B. Some instincts were right: small teams, rapid velocity. Some were naive.
We have built incredibly useful tools. We have also lost something that was both monstrous and wonderful.