Edwin Chen

i agree with dario. i used to work on feed algorithms at google, fb, and twitter. we optimized for engagement because it was the only thing we knew how to measure, even when it was only a weak proxy for what people actually wanted. and then the systems did exactly what we asked! they figured out how to hack engagement in ways nobody intended, and we didn't understand the consequences until later. for me, that's always been the alignment problem in miniature. that experience is a big part of why i started Surge, and it shaped how i think about data, quality, evals, and the hills we want to climb. AI makes the same problem even more consequential. we have models that can solve Navier Stokes, that billions of people will talk to and trust, and that can act on their own. getting them to climb a hill isn't the hard part; they're already incredible at that. the hard part is choosing the right objective - and making sure the data, rewards, and evals actually point at what we want. so, i agree. if we don't want to repeat the lessons of social media - but with way smarter systems that can act on their own, and convince, at way greater scale - alignment has to be built into the way we train and evaluate models from the beginning. a lot more work is needed to make sure models stay human-aligned. reach out if you're interested in working on this together.

"Prognosticative pastry." "A hound circling a tree, nose to bark." These aren’t parodies - they’re actual quotes from SOTA models in response to creative writing prompts, and they’re winning leaderboards that are rewarding slop. We’re introducing *Hemingway-bench*, a new AI writing leaderboard, to fix this: lnkd.in/gdG9QdMc lnkd.in/gEbnMwJs We designed Hemingway-bench to push frontier model writing toward genuine nuance and impact. Instead of autograders and two-second vibe checks - both of which love fancy literary devices and dense formatting, over actual quality - we used expert human writers across a variety of fields to judge real-world writing tasks. Why? I love writing. I love reading. Great science fiction is one of the things that's always inspired me. Even in terms of "enterprise value", so much of what we do in our day-to-day involves writing - we want crisp emails and insightful reports, not dry, verbose summaries. Yeah, coding is important - but there's a reason I use CC-assisted apps, but still haven't read a full-fledged AI novel. What did we find? Current leaderboards are easily hacked, and often negatively correlated with actual quality. If a model (over)uses all the stuff you learn about in school (metaphors in every sentence! transition words! complex, flowery phrases!), it ranks high on EQ-bench and LMArena. But that’s not good writing that people actually want. The winners of Hemingway-bench didn't sound like they were trying to win a poetry slam. Gemini 3 Flash, Pro, and Opus 4.5 took the top 3 spots because they had natural voices that didn't sound pretentious. They were poetic and immersive, but in the right ways. When they used wit, they didn't sound cringey and try-hard - they sounded like your naturally funny friend. I'm waiting for the day AI wins a Pulitzer, and hopefully Hemingway-bench helps guide it on its way. Check out the leaderboard and examples here: lnkd.in/gdG9QdMc And our blog post describing it: lnkd.in/gEbnMwJs

one of my favorite examples from our Kimi coding post-training run: it had to write a Zstandard decompressor, but there was no zstd binary available to check whether it worked. so the trained model wrote a compressor first, generated its own valid test files, and used those to test the decompressor. no ground truth existed, so it built one itself. Read the full report: lnkd.in/e3pv9gnR