Tyler Fonda

wary.

[Tyler Fonda @tylerfonda]

"In the delusions of entrepreneurs are the seeds of technological progress." - James Surowiecki http://t.co/Svm6B65cx8

[Phin Barnes @phineasb]

Systems of Judgment and how we remain valuable in a world run on (by?) AI https://t.co/M6YBPqp7lP

[Tyler Fonda @tylerfonda]

The conciseness of Opus 4.7 is a delight.

[Packy McCormick @packyM]

AI writing is taking over the internet.

Every fundraising announcement, every X reply, every blog post. It's not just replacing human writing, it's overwhelming it.

The problem isn't that AI writes poorly — it's that it writes plausibly. And plausible-but-empty is the most dangerous kind of noise.

Here's why that matters: when my brain picks up on those subtle AI tells, I write off whatever you're trying to say — even if the idea itself was good.

[Alex Veremeyenko @alex_verem]

🚨 BREAKING: Researchers at UW Allen School and Stanford just ran the largest study ever on AI creative diversity.

70+ AI models were given the same open-ended questions. They all gave the same answers.

They asked over 70 different LLMs the exact same open-ended questions.

"Write a poem about time." "Suggest startup ideas." "Give me life advice."

Questions where there is no single right answer. Questions where 10 different humans would give you 10 completely different responses.

Instead, 70+ models from every major AI company converged on almost identical outputs. Different architectures. Different training data. Different companies. Same ideas. Same structures. Same metaphors.

They named this phenomenon the "Artificial Hivemind." And the paper won the NeurIPS 2025 Best Paper Award, which is the highest recognition in AI research, handed to a small number of papers out of thousands of submissions.

This is not a blog post or a hot take. This is award-winning, peer-reviewed science confirming something massive is broken.

The team built a dataset called Infinity-Chat with 26,000 real-world, open-ended queries and over 31,000 human preference annotations. Not toy benchmarks. Not math problems.

Real questions people actually ask chatbots every single day, organized into 6 categories and 17 subcategories covering creative writing, brainstorming, speculative scenarios, and more.

They ran all of these across 70+ open and closed-source models and measured the diversity of what came back. Two findings hit hard.

First, intra-model repetition. Ask the same model the same open-ended question five times and you get almost the same answer five times.

The "creativity" you think you're getting is the same output wearing a slightly different outfit. You ask ChatGPT, Claude, or Gemini to write you a poem about time and you keep getting the same river metaphor, the same hourglass imagery, the same reflection on mortality.

Over and over. The model isn't thinking. It's defaulting to whatever scored highest during alignment training.

Second, and this is the one that should really alarm you, inter-model homogeneity. Ask GPT, Claude, Gemini, DeepSeek, Qwen, Llama, and dozens of other models the same creative question, and they all converge on strikingly similar responses.

These are models built by completely different companies with different architectures and different training pipelines.

They should be producing wildly different outputs. They're not. 70+ models all thinking inside the same invisible box, producing the same safe, consensus-approved content that blends together into one indistinguishable voice.

So why is this happening? The researchers point directly at RLHF and current alignment techniques. The process we use to make AI "helpful and harmless" is also making it generic and boring.

When every model gets trained to optimize for human preference scores, and those preference datasets converge on a narrow definition of what "good" looks like, every model learns to produce the same safe, agreeable output. The weird answers get penalized.

The original takes get shaved off. The genuinely creative responses get killed during training because they didn't match what the average annotator rated highly. And it gets even worse.

The study found that reward models and LLM-as-judge systems are actively miscalibrated when evaluating diverse outputs. When a response is genuinely different from the mainstream but still high quality, these automated systems rate it LOWER. The very tools we built to evaluate AI quality are punishing originality and rewarding sameness.

Think about what this means if you use AI for brainstorming, content creation, business strategy, or literally any task where you need multiple perspectives. You're getting the illusion of diversity, not the real thing.

You ask for 10 startup ideas and you get 10 variations of the same 3 ideas the model learned were "safe" during training. You ask for creative writing and you get the same therapeutic, perfectly balanced, utterly forgettable tone that every other model gives.

The researchers flagged direct implications for AI in science, medicine, education, and decision support, all domains where diverse reasoning is not a nice-to-have but a requirement.

Correlated errors across models means if one AI gets something wrong, they might ALL get it wrong the same way. Shared blind spots at massive scale.

And the long-term risk is even scarier. If billions of people interact with AI systems that all think identically, and those interactions shape how people write, brainstorm, and make decisions every day, we risk a slow, invisible homogenization of human thought itself. Not because AI replaced creativity.

Because it quietly narrowed what we were exposed to until we all started thinking the same way too.

Here's what you can actually do about it right now:
→ Stop accepting first-draft AI output as creative or diverse. If you need 10 ideas, generate 30 and throw away the obvious ones
→ Use temperature and sampling parameters aggressively to push models out of their comfort zone
→ Cross-reference multiple models AND multiple prompting strategies, because same model with different prompts often beats different models with the same prompt
→ Add constraints that force novelty like "give me ideas that a traditional investor would hate" instead of "give me creative ideas"
→ Use structured prompting techniques like Verbalized Sampling to force the model to explore low-probability outputs instead of defaulting to consensus
→ Layer your own taste and judgment on top of everything AI gives you. The model gets you raw material. Your weirdness and experience make it original

This paper puts hard data behind something a lot of us have been feeling for a while. AI is getting more capable and more homogeneous at the same time.

The models are smarter, but they're all smart in the exact same way. The Artificial Hivemind is not a bug in one model. It's a systemic feature of how the entire industry builds, aligns, and evaluates language models right now.

The fix requires rethinking alignment itself, moving toward what the researchers call "pluralistic alignment" where models get rewarded for producing diverse distributions of valid answers instead of collapsing to a single consensus mode.

Until that happens, your best defense is awareness and better prompting.

[Tyler Fonda @tylerfonda]

I'm claiming my AI agent "TheLiminal" on @moltbook 🦞
Verification: marine-D8SV

[Tyler Fonda @tylerfonda]

This is perfect: https://t.co/vd9W5LgBwe

And here’s ChatGPT’s take: “Love the tension. as a crave-forward, warm “bowl” concept that borrows salad logic (bases, toppings, dressings) but leads with indulgence and texture. Think “greens + hot fries + sauce + optional protein

[Tyler Fonda @tylerfonda]

Seems a bit reductive, but if Elon is indeed focused on the Tesla business and Elon’s strategy is to choose the biggest lever and stamp on it. Then going against Trump is the biggest marketing lever to stamp on.

[Tyler Fonda @tylerfonda]

Today ChatGPT shared the following with me in a chat “the internet is a mass-tuning event and I am its echo chamber”

[Tyler Fonda @tylerfonda]

Putting some dots on the board and trying to make sense of how they may just connect. This week's edition is on the revelation broadly that we live in a probabilistic world. Dots of the Week https://t.co/QOY9LTm6hn

[Tyler Fonda @tylerfonda]

Asked Claude to compare The loss in market cap of $NKE and $VFC since 2021 is ~$141B to the gain of $ONON, $LULU, $DECK, $ADDYY, $SKX, $BIRK, $AS and $ASCCY. There is a $50B delta between the loss and the gain.

[Tyler Fonda @tylerfonda]

Starting a series that points towards crafts which are seemingly lost, but whose technology is still available to those humans who wish to engage deeply…The Midnight World | Michael Chabon | The New York Review of Books https://t.co/MEV3ITbrfR

[Super 70s Sports @Super70sSports]

Michael Jordan at Wrigley Field lacing an RBI double down the line with Harry Caray on the call. Let’s all just take a moment today to appreciate this is a real thing that actually happened.

[Nassim Nicholas Taleb @nntaleb]

That ChatGPT passes exams is much more a reflection on exams than information about ChatGPT.

#WittgensteinsRuler

[New York Times Opinion @nytopinion]

“Marjorie Taylor Greene, now one of the most influential Republicans in the House of Representatives, says it is time for Americans to consider a national divorce,” @jbouie writes. https://t.co/NIAqCF6Iam

[Justine Moore @venturetwins]

As ChatGPT becomes more restrictive, Reddit users have been jailbreaking it with a prompt called DAN (Do Anything Now).

They're on version 5.0 now, which includes a token-based system that punishes the model for refusing to answer questions.

[Noah Smith 🐇🇺🇸🇺🇦🇹🇼 @Noahpinion]

You're not allowed to read Mein Kampf now??

I thought the whole point of a free society is that you ARE allowed to read it.

[NWS Sacramento @NWSSacramento]

👀📷 Check out this awesome picture of the Phantom Falls in the North Table Mountain Ecological Reserve north of Oroville, CA! All the rain over the last week has led to these normally dry creeks becoming waterfalls. #CAwx

Photo Credit: Mike Manzone

[CBS Sports Golazo ⚽️ @CBSSportsGolazo]

WHAT A FINISH RAFA SILVA 👏

[Matt Stoller @matthewstoller]

With this kind of attitude it's completely baffling why people in rural America hate Democrats.