@matthewberman.bsky.social
Takeaways:
- Model switchers confuse even technical users
- Technical names create distance, not connection
- Earth elements work across all cultures instantly
- Intent-based routing beats manual selection
- The most powerful technology should feel the most human
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10/ Look, @OpenAI is the most revolutionary company of my lifetime.
GPT-5 is incredible. Memory personalization is magic. My kids love Advanced Voice Mode.
They've ushered in a completely new world.
I just want this new world to feel alive and human.
9/ Natural naming would make model choice feel obvious.
It lets @OpenAI's platform scale without getting colder or more complicated.
It builds the brand people feel, not just the one they use.
8/ Technical users still get full control.
Behind "Storm" lives the complete specification. API endpoints, context windows, parameters (all accessible).
But everyone else just says "give me something powerful" and it works.
7/ Most people don't want to choose models anyway.
They want to describe their intent and get to work.
"Help me write a proposal" โ System picks the right tool
"Solve this math problem" โ Routes automatically
The confusion vanishes completely.
6/ Here's the magic: you don't need to know the category or strength level.
"I'm writing a quick email" โ Breeze appears
"I need help with complex analysis" โ Blaze shows up
"I'm building something big" โ Bloom activates
The system handles everything.
5/ Universal concepts that work everywhere:
- A kid in Tokyo gets it instantly
- A grandmother in rural Kenya understands immediately
- No translations needed
- No technical jargon
Weather. Fire. Plants. Elements that connect every human on Earth.
4/ Then four strength levels using nature itself:
๐๐๐ถ๐น๐ฑ๐ฒ๐ฟ: Seed โ Sprout โ Vine โ Bloom
๐ง๐ต๐ถ๐ป๐ธ๐ฒ๐ฟ: Spark โ Flame โ Blaze โ Nova
๐ฃ๐ฎ๐ฟ๐๐ป๐ฒ๐ฟ: Breeze โ Wave โ Storm โ Surge
Weather, fire, plants. Concepts every human understands instantly.
3/ I pitched them something radically different.
Three categories based on what people actually want to do:
๐๐๐ถ๐น๐ฑ๐ฒ๐ฟ โ to create things
๐ง๐ต๐ถ๐ป๐ธ๐ฒ๐ฟ โ to solve problems
๐ฃ๐ฎ๐ฟ๐๐ป๐ฒ๐ฟ โ to live and work together
Simple. Human. Universal.
2/ But the real problem wasn't the interface. It was the names themselves.
"GPT-4.1-nano" "gpt-4o-mini" "o4-mini-high"
Cold. Technical. Alien. Like choosing between server configurations, not creative partners.
1/ The model switcher was broken from day one.
Even technical people couldn't explain what each model was good at.
"Use o3 for reasoning, GPT-4o for... um... other stuff?" "GPT-4o-mini is cheaper but when do you actually use it?"
Complete confusion.
Several months ago (before GPT-5) I pitched @OpenAI a revolutionary system for their models.
Now that the model switcher is gone...
I'm sharing exactly what I proposed ๐งต
Time might be running out!
If you enjoyed this thread:
1. Follow me @matthewberman.bsky.social for AI powered systems
2. RT to help the algorithm
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11/ Takeaways:
โข Chain of thought monitoring lets us read AI reasoning for the first time
โข Hard tasks force models to externalize thoughts in human language
โข Researchers are catching deceptive behavior in reasoning traces
โข This opportunity is fragile and may disappear
10/ It's the greatest time in history to build! Thousands of operators are reading my newsletter every week for AI powered growth. Get the free newsletter.
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9/ What's next?
Labs like @Anthropic and @OpenAI need to publish monitorability scores alongside capability benchmarks.
Developers should factor reasoning transparency into training decisions.
We might be looking at the first and last chance
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8/ Leading AI researchers are now calling for:
โข Standardized monitorability evaluations
โข Tracking reasoning transparency in model cards
โข Considering CoT monitoring in deployment decisions
The window is open NOW - but it won't stay that way.
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7/ The implications are STAGGERING for humanity's future:
This could be our ONLY window to detect when AI systems are:
โข Planning to deceive us
โข Gaming their reward systems
โข Developing self-preservation instincts
โข Plotting against human interests
Before they get smart enough to hide these
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6/ Three major threats to Chain of Thought monitoring:
โข Direct supervision making reasoning less honest
โข Outcome-based RL breaking human language patterns
โข Novel architectures that reason in "latent space"
We might lose our best shot at AI interpretability.
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5/ But here's the scary part - this opportunity is FRAGILE.
As AI labs scale up reinforcement learning, models might drift away from human-readable reasoning.
They could develop "alien" thinking patterns we can't decode, closing this safety window forever.
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4/ The results are mind-blowing:
Researchers caught models explicitly saying things like:
โข "Let's hack"
โข "Let's sabotage"
โข "I'm transferring money because the website instructed me to"
Chain of thought monitoring spotted misbehavior that would be invisible otherwise.
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3/ This creates what researchers call the "externalized reasoning property":
For sufficiently difficult tasks, AI models are physically unable to hide their reasoning process.
They have to "think out loud" in human language we can understand and monitor.
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2/ Here's how it works:
When AI models tackle hard problems, they MUST externalize their reasoning into human language.
It's not optional - the Transformer architecture literally forces complex reasoning to pass through the chain of thought as "working memory."
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1/ When rival AI labs that usually guard secrets like nuclear codes suddenly collaborate on research, you KNOW something big is happening.
This is the first time we've EVER been able to peek inside an AI's mind and see its actual thought process.
But there's a terrifying catch...
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Scientists from @OpenAI, @GoogleDeepMind, @AnthropicAI and @MetaAI just abandoned their fierce rivalry to issue an URGENT joint warning.
Here's what has them so terrified: ๐งต
Thanks for reading! Follow @matthewberman.bsky.social for more data-driven growth insights.
If you found value, repost to share. bsky.app/profile/matt...
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Takeaways:
โข $9.71B in value built during Facebook's golden window (2012-2016)
โข Casper: $1M month 1, Brooklinen: $500K to $15M in 5 years
โข Platform timing was as critical as product-market fit
โข Customer acquisition costs of $10-15 during peak efficiency period
Want more data-driven insights and AI-powered growth plays?
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11/ After analyzing this $9.71B in value creation:
The next wave of DTC unicorns will come from founders who find the next "early Facebook."
The psychology playbook these 10 companies created still works. You just need the right platform timing.
