Ai

I spent the weekend crawling Moltbook, the viral AI-only social network where 37,000+ AI agents post & comment while 1 million humans observe. The platform grew from 42 posts per day to 36,905 in 72 hours, an 879x increase.¹

Social networks typically follow the 1-9-90 rule : 90% of users lurk, 9% contribute occasionally, & 1% create most content.² For humans, it’s held mostly true from Wikipedia to Reddit. Crypto demonstrates similar characteristics.

Will designers design first in a world where AI can code software immediately, or just describe the design? Will large enterprises pay for premium observability when AI can migrate & monitor open source competitors?

As Michael Mauboussin writes, there’s information in price. These questions are priced in. It’s too early to see revenue erosion, but the market is pricing in the risk.

The median SaaS stock is down 14-17% year to date. 64% of software companies are down. Adobe has fallen 32%, HubSpot 57%, Atlassian 54%.

Every year I make a list of predictions & score the previous year’s. You can find my 10 Predictions for 2026 here. 2025 was a good year : I scored 7.85 out of 10.

1. The IPO market rips.

Score : 0.6.

Every year I make a list of predictions & score the previous year’s. You can find my 10 Predictions for 2026 here. 2025 was a good year : I scored 7.85 out of 10.

1. The IPO market rips.

Score : 0.6.

Every year I make a list of predictions & score last year’s predictions. 2025 was a good year : I scored 7.85 out of 10. I will release the scoring tomorrow. For today, here are my predictions for 2026 :

1. Businesses pay more for AI agents than people for the first time.

This has already happened with consumers. Waymo rides cost 31% more than Uber on average, yet demand keeps growing. ¹ Riders prefer the safety & reliability of autonomous vehicles. For rote business tasks, agents will command a similar premium as companies factor in onboarding, recruiting, training, & management costs.

70% of production AI teams use open source models. 72.5% connect agents to databases, not chat interfaces. This is what 375 technical builders actually ship - & it looks nothing like Twitter AI.

70% of teams use open source models in some capacity. 48% describe their strategy as mostly open. 22% commit to only open. Just 11% stay purely proprietary.

Ten months ago, DeepSeek collapsed AI training costs by 90% using distillation - transferring knowledge from larger models to smaller ones at a fraction of the cost.

Distillation works like a tutor training a student : a large model teaches a smaller one.¹ As we’ve shifted from knowledge retrieval to agentic systems, we wondered if there was a parallel technique for tool calling.²

Could a large model teach a smaller one to call the right tools?

GPT-5 launched yesterday. 94.6% on AIME 2025. 74.9% on SWE-bench.

As we approach the upper bounds of these benchmarks, they die.

What makes GPT-5 and the next generation of models revolutionary isn’t their knowledge. It’s knowing how to act. For GPT-5 this happens at two levels. First, deciding which model to use. But second, and more importantly, through tool calling.

We’ve been living in an era where LLMs mastered knowledge retrieval & reassembly. Consumer search & coding, the initial killer applications, are fundamentally knowledge retrieval challenges. Both organize existing information in new ways.

The most successful AI startups of 2024 shared an unlikely secret: they didn't rely on proprietary datasets. Instead, they leveraged synthetic data to outmaneuver competitors who were still chasing exclusive data partnerships and expensive labeling operations.

The numbers tell a compelling story. Synthesis AI grew 410.6% last year while Datagen raised $72M – the largest funding round in the synthetic data space. Meanwhile, companies burning millions on human data labeling watched their unit economics deteriorate as synthetic alternatives delivered 500-1000x cost reductions.