State of AI 2026: What Actually Changed
What's the actual state of AI in 2026? The reality differs from both the hype and the skepticism.
Here's what matters.
Open-Weight Models Shifted Everything
DeepSeek R1 and similar open-weight models changed the game:
- Accelerated global competition
- Enabled experimentation by anyone
- Changed deployment dynamics through permissive licensing
The closed model advantage shrank. Now anyone can run competitive models.
Where Durable Advantages Come From
Guests emphasized: durable advantages come less from secret algorithms.
What matters now:
- Budget for compute
- Hardware access
- Execution capability
The moat shifted from algorithms to resources.
Post-Training Methods
Much of recent capability progress comes from:
- RLHF (Reinforcement Learning from Human Feedback)
- RLvR (Reinforcement Learning from Verification)
- Inference-time scaling
- Systems optimizations
The training doesn't stop after the model ships.
What This Means
The AI landscape in 2026 is more accessible than expected:
- Open models enable more players
- Advantages are buildable, not just buyable
- Competition accelerates innovation
The winners will be those who execute well, not just those with the biggest models.
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