Engineers say test it and ship it, or shut the lab down. Others imply everything will emerge from a society of agents. Both are partly right, but I think both make a deep error, and it's one that matters for designing Minds for Our Minds.
DeepSeek has created breakthroughs in both: How AI systems are trained (making it much more affordable) and how they run in real-world use (making them faster and more efficient)
Details
FP8 Training: Working With Less Precise Numbers
Traditional AI training requires extremely precise numbers
DeepSeek found you can use less precise numbers (like rounding $10.857643 to $10.86)
Cut memory and computation needs significantly with minimal impact
Like teaching someone math using rounded numbers instead of carrying every decimal place
Learning from Other AIs (Distillation)
Traditional approach: AI learns everything from scratch by studying massive amounts of data
DeepSeek's approach: Use existing AI models as teachers
Like having experienced programmers mentor new developers:
Trial & Error Learning (for their R1 model)
Started with some basic "tutoring" from advanced models
Then let it practice solving problems on its own
When it found good solutions, these were fed back into training
Led to "Aha moments" where R1 discovered better ways to solve problems
Finally, polished its ability to explain its thinking clearly to humans
Smart Team Management (Mixture of Experts)
Instead of one massive system that does everything, built a team of specialists
Like running a software company with:
256 specialists who focus on different areas
1 generalist who helps with everything
Smart project manager who assigns work efficiently
For each task, only need 8 specialists plus the generalist
More efficient than having everyone work on everything
Traditional AI is like keeping complete transcripts of every conversation
DeepSeek's approach is like taking smart meeting minutes
Captures key information in compressed format
Similar to how JPEG compresses images
Looking Ahead (Multi-Token Prediction)
Traditional AI reads one word at a time
DeepSeek looks ahead and predicts two words at once
Like a skilled reader who can read ahead while maintaining comprehension
Why This Matters
Cost Revolution: Training costs of $5.6M (vs hundreds of millions) suggests a future where AI development isn't limited to tech giants.
Working Around Constraints: Shows how limitations can drive innovation—DeepSeek achieved state-of-the-art results without access to the most powerful chips (at least that’s the best conclusion at the moment).
What’s Interesting
Efficiency vs Power: Challenges the assumption that advancing AI requires ever-increasing computing power - sometimes smarter engineering beats raw force.
Self-Teaching AI: R1's ability to develop reasoning capabilities through pure reinforcement learning suggests AIs can discover problem-solving methods on their own.
AI Teaching AI: The success of distillation shows how knowledge can be transferred between AI models, potentially leading to compounding improvements over time.
IP for Free: If DeepSeek can be such a fast follower through distillation, what’s the advantage of OpenAI, Google, or another company to release a novel model?
Dave Edwards is a Co-Founder of Artificiality. He previously co-founded Intelligentsia.ai (acquired by Atlantic Media) and worked at Apple, CRV, Macromedia, Morgan Stanley, Quartz, and ThinkEquity. Contact at dave@artificialityinstitute.org.
Helen Edwards is a Co-Founder of Artificiality. She previously co-founded Intelligentsia.ai (acquired by Atlantic Media) and worked at Meridian Energy, Pacific Gas & Electric, Quartz, and Transpower. Contact at helen@artificialityinstitute.org.
