The opportunities and risks of open-source LLMs was a big theme of the day, and Ali Farhadi of the Allen Institute on AI, Jonathan Frankle of Databricks and Laurens van der Maaten of Meta had a lively discussion of the pros and cons, though mostly stressed the pros. Jon Turow of Madrona Venture Group led the discussion.

Farhadi made a case that open-source, rather than being a threat to security, is the only thing that can ultimately assure it. “This is the first time we are deploying a piece of technology at such a massive scale with such a shallow understanding of what’s going on,” he said. The only way to change that, he said, was with open-source initiatives driving towards “something we can understand, control, predict, and rely on.”

He echoed others in noting that there were “different types of openness,” and said his goal was to be “open in every aspect.”

Frankle, though strongly supporting open-source, said he had held back on releasing features at times due to how they might be used. “There are risks in open-sourcing; we have to balance that.”

But as a Databricks shareholder, he said, “I’m here to get ROI with these models,” and open-source changed the math in a huge way.

Van der Maaten contended that open models were “closing in on closed models” when it comes to performance, and emphasized that Meta’s approach with Llama was a strategy, “not altruism.” Small open-source models trained at specific tasks were already as good or better than closed models, Frankle added.

The panel pushed back on the idea that new foundation models would be impossibly costly — $10 billion or more.

Frankle said Databricks’ program cost just $5 million initially, and estimated the cost of the Llama 3 program at around $300 million, which van der Maaten didn’t dispute.

“I question the $10 billion,” added Farhadi. “We haven’t even explored optimizing.”


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