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Every major AI lab signed the Open Weights letter defending open models. Meta, OpenAI, Google, Microsoft, Nvidia.
Anthropic was the only holdout.
Yesterday, its CEO, Dario Amodei, published a thoughtful defense of that decision to not fully support open weight or open source models.
Here's what nobody's connecting: the money trail.
Roughly 80% of Anthropic's revenue is businesses paying per token. Free Chinese open models attack that exact revenue stream weeks before Anthropic is set to go public.
On today's show we break down what Dario actually said, what he said before, and why we think this was written for Washington policymakers and not for the rest of us.
Anthropic Responds: Why Claude’s CEO didn't sign the open model pact and the real reasons why -- An Everyday AI Chat with Jordan Wilson
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Topics Covered in This Episode:
Anthropic Refuses Open Model Pact
Dario Amodei’s Public Letter Analysis
Anthropic’s 80% Revenue Token Expose
Chinese Open Model National Security Fears
Microsoft & Nvidia’s Open Weights Coalition
Regulatory Capture and Washington Influence
Timing Related to Executive Order Deadline
IPO Motivations Behind Anthropic’s Decisions
Contradictions in Anthropic’s Open Model Stance
Impact of Open Source on Token Business Model
Timestamps:
00:00 Anthropic's stance on open models
04:19 Discussing Anthropic's response to open models
06:39 Understanding open weight models
12:08 Future AI and cybersecurity risks
15:04 Discussion on open-source AI models
19:30 Discussing Anthropic's business challenges
21:08 Cutting costs with open-source models
26:17 Anthropic's recent stock downturn
27:11 AI investment and cost efficiency shift
30:14 Anthropic's stance on open source models
36:32 INTROPICS IPO and regulatory discussions
37:37 Wrapping up and subscribing
Keywords:
Anthropic, Claude, open model pact, open source AI, open weights, American AI leadership, Dario Amodei, IPO, regulatory capture, DC lawmakers, Chinese open source models, token revenue, per token business model, NVIDIA, Microsoft, Meta, OpenAI, Google, IBM, national security, AI safety, government mandates, chip controls, AI regulation, chip ban, industrial scale distillation, mandatory safety testing, inference, AI ecosystem, Opus 5, Fable 5, GPT-5, GLM 5.2, cost per task, token efficiency, model router, proprietary models, closed source AI, cybersecurity risks, Chinese cyberattacks, biological attacks, Glasswing program, open source vs proprietary, tech lobbying, Trump AI order, federal deadline, AI policy, artificial general intelligence, artificial superintelligence, AI monetization, S-1 filing, public company, venture capital, AI benchmarks, model switching, API pricing, model containment, Hugging Face incident, AI startup monopoly, safety vs business protection, market competition, AI cost reduction.
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