Core Claim

The comparison between DeepSeek and TikTok is flawed. The real issue isn't about user data collection in a social media app—it's about infrastructure control and whether open or closed AI models will dominate the future.

There's an assumption that scaling up compute leads to better AI. But perhaps the real innovation will come from those who find a different path.

The Strategic Landscape

When a country releases a "free" AI model into the global ecosystem, the strategic calculus is straightforward:

1. Deploy after others have paved the way - pick up where competitors experienced bottlenecks and pain points
2. Market it aggressively - make it look and feel different
3. Encourage global utilization - each user becomes an unpaid play tester, growing the model's capabilities
4. Gather data at scale - not just personal data, but data about how people use AI, what they need, what problems they're trying to solve

This isn't conspiracy—it's basic competitive strategy.

Data Collection vs. Infrastructure Control

U.S. lawmakers understood TikTok's risks because they were tangible:

• User data being collected
• Content algorithms influencing what people see
• Direct access to personal information

DeepSeek's risks are more systemic and harder to grasp:

• Setting technical standards and frameworks
• Creating ecosystem dependencies
• Shaping what counts as "normal" in AI development
• Long-term influence over how AI governs outputs

The TikTok analogy breaks down because it focuses on the wrong category of risk. The better frame: open vs. closed models.

Open vs. Closed: The Real Debate

The more productive comparison:

When an open model becomes foundational, whoever controls its development gains influence over:

• Technical standards
• Training approaches
• What gets built on top

This is infrastructure-level power—less visible than data collection, but potentially more consequential.

The Compute Assumption

Much of the AI race is focused on:

• Who has the most GPUs (especially NVIDIA)
• Who can throw the most money at training
• Who can scale compute fastest

The assumption: more compute = better AI.

But is that the right frame?

DeepSeek reportedly achieved comparable performance with significantly fewer resources. This suggests:

• Efficiency innovations may matter more than raw compute
• The "throw money at it" approach may not be the only path
• Different architectures or training approaches could leapfrog brute-force scaling

Perhaps the real innovation will come from those who find a different path.

What We Don't Know Yet

This is still a developing story. Key uncertainties:

• How much of DeepSeek's efficiency claims are real vs. marketing?
• What are the actual data flows and who has access?
• How will open-source AI governance evolve?
• Will efficiency innovations spread across the industry, or remain siloed?

History may prove some current interpretations wrong. The landscape is shifting faster than analysis can keep up.

Questions Worth Asking

1. If open models become foundational, who sets the rules?
2. Is infrastructure control more or less concerning than data collection?
3. What does "winning" the AI race even mean—and for whom?
4. If compute isn't the only path to capability, what alternatives exist?
5. How do we think about national interest when AI development is inherently global and open-source?

Key Formulations (Preserve These)

"The comparison between DeepSeek and TikTok is flawed. The real issue isn't about user data collection—it's about infrastructure control."

"There's an assumption that scaling up compute leads to better AI. But perhaps the real innovation will come from those who find a different path."

"This is infrastructure-level power—less visible than data collection, but potentially more consequential."

"The landscape is shifting faster than analysis can keep up."