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Recently, there’s been a lot of discussion about the scalability of AI systems. From our perspective, scalability is not just a technical requirement — it’s the core of any AI company’s product–market fit strategy.

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Over the years, we’ve helped build AI systems that scaled successfully into thousands of real-world deployments. We’ve also seen some systems struggle to scale. Here are a few lessons we’ve learned about what makes scalability possible:

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1️⃣ Train on the right data.

The system must be trained on highly accurate, well-labeled datasets whose distribution matches the production environment. Without this, even the most sophisticated model will stumble. For example, Tesla’s FSD reportedly underperforms in China because it wasn’t trained on local driving data.

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2️⃣ Design for human intervention.

Even the best AI can cover maybe 90% of cases, but there will always be edge cases. A scalable system must include mechanisms for human-in-the-loop correction. Think of autonomous driving: despite huge progress, no car has yet achieved Level 3 autonomy.

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3️⃣ Ensure user incentives and trust.Scalability also depends on adoption.

The system must be fun, reliable, and deliver clear ROI. For example, one of our employee owns a Tesla Model Y, and while he tried FSD for a month and found it fun, he didn’t keep the subscription. Why? It’s still hard to fully trust, and the cost didn’t feel justified.

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💡 Our takeaway: scalability is not just about models and data pipelines, but also about human factors, trust, and economics.

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👉 What are your thoughts? What’s been your biggest challenge in making AI systems scale?