Two Strategies for Building Intelligence
For three years, the AI industry operated on a simple assumption: scale wins. More parameters, more GPUs, more data, more electricity. The companies with the largest compute budgets would build the best models, and everyone else would rent access.
The Magnificent Seven — the US tech giants — bet accordingly. Microsoft committed over $100 billion to AI infrastructure. NVIDIA's H100 chips became the scarcest resource in technology. The logic was straightforward: intelligence is a function of compute, and compute is a function of capital.
Then, in early 2025, a relatively unknown Chinese lab called DeepSeek published results that forced the entire industry to reconsider.
The Efficiency Thesis
DeepSeek demonstrated that a model could match GPT-4's performance benchmarks at a fraction of the training cost and with significantly less compute. The numbers weren't marginal. They were dramatic enough that NVIDIA's stock dropped 17% in a single day — the largest single-day market cap loss in US stock market history.
What DeepSeek proved wasn't that the US approach was wrong. It was that there's more than one path. Scaling laws — the principle that model performance improves predictably with more data and compute — are real. But architectural innovation can achieve similar performance gains without proportional increases in hardware. Better math, it turns out, can substitute for more silicon.
This distinction matters enormously. If intelligence scales only with compute, then the game is a capital race and whoever spends the most wins. If intelligence also scales with architectural efficiency, then smaller players with strong engineering talent can compete. The implications ripple far beyond Silicon Valley and Shenzhen.
Two Models, Two Strategies
The US and Chinese AI ecosystems have settled into genuinely different strategic approaches, and neither is obviously wrong.
The US approach is built on closed models and cloud distribution. OpenAI, Anthropic, and Google offer their best models as services — you pay per token or per subscription. The models are proprietary. The competitive moat is the model itself. This is the "Apple" strategy: premium product, controlled ecosystem, high margins.
The Chinese approach is converging on open-weight models. DeepSeek, Alibaba's Qwen, and Moonshot's Kimi release their model weights for anyone to download, modify, and deploy. The competitive moat isn't the model — it's the ecosystem that forms around it. This is the "Android" strategy: ubiquitous, customizable, free at the base layer, monetized through services and integration.
Neither strategy is charity. The US approach captures value directly through pricing. The Chinese approach captures value through adoption — when your open model becomes the default foundation that thousands of companies build on, you own the ecosystem even if you gave away the weights.
Different Definitions of Intelligence
There's a subtler divergence that the headlines miss. The two ecosystems aren't just building differently — they're building for different use cases.
The US conversation about AI is dominated by AGI, safety research, and consumer-facing assistants. The products are chatbots, coding tools, image generators. The aspiration is general intelligence — a system that can do anything a human can do.
The Chinese AI ecosystem is building for industrial integration. AI embedded in factory floors, logistics networks, supply chain optimization, classroom instruction. The aspiration isn't a system that can do anything — it's a system that can do specific, economically valuable things reliably and cheaply.
I don't think one approach is more "right" than the other. I think they're optimizing for different outcomes. The US is building for the cloud economy — intelligence delivered as a service over the internet. China is building for the real economy — intelligence embedded in physical systems and industrial processes.
Both are enormous markets. The question is which one grows faster.
What This Means for Indian Engineers
I've been asked this question more than any other in the last six months: where does India fit?
India has something that most countries don't — a deep bench of engineering talent at global quality levels. What India doesn't have is the capital to compete on pure compute. A $100 billion data center buildout isn't on the table. That's not a criticism — it's a constraint, and constraints shape strategy.
The efficiency breakthrough is directly relevant here. If building competitive AI requires matching dollar for dollar with Microsoft and Google, India is out of the race. But if architectural innovation and engineering skill can achieve comparable results at lower cost — which is exactly what DeepSeek demonstrated — then India's talent base becomes a genuine competitive asset.
The open-weight ecosystem matters too. When foundational models are freely available, the value shifts from "who can train the biggest model" to "who can adapt models to specific domains and deploy them where they're needed." Indian agricultural data. Indian healthcare workflows. Indian languages that global models handle poorly. Indian infrastructure constraints that demand edge deployment and offline capability.
These aren't peripheral opportunities. A model that understands Hindi medical terminology and runs on a phone with intermittent connectivity serves a market of hundreds of millions of people. The teams that build these products need to understand the domain, the language, and the constraints — and that understanding is local.
The engineers I talk to who are positioning themselves well for 2026 and beyond aren't trying to compete with OpenAI on general English capability. They're becoming domain experts — in fintech, healthcare, agriculture, education — and using open-weight models as foundations for specialized products that global labs can't build because they don't understand the context.
The Takeaway
The AI industry in January 2026 looks fundamentally different from January 2024. The assumption that raw compute determines outcomes has been challenged. Open-weight models have created a viable alternative to the subscription economy. And the definition of "winning" in AI has fractured into at least two distinct visions.
For anyone building in this space — engineer, founder, or investor — the strategic question isn't "US or China." It's "which layer of the stack am I competing on, and do I have a genuine advantage there?"
The era of a single path to AI dominance is over. What's replaced it is more interesting: a landscape where architectural skill, domain expertise, and deployment strategy matter as much as capital. That's a world where more players can compete. Including India.
This connects to the broader pattern I've been tracking. The technical case for small models explains why efficiency isn't just a cost play. The $70 billion flowing into Indian AI shows the capital side. And the execution challenge is why talent and sequencing matter more than ambition.