Liang Wen achieved the top score on the college entrance exam in Zhanjiang in 2002 and attended Zhejiang University for engineering.

The open-source model DeepSeek releases is the same as the deployed model: identical weights, not a weaker public release.

Despite DeepSeek's compute constraints, Liang argues that Nvidia is 'digging its own grave' because CUDA's moat—the software lock-in preventing developers from rewriting code—is eroding as AI writes code faster than humans, making CUDA rewriting feasible.

High Flyer bought GPUs aggressively before their purpose for AI was widely known: 100 cards in 2015, 1,000 in 2019, 10,000 by 2021. Liang says this purchasing was driven by curiosity.

When R1 went viral and millions of consumer users arrived overnight, DeepSeek made almost no effort to retain or monetize them; the API runs with minimal staff (only a few maintainers, no sales team, no customer service), reflecting Liang's view that consumer apps are distractions from AGI research.

DeepSeek's pricing strategy parallels Costco's approach of deliberately capping markups at about 14% for decades; the low-margin cap creates trust and makes it impossible for competitors to undercut while being profitable themselves, forcing rivals into territory DeepSeek has already made its home.

Serious figures like Yann LeCun argue that language models alone cannot reach human-level intelligence; they contend that world models—systems that can predict the effects of actions—are necessary for AGI. Liang is betting the entire company against this thesis.

Liang concludes that the gap between Chinese and US AI capability is fundamentally a compute gap, and because fewer chips means fewer experiments, the talent gap is downstream of the compute gap.

The speaker's concluding promise is that the strongest model DeepSeek builds will always be released open-source, identical to the version DeepSeek runs internally.

Liang founded High Flyer, a quant fund that trades markets with machine learning, and built it into one of China's largest quant funds.

DeepSeek's research team was spun out from High Flyer in 2023.

Liang's career path from quantitative finance to AI mirrors Jeff Bezos's path from D.E. Shaw to Amazon, and this background explains DeepSeek's focus on cost, price, margin, and operational discipline.

Liang wants thousands of companies built on top of DeepSeek; each company shapes the ecosystem to be more DeepSeek-dependent and increases switching costs and network effects.

When someone on the investor call challenged the 6x profit margin as too high, Liang agreed there was room to cut but noted that at the current price, demand no longer increases, so he leaves it unchanged.

DeepSeek deliberately launched one model at a high price to constrain demand due to fears of overwhelming the system; when price was cut to one-quarter, internal company chat 'erupted in cheers,' revealing that the team prioritized user adoption and ecosystem growth over revenue.

DeepSeek's open-source strategy does not kill the business because model weights are the recipe (replicable) while serving a frontier model cheaply is the hard part (infrastructure, kernels, cluster optimization, utilization); a competitor can download DeepSeek's open model tomorrow but would still need to serve it at multiples of DeepSeek's cost.

Open-source models only threaten a company if that company is charging 100x the cost of the open alternative; at DeepSeek's pricing, running the business model of DeepSeek using DeepSeek's own open weights would lose money for competitors.

In Liang's framing, apps (like consumer interfaces) and APIs are 'sesame seeds' while AGI is the 'watermelon'; the company picks up seeds on the way but doesn't stop to harvest them, meaning peripheral products are accepted passively but not pursued strategically.

DeepSeek refuses to build video generation capabilities because, despite being 'a good business,' video generation is not on the path to AGI and would divert resources.

DeepSeek refuses to invest heavily in world models as a core product direction because world models are not currently the bottleneck to AGI.