Investors
2 Curated Dialogues
张璐
Frontier Insights
Core Frontier Thesis: Open-source AI (e.g., DeepSeek) is structurally leveling the playing field with closed-source frontier models, drastically slashing unit economics and accelerating enterprise penetration. Concurrently, compute dominance is shifting toward absolute vertical sovereignty—exemplified by Musk’s cross-entity, orbital-scale industrial operating systems.
Strategic Imperatives: Prioritize high-margin, verifiable B2B agentic workflows (healthcare, industrial automation) where proprietary data moat-building outpaces raw foundation-model hype, rather than chasing generic consumer agents.
Risks & Warnings: Reliability bottlenecks in autonomous agent execution, escalating talent wars, and structural capital concentration threaten venture returns as hyperscalers commoditize baseline inference.
Key Views & Dialogues
159: Musk’s Terafab Space Compute, Nvidia Reclaims the CPU | Talking New AI Compute Trends with Zhang Lu of Fusion Fund
Terafab combines Tesla, SpaceX and xAI around compute sovereignty, targeting 1 TW of annual capacity with 80%-90% deployed in space; radiation, maintenance, launch costs and demand maturity could delay the window seven to ten years. NVIDIA’s Vera CPU and Groq extend its heterogeneous AI factory, while inference could reach 60%-70%; Blackwell and Vera Rubin may support over $1T in 2027 revenue if token demand grows.
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- Terafab is not really competing for a chip fab; it is competing for compute sovereignty across Musk’s ecosystem. The plan is to integrate Tesla, SpaceX and xAI from chip design and manufacturing through deployment and applications, with an ultimate target of producing 1 TW of AI compute annually and deploying 80%-90% of it in space. Zhang Lu described it as “a giant cross-company industrial operating system,” but expects both the rollout timeline and funding needs to far exceed Musk’s own expectations.
- Orbital data centers are not a mature startup bet over the next 2-3 years. Cosmic radiation, chip packaging, launch, maintenance and repairs all present major technical and cost challenges; serving Earth-based applications would also introduce latency. Zhang’s estimated maturity window is seven or even ten years. “If everyone wants to explore building data centers in space, why not build them in nearby Canada?”
- SpaceX needs the entire space economy—not just rockets and Starlink—to support a trillion-dollar IPO narrative. Zhang believes orbital deployment also reflects Musk’s deeper goal: “He doesn’t want any government to regulate him.” With jurisdiction over space, orbital enforcement and lunar resource ownership still unclear, SpaceX could become both the main enabler of the space economy and a future rule-setter.
- The nearer-term opportunity than orbital data centers is serving a space industry that is “AI native, robotics native.” Microgravity factories could explore crystals, materials and protein structures difficult to produce on Earth; AI could manage satellite traffic and data trading, while robots extract water, hydrogen and oxygen from lunar regolith to build lunar “gas stations.” Genuine demand for space-based compute will emerge only after these local applications scale.
- The more investable opportunities today are the efficiency layer of terrestrial AI infrastructure and other space infrastructure. Interconnect, optical switches, power consumption, memory, security, inference costs and system integration all present clear bottlenecks; startups must also decide whether they will serve future hyperscale clusters or build capital-intensive clusters themselves. Zhang’s direct conclusion: “I don’t think it’s a good time. I think it’s still a little too early.”
- Nvidia is rewriting itself from a GPU company into an “AI factory” that produces tokens, while inference turns revenue from one-off demand into recurring cash flow. The training-to-inference compute mix has shifted from roughly 70%-80% versus 10%-20% toward an even split, and could eventually invert to 20%-30% versus 60%-70%; Jensen Huang therefore expects Blackwell and Vera Rubin to support more than $1T in data-center revenue by 2027.
- The Agent era will lift demand for heterogeneous compute across CPUs, GPUs, LPUs and NPUs, making platform control more important than winning on any single chip. Nvidia is using Vera CPU to cover tool calls, code execution, multi-Agent workloads, reinforcement learning and simulation, while adding Groq’s low-latency, high-throughput inference capabilities to Vera Rubin; even if its CPUs do not always beat AMD’s, control of the full system, rack and one-stop purchasing creates a major integration advantage.
- Enterprise AI has entered a phase in which budgets, deployments and exits are all accelerating. Regulated industries favor private-cloud or on-premises deployments of small language models to balance accuracy, privacy, latency and cost; companies in the Fusion Fund network have reached AI budgets of up to $12B, while some sub-10-person companies grew annual revenue from zero to $20M. Finance, healthcare, insurance, industrials and supply chains could be among the fastest adopters, and some portfolio companies founded less than two years ago were acquired last year at 10-20x returns.
100: How Silicon Valley Sees DeepSeek: A Conversation with Zhang Lu of Fusion Fund on Open Source, Agents and “Beyond AI”
DeepSeek R1 suggests open-source models can catch closed systems, while unsupervised reinforcement learning produced chain of thought without process labels. V3 used 2,048 H800s and beat Llama 3.1, trained on 16,000 H100s, on some benchmarks; lower costs may widen AI adoption and GPU demand, though proprietary data still favors closed-source players.
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- Zhang Lu sees DeepSeek R1’s breakout as a victory for the open-source ecosystem, not as a Chinese company independently catching up with OpenAI. From V2 and V3 through R1, DeepSeek has consistently published technical reports and intermediate experiments, allowing teams worldwide to share architectural exploration. That openness is especially scarce as OpenAI is mocked as “CloseAI” and Mistral and AlphaFold 3 have tightened access to details. For investors, if open source catches up with closed source, the most direct beneficiaries are startups that can call, distill and modify models at low cost.
- R1’s most important industry implication is not any single benchmark, but that unsupervised reinforcement learning can produce chain of thought without process labels. Zhang Lu does not see this as overturning the scaling law; rather, it suggests that after reducing the need for expensive labeling, more compute could still lift capabilities by “another order of magnitude.” Lower costs are only the result of this technical path; the real upside is that model training, inference and self-exploration have been reopened as areas for innovation.
- DeepSeek’s cost cuts triggered near-term concern about Nvidia, but Zhang Lu’s long-term view is the opposite: cheaper models will pull AI into every industry sooner and ultimately lift GPU demand. The comparison raised by the hosts was that V3 used 2,048 H800s and beat Llama 3.1 on some benchmarks, while the latter was trained on 16,000 H100s. Zhang Lu’s point is that lower per-task costs will bring AI to more use cases that previously could not afford it; this is not the end of compute demand.
- Meta will face brand pressure over “why a small company did it better,” but it may also become a long-term beneficiary of the open-source boom. DeepSeek’s disclosed V3 training cost of roughly $5.57M covers GPU hours only, excluding upfront investment, R&D and personnel, so it cannot be directly compared with Meta’s full spending. Counting only the “raw ingredients for cooking” is not the same as counting the kitchen, pots and pans. DeepSeek’s new architectural direction could help Llama iterate, while Meta can continue differentiating itself from closed-source players such as Google through the open-source ecosystem.
- The closed-source camp has not stopped, and competitive advantage is moving beyond model scale toward proprietary data, internal tools and commercial data flywheels. Anthropic is acquiring more high-quality B2B data through enterprise orders; xAI may tap 2D/3D industrial data from Tesla factories and supply chains, as well as SpaceX and Starlink. Gemini, Claude and ChatGPT each have advantages in accuracy and writing style. Zhang Lu heard from a founding member of an unnamed company that 70%—80% of its internal code is already written by proprietary AI tools; undisclosed applications are being used first to accelerate the company’s own iteration.
- The Agent thesis is right, but today’s products look more like “very junior interns” than analysts. When Zhang Lu tested Operator, its search speed was “like an old lady,” and it still fabricated information. Enterprise customers are therefore more likely to buy narrow, specialized and accurate industry Agents than a generic assistant that costs $200 per month but tries to do everything. Compared with traditional RPA, the breakthrough is lowering the user barrier to “can you chat?”; deployment still requires workflow integration, inference optimization and cost infrastructure.
- Outside AI, Tech Bio and space tech are the two areas to watch most closely, though both are being accelerated by AI as a “catalyst.” Longevity has shifted from whether people can live to 150 or 200 toward whether they can reach 100 or 120 with a clear mind and healthy body. In space, there is a possibility that over the next 3—5 years the cost of sending one person into space could fall to $50K—$100K, while launching a single satellite could fall to $10K—$20K. Zhang Lu also warned that in 2025, “uncertainty will become the only certainty”; early-stage companies will derive more of their edge from rapid iteration, industry data, commercial channels and resistance to the giants’ control of resources.