Andrew Kang
Key Views & Dialogues
Building the SoftBank of Robotics | Andrew Kang
Humanoid robotics may be approaching the pre-ChatGPT phase of AI, with $50,000 robots requiring only 20,000 units for $1B revenue and a projected tens-of-billions industry within two to four years. Robo Strategy (NASDAQ: BOT) seeks to challenge SoftBank through public markets, favoring vertically integrated players with embodiment-specific data; US restrictions on Chinese robots could help, while robotics data businesses face a two-to-five-year durability test.
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- Kang’s core thesis is that humanoid robotics today is where AI sat in 2022 and crypto sat in 2014–15 — a pre-ChatGPT-moment industry the general investment community still misprices. His unit math: at $50K per robot, $1B of revenue is only 20,000 units and $10B is 200,000 — trivial next to phones or cars made “in the hundreds of millions or billions” — and he expects industry revenue in the “tens of billions somewhere between the next two and four years.” His edge claim: “the more difficult an industry is to understand, the more alpha you can generate.”
- The TAM answer to humanoid skeptics is the general-purpose-device argument: “all human physical labor is your TAM,” and scale economics beat special-purpose machines the way smartphones beat cameras and GPUs beat ASICs. Proof by existence: BMW, Volkswagen and BYD still employ hundreds of thousands to million-plus workers despite decades of industrial automation, because most factory work changes too often to justify fixed automation below multi-million-unit SKUs.
- Robo Strategy (NASDAQ: BOT) exists because Tom Lee raised $18B in 8–9 months “for an asset that went down 50%… in a thesis that wasn’t that good” — so an asset class actually going up could raise “maybe an order of magnitude more.” Kang’s ambition is explicit: use public capital markets to reach beyond a16z-scale ($15B last fund series) and “become a new challenger to SoftBank,” applying a similar private-to-public valuation concept to the one TransDigm and Constellation Software use (buying at 3–8x earnings, marking at 15–40x).
- Post-ChatGPT power laws changed the venture math: you can now invest at multi-billion valuations and still expect “a 50x, 100x, 500x investing at a $2 billion valuation” — which is what makes deploying billions viable at growth stage. Portfolio construction is a barbell: 6–10 companies at ~70% of the fund, 20–30% in pre-seed-to-Series-B optionality, and “let winners ride.”
- He concentrates in vertically integrated players because embodiment-specific, on-robot data is “absolutely necessary” — a model trained in the wrong body is like learning basketball at 7 feet then playing at 5'10": “all wobbly.” The “Scale AI of robotics” cohort is the Buzz Lightyear meme — a thousand identical companies — and a “short-term cash cow” whose revenues might plummet “two, three, four, five years down the line” once robots self-improve; “it almost feels like trading as opposed to investing.”
- On US vs China he rejects the who-wins framing: both build great products, but American companies win for shareholders on 20–50%+ margins and stronger capital markets — “you can sell 10 times less product but be a bigger business.” Plus bipartisan bills restricting Chinese robots in the US are “very high probability,” potentially ring-fencing the biggest market (30–60% of any major product): “America’s the place you want to invest.”
- His philosophy is pricing risk/reward, not contrarianism: citing Coatue data that bigger public companies 10x more often than small ones, he backs the strongest founders at higher valuations — “winners win” — versus the Character AI/Inflection AI trap of buying “grittier and cheaper.” One admitted change of mind: more bearish on model-only companies, more bullish on open source — “Kimi getting as good as Claude on some benchmarks” will repeat in robotics — which he reads as bullish for hardware companies with secured supply chains, not for restricted Chinese robots.
Why Robotics Is The Next Trillion Dollar Industry - Andrew Kang
Andrew Kang says robotics is shifting from mechanical design toward intelligence, with reliability now the next threshold for physical intelligence. He expects GPT-5-level robots in roughly a year and home-capable robots in about two years, while hardware, manufacturing, supply chains, model commoditization, and labor displacement shape the opportunity.
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Andrew Kang’s core bet is that intelligence—not mechanical design—was the missing layer that kept general-purpose robots in science fiction, and ChatGPT made the coming physical-intelligence inflection visible. After discovering Figure AI in late 2023/2024, he pressure-tested the thesis against skeptical venture investors, competitors, references, and co-investors, eventually concluding that “the rest of the venture capital landscape was missing something.” The opportunity is enormous, but concentrated: as with AI, many robotics startups may fail while the top companies compound 30x or more.
Kang argues that conventional valuation models break when technological progress turns reflexive and growth reaches 100%, 200%, or 300% rather than a steady 8% or 20%. Automating research could increase experimentation 2x, 5x, or 10x and cut 90-day iteration cycles to 45 days, with each improvement accelerating the next. His portfolio implication is blunt: acknowledge risk management and possible 30%-50% drawdowns, but stop allowing short-term macro anxiety to eject you from exceptional companies whose opportunity may be almost infinite.
Robotics is already at what Kang calls the “ChatGPT-3 level,” but real deployment requires moving from being right roughly half the time to near-zero failures. He thinks “GPT-5-level” physical intelligence could arrive in roughly a year because robotics can reuse advances in pre-training, annotation, RLHF, mid-training, and automated experimentation rather than rediscovering them. Home-capable robots might follow in about two years, broader everyday visibility in three to four years, and—if production grows roughly 10x annually—hundreds of millions or billions of units in the early 2030s.
The economic prize spans both premium consumer hardware and a wholesale restructuring of labor-intensive industries. Kang expects at least one robot per household, potentially one per person, with brand, taste, and status supporting Apple- or Tesla-like margins; meanwhile, Walmart’s roughly $700 billion of revenue, $20 billion of net income, $30 billion of operating income, and approximately $100 billion of physical-labor expense illustrate how automation could move margins from 3% toward 10%-15%. He does not celebrate displacement: formerly “anti-socialism,” he now sees a credible case for UBI and a safety net if automation breaks the traditional labor system.
Humanoid robotics should support multiple major winners rather than collapse into one dominant supplier. Kang says large technology companies were “asleep at the wheel”: Google sold Boston Dynamics and scrapped Everyday Robots, while Google and Nvidia now emphasize intelligence and operating systems rather than wholly owned humanoid hardware. Tesla Optimus should become substantial, but Tesla is not clearly number one today; Kang instead expects an automobile-like market with perhaps 15-20 large players and greater value concentration among the leaders.
The most durable near-term moats may sit in hardware, manufacturing, deployment, and supply chains—not standalone physical-AI models. Kang hedges that he “may be wrong,” but points to Chinese open-source labs, Nvidia’s research, DreamZero reaching the frontier when released, and a shrinking open-versus-closed model gap as evidence that intelligence will commoditize once it is “good enough.” Actuators already represent roughly 30%-50% of a robot’s bill of materials, onboard GPUs are mandatory, and deployed fleets produce proprietary real-world data that hardware companies can use to improve their models.
Robo Strategy is designed to turn scarce private-market access into a permanent-capital compounding vehicle rather than a leveraged robotics proxy. Kang estimates new-generation private robotics companies are collectively worth only $100-$200 billion—versus roughly $50 billion for all Pokémon cards—and believes the industry could grow 100x or 1,000x. Borrowing MicroStrategy’s “accretive issuance” loop while avoiding unnecessary leverage, he wants to build a SoftBank-scale public venture platform whose access advantage is multiplied 100 times because investors cannot simply buy leading private robotics companies through an ETF or exchange.