Max Hodak: The Real Reason Most Deep Tech Startups Die Is Bad Plumbing

Each hex on the implant grid is essentially a solar cell. The laser projection both powers and drives the device wirelessly, eliminating any through-skin connector that would invite bacterial infection. Hodak is direct about the evidence: three clinical trials to date, major trials completed in 2025, device approved in Europe, results in the New England Journal of Medicine, a patient reading a full novel with the implant. "That is a real, not a marketing, data point." The device — marketed as PRIMA — treats geographic atrophy caused by age-related macular degeneration, a leading cause of blindness for which there was previously no treatment for central-vision loss. Science announced PRIMA's European commercial launch after receiving CE Mark clearance in July 2026, positioning it as the first BCI device cleared for commercial sale and, per the company, the only therapy that restores functional central vision for the condition. The pivotal European trial enrolled 38 legally blind patients; per the peer-reviewed NEJM results, 80% showed meaningful improvement in visual acuity and regained the ability to read letters, numbers, and words. Science has raised roughly $490 million since its 2021 founding, including a $230 million Series C — a scale far beyond the illustrative $20 million Series A Hodak uses above to walk through deep-tech burn math.

The technology origin story is itself a case study in deep tech M&A. The implant was invented at Stanford roughly 15 years ago and licensed to a European company. Science explored the full decision space: two retinal targets (bipolar cells or optic nerve) crossed with two modalities (electrical or optical). The electrical-bipolar route led to a French company that represented the state of the art — which Science acquired, gaining both the license and the technology. "Business is just a fancy word for talking to people and doing things," Hodak said. They cold-emailed surgeons: "Hey, we have a weird surgery to develop. Do you want to be a consultant?" People replied.

The hardest engineering frontier is physical. Power and thermal constraints push toward implanting as little as possible, since skin is a critical immune barrier. The most underrated problem, Hodak says, is packaging — what his European colleagues call "tropicalization": keeping the device in and the body out. "There are no truly passive surfaces anywhere in the body. Even bone is constantly getting remolded." The classic solution, a laser-welded titanium can used in pacemakers and deep brain stimulators, cannot fit in the eye. A predecessor retinal device that strapped a titanium box and battery to the outside of the eyeball required a 4.5-hour surgery and "didn't work." Science's wireless laser projection sidesteps that constraint, but next-generation conformal coatings remain an open materials-science problem.

Hodak's framing of brain-computer interfaces departs from the AI-adjacent narrative that dominates Silicon Valley. "In the near term, BCI is really a longevity story — essentially healthcare and biotech," he argued. "The brain is the thing that makes you you. It's the only thing that in principle you can't transplant. You can get a new heart or a new liver. You cannot even in principle get a new brain."

Neural engineering, he noted, delivers effect sizes that conventional medicine rarely sees. A deep brain stimulator takes a Parkinson's patient "from not being able to hold a cup of water to being able to write cursive in like 10 seconds" — a contrast to drugs whose effects are "a little bit, dwindling over time." A newborn's cochlear implant activation, he said, is arguably the strongest patient testimonial in all of medicine.

The long-term vision compresses into two moves. First, consciousness is a practical problem, not a philosophical one: "The brain is composed of ordinary matter arranged according to the rules of chemistry, only things found on the periodic table." With a sufficiently capable BCI able to read and drive every neuron, "I think we'd figure out consciousness pretty fast." If the endpoint of AI is superintelligent machines, "the end of the BCI quest is conscious machines" — and eventually, superintelligent conscious machines that humans can participate in. "At some point, the boundary between these technologies becomes less meaningful."

The throughline that makes Helix coherent is AI. Hodak is explicit that bespoke internal software is only now a rational choice at the startup stage because agents and "vibe coding" have changed the economics. The design principle is AI-native: "Gather all of the context, all the stuff happening in your company, and be able to make that available efficiently to agents, because those are clearly a big part of the future."

He reports AI as a multiplier, not a replacement. The biggest impact areas so far are coding and regulatory compliance. On coding: "I've written a lot of code in my life. I don't think I've looked at the source very much the last six months." On compliance, the quality-system bureaucracy is "the quintessential heavy bureaucracy" — the idea of quality itself is sound, but humans are bad at reading and interpreting requirements. Standards cover everything from lithium-ion battery connectors on PCBs to electrical insulation to whether shipping-label corners curl inside a vibration box. Historically, you hired regulatory experts to enumerate standards and build evidence spreadsheets over many months. AI now generates the standards lists and evidence tables almost immediately. "Regulations are written in blood and largely good ideas — it's just hard for humans to do it."

On build-versus-buy, Hodak is blunt: "There's no company that loves their ERP system. I don't know there's anyone who's really like, I want to spend more time in NetSuite." His precedents include Y Combinator's internal software, Facebook's internal tools, and the "pretty giant" piece of software called Warp Speed that runs much of SpaceX and Tesla's manufacturing and R&D. A company growing up around software fitted to its own shape is powerful in ways off-the-shelf software is not. Historically, custom software was too expensive, so everyone bought. "That was I think a worse world and that world has changed." The concrete failure of buying: Greenhouse forced a small human bottleneck at the top of the hiring funnel and foreclosed the voting mechanism. Custom software enabled "smart inferences about who would know about an applicant." His rule: build what works for you, and "bake it into the company so when you put something there it stays there."

Hodak's closing arc is about judgment — developing it, funding it, and being forced to deploy it alone. On fundraising, his core accusation is that founders, especially experienced ones, pitch VCs for what seems reasonable to ask rather than what the experiment actually costs. "You're raising some amount of money to go find out some answer. The answer to that might be no."

Some ideas "are worth funding with $50 million or zero dollars, but not $5 million" — the middle amount produces an ambiguous outcome and a frustrating experience. His rules of thumb: define your next value inflection point, price the experiments required to reach it, and "raise twice the money." Accept that 20% to 30% of capital will be wasted — "that's pretty good." There is no guarantee you won't end up "on a bridge to nowhere."

Most provocative is his stance on profitability: "I think that people should push for profitability sooner than they often think that they need to." He describes a company as "kind of constantly dying slowly of this money cancer that we can beat into remission every couple years with the fundraising." Revenue changes what investors value you on — long-term roadmap instead of probability of dying — and unlocks a different set of investors.

His years at Neuralink were, in retrospect, an apprenticeship. He worked alongside someone with "empirically excellent judgment," where a problem would arise with two plausible solutions and the answer was "oh, it's definitely option B, the problem would never recur." The value was reinforcement learning on decisions with real stakes and delayed feedback — "that is an essential part of the education of an entrepreneur that I think many people underrate."

Beneath this sits a physics-flavored theory of action. A thrown ball's ballistic trajectory is "information-minimizing" — it's the path of least action. Whenever you exert action on the universe, you create information. When stuck, you "have to start injecting action, producing entropy." The counterintuitive corollary: a company trapped in a deep local minimum can sometimes be unblocked by removing someone who is individually strong but wrong-fit, because the action itself reshapes the system. "The action space is always larger than it appears."

His final position is almost anti-advice: "There are no general principles. People are looking for shortcuts. That doesn't exist. When you get to that moment in history, you're doing something new." What should not feel normal, he says, is that a smart 20-year-old can come to San Francisco and get millions of dollars to test an interesting idea — "that shouldn't feel normal."

He borrows Paul Graham's map of city vibes — Cambridge tells you to be smarter; New York, wealthier; San Francisco, more powerful — and reframes ambition accordingly: "This is not about money. This is about power," including the shareable power of restoring sight or "giving life to the cancer patient." That path costs a decade of your life "that you will never get back no matter how it turns out." He admits he sometimes thinks life would be easier if he worked on AI instead of brain-computer interfaces, "but somebody has to do it."

The interview closes on one telling confession: his most contrarian infrastructure choices were never scrutinized by his board "because I control the company." A reasonable board, he notes, would question a founder who announced they planned to vibe-code their purchasing system. He never got those questions.

For investors and operators watching the deep tech landscape, Hodak's framework offers a specific lens: the companies worth betting on may not be the ones with the most elegant technology, but the ones that treat their own operations as an engineered product — with measurable throughput, deliberately designed feedback loops, and a founder who understands that judgment, unlike purchasing, can never be delegated.