Reed Jobs would rather talk about curing cancer than his last name Connie Loizos 5:16 PM PDT · July 11, 2026 Reed Jobs is easy to like. He’s motormouthed, self-deprecating, prone to video-game analogies, and clearly loves his work. He doesn’t particularly want to discuss the fact that he is Steve Jobs’s son, but he’s not uptight about it, either. When our producer, Maggie, asked if he was on a MacBook for our video call Thursday morning, he didn’t miss a beat: “Are you kidding?”
What he’d much rather talk about is Yosemite, the oncology-focused venture firm he launched in 2023 to, in part, build biotech companies from scratch, out of early academic research, using a mix of philanthropy and outside investment capital. Three years in, Jobs is ambitious about turning Yosemite into a serious player, not just because he wants to win but because he thinks the opportunity in front of him is expanding faster than he expected thanks to AI’s impacts on both drug discovery and clinical trial design.
Among the portfolio companies he’s proudest of are Azalea , born from a grant to Jennifer Doudna’s lab and now in the clinic, and Quarry , a company built with serial founder Craig Crews around a novel therapeutic approach called induced proximity, wherein a drug works by physically dragging a disease-causing protein next to the cell’s own breakdown system (instead of trying to block it directly).
When we last sat down with Jobs at TechCrunch Disrupt nearly three years ago, Yosemite was brand new and biotech was still reeling from its post-pandemic crash. Now, the firm has a team of 17; a cluster of blockbuster drugs are all losing patent protection in roughly the same window, creating all kinds of new opportunities; and AI has gone from a curiosity to, in Jobs’s words, a huge part of what Yosemite does. We caught up on all of it.
TC: You announced the first close of your second fund earlier in the year, targeting $350 million. What’s the state of the union at Yosemite?
RJ: One of extreme activity right now. We’ve had incredible traction, and we’ve brought on a lot of really important new partners. Yosemite is a unique venture organization for two reasons: we only work in oncology — that’s 40% of biotech — and we like to make our own companies ourselves. We don’t think the cures for cancer are sitting out in pharma waiting to be discovered; we think we need to go make them with new knowledge. To de-risk those ideas early, when they’re still gentle ideas in university labs, we use a little philanthropy in a completely no-strings-attached way. Two of our 20 companies in the first fund came directly out of a grant.
How much of that $350 million is going into companies you’re spinning up yourselves versus companies you’re joining?
About a third goes into companies we’re making ourselves — either our own ideas or ones we build alongside academics, at places like Yale, Berkeley, and Stanford. That takes a lot of time and energy, which is why it’s only a third. The rest goes into companies other people made that we want to join. Separately, 2.5% of the fund’s [assets under management] goes into a donor-advised fund — that’s completely no-strings-attached grant money, plus $1 million a year from our management fees.
It’s early days, but what’s the case you make to prospective LPs on performance relative to other life science VC firms?
It’s extremely early for us, but Yosemite has the ability to create new areas of medicine before other firms get there. My team has pioneered a couple of these: epigenetic gene editing [technology that changes how strongly a gene is expressed, rather than altering the underlying DNA sequence itself], and safe delivery of gene editing to specific cells — a bottleneck for the whole field for the better part of a decade. If you want to be first, and you want to help discover new areas, that’s what we’re going to be best at.
Earlier on, you were worried about how conservative biotech investors had become. Has that changed?
It has, actually. When I launched Yosemite in 2023, the XBI [ETF/index] was still down massively from its 2021 highs and pharma hadn’t gotten acquisitive yet. What’s changed in the last three years: interest rates are better, and pharma is entering its largest patent cliff in history while sitting on record cash reserves from the pandemic. That’s added up to an acquisitive spree over the last eight months or so. We’ve seen huge exits, like Eli Lilly buying Kelonia for $7 billion , and massive wins in antibody drug conjugates. One high-profile one: Revolution Medicines, going after KRAS [one of the most commonly mutated cancer-driving genes, long considered nearly impossible to target with drugs] in pancreatic cancer, has doubled the survival rate for [the most common form of pancreatic cancer] — from 12 to 24 months. That’s only happened in the last year.
Last year you talked publicly about your concerns over proposed NIH cuts.
Unfortunately, there’s still pressure from the federal government, but it’s less of a long-term threat than it was. Last year, for the first time in history, an administration asked for a cut of up to 40% of the NIH budget. For context, the biggest cut that ever happened was 1% in 2009, in response to the global financial crisis, and that cost 7,000 NIH scientists their jobs. Gratefully, the Senate and House — this is extremely bipartisan — totally rejected the 40% cut. This year they came back asking for 12%, still the biggest cut of all time by an order of magnitude, and I expect the same rejection. NIH funding has more than 90% approval. Personally, I think we should go on offense — I’d increase it to something like $100 billion. On a dollar basis, it hasn’t grown in about a decade, so relative to inflation, it’s actually shrunk.
American hospitals are some of the most technologically naive places in the economy — there’s still a huge amount done on fax, on floppy disk. One example: call centers, like 911 triage, are expensive to keep open 24/7 and are ripe for AI. There’s also electronic health records, radiology, pathology. But where I get really interested is clinical trials — the biggest cost and time sink in drug development. A Phase 3 cancer trial costs about $260 million, and only one in three succeeds. The biggest cost is patient recruitment and retention. AI could help build a synthetic control arm [a computer-generated stand-in for the untreated comparison group, built from existing patient data], so instead of recruiting a full control group, you only recruit the active arm — that halves the patients you need and massively increases speed. The FDA is leaning into this right now.
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