Abe Murray

The through-line

Abe Murray's positions all trace back to a single formative fact: he learned business in his family's aquaculture and fishing operation, dropped out of high school to run boats and factories, and got what he calls his first MBA as a kid [1]. That experience taught him two things he has never let go of. The first is how unglamorous physical work actually is: "how hard it is to do things in the world right stuff breaks um stuff gets dirty," plus regulatory compliance, food safety, and the obligation to ship things safely and ethically into the environment they land in [1]. The second is that automation is not a thesis so much as a necessity. In the early and mid 1990s his family could not hire labor even paying three and four times minimum wage, so they were forced to automate, in the factories and on the boats [1].

After a long career in technology he describes himself as having "circled back around to investing in this world and reality" [1]. The consistent preoccupation is that hard, physical, regulated, unsexy work is where value gets created, that the people who do it well are often not the ones with fancy credentials, and that an investor in this space should have lived the operating problem rather than only modelled it.

On automation as a response to a real shortage

Murray's view of robotics is not futurist. It comes from having watched a family business unable to staff its factories despite paying multiples of minimum wage, and having to automate as a matter of survival [1]. That lesson "stuck with me," and he draws a direct line from it to backing food automation, robotics, and deep tech companies today [1]. Alongside it sits the operational realism: he became an FDA certified food safety person for the factories and boats, and he has thought a lot about doing the work safely and ethically, which he treats as directly relevant to how he evaluates companies doing physical work in food and healthcare [1].

On credentials, hiring bars, and where talent actually comes from

Murray leads with the high school dropout line deliberately, because it "kind of jerks people out of what they might be thinking about as the usual techie background" [1]. He calls himself a state school kid and says he keeps a chip on his shoulder about it, even after Harvard Business School: "you don't have to come from a fancy School you just have to work real hard and have learned a lot and do something useful in the world" [1]. This is not sentiment for him but a hiring position he has held as an engineering and product leader and now applies to founders.

He is candid that the system does not cooperate. He tried hard to recruit at the University of Rhode Island, where he studied, because the student body there is genuinely diverse, full of first-generation graduates and immigrants, "and it was just hard to get any of them over the hiring bar for Google," which he found frustrating [1]. He also admits the same barrier applied to him: sitting in a cubicle at BAE Systems looking at Google job applications, he "didn't even dare apply," and that gap is part of what pushed him to apply to Harvard Business School, which felt like a long shot [1].

On ambition, luck, and doing the job in front of you

His advice to young people is concrete and slightly unfashionable. Put in the time and get the grades, because you can get an excellent education anywhere [1]. Then, whatever job you are given, "knock it out of the park ask for more work crazy hard especially when you're young and it's it's going to work out" [1]. He is honest that he applied to exactly one job out of undergrad, because the dot-com crash had wiped out the startups he wanted to join, and that he could not dream bigger than that at the time [1]. The other half of the advice is about probability: "get lucky is another piece of advice I give young kids like just go get lucky and part of that is taking shots to get lucky just knowing that it's not going to happen unless you try" [1]. He had a backup plan when he applied to business school, which was to start a company, and when he got in he did both, building a B2B SaaS company with a classmate while enrolled [1].

On ownership mentality and zero to one

Ownership mentality is the trait he says he took from the family business into every team since, and the one he asks of everyone he works with [1]. He credits it with getting him repeated zero-to-one assignments inside Google: building the ebook business from scratch, which was the first Google team to take customer dollars rather than user ad revenue, then work in Android, then shipping AI across the company from Google Research [1]. He found the research role a step removed from what he actually loves, because "we'd build Tech that other people would put into their zero to1 product," which is what sent him back toward startups [1].

That pull took him to Verily Life Sciences, then Google Life Sciences, at a point when it was about to raise its Series B, had a couple of hundred people, no Boston team, and no engineering execution machine [1]. His description of the job is that they showed up and built the execution machine, stood up a digital health and value-based care management practice, and worked out what to do as they went [1]. Over five years the company raised roughly $2 billion and passed a thousand people, shipping regulated medical devices and systems out of Boston [1]. He contrasts the culture directly with Google's, where he saw "more maybe a nine-to-five mentality," against Verily where "we were killing ourselves and loving it" [1], and he measures the outcome in patients: helping people get diabetes under control, caring for heart failure patients, running population health for large payers and providers [1].

On why healthtech is the right training for deep tech

Murray argues that the things people assume make deep tech hard are the same things that make healthtech hard, and that he learned them the expensive way. People say deep tech has a capex challenge, a hardware component, and a complex go-to-market, and all of that is true in healthcare too [1]. The team composition is the tell: instead of the classic B2B SaaS trio of product manager, UI designer, and engineer, at Verily the work involved clinicians, doctors and nurses, and clinical trials [1]. The sales motion rhymes as well. Selling deep tech often means selling to a construction developer who has never worked with deep tech before and has to be brought along, which he compares to approaching hospital systems that had not integrated much technology and were being asked to do new things [1]. He also carried across the specific problem of making Google's AI tools useful inside healthtech, where it had to be done safely and applied to datasets that looked nothing like Google's [1]. He calls the whole experience a proving ground he did not know he was in [1].

On how AlleyCorp invests

He is careful to frame his work as the firm's rather than his own, describing AlleyCorp as a New York based venture capital firm that "they've been a venture Studio before people talked about Venture studi," incubating and investing in transformative companies including MongoDB and Business Insider [1]. It is one of the most active early stage investors, doing pre-seed and seed with some Series A activity, organised around focus general partners by category: deep tech and robotics, which is his, plus enterprise software and consumer tech, a strong healthcare practice, marketplaces, and economic infrastructure [1]. He values the cross-pollination, since his healthcare expertise from Verily feeds the vision care work and vice versa [1].

The staffing model follows from the strategy. Because the firm builds companies from scratch or backs two people with an idea and a prototype, in the model Kevin Ryan established, the investing team is deliberately made of operators who love going zero to one and have scaled companies [1]. Murray acknowledges the cliche about value-add venture and then states the ambition plainly: "we want to be the first call we want our Founders to pick up their phone call us when they want to think something through and we want our team to have actually lived some of that with them" [1]. His example is Brandon Jones, recruited out of Harvard Business School, a Princeton mechanical engineer who built robots there and then built manufacturing automation lines for rocket engines at scale at SpaceX, and Murray endorses the line that years working for Elon are worth a couple anywhere else [1].

On picking a big idea

Murray accepts that the job is to multiply capital and return money, and then says that is not the guiding criterion: "I think we're playing the game to do things that we're proud of and that matter in the world" [1]. His reasoning is about the cost of the work rather than about virtue. Every huge outcome that looks effortless from outside came from a decade of a founding team working extremely hard, so if he is going to sign up for that alongside them, he wants it to be useful work [1]. He anchors this in something he heard from Larry Page: "you're going to work just as hard on a small idea as a big idea so pick a big idea" [1]. The conventional filters sit underneath that, huge market, phenomenal team, defensible technology, but the first screen is whether he will love doing it [1]. His stated map of the deep tech territory covers automation, aerospace, advanced manufacturing, and material science [1], and the portfolio he points to spans construction tech, Coup insurance for the robotics industry, Glacier, and Eyebot in vision care, where he can draw on having run clinical trials for regulated medical devices and having talked to teams working on diabetic retinopathy [1][2]. He has also spoken more broadly about how to fund and grow transformative companies and what sustains startup success over time [3].

Takeaways

• Automation is a response to a labour shortage he lived through: in the early to mid 1990s his family business could not staff factories at three to four times minimum wage and was forced to automate both on shore and on the boats [1].
• Physical and regulated work is harder than software people expect, because "stuff breaks um stuff gets dirty," regulators are involved, and safety is a design constraint rather than an afterthought [1].
• Elite credentials are a poor filter: "you don't have to come from a fancy School you just have to work real hard," and he found the standard hiring bar screened out exactly the state school students he wanted to recruit [1].
• Take the shot even when rejection seems likely, and keep a backup plan; his was to start a company if Harvard Business School said no, and he ended up doing both [1].
• Verily is his template for deep tech go-to-market: hardware, capex, regulatory review, clinicians in the build team, and buyers who have never adopted technology like this before [1].
• AlleyCorp staffs its investing team with operators so it can be the founder's first call, and organises coverage through focus general partners by category, from deep tech and robotics to healthcare, marketplaces, and economic infrastructure [1].
• Choose the largest version of the problem, because on Larry Page's logic the effort is the same either way: "pick a big idea" [1].