Who Is Jorge Colindres?
Jorge Colindres is the cofounder of Radical AI, a deep tech company that's building an entirely new system for discovering and manufacturing materials. On the surface, Radical AI is an ML+robotics lab attacking the hardest problems in materials science — hypersonic flight, lithium ion batteries, nuclear fusion. But in Jorge's vision, it's something bigger: a redefinition of how science itself should work. He sees a bottleneck that nobody else was willing to tackle: the decades it takes to discover and bring new materials to market. His bet is that by combining machine learning with fully automated lab systems, they can compress 10-20 years of materials development into months. At 37 people and $65 million raised, Radical AI is already proving it — they've validated hundreds of high-entropy alloy compositions in months, compared to the 3,500 academia achieved over 40 years.
But before Radical, Jorge was a venture capitalist. Reading 200+ machine learning research papers, he grew frustrated. He watched AI companies build calendar optimizers and email copy generators while the technology capable of solving humanity's hardest problems went to waste. The moment of clarity came sitting next to his co-founder Joseph: "Why are we not solving cancer with machine learning?" That question became the company.
The Archetype: The Creator
The Creator
The Sage
Tests & Allies
Jorge embodies the archetype of The Creator — the person who sees something broken in the world and has to fix it, not because it's profitable, but because it's intolerable. His frustration wasn't performative. When he talks about the disconnect between ML's promise and what the industry was actually building, you can hear the real irritation: "I kept seeing companies that I did not think were actually going after the biggest, hardest problems." That frustration drove him down a rabbit hole of 200+ research papers. He had to understand. Was the technology not impressive, or were people just picking low-hanging fruit?
The Creator in Jorge shows up most in his willingness to question the entire operating model. Legacy materials companies — Dow, DuPont, 3M, BASF — have been around 100+ years and make billions. They're not incentivized to invent the future. So instead of selling designs to them, Radical AI is building a fully verticalized company to discover, process, scale, and manufacture materials end-to-end. That's not an incremental improvement. That's a deliberate choice to rebuild the entire category from first principles.
His secondary archetype is The Sage — the need to understand how things work at a fundamental level. He reads voraciously (24 books a year before the startup), he writes code, he asks about microscopy analysis and quantum mechanical data. The Sage in him demands clarity: you have to understand the problem first before you solve it. "It all comes down to what problems are you trying to solve. If you can start with the problem, you have a better chance at really understanding the right solution."
"If you want to be successful and if you want to achieve the things that you set out to do, then you really need to focus. You need to really drill into what are the most important things and then go after those things very directly."
The Hero Match
Prometheus
Prometheus is the god who stole fire from the heavens and gave it to humanity — knowing full well the punishment would be eternal. He's not motivated by glory or reward. He's driven by an obligation to human progress. That's Jorge. He talks about the obligation with surprising honesty: "You have an obligation almost at the purest of levels as a human being to try to make an improvement for the rest of humanity. And I think that's what everyone here believes as well."
The Prometheus parallel runs deeper. Jorge's willingness to take on pain — "one of the most painful endeavors I have ever undergone" — is tied directly to the belief that nobody else will. A VC reading 200 papers and deciding to blow up their career to start a materials science company they barely understood. That's the Prometheus moment. He couldn't not do it. And like Prometheus, he's building something that will benefit humanity for centuries, even if the immediate reward is struggle, 5-day-a-week in-person work, and the weight of 37 people betting their careers on a thesis about how science should work.
Tony Stark — Iron Man
If Prometheus is the mythological match, Tony Stark is who your Slack audience sees. Stark builds better tools because the existing ones are garbage. He's not ideologically opposed to legacy systems — he's just pragmatically convinced they won't solve the problem. He takes the approach of: fine, I'll build it myself. More importantly, Stark understands that the real competitive advantage isn't any single tool. It's the system. The lab coat, the suit, the mind — they're inseparable. That's Radical AI's data moat. It's not just the ML model or the robotic lab in isolation. It's the feedback loop they built to connect them.
And there's a Stark-like confidence in how Jorge talks about risk: "You do need to lean into risk in order to achieve the ultimate reward. Leaning into risk means making quicker decisions with less information." That's not recklessness. That's clarity. Stark doesn't hesitate because he's thought through the decision framework. Jorge operates the same way.
The Story Behind Radical AI
It was 2022. Jorge was investing in deep tech and software at a VC firm, sitting next to his co-founder Joseph, also a VC with a deep tech focus. AI companies kept pitching them. Wave after wave of ideas. And Jorge felt something close to betrayal. Not anger — worse. Disappointment at the gap between what was possible and what was being built.
He decided to find out if he was wrong. Over the next year, he read 200+ machine learning research papers. Not abstracts. Papers. He wanted to understand the fundamental capability of the technology. And the conclusion was unavoidable: the technology was powerful. But the applications were safe, incremental, low-hanging fruit. The moment came when he turned to Joseph and said it out loud: "Why are we not solving cancer with machine learning?"
Joseph didn't say yes immediately. He said: "I don't know about cancer, but let's look into different spaces." They discovered their third co-founder, Herd Seder, a leading academic in machine learning applied to materials science. The three of them realized what they were building wasn't optional. "Science was moving in this direction. Whether we started a company or not, it was inevitable. We knew we had to give it a shot."
That was the call to adventure. But the real moment of crossing the threshold came later. Jorge went from talking about the idea to walking away from venture capital. From having opinions about how the world should work to building proof that it could.
The Founder's Journey ↔ The Company's Journey
Venture capitalist frustrated by wasted potential → reads 200 papers obsessively → finds co-founders with complementary expertise (Joseph: materials scientist, Herd Seder: academic authority) → commits fully, burns boats, moves to building → discovers the torch test moment, first proof the system works.
Two people in a living room with a conviction about how science should work → $65 million raised → 37 people across robotics, mechanical engineering, software, ML, materials science → validated hundreds of HEA compositions in months (vs. academia's 3,500 in 40 years) → torch test showing materials outperforming 1960s industry standards at 3000 degrees → now verticalizing toward manufacturing.
The parallel is exact. Jorge's personal conviction that science was broken translated into building a system that fixes it. Every stage of his journey — the frustration, the deep research, the willingness to look incompetent, the commitment despite pain — is embedded in Radical's operating model. A founder who knows what deep learning can do, paired with a materials scientist, paired with an academic. A willingness to fail 95% of the time and count the failures as data, not waste. A 5-days-a-week in-person culture because you can't build something this ambitious remotely.
How Jorge Leads
Jorge is a consensus-builder, but not in a passive way. He builds consensus around a firm conviction. He's thought about the problem enough to have a position, but he's open about how he arrived there and why. When he narrates decisions, it's "we discovered" and "the team realized," but there's no ambiguity about who set the direction.
He leads through clarity. He can explain what Radical AI does to an elementary schooler, to investors, and to engineers, and the explanation changes form but not substance. That precision — the ability to distill a complex system into its essential components — is rare in technical founders. He talks about the ML pipeline, the robotic lab feedback loop, the data moat, and he makes each piece feel inevitable, not clever.
His decision-making is risky but not reckless. He talks about "making quicker decisions with less information" as a principle for operating when you're in genuinely new territory. But he also emphasizes long-term thinking over short-term wins. "If you have long-term vision but no intermediate progress, then it's a fantasy. And if you only have short-term results and no long-term vision, then what are you even doing?" He's thought about the paradox and resolved it: you need both, operating on different clocks.
The core tension: Impatience vs. Patience. Jorge is impatient about the big picture — science is too slow, he's going to fix it. But he's patient about the path, willing to go through all 95% of failures because each one is data. This tension makes him push for speed without breaking quality. It's what lets him hire people with no materials background and make it work.
Founder Superpowers
Turning Frustration Into Research Marathons
Most people feel frustrated and vent. Jorge felt frustrated and read 200 papers. "I read north of 200 research papers within the domain of machine learning because I wanted to get to a root understanding of that question." That conversion — emotional energy into systematic investigation — is how Radical AI was born. He can't move forward without understanding, and that refusal to act on incomplete information is what makes his conviction unshakable once he does move.
Making Invisible Data Visible
Jorge built a company around an insight most scientists know but no one acts on: "We publish on what worked, despite the fact that there are 90, 95% of iterations that did not work. We just don't talk about those in the paper." While everyone else publishes the 5% that worked, Radical captures the 95% that didn't — and turned it into the company's moat. Seeing value where others see waste is what makes their data strategy genuinely defensible.
Compressing Timescales by Stacking Clocks
Most founders choose long-term vision or short-term execution. Jorge runs both simultaneously. "Not in a balance, but an understanding of how they relate to one another." He thinks in decades for direction (verticalization, manufacturing, redefining science) and in quarters for proof (HEA compositions, torch tests, the next hire). That stacking is what compresses 20-year material timelines into months.
What It's Like to Work With Jorge
Jorge doesn't lead through charisma. He leads through alignment. In the interview, he's measured, rarely animated except when describing the research rabbit hole or the torch test. He thinks out loud in complete thoughts, which means his answers are thorough but not spontaneous. He respects silence — when asked a hard question, he pauses and then commits to a real answer, not a prepared one.
The most telling moment in the conversation isn't a quote. It's how he talks about his wife. "The number one person I talk to is my wife always. She is my biggest supporter. I could not be where I am without her." No equivocation. No performance. Just clarity about where he gets support when the mission becomes painful. For a team, that's a signal: this is a founder who knows his limits and admits them, which means he'll admit when something isn't working.
He takes feedback literally. When Angelina sketches a naive version of how the system works — "I'm not a materials scientist, I'm just making things up" — he doesn't correct the caricature. He uses it as a framework for explaining how it actually works. That's generosity. It means when an engineer on his team proposes something, he's likely to engage with the spirit of the idea rather than dismissing rough first drafts.
What might surprise someone joining Radical: Jorge is not trying to convince you that you should be there. He's filtering for people who are already convinced the mission is worth the pain. "You are not here for a job, you are here for a mission." That's not inspirational fluff in his mouth. It's a statement of fact. If you're looking for flexibility, remote work, or a stepping stone, Radical isn't for you. If you're looking for the torch test moment — real evidence that what you're building works — he'll give you that.
Why This Matters (For You)
If You're in Materials Science or Advanced Manufacturing
Jorge's vision at Radical AI directly challenges the bottleneck you're living in: the 10-20 year cycle from material discovery to production. If you're an R&D director, materials scientist, or procurement decision-maker in aerospace, energy, or advanced manufacturing, listen to his core insight — traditional materials development publishes the 5% of iterations that work, but discards the 95% that failed. Radical AI captures that hidden data and feeds it back into ML systems that compress decades into months. The result isn't just faster discovery. It's access to materials compositions and performance characteristics that wouldn't have been found within your organization's timeline budget. Jorge's bet is that this system will become the standard for materials research. If your company's roadmap depends on discovering or validating new materials faster, his thesis — and Radical's approach — is directly relevant to your strategic options.
If You're an Engineer Building with AI Systems
Jorge's approach is ruthlessly problem-first. Before you build a bigger model, before you optimize for speed, understand what you're trying to solve. Then build the minimal system that solves it. Radical didn't say "we need the best ML model." They said "we need to discover materials faster," which led to ML + robotics + the feedback loop. The moat isn't any single tool — it's the system that captures data nobody else can. Take that lesson: your competitive advantage is probably not in the architecture. It's in the data you can accumulate that others can't.
If You're Early in Your Career
Jorge's path is nonlinear: founding engineer → VC investor → founder. That jump looks risky, but it wasn't random. He spent a year learning the landscape, understanding what was possible, and then betting everything on a thesis. The lesson isn't "take big risks." It's "do the homework first, then commit fully." He read 200 papers. He didn't just have a hunch. And once he committed, he didn't hedge — 5 days a week in person, working through the pain because he'd already made the decision that the problem was worth it. Long-term thinking means you accept that the first 5 years will be hard, so you might as well go all-in.
If You're Considering Joining Radical AI
Radical selects for mission alignment over pedigree. 70-80% of the team doesn't come from materials science. They came because they believe the scientific method needs to be rebuilt. That means Radical is betting on your ability to think and iterate in genuinely novel territory, not your materials science credentials. You'll work in-person 5 days a week because that's the pace required to move fast on something this ambitious. And you'll have direct access to the lab and the torch tests and the proof points — the moments that make the abstraction real. If you want to see the physical manifestation of the problem you're solving, Radical does that.
Go Deeper
The full conversation with Jorge Colindres is on its way. Check out other episodes in the meantime.
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