Colossal Biosciences CEO Ben Lamm Sees Biology as an Engineering Challenge

Colossal Biosciences CEO Ben Lamm sees de-extinction as a way to speed up biological research. The company’s work on extinct species may draw public attention, but his larger bet is that combining artificial intelligence, computational modeling and gene editing can make biology easier to study and engineer.

Time as a business advantage

Lamm has described compressing time as his defining business skill. In a 2017 interview with Dallas Innovates, he called it “a subset of time travel and really compressing time.” That idea links his earlier work founding defense and space technology company Hypergiant with his current focus at Colossal.

At Colossal, the effort has two sides, Lamm says: using present day tools to investigate the biological past, then applying what researchers learn to future problems. AI can search biological possibilities at a scale beyond manual analysis, while gene editing lets scientists test hypotheses. Computational modeling can also reduce some of the time spent on experiments, though the article offers no specific measurements of those time savings.

“De-extinction is time compression in two ways,” Lamm says. “On one hand, we now have the technical tools to understand the past.”

Beyond the extinct species

Lamm’s broader case is that de-extinction projects can generate methods and knowledge useful beyond recreating extinct animals. He argues that a deeper understanding of biology could help researchers investigate disease and biological maladaptations that now take decades to understand.

“The more completely we understand biology through de-extinction,” he says, “the more we can compress future time.” The animals provide a demanding research target; advances in reproductive biology, genome assembly, modeling and gene editing could then support work in other fields.

That ambition carries a substantial leap in logic. Faster simulations and more capable editing tools can accelerate parts of research, but the source provides no evidence that biological discovery as a whole can now run on startup timelines. Lamm’s claim that biology is becoming an engineering discipline is best read as a direction of travel, not a settled description of the field.

Can biology’s timelines shrink?

Business models often prize rapid iteration, while biological processes can take years or longer to observe and understand. Lamm believes technology is narrowing that gap. “We're moving from observing biology to programming it because we've compressed the most difficult variable: time,” he says.

He also draws a distinction between Colossal’s public image and its research purpose: “People misunderstand what we're building. They think we're in the mammoth business. But we're really in the time-compression business.” The company’s high-profile goals, in this framing, are difficult enough to prompt new tools and techniques.

The commercial significance rests on whether those capabilities can transfer beyond de-extinction. If they can, Colossal’s work may contribute to a broader shift from using software to process information toward using biological tools to shape living systems. The source presents that prospect as Lamm’s thesis; it doesn’t establish how quickly or reliably the transition will happen.

A compounding innovation thesis

Lamm has repeatedly pursued markets where several technologies converge, creating problems that once seemed more like science fiction than engineering. His view is that progress can compound: new tools make discoveries possible, and those discoveries make the next round of work faster.

“People consistently overestimate what happens in a year and underestimate what happens in ten,” he says. “The tools compound. Once they reach escape velocity, progress stops looking linear.”

For Colossal, the practical test is whether its research can produce repeatable advances that shorten the path from biological question to useful result. De-extinction gives the company a visible, technically demanding goal; the wider value depends on what its methods can do after they leave that proving ground.