Tech
How AI is helping solve the labor issue in treating rare diseases
Modern biotech has the tools to edit genes and design drugs, yet thousands of rare diseases remain untreated. According to executives from Insilico Medicine and GenEditBio, the missing ingredient for years has been finding enough smart people to continue the work. AI, they say, is becoming the force multiplier that lets scientists take on problems the industry has long left untouched.
Speaking this week at Web Summit Qatar, Insilico’s president, Alex Aliper, laid out his company’s aim to develop “pharmaceutical superintelligence.” Insilico recently launched its “MMAI Gym” that aims to train generalist large language models, like ChatGPT and Gemini, to perform as well as specialist models.
The goal is to build a multimodal, multitask model that, Aliper says, can solve many different drug discovery tasks simultaneously with superhuman accuracy.
“We really need this technology to increase the productivity of our pharmaceutical industry and tackle the shortage of labor and talent in that space, because there are still thousands of diseases without a cure, without any treatment options, and there are thousands of rare disorders which are neglected,” Aliper said in an interview with TechCrunch. “So we need more intelligent systems to tackle that problem.”
Insilico’s platform ingests biological, chemical, and clinical data to generate hypotheses about disease targets and candidate molecules. By automating steps that once required legions of chemists and biologists, Insilico says it can sift through vast design spaces, nominate high-quality therapeutic candidates, and even repurpose existing drugs — all at dramatically reduced cost and time.
For example, the company recently used its AI models to identify whether existing drugs could be repurposed to treat ALS, a rare neurological disorder.
But the labor bottleneck doesn’t end at drug discovery. Even when AI can identify promising targets or therapies, many diseases require interventions at a more fundamental biological level.
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GenEditBio is part of the “second wave” of CRISPR gene editing, in which the process moves away from editing cells outside of the body (ex vivo) and toward precise delivery inside the body (in vivo). The company’s goal is to make gene editing a one-and-done injection directly into the affected tissue.
“We have developed a proprietary ePDV, or engineered protein delivery vehicle, and it’s a virus-like particle,” GenEditBio’s co-founder and CEO, Tian Zhu, told TechCrunch. “We learn from nature and use AI machine learning methods to mine natural resources and find which kinds of viruses have an affinity to certain types of tissues.”
The “natural resources” Zhu is referring to is GenEditBio’s massive library of thousands of unique, nonviral, nonlipid polymer nanoparticles — essentially delivery vehicles designed to safely transport gene-editing tools into specific cells.
The company says its NanoGalaxy platform uses AI to analyze data and identify how chemical structures correlate with specific tissue targets (like the eye, liver, or nervous system). The AI then predicts which tweaks to a delivery vehicle’s chemistry will help it carry a payload without triggering an immune response.
GenEditBio tests its ePDVs in vivo in wet labs, and the results are fed back into the AI to refine its predictive accuracy for the next round.
Efficient, tissue-specific delivery is a prerequisite for in vivo gene editing, says Zhu. She argues that her company’s approach reduces the cost of goods and standardizes a process that has historically been difficult to scale.
“It’s like getting an off-the-shelf drug [that works] for multiple patients, which makes the drugs more affordable and accessible to patients globally,” Zhu said.
Her company recently received FDA approval to begin trials of CRISPR therapy for corneal dystrophy.
Combating the persistent data problem
As with many AI-driven systems, progress in biotech ultimately runs up against a data problem. Modeling the edge cases of human biology requires far more high-quality data than researchers currently can get.
“We still need more ground truth data coming from patients,” Aliper said. “The corpus of data is heavily biased over the Western world, where it is generated. I think we need to have more efforts locally, to have a more balanced set of original data, or ground truth data, so that our models will also be more capable of dealing with it.”
Aliper said Insilico’s automated labs generate multi-layer biological data from disease samples at scale, without human intervention, which it then feeds into its AI-driven discovery platform.
Zhu says the data AI needs already exists in the human body, shaped by thousands of years of evolution. Only a small fraction of DNA directly “codes” for proteins, while the rest acts more like an instruction manual for how genes behave. That information has historically been difficult for humans to interpret but is increasingly accessible to AI models, including recent efforts like Google DeepMind’s AlphaGenome.
GenEditBio applies a similar approach in the lab, testing thousands of delivery nanoparticles in parallel rather than one at a time. The resulting datasets, which Zhu calls “gold for AI systems,” are used to train its models and, increasingly, to support collaborations with outside partners.
One of the next big efforts, according to Aliper, will be building digital twins of humans to run virtual clinical trials, a process that he says is “still in nascence.”
“We’re in a plateau of around 50 drugs approved by the FDA every year annually, and we need to see growth,” Aliper said. “There is a rise in chronic disorders because we are aging as a global population … My hope is in 10 to 20 years, we will have more therapeutic options for the personalized treatment of patients.”
Tech
Embattled startup Delve has ‘parted ways’ with Y Combinator
The controversy around Delve appears to have cost the compliance startup its relationship with accelerator Y Combinator.
Delve is no longer listed among YC’s directory of portfolio companies, and the Delve page seems to have been removed from the YC website. In addition, the startup’s COO Selin Kocalar posted on X that “YC and Delve have parted ways.”
“I still remember the day we took our YC interview at MIT,” Kocalar said. “We’re so grateful to the community and every founder friend we’ve made.”
YC isn’t the first investor to distance themselves from Delve. Insight Partners also appears to have deleted posts about its investment in the company, although its primary blog post was later restored.
Meanwhile, Delve continues to push back against anonymous claims that it misled clients by telling them they were compliant with privacy and security regulations while allegedly skipping important requirements and auto-generating reports for “certification mills that rubber stamp reports.”
Those claims were first published in an anonymous Substack post attributed to “DeepDelver,” who described themselves as a former Delve customer who became suspicious after receiving leaked data about the startup’s clients.
DeepDelver published subsequent posts sharing what they said were Slack and video posts from the company, as well as accusing Delve of passing off an open source tool as its own, without giving credit or reaching an agreement with the developer. A security researcher also said he was able to access sensitive Delve data.
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Meanwhile, Delve became part of a related controversy when malware was discovered in an open source project developed by Delve customer LiteLLM.
In the company’s latest blog post, Delve’s COO Kocalar and CEO Karun Kaushik declared their intention to set “the record straight on anonymous attacks.” Among other things, they claimed that the company has hired a cybersecurity firm “to help us understand what happened,” and said the “evidence points to a malicious attack rather than a genuine whistleblower.”
“It appears that an attacker purchased Delve under false pretenses, maliciously exfiltrated data, including Delve’s internal company data, and used it to launch a coordinated smear campaign against us,” they said. The blog post also includes a screenshot that they said “shows the attacker exfiltrating our audit tracking spreadsheet via file.io.”
Beyond this accusation, Delve also described DeepDelver’s criticism as “a mix of fabricated claims, cherry-picked screenshots, and data taken out of context.” For example, they said DeepDelver “dismisses our AI while acknowledging it automated 70% of a security questionnaire.”
On the question of using open source tools, Delve said it “built on an Apache 2.0 open-source repository, which explicitly permits commercial use, and significantly rebuilt it for compliance use cases.”
However, the executives also said they’ve been taking steps to ensure customers “feel confident in our platform and compliance outcomes.”
Those steps supposedly include cleaning up the company’s network to remove auditing firms “that don’t meet our standards,” “offering complimentary re-audits and penetration tests to all active customers,” and making it “unambiguously clear” that Delve’s templates for things like board meeting notes “are designed to be starting points only.”
In a post on X, Kaushik made many of the same points but also said, “[W]e grew too fast and fell short of our own standard. To our customers, we deeply apologize for the inconveniences caused.”
TechCrunch has reached out to Y Combinator and DeepDelver for any response to Delve’s comments.
Tech
Peter Thiel’s big bet on solar-powered cow collars
Founders Fund has made its name backing what Peter Thiel calls “zero to one” companies — businesses that don’t just improve on existing ideas but create something entirely new. Its portfolio includes Facebook, SpaceX, and Palantir. Its latest bet is a New Zealand startup that puts solar-powered smart collars on cows.
Halter, which closed a $220 million Series E at a $2 billion valuation last month, with Founders Fund leading the round, isn’t the kind of company that tends to dominate technology headlines. There is no agentic AI involved, no humanoid robots. There is, however, a very large and largely unsolved problem: How do you manage cattle spread across some of the most remote terrain on earth, without dogs, horses, motorbikes, or helicopters?
Craig Piggott, Halter’s 30-year-old founder and CEO, has spent nine years working on an answer. “If you manage a pasture-based farm, whether it’s dairy or beef, the most important variable is how you manage the productivity of your land,” Piggott told TechCrunch in a recent interview. “Fences are the lever — they control where animals graze and how you rest the land. Being able to do that virtually just made a lot of sense.”
The system Halter has built combines a solar-powered collar, a network of low-frequency towers, and a smartphone app to let farmers create virtual fences, monitor every animal around the clock, and move their herds without ever leaving the farmhouse. Cattle are trained to respond to audio and vibration cues from the collar — a process Piggott that likens to the way a car beeps as it approaches a wall while parking. Most animals, he says, learn within three interactions with a virtual fence. “Then you’re able to guide them and shift them around on sound and vibration alone.”
The collar does more than herd. Because it is always on and collecting behavioral data, it also tracks animal health, monitors fertility cycles, and flags when individual animals may be sick, capabilities that Piggott says have improved dramatically as Halter has accumulated what is likely the world’s largest dataset of cattle behavior. The company is now on its fifth generation of hardware, and its reproduction product is currently in beta with U.S. customers.
“The product that ranchers use today is radically different to what they bought a year ago,” Piggott said. “Every week, we’re releasing new things to our customers.”
Piggott grew up on a dairy farm in New Zealand before studying engineering and landing a brief stint at Rocket Lab, the rocket company that gave him his first glimpse of what a technology startup could be. “Rocket Lab was kind of my introduction to technology and startups and the world of venture capital,” he said. “Realizing you could raise money, hire a team, and chase an ambitious mission was inspiring. I wanted to do that in agriculture.” He started Halter at 21. “Probably a bit naive in hindsight,” he acknowledged, “but that was fine.”
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Nine years later, Halter’s collar is on more than a million cattle across more than 2,000 farms in New Zealand, Australia, and the United States, where the company operates in 22 states. The financial proposition for farmers is straightforward: By giving ranchers precise control over where their herds graze, Halter can lift the productivity of their land by as much as 20% — not by saving labor costs (though that happens, too), but by ensuring cattle graze more efficiently and leave less grass behind. “In some cases, we see customers literally doubling the output off their land,” Piggott said. “The upper ceiling for returns is very, very strong.”
Halter isn’t alone in spying the opportunity. Pharmaceutical giant Merck already makes its own virtual fencing system for cattle, called Vence, and newer entrants are circling too — at Y Combinator’s most recent “demo day,” a startup called Grazemate presented a vision for herding cattle with autonomous drones (no collars necessary).
Piggott seems unbothered by either. Asked about drones, he answers: “Can I see drones playing some small part in the future? Probably. But I don’t think a drone is the right form factor for the core fencing element of virtual fencing. A collar will probably be the right form factor for a very long period of time.” And as for the bigger competitive picture, he argues the real obstacle isn’t rival technology at all. “The biggest competition is just not changing anything,” he said. “It’s doing what you did last year.”
What sets Halter apart, Piggott argues, is the sheer engineering difficulty of what it has spent nine years solving — a system managing a thousand animals needs to be reliable to many nines of uptime, because even a 1% failure rate means ten animals out at any given time. “Chasing those many nines of reliability takes time,” he said, “and that long tail is what we proved out in New Zealand over many years before we started to expand globally.”
Halter is also something of an outlier in the agricultural technology sector, which has slumped in recent years as startups struggled to persuade farmers to adopt new products while managing high operational costs. Piggott attributes Halter’s traction to its relentless focus on financial return. “From day one, Halter has been built around a really strong financial ROI,” he said. “If you can lift the productivity of land by 20%, that flows through the entire business.”
Unlike most technology companies, Halter doesn’t view the United States as the center of its universe. “The U.S. market is important for us, but it’s not the world’s biggest market,” Piggott said. “Agriculture is spread around the world, and we need to get there too.” The company has now raised roughly $400 million in total and is prioritizing expansion across the U.S., South America, and Europe.
But the scale of the remaining opportunity is perhaps best captured in a single number — one that no doubt resonated with Founders Fund and Halter’s earlier backers, too. Halter’s collar is on one million cattle, while there are one billion more in the world. With less than 10% penetration in its home market of New Zealand alone, “We have a long way to go, and a lot of product still to build,” Piggott said.
You can listen to our conversation with Piggott on this newest episode of the StrictlyVC Download podcast, which drops Tuesdays.
Tech
OpenAI acquires TBPN, the buzzy founder-led business talk show
OpenAI has acquired popular tech industry talk show TBPN — Technology Business Programming Network — making this the AI giant’s first acquisition of a media company. The show will report to OpenAI’s chief political operative, Chris Lehane.
TBPN, hosted by former tech founders John Coogan and Jordi Hays, is a daily live show that airs on YouTube and X for three hours, focusing on tech, business, AI, and defense.
The show has gained a cult following in Silicon Valley, a safe space where industry power players can speak candidly and be questioned by fellow insiders. The show has a reputation for being something of a Sports Center for the tech industry — a place where top tech CEOs like Mark Zuckerberg, Satya Nadella, Marc Benioff, and, yes, Sam Altman, come to chop it up, react to the news of the day, and occasionally make some of their own.
TBPN will continue to live on as its own brand, which OpenAI will help scale. Not that it necessarily needed help on that front; TBPN has grown into an empire that’s on track to pull in more than $30 million this year, according to The Wall Street Journal.
OpenAI already has its own podcast for long-form conversations with the people building tech at the company.
OpenAI will also tap the founders’ “amazing comms and marketing instincts” outside the show, according to OpenAI’s head of AGI deployment, Fidji Simo, who said TBPN will “bring AI to the world in a way that helps people understand the full impact of this technology on their daily lives.”
Simo went even further, noting that TBPN’s prowess is necessary for an atypical company like OpenAI where “the standard communications playbook just doesn’t apply.”
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She said TBPN will have editorial independence and continue to “run their programming, choose their guests, and make their own editorial decisions.”
Still, the acquisition might give some pause. After all, OpenAI is a valuable AI lab on the brink of an IPO buying a buzzy talk show that often discusses the company and its competitors. And once the deal closes, TBPN will operate under OpenAI’s strategy team and report to Chris Lehane, the man who invented the phrase “vast right-wing conspiracy” as a tool to deflect press scrutiny of the Clinton White House.
Lehane, who has been described as a master of the “political dark arts,” is also behind the crypto industry super PAC Fairshake, which spent hundreds of millions to kneecap anti-crypto candidates in the 2024 election. He joined OpenAI that same year and has been in President Trump’s ear ever since, whispering recommendations for sweeping and controversial policies like preventing states from regulating AI and easing environmental restrictions that might slow data center construction.
OpenAI CEO Sam Altman, who said in a social media post that TBPN is his favorite tech show, seems to believe the acquisition won’t change TBPN’s commentary and even criticism of the company.
“I don’t expect them to go any easier on us, am sure I’ll do my part to help enable that with occasional stupid decisions,” he wrote.
TBPN, meanwhile, sees the acquisition as a means to do more than just commentary.
“While we’ve been critical of the industry at times, after getting to know Sam and the OpenAI team, what stood out most was their openness to feedback and commitment to getting this right,” Hays said in a statement. “Moving from commentary to real impact in how this technology is distributed and understood globally is incredibly important to us.”
Got a tip or documents about the AI industry? From a non-work device, contact Rebecca Bellan confidentially at rebecca.bellan@techcrunch.com or Signal: rebeccabellan.491.