Michael Sasso

Bloomberg

July 12, 2026, 8:45 p.m. ET

Artificial intelligence is poised to usher in an unprecedented wave of entrepreneurs in the United States, even as concerns linger about potential job losses. 

While it's true that many of these new ventures may not survive for long—thanks to AI enabling questionable business models to take shape, often characterized as disruptive “AI slop” prevalent on social media—the sheer volume of AI-driven startups is impressive. According to Aaron Terrazas, an economist affiliated with small business services provider Gusto, this surge is likely to generate a number of enduring companies, even after the less viable startups fade away.

How AI is fueling an unprecedented surge in new startups in the US

A bright, sunlit startup incubator where diverse entrepreneurs huddle around a large wooden table strewn with prototype AI gadgets and rocket models labeled “AI.” Whiteboards behind them are filled with excited sketches and upward-trending charts. One tiny rocket is mid-launch, its smoke trail curving into a soaring graph line. A single US flag hangs on the wall, and natural morning light floods the room, emphasizing the energy of a record-breaking wave of new AI startups.

Why AI is driving a record boom in new US startups - The Detroit News

- Artificial intelligence is expected to create a record number of new entrepreneurs in the US.  
- Many startups may fail due to dubious business plans enabled by AI technologies.  
- Despite failures, the AI startup surge could lead to lasting companies and innovations.  

Michael Sasso

Bloomberg

July 12, 2026, 8:45 p.m. ET

While it may still spawn a jobs apocalypse, artificial intelligence is set to spark a record number of new entrepreneurs in the US.

Many of these startups could fail quickly, as AI helps dubious business plans move forward - a new wrinkle on the “AI slop” that’s all over social media. But the AI-enabled startup surge is so robust it should yield many lasting companies even after the weaker ones peter out, said Aaron Terrazas, an economist who works with small business services firm Gusto.

Why AI is driving a record boom in new US startups

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Nvidia Stock: Reasons Behind 15% Price Hike and Investment in AI Startups

A sunlit modern trading floor bathed in warm natural light, dominated by a huge Nvidia logo painted on a sleek wall. Multiple screens display a rising stock chart with “NVDA +15%” in bold green. From a secure vault stamped with the Nvidia emblem, streams of golden coins and glowing banknotes arc toward a cluster of miniature futuristic office towers labeled “AI Startups.” In the foreground, two silhouetted executives shake hands over a glossy table, sealing the investment deal.

Nvidia Stock: Why It May Be Raising Prices 15% and Funding AI Startups - Barron's

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Nvidia Stock: Why It’s Raising Prices 15% and Funding AI Startups

General Intuition, a New York-based startup focused on developing a foundational model for generalized AI agents capable of navigating both space and time, is currently in discussions to secure funding that would elevate its pre-money valuation to $6 billion. Notable new investors in these negotiations include Valor Equity Partners, Point72 Ventures, and Seven Seven Six, as reported by sources familiar with the situation.

Current backers, such as Khosla Ventures and General Catalyst, are also expected to take part in this funding round. This capital infusion follows a recent successful raise of $320 million at a $2.3 billion valuation, underscoring the startup’s rapid growth within the burgeoning field of physical AI.

The latest developments align with prior reports regarding General Intuition, which has emerged as a leading player in the creation of innovative AI solutions. The company was founded by CEO Pim de Witte last October, originally as a spinoff from his video game clip-sharing platform, Medal. General Intuition leverages extensive gameplay data—including hundreds of millions of hours of recorded action labels, which detail player inputs and timings—as a foundational dataset for its model.

In a recent statement to TechCrunch, investor Vinod Khosla highlighted the significance of these action labels in fostering what he describes as the “emergence of intuition.” This pertains to the model's capability to generalize across various tasks not specifically included in its training.

General Intuition is in the process of finalizing this funding round, which is reportedly oversubscribed, reflecting strong interest from investors. The company plans to utilize the new capital to refine its general model, particularly focusing on robotic applications. This includes investments in computing infrastructure—having partnered with CoreWeave—and increasing its workforce.

Valor Equity Partners, known for its support of SpaceX, is poised to enter the AI domain with this investment, marking a significant moment as it would be the first AI lab the fund has engaged with since backing SpaceX.

Further updates on this developing story will be provided as they become available.

Valor and Point72 support General Intuition with a $6B valuation as the AI startup expands into robotics.

A sunlit robotics workshop bathed in warm natural light, sleek robotic arms assembling a cutting-edge humanoid robot atop a wooden workbench; on the white wall behind, a large General Intuition logo, flanked by gold-plated investor plaques labeled Valor and Point72, and a discreet metal plate engraved “$6 B” in the foreground to underscore the startup’s new valuation.

Valor, Point72 back General Intuition at $6B valuation as AI startup pushes into robotics - TechCrunch

- General Intuition is raising funding at a $6 billion valuation from notable investors.  
- The company recently secured $320 million at a $2.3 billion valuation.  
- Funds will improve general models with a focus on robotic embodiments and infrastructure.  

New York-based General Intuition, the startup building a foundation model that trains generalized AI agents to move through space and time, is in talks to raise funding at a $6 billion pre-money valuation from new investors including Valor Equity Partners, Point72 Ventures, and Seven Seven Six, according to sources familiar with the matter.

Existing investors including Khosla Ventures and General Catalyst are also participating in the round. The fresh funds would come just weeks after General Intuition raised $320 million at a $2.3 billion valuation.

The news confirms earlier reporting on the deal for what is becoming one of the hottest startups tackling physical AI.

CEO Pim de Witte spun out General Intuition last October from his video game clip-sharing platform Medal, using its hundreds of millions of hours of gameplay and “action labels” — records of which buttons a player pressed and when — as an initial dataset.

First sneak peak in the WSJ on how GI’s large action models work! https://t.co/6fuOcu9fkb — Pim de Witte (@PimDeWitte) August 21, 2026

Investor Vinod Khosla recently told TechCrunch that he believes those action labels will be a key part of the “emergence of intuition,” or the ability for a model to truly generalize across tasks that it wasn’t explicitly trained on.

General Intuition is still finalizing the round, and a source close to the deal said that it is oversubscribed as the startup continues to field interest from investors. The company intends to use the funds to focus to improve its general model with a focus on robotic embodiments. That means spending more on compute infrastructure (the company has a partnership with neolab CoreWeave) and hiring more talent.

Valor Equity Partners is known for backing SpaceX. TechCrunch has reached out to confirm its investment in General Intuition, which would be the first AI lab the fund has invested in since SpaceX.



This story is developing. Please check back in for updates.

Valor, Point72 back General Intuition at $6B valuation as AI startup pushes into robotics

Askya Investment Partners is welcoming applications for a unique six-week program designed specifically for African AI startups, committing at least $200,000 to support one of the participants. A total of ten startups will be selected, and notably, participants are not required to contribute any cash or relinquish equity.

Established in 2024 by Babacar Seck, the firm leverages Seck's impressive experience, which includes roles as an advisor at AXA and as a founding member of Proparco's $300 million venture capital initiative. Seck has also served as CEO of Digital Africa and has invested in nearly 20 startups, such as Moniepoint and GoMyCode, generating over $120 million in returns for investors.

Seck emphasizes that this initiative is distinct from typical accelerators, which primarily focus on fundraising and preparing startups to pitch to investors. Instead, Askya's program addresses fundamental issues that many African startups face, such as weak governance and technology challenges, rather than shortcomings in the founders themselves.

The program aims to provide participants with essential coaching from experienced operators and builders. Founders will also benefit from masterclasses that delve into topics like go-to-market strategies, distribution, pricing models, governance, and technology development.

One of the significant challenges that Askya aims to tackle is the disconnect within the ecosystem. Seck notes that data centers often pursue customers without a clear understanding of demand, and many large corporations express a desire to work with startups but lack insight into which ones to engage. Moreover, universities are training AI engineers who frequently leave the continent for better opportunities abroad.

To address these issues, Askya plans to involve telecommunications companies, banks, and technology firms in the program, showcasing real challenges to aspiring founders. Additionally, the firm is collaborating with Deep Learning Indaba to enhance connections within the African AI community.

In terms of expectations, Askya is specifically looking for companies that are African-founded and based on the continent. Participants should be full-time entrepreneurs with operational products and existing customers. Although the program isn't open to companies at the ideation stage, startups at the pre-Series A level are encouraged to apply.

Seck identifies three key criteria for applicant evaluation: the relevance of the problem being addressed, the innovation of the product in comparison to existing options, and the capabilities of the founding team. A crucial fourth criterion is the founders' commitment to building sustainable, long-term businesses. Askya is not interested in quick exits; instead, it aims to nurture substantial ventures capable of lasting through generations.

When asked about the essence of AI as an interesting venture, Seck remarks that AI fundamentally serves as a tool for enhancing productivity across various sectors, potentially transforming industries such as energy, healthcare, and economic inclusion.

Seck classifies innovative African startups into two categories: the AI stack, which explores applications like voice AI for African languages, and AI solutions for enhancing productivity within the economy. He points out the significant potential for AI applications in energy optimization and health insurance, where improvements in existing systems can yield substantial financial benefits.

Moving away from traditional accelerator frameworks, Askya’s program is centered on comprehensive coaching and interaction with industry stakeholders to create tangible pathways for startups. Participants will receive valuable insights from seasoned industry professionals while also aligning themselves closely with the pressing needs of corporates.

The selection process involves an online application that assesses whether companies align with the program's key expectations. Candidates must demonstrate their commitment to solving relevant problems while showing early signs of execution and traction in their business endeavors.

Incorporating partners from various sectors, including telecommunications and financial institutions, is vital for facilitating immediate market access for the startups. Workshops led by corporate partners will enable participants to tackle real-world challenges and potentially become distributors.

Coaches for the program include accomplished entrepreneurs and operators, many of whom have scaled enterprises robustly and possess experience in navigating complex markets. This diverse expertise will not only enrich the program but also offer founders critical insights and strategies that are typically unavailable outside of established networks.

Ultimately, Askya aims to revolutionize how African startups approach AI by emphasizing the importance of robust foundations and strategic connections within the ecosystem. This six-week initiative marks the beginning of a broader commitment to empowering African entrepreneurs and driving meaningful economic change on the continent.

Askya's $200,000 wager on the future of Africa's AI startups

A sunlit co-working hub in Africa where adult entrepreneurs gather around a giant $200,000 check emblazoned with the Askya logo. Warm natural light pours through large windows onto laptops and sketches, while a bold, stylized map of Africa mural anchors the optimistic scene of the next generation of AI startups.

Askya's $200,000 bet on Africa’s next generation of AI companies - TechCabal

- Askya Investment Partners launches a six-week programme for African AI startups, investing at least $200,000.  
- The programme focuses on strengthening startups through coaching and connecting them to corporates.  
- Criteria for selection includes solving real problems and demonstrating commitment to sustainable growth.  

Askya Investment Partners is opening applications for a six-week programme for African AI startups, with a minimum commitment of $200,000 to at least one participant. Ten companies will take part, and none of them has to pay cash or give up equity to participate.

Founded in 2024 by Babacar Seck, the firm’s pitch rests heavily on his track record, as Seck spent years at AXA as an advisor to the chairman and chief executive before becoming a founding member of Proparco’s $300 million venture capital programme and later chief executive of Digital Africa.

Across those roles, he backed nearly 20 startups, including Moniepoint, Jumia, GoMyCode and Complete Farmer, and was the first generation of institutional venture capital fund managers on the continent. By the firm’s account, those investments returned more than $120 million in profits to investors, including one New York Stock Exchange listing. He has also chaired a Smart Africa AI council investment working group, developing a continental AI investment strategy.

Seck is deliberate about how he describes the programme. It is not an accelerator, he says, because accelerators are built around fundraising. They teach founders to pitch, polish their deck, and end with a demo day that is effectively a pitching day, treating the fundraise as the outcome.

Askya’s programme starts from a different diagnosis. Many African companies find real demand for their products but still struggle to scale because the fundamentals underneath them are weak—governance, hiring, and technology. That, Seck argues, is rarely a problem of founder intelligence or integrity. It is a problem of exposure.

The programme will therefore pair founders with operators and builders who can coach them, alongside masterclasses covering go-to-market strategy, distribution, pricing, governance, and technology.

The second problem Askya is trying to solve is disconnection. Seck describes an ecosystem whose parts do not talk to each other. Data centres chase customers while everyone insists Africa needs more data centres. Global cloud providers quietly report that some of their biggest African customers are startups, a few spending upwards of $30 million a year. Corporates and governments say they want to work with African startups but do not know which ones are good. Universities train AI engineers who take jobs abroad, because too few companies on the continent operate at the scale needed to keep them.

Askya will bring telcos, banks, and technology companies into the programme to put their real problems in front of founders. It is also partnering with Deep Learning Indaba to connect participating companies to the wider African AI community.

On what Askya is actually looking for, Seck is equally specific.

Companies must be African-founded and based on the continent, with full-time founders, a working product, and their first customers. They cannot be at the idea stage, but Askya is not demanding millions in revenue either. The firm is open to companies up to the pre-Series A stage.

Beyond those basics, Seck says there are three tests. Is the company solving a real problem or building a solution in search of one? Is its product meaningfully better than the alternatives, given what African customers actually value? And does the team have what it takes to build it?

The fourth criterion — the one Seck returns to most often — is commitment. Askya is not looking for founders chasing a quick investment and exit. Internally, the firm calls the kind of companies it wants to build “tech Dangotes”: generational businesses capable of lasting decades.

As Seck puts it, building a great business does not take five years. It takes seven, 10, or 15.

This interview has been edited lightly for length and clarity.

What does your ideal African AI startup look like?

Babacar Seck: When we talk about why AI is interesting to us, the fundamental reason is that AI is a productivity enhancement tool. That is really the core of it. Everything we see and like derives from that, because if you look at the impact of AI in the world today, where it is impactful, it is driving up productivity and effectiveness.

We think about it as a tool that can help us solve our problems in energy, healthcare, industrialisation, economic inclusion, and other sectors.

If we zoom in on where we have seen exciting startups, I would separate them into two blocks. Internally we call the first one the AI stack — the different layers of the AI value chain. One very exciting area is voice AI in African languages. You have probably seen many startups operating in that space. They have not fully cracked it yet, but think about it: today in Africa, most people do not speak English, French, or Portuguese at home. They speak their mother tongue, the language they are more comfortable in. The data in South Africa shows 80% do not speak English at home, which surprised many people, including us.

The minute you have an application — and several are being developed — that lets companies interact with their customers in the customer’s own language, you are bridging a divide. Not only in company-to-customer interactions, but also in government-to-citizen ones. We are starting to see this with NGOs using WhatsApp voice messages in African languages to reach people who are often not literate and who live in rural areas where English or French education is less developed. It is a tool with the opportunity to bridge that divide.

The other block we call AI for the real economy. This is the pure productivity play. Everywhere you have structured data at scale, you can build innovative small AI models that enhance performance. This is very exciting in the African context, because we are digitising in the age of AI. We are creating structured pools of data at the same time as we are applying AI to them. It creates a leapfrog effect, because you are building an economic moat much faster as you scale.

Think about the energy space. We see B2B companies in Nigeria that help you optimise your energy installation. Because of the weakness of the grid, people overcompensate, especially for critical industrial operations that cannot go down—telecom towers, manufacturing, and so on. You oversize on solar power, batteries, and generators, and you do not optimise, so you spend a lot more on energy consumption. With AI you can optimise that, using not just data from the platform but also external data—weather, the cost of diesel today, the cost of the grid, and the temperature—and work out how to optimise the mix. You can right-size and also optimise day-to-day use.

We see tangible impact. This is not talk. We see 20% to 30% cost reductions on energy consumption for businesses using such solutions, which is huge, and it has even more potential as the AI gets better.

Another example emerging as a trend is health insurance. Health insurance is not a big thing in Africa today. Even in Nigeria, one of the continent’s biggest economies, maybe 5% of adults are insured. One of the reasons is that it is very expensive. If you go deeper and ask why, it is because there is so much fraud. I come from the insurance sector, so I know this firsthand. No insurer can tell you exactly how much fraud they have, but it is generally estimated to be at least half of claims. Many insurers either lose money or do not make big margins on health insurance because of fraud, and that is also why they do not expand aggressively. In Nigeria it is mostly companies insuring their employees. People do not go out and buy health insurance for themselves because it is not affordable.

What companies do with AI in that space is leverage image recognition, which has become much more advanced. They can authenticate documents and proof of a medical act with more accuracy and make immediate payouts, which you would not do if you had high suspicion of fraud. You may have experienced going to a hospital with insurance and still being asked to pay, even within the network, because the insurer is going to take a year to pay them. This changes the whole working capital flow in that entire sector, and we see companies doing it at a growing scale.

Only one startup gets a cheque. What do the other nine get?

Babacar Seck: You noticed we did not call this an accelerator. It is an AI growth platform. Most accelerators are focused on fundraising. They teach you how to pitch, how to build a good deck and business plan. You get some coaching, and at the end you have what they call a demo day, which is not really a demo day — it is a pitching day. You pitch to investors and you raise, and the raise is the outcome of the programme.

Our programme is structured differently because we saw two problems in the ecosystem.

The first is that many companies that manage to get demand for their product — what we now call product-market fit — have weak fundamentals, and that stops them from growing into enduring companies. You have covered so much of this: whether it is governance, managing people, or technology that does not scale and breaks at some point. Most of the time those are not the result of founders who are unintelligent or dishonest. It is limited exposure to people who have already encountered those challenges.

We address that by centring the programme on builders and operators coaching entrepreneurs. The people coaching in the programme have built businesses themselves. It is a mix of coaching and masterclasses over the six-week programme, giving founders a strong foundation on those topics and on the technology side, guiding them on how to build companies that endure and scale. There is a big focus on go-to-market, distribution, sales, and pricing, with that core layer of governance, technology, and company building underneath. Every company that goes through the programme gets that, and between you and me, that is the highest value, because those are the things you cannot otherwise get. You cannot access those people unless you know them, outside a structured programme.

The other challenge we saw comes from being deeply involved in the AI ecosystem — through advocacy in the Financial Times and the Africa CEO Forum. I also chaired a Smart Africa AI council investment working group, where we devised an investment strategy for African AI at the continental level. One thing we noticed is that all the big issues we face, with the exception of regulation, come down to companies not scaling.

When you speak to infrastructure players and data centres, you realise most data centres are chasing customers, yet we say we do not have enough data centres in Africa. One of the gaps is that these operators are not plugged into demand. But when you speak to the global cloud players — and because we are on record I will not say which ones — they will tell you their biggest customers in Africa are actually startups, with some scale-ups spending upwards of $30 million a year on cloud.

You see the same disconnect with corporates and government. They say they would love to partner with African startups, but they do not know them. They do not know who is good. They see some news online, they engage, some of them have accelerators as a more structured way to engage, but it has not been translating.

And when you speak to universities, they tell you they cannot find jobs for the talent they train. We are training a growing number of AI engineers — at Carnegie Mellon in Kigali, at the African Institute for Mathematical Sciences, and at some really good Nigerian universities — and those people all find jobs abroad, because the platforms on the continent do not exist at the right scale to retain them.

All of this comes back to scaling the startups while connecting them to the ecosystem. That is why this platform is also about connecting to these nodes. We are partnering with Deep Learning Indaba, the largest community of AI professionals, researchers, and learners on the continent, which held its event in Lagos last week — we were there — and a few other organisations, so we can plug these startups into the ecosystem that can scale them.

It is the coaching, the plugging into the ecosystem, and the investment is the cherry on the cake. We are not saying we will only invest in one. We might invest in several, but our commitment at a minimum is one.

People are not coming for the money. We want to attract people who want to scale their businesses and give them solid foundations.

Does the $200,000 come from your fund? What are the terms?

Babacar Seck: It comes from our fund. The terms will be on a case-by-case basis, so it is fully flexible.

What is the selection process, and what are you looking for?

Babacar Seck: Let me describe the process, then tell you what we optimise for.

We have an online application form with a few questions that help us understand whether companies meet the high-level criteria. Those are: African-founded, based on the continent, solving problems here. Founders need to be full-time, need to have a product, and need to have first customers. It is not idea stage, but we are also not requiring millions of dollars in revenue. We are flexible on the maximum stage, up until pre-Series A. The minimum is showing some level of execution and commitment to your business.

In terms of what we optimise for, the first is driving productivity on the continent. Are they solving a real customer problem? Very often, when you dig in, companies are building a solution but not solving a problem. At the core is focusing on pain points that are significant and at scale. That tells us there is potential demand for what they are building.

Second, is it innovative? Is this meaningfully better than the alternatives people have, given customer preferences? In Africa, some telcos discovered it is better to optimise for cost rather than quality, because people are price sensitive. That is not the obvious thing you would do in other regions, but in Africa that is what matters. If you can vastly optimise for what customers seek most, you have something. In the Nigerian payments space, what people needed was access, distribution, and reliability, more than complex features. That is what merchants wanted, and the players who provided it now dominate the market.

Third, can they build it? Does the founding team have the skills required? If you are building an AI solution and nobody on the team has experience building it or any competitive advantage in doing so, that leads us to more questions.

One very important thing for us is commitment. Are you committed to solving this? We want demonstration of that, not just words. How have you been trying to solve this problem before you even built this business? Why do you care? We are not trying to do a quick invest and flip. I think it was TechCabal that ran an interview I gave, and you titled it “the exit is not the goal,” and then I got calls from my investors — but we stand by it. The goal is building great, generational, enduring businesses. Internally we call them tech Dangotes. That is what we are looking for.

Nobody needs to teach you that it does not take five years to build a great business. It takes seven, ten, or fifteen at best. If you are not committed to doing this to your maximum over the next decade or more, you are likely going to give up. The level of the team’s commitment to the problem is the most important thing.

Your deck mentions corporates. Why involve them, and which ones are involved?

Babacar Seck: We are finalising the partnerships, so we cannot announce names yet. The profile is telcos, banks, and tech corporations.

It is important to have corporates involved because they drive the demand in our market and they are extremely hard to read. We have seen startups engage with a telco for a year and a half, and nothing moves because of not having the right person on the other side or corporate complexity. Having them in the programme helps us create that route to market much faster. And the corporates are the ones with the problems — you want to be close to the problem for the startups to be able to innovate.

There will be workshop sessions run by corporates where they present their challenges and priorities, and startups get the opportunity to engage and propose solutions. Corporates can also be distributors, especially telcos, because of their customer base, and banks, because of regulatory factors. Both are very good partners to startups. It is about being close to the problem, and it is about distribution.

What talent do you have on the continent coaching these founders?

Babacar Seck: It is a mix of founders and operators.

For founders, we have people who have built and run businesses past $50 million in revenue coming to share their lessons learned—from success but also from failure—across multiple sectors. We have them from both Francophone and Anglophone markets to highlight the differences between those markets.

On the operator side, we have people like the head of AI for one of the hyperscalers in Africa and the founding CEO of Mansard, plus a few more from Senegal, Guinea, Kenya, and outside Africa.

In AI specifically, we looked for people working at big companies who have built products there and also built and scaled startups. We have people from NVIDIA and Google involved.

There are three categories really: the AI builders who have been founders and also worked in big tech, the founders I mentioned, and the operators. Among the operators we also have someone who heads technology for Egon Zehnder, the biggest leadership consulting firm, which helps companies hire CEOs and CTOs globally.

I asked some of them what a workshop like the one they are giving normally costs. It is typically around $50,000. Here, founders are getting it for free.

Why do you think this can be run in six weeks?

Babacar Seck: The most important things with the problem we are trying to solve are design and iteration.

The design is having the core principles in place: you are learning from builders, you are getting access to partners and customers, and it is not a distraction from your core business. Those are the three most important things. The rest is iteration.

We are starting with six weeks. This is something we are going to scale. It is not a one-off. It is a real commitment we are making. As I mentioned, we think AI is going to become one of the most important drivers of economic development in the world. It will determine the difference between who is rich and who is poor and which country grows and which does not.

This challenge is a today problem, even a yesterday problem. Africa has not committed to scaling its AI ecosystem the way it should. We have been advocating that Africa should be a producer of technology, not just an importer of these solutions; otherwise, our productivity gap with other economies will keep widening. We have been advocating for this, but at this point it is our responsibility to build it. This is version one. This is the start. Even in its current form, we think it can have a huge impact on the companies that go through it. We are putting our money where our mouth is, backing founders and bringing what they need to scale these businesses to solve Africa’s needs—bringing painkillers, and I would argue antivenom, into the market against all these risks and needs people have.

True scale demands moving beyond surface-level integrations to robust execution. We’ve filtered the noise out of Moonshot 2026, optimising the conference strictly for high-calibre connections between startup founders, global financial operators, enterprise leaders, and individuals rewiring Africa’s technical frameworks. Get 20% off Early Bird tickets for a limited time.

Askya's $200,000 bet on Africa’s next generation of AI companies

In Salt Lake City, the University of Utah has unveiled a groundbreaking supercomputer named Redtail, now recognized as one of the top 160 fastest supercomputers globally. Within its first month of operation, Redtail achieved an impressive 5th place among public universities in the U.S. on the Top500 list and secured the 76th position on the Green500, which ranks the most energy-efficient supercomputers.

This advanced computational system, which boasts a $50 million price tag, was financed through a collaborative effort involving the state of Utah, the Huntsman Family Foundation, Hewlett Packard Enterprise, and NVIDIA. It aims to provide vital computational resources for researchers, students, and businesses throughout Utah, facilitating the kind of AI-driven projects that require substantial processing power.

Manish Parashar, the university's inaugural chief AI officer, expressed enthusiasm about the potential of this new technology. “It shows that our state, faculty, students, researchers, and entrepreneurs have access to world-class technology,” he remarked, emphasizing the competitive edge it provides.

The initial interest in Redtail is significant, with researchers already applying its capabilities to diverse projects like climate modeling, air quality improvement, drug discovery, aerospace engineering, and notably, creating digital replicas of human vascular systems aimed at enhancing precision medicine. “This project addresses how to craft a digital twin of a human vascular system for more customized and effective treatment,” Parashar explained.

Coinciding with Redtail's introduction, the University of Utah was invited to join a National Science Foundation network dedicated to AI-driven Cloud Laboratories. Parashar mentioned that the Cloud Lab seeks to automate research processes, allowing scientists to collaborate regardless of their physical locations. Redtail’s robust processing abilities will be instrumental in developing models and AI functionalities for such initiatives.

This investment aligns with a broader strategy in Utah, where efforts to strengthen AI capabilities have intensified over the past year. The state has launched a $100 million Pro-Human AI Initiative, focusing on AI applications for education, air quality, and wildfire management. Additionally, the establishment of an $18.6 million Health AI Vault is enhancing research into cancer and medical AI. Recently, the university was recognized as a national leader in AI, ranking among the top 5% of educational institutions for its AI-related efforts.

The financial investment landscape also reflects this growth. Local AI startup Jump raised $80 million earlier this year, while Databento attracted $97 million in its Series B funding. Zanskar, specializing in geothermal energy through AI, secured $115 million. Collectively, Utah's AI sector has seen an influx of hundreds of millions in venture capital in 2026.

Jump's founder, Parker Ence, credits local investors with significantly contributing to his company’s success. “The funding we received from regional firms like Sorenson and Pelion was crucial for our growth,” he shared. He also highlighted the talent capable of competing with coastal companies, asserting that his entire executive team is composed of Utah locals.

Additionally, the Silicon Slopes community has initiated Start School, a program for aspiring entrepreneurs emphasizing that modern AI tools enable solo founders to launch successful ventures without needing venture capital.

On the regulatory front, Utah is making strides in AI governance, distinguishing itself from other states still grappling with regulatory frameworks. Margaret Woolley Busse, the executive director of the Utah Department of Commerce and the architect of the Office of AI Policy, noted a surprising surge of interest in healthcare-related AI innovations. “Business innovators here are developing transformative AI solutions that could greatly enhance residents' lives,” she mentioned, pointing to the state's proactive regulatory approach.

Utah has enacted several AI-specific laws, including mandates for consumer awareness when interacting with AI in sensitive contexts, protections against unauthorized deepfakes, and inclusive policies for AI-generated content.

As for businesses navigating the AI landscape, Kavita Ganesan, founder of Opinosis Analytics, an AI consultancy in Salt Lake City, noted a shift in focus among companies. While enthusiasm for AI was high, discussions have evolved into more practical considerations on implementing the technology effectively. “Organizations are not struggling with access to supercomputers or regulatory frameworks,” she observed, emphasizing that many are still exploring foundational aspects, such as identifying suitable use cases for AI and integration into existing operations.

Despite the notable advancements, Parashar acknowledged that Utah's journey in AI is just beginning. “While our progress is commendable, we are still at an early stage,” he stated, urging a forward-looking approach to prepare for future developments in AI.

The University of Utah is set to host its annual One-U Responsible AI Initiative symposium on October 6, further underlining its commitment to responsible AI advancement and collaboration.


Exploring Utah's expanding investment in AI, from supercomputers to startups.

Bright midday sunlight floods a sleek innovation hub: on the left, engineers monitor towering supercomputer racks glowing softly behind a glass wall; on the right, a small team of entrepreneurs huddles over laptops around a wooden table brainstorming. A single Utah state flag hangs prominently on the wall, casting a warm glow across the scene.

From supercomputers to startups: Inside Utah's growing bet on AI - KSL News

- The University of Utah launched the Redtail supercomputer, ranking 5th among U.S. public universities.  
- Redtail, funded by a $50 million partnership, supports advanced AI research and applications statewide.  
- Utah's proactive AI regulations aim to ensure public trust and improve resident lives.  

SALT LAKE CITY — The University of Utah just turned on a supercomputer that ranks among the 160 fastest in the world.

Redtail, as it's called, also placed 5th among U.S. public universities on the Top500 list and 76th on the Green500, which tracks the world's most energy-efficient machines. All of this in its first month of operation.

The system cost $50 million, funded through a partnership between the state of Utah, the Huntsman Family Foundation, Hewlett Packard Enterprise and NVIDIA. It's designed to give researchers, students, and eventually businesses across the state access to the kind of computing power that serious AI work requires.

"It demonstrates that the state, the faculty, the students, the researchers, the entrepreneurs have access to state-of-the-art technology that is on par with anybody else in the world," said Manish Parashar, the university's first chief AI officer. "And they can bring that to their work."

The projects already lining up give a sense of the scale. Through a Redtail challenge designed to attract early users, researchers are applying the system to climate modeling, air quality, drug design, aerospace engineering, and something Parashar is particularly excited about: building digital twins of human vascular systems for precision medicine.

"How do you build a digital twin of a human vascular system so that you can do more targeted precision treatments, treatments that are targeted to an individual, that can be more effective?" Parashar said. "These are the types of projects we are seeing emerge."

The same week Redtail earned its rankings, the university was also selected for a National Science Foundation network of AI-driven Cloud Laboratories. Parashar described the Cloud Lab as a way to automate research processes that traditionally require scientists to be physically in the room. The result is that researchers anywhere can participate in work that used to be limited by geography.

"Redtail provides the computational platform that can help build the models and the AI that can drive something like the Cloud Lab," he said.

The bigger picture

The infrastructure investment is significant on its own. But it fits into a pattern that's been building across Utah for the last year.

The state's $100 million Pro-Human AI Initiative, launched at the governor's level, continues to expand. New faculty have been brought in to focus on AI applications in education, air quality, and wildfire monitoring. An $18.6 million Health AI Vault is advancing cancer research and medical AI. And the university was recently evaluated as a national "trailblazer," ranking in the top 5% of academic institutions for its AI response.

The money side tells its own story. Jump, a Utah-based AI company, closed an $80 million growth round earlier this year. Databento, an AI-driven financial data firm, landed a $97 million Series B with more than $300 million in investor demand. Zanskar, an AI-native geothermal company, raised $115 million. Collectively, hundreds of millions in AI venture capital have flowed into Utah in 2026 alone.

Systems in the Redtail AI Factory, which will support research and innovation across Utah. (Photo: University of Utah)

Parker Ence, founder of Jump, said the local investing community played a real role in his company's growth.

"Taking investment from Sorenson and Pelion was a key early step for us that set us up for success in later fundraising rounds," Ence told me. "As a minor contributor to the Governor's AI Task Force, I've been impressed with the level of thought going into advancing economic opportunity for Utahns while balancing safety and pro-human outcomes."

He also pushed back on the idea that Utah companies need to recruit from the coasts.

"The idea that you have to go outside of Utah to attract stellar talent isn't true," he said. "Our entire exec team, which I would put up against any vertical-focused AI company, is all Utah-based."

Meanwhile, Silicon Slopes recently launched Start School, a new program for Utah entrepreneurs built around a single thesis: AI tools have lowered the barriers to starting a company to the point that solo founders can build viable businesses without venture capital.

The rules of the road

Utah has also been moving on AI regulation, an area where most states are still debating where to begin.

Margaret Woolley Busse, executive director of the Utah Department of Commerce and architect of the state's Office of AI Policy, told me the biggest surprise has been the level of interest in healthcare AI.

"Innovators doing business in Utah are actively working on transformative AI solutions that have the potential to significantly improve the lives of residents," Busse said. "The state recognizes the risks associated with these advancements and is taking a proactive role in overseeing regulatory sandbox pilots."

Utah already has several AI-specific laws on the books: a requirement that consumers be informed when they're interacting with AI in high-risk situations; protections against non-consensual deepfakes; libel and slander laws expanded to cover AI-generated content; and safeguards around mental health AI applications.

"Without adequate safeguards, public trust in AI technology will not develop, which could hinder its positive applications in various sectors across the state," Busse said.

What businesses are actually experiencing

The state-level numbers are big. But what does all of this actually look like for a Utah business trying to figure out AI?

Kavita Ganesan, founder of Opinosis Analytics, a Salt Lake City-based AI consultancy, works directly with small businesses, mid-sized firms, nonprofits and government agencies. She told me the conversation around AI has changed over the past year, but not in the direction most people assume.

Innovators doing business in Utah are actively working on transformative AI solutions that have the potential to significantly improve the lives of residents. –Margaret Woolley Busse

"The conversation has shifted from raw enthusiasm to implementing AI the right way," Ganesan said. "As teams become more familiar with the technology, they are seeking true expertise to implement reliable solutions, especially as market hype and low-quality providers create fatigue and disappointment in the field."

I asked her whether Utah's growing AI infrastructure, from Redtail to the Office of AI Policy to the venture funding, is making a practical difference for her clients.

She was direct.

"Most organizations are not held back by a lack of supercomputing power or state policy frameworks," Ganesan said. "They are still working through the foundational phases: trying to vet use cases, identifying real business opportunities, and figuring out how to integrate AI into existing workflows safely and effectively."

It's a reminder that the headline numbers and the day-to-day reality of AI adoption are still in very different places for most companies.

Still early

Parashar acknowledged that Utah's progress, while real, is still early.

"AI is a very fast-evolving technology, and I think we are still in early stages," he said. "We are doing a great job getting to the puck. I think now it's time to think about where the puck is going to be in three years and how do we get there ahead of time, so we're not surprised."

Utah is betting that preparation, not reaction, will separate the states that lead from those that follow. The state is investing in infrastructure, the talent pipeline, policy guardrails and business support before the full wave of demand arrives.

Whether that bet pays off will depend on how quickly the gap closes between the supercomputer in the university basement and the small business owner trying to figure out where AI fits into her Tuesday.

The University of Utah will host its annual One-U Responsible AI Initiative symposium on Oct. 6.

More on KSL

From supercomputers to startups: Inside Utah's growing bet on AI

For years, the process of updating financial software used for bookkeeping has monopolized the efforts of consultants. 

Nicolas Kopp, the CEO of Rillet, an AI-driven accounting startup, expressed this sentiment succinctly to Business Insider: "It's not enjoyable being a consultant implementing your third version of Oracle Fusion and watching three years slip away while you transform Excel spreadsheets and navigate an outdated system."

Oracle Fusion is a robust suite of cloud solutions utilized by large enterprises to oversee their financial and operational needs. The installation of such systems can demand extensive consulting resources, often spanning several years. Rillet aims to streamline this arduous process by leveraging artificial intelligence to maintain real-time financial records as transactions unfold.

Kopp summarized the startup's main goal as follows: "To minimize the time humans spend clicking through applications."

Rillet's rapid ascent also highlights a transformative trend in the consulting industry. With AI assuming a larger share of routine accounting responsibilities, firms are increasingly in search of professionals who possess specialized knowledge and the technical acumen to navigate AI technologies and large datasets.

Recently emerging from a period of secrecy, Rillet announced a significant $100 million Series C funding round at a valuation of $1 billion. This positions the company as a key player among a wave of startups challenging the entrenched enterprise resource planning (ERP) market. With a total funding exceeding $200 million, Rillet stands out as one of the most heavily financed companies offering an AI-augmented alternative to traditional financial software.

Currently, Rillet services over 600 clients across various sectors, including notable names such as Mercor, Neuralink, Temporal, and Skild AI. Interestingly, 40% of its clientele is drawn from industries beyond technology and AI, spanning healthcare, financial services, retail, and biotechnology. Half of its customers have transitioned from legacy ERP systems like NetSuite, Workday, Oracle Fusion, and Sage Intacct, while the remaining clientele has migrated from Intuit.

The evolution towards a more streamlined bookkeeping process is exemplified by Rillet's innovative approach. An analysis conducted by Andreessen Horowitz involving 56 of Rillet's customers revealed that 87% had less than 1% of bookkeeping entries pending for review at month’s end.

This development signals a broader transformation within the enterprise software landscape. Recent downturns among traditional software firms have been coined the "SaaSpocalypse," as advancements in AI prompt businesses to reconsider their investment in extensive software seats, questioning whether newer AI-native solutions can effectively replace them.

Kopp anticipates a shift in how companies procure software. He predicts that the traditional model of billing clients based on employee usage will soon become obsolete, as AI takes over much of the operational workload.

As the deployment of AI reshapes enterprise software operations, it also impacts the types of expertise consulting firms seek. Rillet recently formed a "corporate development alliance" with EY and collaborates with various consulting players like RSM and KPMG.

In the short term, Kopp sees a significant opportunity for consulting firms in areas like change management and system implementation. In the long run, he envisions leveraging years of accumulated accounting knowledge into "skill files, repositories, and agents" that can be integrated with AI systems such as Rillet’s.

"I believe there's substantial potential in developing these agents," Kopp stated. Already, Rillet is assisting a consulting firm in creating analytics and reporting agents for forecasting dashboards and tools for monitoring controls and risks, thereby simplifying the analysis of large datasets.

This shift is reshaping the human talent landscape, especially as the accounting sector continues to wrestle with a prolonged talent shortage.

"The demand for expertise is rising," Kopp noted. "AI levels the field, necessitating highly skilled accountants and accounting professionals within these firms. Otherwise, AI may end up serving as a substitute for their work." He foresees a trend where entry-level positions will increasingly be filled by AI over the next couple of years, creating fresh demand for specialized experts.

Simultaneously, there's a burgeoning category of tech-savvy, innovative workers eager to explore AI applications—commonly referred to in Silicon Valley as "forward-deployed engineers." Their primary role is to work collaboratively with clients to deploy AI tools effectively. Data from Indeed revealed that job postings for this position surged over 5,000% year-on-year in April.

Kopp expects the demand for such talent to continue escalating, particularly for individuals skilled in managing large data sets, working with AI, and designing agents, as consulting firms embrace more transformation initiatives. This trend aligns with a broader movement within the industry, as consulting firms increasingly recruit technical specialists to replace traditional entry-level roles impacted by AI automation.

Moreover, Kopp regards the AI shift as a valuable asset in attracting new talent. 

"A new wave is emerging. It's worthwhile to consider a career in consulting," he remarked. "This shift can serve as a beacon for the younger generation who are more technologically inclined to join these firms."

Rillet's CEO states that AI is altering the hiring practices of consulting firms.

A sunlit consulting firm boardroom with a large frosted-glass wall bearing the unicorn-shaped Rillet logo. At the polished wood table stand two piles: one of fading paper resumes dusting away, the other a neat row of digital tablets glowing with AI-generated candidate profiles. Rillet’s CEO stands at the head of the table, hands confidently on hips, as intrigued recruiters lean forward under warm natural light—capturing AI’s new role in shaping who consulting firms hire.

The CEO of the unicorn AI accounting startup, Rillet, says AI is changing who consulting firms hire - Business Insider

- Rillet aims to revolutionize accounting with AI, speeding up bookkeeping processes significantly.  
- The company has raised $100 million and is valued at $1 billion, attracting diverse clients.  
- Demand for specialized talent in consulting grows as AI automates traditional bookkeeping tasks.

For years, upgrading the software companies use for bookkeeping consumed a consultant's entire job.

"It's no fun being a consultant implementing your third instance of Oracle Fusion and having three years of your life basically go by in a blink of an eye because you're transforming Excel spreadsheets and trying to wrangle a very old system," Nicolas Kopp, CEO of AI-native accounting startup Rillet, told Business Insider.

Oracle Fusion is a suite of cloud software that large companies use to manage their finances and operations. Setting up systems like this can take years of consulting work. Rillet is betting AI can make that process faster by keeping a company's books up to date as transactions come in.

Its "objective function" is simple, Kopp said: "To reduce the time spent that humans go around clicking buttons in applications."

Rillet's growth also offers a glimpse of how AI could reshape consulting talent. As the software takes on more routine accounting work, firms are looking for people with deeper domain knowledge and the technical skills to work with AI and large data sets.

Related video How AI takes a toll on the freelancers who train, test, and refine it

The company, which emerged from stealth two years ago, said last week that it raised a $100 million Series C at a $1 billion valuation.

It's at the forefront of a growing crop of startups taking aim at the decades-old ERP market, and with more than $200 million in total funding, it is one of the most-funded companies building an AI-native alternative to legacy finance software.

The company now serves more than 600 public and private customers, including Mercor, Neuralink, Temporal, and Skild AI, and says 40% of its customer base is outside tech and AI in sectors like healthcare, financial services, retail, and biotech. It has captured half of its customers from legacy ERP systems, including NetSuite, Workday, Oracle Fusion, and Sage Intacct, while the other half comes from Intuit, according to the company.

The post-clicking future

AI-native startups like Rillet are turning the monthly ritual of closing the books into something similar to a normal workday.

In an Andreessen Horowitz analysis of 56 Rillet customers, 87% had fewer than 1% of their bookkeeping entries left to review when the end of the month rolled around.

The shift is part of a broader shake-up taking place across enterprise software. Investors have dubbed the recent sell-off in traditional software companies the "SaaSpocalypse," as advances in AI raise questions about whether businesses will continue paying for large numbers of software seats or whether newer AI-native products can replace them.

Kopp said he expects the way companies buy software to change. Charging customers based largely on how many employees use a product, he said, is likely to become "a thing of the past" as more work is performed by AI agents rather than people clicking through software themselves.

More engineers, more talent

If AI changes how enterprise software is used, then it also changes the kinds of workers consulting firms need to deploy and manage it.

In April, Rillet announced a "corporate development alliance" with EY. It now also works with several consulting firms, including RSM and KPMG, Kopp said.

In the near term, he believes the biggest opportunity for consulting firms is in "change management and implementation." The longer-term opportunity is to turn their decades of accumulated accounting expertise into "skill files, repositories, agents" that can be layered onto AI systems like Rillet.

"I think building agents is a huge opportunity," Kopp said. He added that Rillet is already helping a consulting firm build analytics and reporting agents for forecasting dashboards, as well as tools for controls and risk monitoring that make it easier to review large data sets rather than relying on manual testing.

That shift is changing the human talent they need, especially as the accounting profession continues to grapple with a yearslong talent shortage.

"Number one, the bar is going up," Kopp said. "AI is a great leveler of information. So you need very highly skilled accountants and accounting professionals at these firms. Otherwise, the AI can kind of do your job." Kopp also believes that "generalist" entry-level work will increasingly be absorbed by AI in the coming two to three years and create more demand for domain-specific experts.

At the same time, there's a new class of workers who are tech-savvy, innovative, and eager to experiment with AI — the kind Silicon Valley calls "forward-deployed engineers." They are essentially tasked with working directly with customers to help put AI tools to use. Job postings for the role were up more than 5,000% year over year in April, according to data from Indeed.

Kopp expects demand to keep rising for these types of workers, especially those who can handle large data sets, work with AI, and build agents as consulting firms take on more transformation projects. That's already consistent with a broader shift in the industry: Consulting firms are hiring more technical specialists as AI automates more traditional junior work.

He also sees the AI shift as a recruiting tool in its own right.

"There's a new wave coming. It's worth your time to go into consulting," he said. "They use this a little bit as a lighthouse, I think, for the younger generation, the people that are more tech forward, to come join their firms."

The CEO of the unicorn AI accounting startup, Rillet, says AI is changing who consulting firms hire

In what could be a transformative deal, Anthropic is set to acquire Israeli AI startup Decart for an estimated $7 billion. This acquisition could turn out to be exceptionally profitable for Decart's founders and investors once Anthropic proceeds with its anticipated IPO, which insiders speculate could take place as early as late August.

Interestingly, Anthropic plans to utilize shares as the primary form of payment, rather than cash, keeping Decart's shareholders invested in Anthropic's future value post-acquisition.

This could yield significant rewards considering Anthropic's swift rise in valuation. Just this year, it was valued at $380 billion in February and soared to $965 billion by May, following a substantial $65 billion funding round. Its annual revenue run rate by the end of July hit $65 billion, a notable increase from $47 billion in May and about $9 billion at the close of 2025.

With the upcoming IPO, Anthropic is eyeing a public offering that might match or surpass SpaceX's record-breaking $75 billion IPO. Should Anthropic go public at a valuation higher than what is suggested by the Decart acquisition deal, the shares allocated to Decart's investors could see a considerable increase in value. Conversely, a lackluster IPO could result in diminished financial returns for them.

This unique structure differentiates the Decart acquisition from traditional cash buyouts. Instead of simply cashing out with a fixed $7 billion, the founders and investors are essentially retaining a stake in Anthropic’s future growth trajectory.

Additionally, this deal will bolster Anthropic's presence in Israel, a hub for AI innovation. Decart specializes in enhancing AI efficiency with real-time applications, allowing Anthropic to tap into both its advanced technology and skilled team.

Founded in 2023, Decart boasts a workforce of around 100 employees and has successfully raised $450 million to date.

To navigate this strategic transaction, Decart has engaged boutique investment bank Catalyst as their advisor.

Decart's $7 billion Anthropic agreement may increase in value post-IPO.

A modern boardroom bathed in warm morning light, two silhouetted executives shake hands across a sleek glass table. Behind them, a large wall screen shows a bullish IPO graph soaring upward with “$7 B” and the Anthropic logo beside the rising curve. Decart’s logo is emblazoned prominently on a frosted glass wall in the background, sunlight glinting off its edges.

Decart’s $7 billion Anthropic deal could become far more valuable after the IPO - calcalistech.com

- Anthropic plans to acquire Israeli AI startup Decart for $7 billion, primarily in shares.  
- Decart shareholders' returns depend on Anthropic's IPO valuation, potentially increasing or decreasing payouts.  
- The acquisition bolsters Anthropic's presence in Israel, enhancing its AI technology and talent access.  

The reported $7 billion acquisition of Israeli AI startup Decart by Anthropic could become significantly more lucrative for its founders and investors when the Claude developer follows through with a blockbuster IPO.

Anthropic is expected to pay most of the consideration in shares rather than cash, meaning Decart shareholders will remain exposed to the value of Anthropic after the acquisition closes.

That could prove particularly valuable given how quickly Anthropic's valuation has risen. The company was valued at $380 billion in February and $965 billion in May, when it raised $65 billion. Its annual revenue run rate reached $65 billion by the end of July, up from $47 billion in May and roughly $9 billion at the end of 2025.

1 View gallery Anthropic Claude. ( Photo: Shutterstock )

Now Anthropic is preparing for a potential public offering that could come as soon as the end of August. Bloomberg reported that the company is considering an IPO that could match or exceed SpaceX's record $75 billion offering.

If Anthropic goes public at a valuation above the level implied by the Decart acquisition, the shares received by Decart's shareholders could appreciate significantly. The opposite is also true: a weaker IPO would reduce their potential payout.

The structure makes the Decart transaction different from a conventional cash acquisition. Rather than simply locking in a $7 billion exit, its founders and investors are effectively retaining a stake in Anthropic's next stage of growth.

The deal would also give Anthropic a major foothold in Israel. Decart has developed technology focused on AI efficiency and real-time applications, while Anthropic would gain both its technology and a team in one of the country's largest AI talent hubs.

Founded in 2023, Decart employs approximately 100 people and has raised $450 million to date.

Decart has hired boutique investment bank Catalyst to advise on the transaction.

Decart’s $7 billion Anthropic deal could become far more valuable after the IPO

In a move to broaden its educational offerings, Harvard Business School is incorporating artificial intelligence avatars to deliver personalized feedback to its students.

These innovative avatars have been developed by a startup known as HeyGen and are integrated into the HBS Foundry bootcamp, an eight-week program priced at $699, designed specifically for aspiring entrepreneurs. Although participants enjoy live weekly sessions with instructors, it is the AI avatars that step in to provide evaluations during practice pitches and board meetings.

New York Times reporter Sarah Kessler experienced this firsthand, pitching an AI-driven recreation of Jeff Bussgang, co-founder of Flybridge Capital. While both Bussgang and his digital counterpart were not particularly taken with her idea of creating “Uber for bananas,” Kessler noted that the avatar maintained a eerily static smile throughout her presentation.

Katharina Rings, the project director, initially thought of the AI feature as a simple chatbot. However, following feedback from a trial run, students expressed a desire for a more interactive experience.

Interestingly, while many college students have voiced skepticism about artificial intelligence, those in the Foundry program indicated their appreciation for the avatars. Bussgang himself remarked that while his digital doppelgänger may come across as somewhat “creepy,” he noted, “My students love it.”

Harvard's $699 startup bootcamp features AI avatars of its teachers.

A sunlit, airy Harvard startup bootcamp studio: adult entrepreneurs sit at sleek desks with open laptops, taking notes as lifelike AI avatars of instructors appear on slim desk-mounted screens, gesturing over digital whiteboards. A large Harvard crest hangs on the bright front wall, warm natural light flooding the room to evoke innovation and tradition united.

Harvard’s $699 startup bootcamp offers AI avatars of its instructors - TechCrunch

- Harvard Business School uses AI avatars for feedback in its entrepreneurial bootcamp.  
- Participants found the AI avatars more engaging than traditional chatbot interactions.  
- Despite mixed feelings, students appreciate the virtual avatars' role in practice sessions.  

In Brief

As Harvard Business School seeks to expand its reach, it’s leaning on AI avatars to provide individual feedback.

These avatars were created by a startup called HeyGen and are included in the eight-week, $699 HBS Foundry bootcamp for entrepreneurs. The program offers live sessions with instructors every week, but the AI avatars are the ones providing feedback during practice pitches and board meetings.

New York Times reporter Sarah Kessler actually tried this out herself by pitching an AI-generated copy of Flybridge Capital co-founder Jeff Bussgang. Apparently, both the real Bussgang and his simulacra were unimpressed by her plan to build “Uber for bananas,” but Kessler said the virtual version offered a noticeably frozen smile during her pitch.

Project director Katharina Rings said she initially envisioned the AI component as something closer to a chatbot. However, after HBS released a trial version, students said they wanted a more guided experience.

And while some college students haven’t been shy about expressing their negative feelings towards AI, Foundry participants told Kessler they like the avatars. As for Bussgang, he acknowledged his digital copy is a little “creepy,” but he said, “My students love it.”

Harvard’s $699 startup bootcamp offers AI avatars of its instructors

Michael Polansky is remarkably unassuming for someone operating in a corner of the world known for outsize egos.

Seated at a leafy patio outside a popular bakery in Mill Valley, an affluent town about 15 miles north of San Francisco, Polansky — bespectacled, his fresh face framed by dark hair shot through with gray — has the look and the friendly demeanor of a young professor.

He is, in fact, both the founder of a buzzy AI and biology startup called Outer Biosciences and the creative, business, and romantic partner to Stefani Germanotta — better known as Lady Gaga. It’s an unlikely double life. On the one hand, the couple invariably moves in the world that comes with celebrity; on the other, he’s running an outfit that has spent years figuring out how to keep living human tissue alive outside the body — for over a month, so far — without anyone outside the company knowing.

He didn’t see any of it coming. Polansky grew up in Minnesota and went on to Harvard, where he studied applied mathematics and computer science, graduating in 2006. Afterward, he logged three years at the hedge fund Bridgewater Associates — “a very unique place,” Polansky says over coffee, and one where he had “a really good experience,” even if it wasn’t a place he was going to “wake up and be excited about . . .every day.”

His pilgrimage from Bridgewater to Silicon Valley ran through Minnesota. By coincidence, Sean Parker’s assistant at the time had been Polansky’s neighbor growing up. At a 2007 wedding in their home state, she mentioned that Parker was looking for someone to work with him. Polansky already knew who Parker was and was looking to move west. The two had dinner in New York, “hit it off immediately,” and, as Polansky tells it, he quit Bridgewater the next day and moved to San Francisco.

He first landed a role as a principal at Founders Fund when the firm was run by its four original partners — Peter Thiel, Sean Parker, Luke Nosek and Ken Howery – with Polansky and another young principal at the time, Brian Singerman, sharing an office. “It was a really, really great experience,” Polansky says.

When Parker left Founders Fund after becoming liquid in his Facebook stock and wanted to build out his own family office, he brought Polansky with him. Polansky ran that office — handling Parker’s business, investment, and philanthropic interests, including helping stand up the Parker Institute for Cancer Immunotherapy (where Polansky remains executive director) until COVID, when his life “moved in a different direction.”

That change had a lot to do with Germanotta. Polansky met her in late 2019 at one of Parker’s birthday parties. Charmingly, the meeting came at the urging of her mother, Cynthia Germanotta, president of the Born This Way Foundation, whom Polansky had come to know through his philanthropic work.

“She had said, you know, for months and months, ‘I want to set you up with my daughter,’” Polansky recalls. “I was like, I think you’re making fun of me.” She wasn’t. When their own mothers later met, he says, laughing, “it all made perfect sense.” (His mother and Germanotta’s mother are now close friends.)

Their relationship is a full partnership, professionally and personally. Notably, for Gaga’s most recent world tour, which began in July of last year and ended in April, the couple managed a massive operation across three 747s, something Polansky compares to running “a 200-person startup that travels around the world every day.”

It also includes Haus Labs, the cosmetics brand Germanotta initially built “on her kitchen floor,” Polansky says, instead of simply licensing her name to an existing company. That business, based in El Segundo, California, an operation with roughly 70 employees, is reportedly thriving.

Germanotta also sits on the board of Outer Biosciences, and the two companies collaborate at the margins. For example, Outer Biosciences’ chief scientist, Kyung-Jin Jang, sits on Haus Labs’ scientific advisory board, and the companies have run some joint projects.

Says Polansky, beaming as he talks about her from our sun-dappled table: “People really haven’t gotten to know a certain side of her publicly … She’s such a brilliant businessperson.”

From cancer to tissue in a dish

While Polansky is also a businessperson, he’s not a scientist. He got into life sciences “accidentally,” he says, through more than a decade spent alongside Parker in cancer immunotherapy, a field that was “very fringe” when they entered it.

Outer Biosciences, which Polansky founded in 2022 and where he is CEO, grew out of frustration that the pace of innovation in biology and chemistry has never matched software, in large part because there’s no ethical way to run experiments directly on people. Meanwhile, the proxies scientists rely on instead — animal models, simplified cell cultures, lab-grown organoids — are poor stand-ins for how a real human organ behaves.

So Outer Biosciences took a different approach. Instead of engineering a synthetic organ, the company sources human skin that would otherwise be discarded after surgery – mostly plastic surgery – through what it describes as vetted non-profit and commercial biobanks and brokers operating under “institutional review board oversight and documented donor consent,” principally the National Disease Research Interchange and the Cooperative Human Tissue Network. (Both outfits receive federal funding from the NIH and the National Cancer Institute without being federally operated.)

Polansky is careful to note that there is no single government tissue network that qualifies buyers. He says Outer Biosciences pays fees to these suppliers on a cost-recovery basis rather than purchasing tissue outright. He also says the company spent roughly two years on building that pipeline and handling the protocols required to receive that tissue “within hours” of surgery, while it’s still living.

Asked about the donors’ privacy, he says that every sample arrives already de-identified — stripped upstream by the supplying organizations of names, contact information and other direct identifiers. A proprietary support system developed by his team then feeds the tissue nutrients and removes metabolic waste, extending its viable life well past the industry norm.

That norm, by the way, is a matter of days. That’s enough time to test for acute toxicity, but not for slower biological processes like collagen remodeling, pigmentation change, or barrier repair that take weeks to unfold. (Dermatologists routinely tell patients to expect changes over a matter of weeks for this same reason.)

Outer Biosciences’ system meanwhile keeps tissue alive for up to a month, says Polansky, who says it retains its “day-zero architecture and preserves its day-zero epidermal, stromal and immune-associated molecular programs.” In plainer English, that means that 30-day-old tissue cared for by the company looks a lot like day-one tissue, but it isn’t fully indistinguishable from it.

Sunburn is one of the clearer examples of what that extra time buys Outer Biosciences. Polansky says that its researchers can induce UVB damage in living tissue, then track the stress, inflammatory, and recovery-related responses that follow over the following weeks as an information-gathering exercise. He says the team isn’t “healing” the skin but rather watching an injury happen and then watching the biology that follows it over time.

Perhaps anticipating pushback from the scientific community, Polansky is careful about how he frames the company’s achievements when this reporter asks questions about rival technologies. The startup’s value, he says, isn’t any one piece of what it’s doing but how the pieces fit together: human tissue that can be kept alive for weeks, a diverse donor pool that Outer Biosciences’ team can subject to controlled experimental conditions, and repeated molecular measurements taken along the way.

And it’s all fed into one, closed self-enforcing system. An AI model predicts which untested chemicals are likely to have a beneficial effect on a specific skin function. Those chemicals get run through the living-tissue system. Then the results, whether the prediction was right or wrong, get fed back into the model, improving the next round of guesses.

It’s a giant improvement from where things started, says Polansky. Early on, the company relied on a “brute force” approach, mining scientific literature and partnering with the National Cancer Institute on natural compounds from extreme environments. That phase produced a couple of leads over about 18 months, but with AI layered in, the company is now generating a new candidate roughly every six weeks, with six leads currently active in its pipeline and several dozen additional “hits” logged.

What makes that pace truly astonishing, Polansky says as the crowd around us thins out, is the size of the current universe of skin-active ingredients.

It’s almost impossible to know the exact number, but it’s small. “Active ingredient” means something different informally than it does formally. While the FDA maintains rules covering 13 categories of over-the-counter skin drugs (think sunscreen, antifungals), across all of them, only about 120 to 130 active ingredients are approved. Add cosmetic ingredients backed by actual research, says Polansky, and that number is closer to 200.

This, of course, presents opportunities.

Outer Biosciences is discovering cosmetic ingredients, not drugs, so there’s no FDA approval to seek out. Instead, the route runs through two steps: first, getting the ingredient a standardized industry name; then safety testing under guidelines set by the OECD, a Paris-based international body whose member countries agree to accept each other’s properly run studies — meaning a study done correctly in one participating country is accepted in the other 40-plus.

A partner outfit then commercializes the whole thing. Indeed, rather than build its own consumer brand, Outer Biosciences right now plans to license or sell its finished ingredients to beauty or pharma outfits that will then formulate these into actual products (a serum, a cream) and bring them to market under their own brands. Already, four of Outer Biosciences’ six current leads look likely to reach commercialization, Polansky says.

In the meantime, the company is generating money from collaborative research partnerships, including a pharmaceutical partner that’s studying why certain cancer drugs cause severe skin rashes, and consumer beauty brands that are testing whether Outer Biosciences’ data holds up against their own product-development and marketing needs.

If Polansky is raising more money for the company currently, he won’t say. To date, the company has raised roughly $23 million, with early backers including Wing Ventures, Initialized, and Polansky’s own investing firm, Hawktail, among others. The company employs 19 people, with all but Polansky based just outside Cambridge, Massachusetts.

Asked why he’s chosen now to talk about the company after years of near-total silence — he says he barely discussed it even with close friends — Polansky points to the data the team is beginning to amass and the confidence that has given them. “Trying to do this in private is hard,” he says, as the wait staff start flipping chairs onto tabletops, signaling it’s closing time at the bakery. “We kind of want to start working in public now,” he adds, shrugging.

Outer Biosciences is not the only company chasing this idea, even if its use of real tissue, rather than synthetic, is distinctive.

Vivodyne — a Philadelphia-based competitor building lab-grown human organ tissue paired with predictive AI to replace animal drug testing — just this month announced it has raised close to $80 million to date, including a $38 million seed round and a $40 million Series A, both led by Khosla Ventures.

Other rivals are pursuing various flavors of organ-on-a-chip and microphysiological systems for preclinical testing.

Polansky doesn’t seem especially preoccupied with any of them — less, it seems, out of arrogance than because he seems to have his hands full. Besides, there’s plenty of room for everyone in this moment. Unlike AI companies training on scraping the internet, there is no “biology internet” to scrape.

And Outer Biosciences has two other reasons to focus on its own knitting. First, the data it generates doesn’t exist anywhere else, which, conceivably, makes the company’s position more defensible, if a lot slower, to build than “traditional” software-based AI startups. It’s also cheaper to run, with modest compute demands compared with training a large language model. In fact, all of the company’s AI work currently runs on-premise, not in the cloud, because “we don’t want the data in the cloud,” Polansky says.

Whether over time, Outer Biosciences becomes a standalone commercial-ingredients business, licenses its discoveries, or eventually reorganizes around a single breakout compound, Polansky says he hasn’t settled on – and the team doesn’t need to. The more important goal, he says, is a predictive model that’s accurate enough that the company can spot promising directions in skin biology without having to run every experiment physically first, opening up a rate of discovery in dermatology that doesn’t currently exist.

For now, instead, the work of turning a promising compound into a real product — the formulation, the manufacturing scale-up, the supply chain, the safety testing — is still done manually by the same scientists who discover the compounds in the first place. Building out a product-development team, with people who’ve done this kind of work before, is next on the roadmap.

“I think it’s going to be fun,” he says, “to have people know that this is what we’ve been doing.”

Michael Polansky is training an AI model on skin that’s still alive

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Michael Polansky is training an AI model on skin that’s still alive - TechCrunch

Rillet co-founder and CEO Nicolas Kopp seems justifiably confident as we talk over Zoom a day after his company announced a $100 million raise at a $1 billion valuation. The U.S. has a shortage of accountants right now, which is driving growth of his AI-native accounting platform so much, he raised that cash in 48 hours without even trying.

Rillet emerged from stealth two years ago. Since then, it has raised $200 million from top investors like Iconiq, Andreessen Horowitz, and Sequoia. It’s also amassed 600 customers, most of whom are looking to ditch legacy accounting systems like Oracle and NetSuite, Kopp says.

A few weeks ago, Rillet held a board meeting and shared with investors its growth since its $70 million Series B last summer. Annualized revenue rate had doubled in the last quarter alone; the startup added new clients, many of them public companies, and an alliance with EY to introduce AI tools to the auditing giant.

His customers aren’t piloting Rillet either, he said — they’re yanking out ERP and accounting software from competitors like Intuit, NetSuite, or Oracle.

After that board meeting, text messages were fired, calls were made, and 48 hours later, Rillet was a unicorn. The company wasn’t even looking to raise, Kopp said.

Seth Pierrepont, the general partner at Iconiq who led the round, said that the deal came together fast but “it wasn’t a cold start,” he described.



“Rillet had already proven it could win against the incumbents that have owned this category for decades,” Pierrepont told TechCrunch. Iconiq also co-led the company’s Series B, and with this latest round, Pierrepont joins the Rillet board. “A year of watching the team deliver on that made doubling down and leading the Series C an easy call.”

Julien Bek, Sequoia’s lead investor on the deal, also said that, though 48 hours might look rushed from the outside, from their perspective, re-investing in Rillet was a “very easy decision,” after the company’s growth in the past year.

“Rillet’s initial wedge is accounting, but ultimately they are reinventing the entire finance function,” Bek told TechCrunch, adding that agentic finance could become “one of the largest application software opportunities of the AI era.” Sequoia led Rillet’s Series A last summer.

“When the opportunity came together,” Bek continued. “We already had all the context we needed.”

Rillet is one of many AI-native startups now giving legacy players a run for relevance. Earlier this year, software stocks on the public market dipped as investors worried about how emerging AI tools would affect them. Kopp thinks there’s some truth to that.

“AI is going to come hard at these legacy players,” he said, because it is giving customers compelling alternatives.

Rillet, for example, was built for AI agents, not humans, letting humans work alongside the AI agents on corporate bookkeeping. Rillet clients range from laundromats to a major sports franchise. Some 50% of Rillet customers come from Intuit, 30% from NetSuite and Sage Intacct, and 20% from Oracle, SAP, Workday, and Microsoft products, he said.

Security is critical when working with sensitive client data, Kopp said. Rillet includes model routing, so customers can redirect requests to the foundational model of their choice (like OpenAI or Anthropic), and Rillet’s harness prevents these models from training on their data, he says.

Rillet product imagery Image Credits:Rillet

There’s also no cross-training — meaning one customer’s data remains proprietary. The agents also have memory, so they can remember and store historical actions they can then use for their own process and improvement.

About three months ago, Rillet released a governance feature letting accountants see and audit every decision the AI agent has made — including what numbers the agents pull and how they calculated them. Creating this was harder than it looks, Kopp said, because the team had to compress agent data into a format humans could understand.

Kopp said this feature was only possible to build recently because AI agents have gotten so powerful so quickly. They can, for example, now support multi-step workflows over longer periods of time. Because of that, auditing what they are doing has become even more important for clients.

“We barely scratched the surface of potential and opportunity that this technology has,” he said.

Right now, regulations for public companies require that every transaction made by an AI agent be approved by another human. He thinks regulators and top names are watching how the accounting industry evolves around this new technology. He’s hopeful that new rules and regulations will evolve that align more with where everything is headed.

“It’s a very normal process,” he said. “Similar to when the cloud came, of just getting everybody familiar with what’s going on and how it helps the profession.”

Kopp also doesn’t think mass job displacement from AI is coming anytime soon, especially in accounting. (Stanford released a report a few weeks ago that found no widespread job displacement yet.) He insists that Rillet isn’t a human replacement, not even for junior accountants. They can use Rillet to help automate and assist with some of the profession’s grunt work.

He also pointed out the expected shortage of accountants in the U.S. The number of those graduating with an accounting degree has been declining since at least 2010. In a recent report, the Controllers Council Organization found that 61% of finance leaders have struggled to find finance, accounting, and CPA talent in the past year. The pullback is not entirely shocking: accountants’ hours are long, the pipeline to the top is arduous, the pay often doesn’t match the workload, and the work doesn’t appeal to everyone.

At the same time, the Bureau of Labor Statistics has projected that accounting-related needs are expected to grow by at least 5%, adding 72,800 jobs by 2034. It also doesn’t expect AI to reduce the demand for accountants, even as the technology becomes more widespread. “The automation of routine tasks, such as data entry, will instead make accountants’ advisory and analytical duties more prominent,” the BLS said.

“I just don’t see people losing their job anytime soon,” Kopp said. “These people have started their professions to help businesses make better financial decisions,” he added. “We can fully enable them to do that.”

This piece was updated.

How AI accounting startup Rillet raised $100M and became a unicorn in 48 hours

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How AI accounting startup Rillet raised $100M and became a unicorn in 48 hours - TechCrunch

The near-bottomless demand for unique AI training data from top labs and corporations is driving a massive boom for a cohort of data-labeling startups.

One of these fast-growing businesses is Micro1, a four-year-old startup that expanded its gross annual run rate from $100 million to $500 million over the past eight months, according to a person familiar with the company. Like its peers that hire domain experts such as doctors, lawyers, and scientists on a contract basis, Micro1 retains roughly 60% to 70% of that figure, putting its net annual run rate between $150 million and $200 million.

While Micro1 still lags competitors like Mercor (which hit $2 billion in gross annualized revenue this summer) and Handshake (which reached $1 billion earlier this year), the startup’s revenue growth shows that there is more than enough demand to support multiple players supplying AI training data.

The rapid growth is bound to continue, with some researchers hypothesizing that future AI spending on data could rival spending on compute.

That outlook bodes well for Micro1, which is seeing its contract sizes grow at an accelerated pace and expects its margins to expand over time. The startup is increasingly generating synthetic data without human involvement, such as by creating automated descriptions of video content. Additionally, some of the data it generates can be sold to multiple customers, driving gross margins for this “off-the-shelf” data as high as 80% to 90%, a person familiar with the startup’s finances told TechCrunch.

Selling the same datasets to multiple clients has sparked recent controversy, with critics arguing that distributing off-the-shelf data to Chinese AI developers helps make their models as powerful as top U.S. models.

Micro1’s founder, Ali Ansari, said last month on X that unlike some of its competitors, the startup doesn’t sell its data to Chinese model makers. “Some human data companies work with foreign adversaries. [A]nd the results show today in Kimi K3. We believe it’s shameful to claim American AI dominance desires while selling millions worth of data to countries that we are in adversarial competition with.”

Like Mercor, Micro1 began as an AI recruiting startup. But after noticing that data-labeling clients were using his AI platform to vet and recruit engineers for annotation, Ansari decided to pivot and enter the data-labeling business, too.

Ansari previously told TechCrunch that in addition to having its experts evaluate model outputs — a concept known as reinforcement learning gyms — the company is building a robotics pre-training dataset by having hundreds of generalists record everyday object interactions in their homes.

Micro1 raised its Series A at a $500 million valuation last September, and TechCrunch understands that the startup may have recently raised another round at a significantly higher valuation.

Micro1 didn’t respond to a request for comment.

AI data startup Micro1 reaches $500M gross run rate amid AI training boom

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How AI is fueling an unprecedented surge in new startups in the US

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