Technology

Experts don’t think AI is ready to be a ‘co-scientist’

Last month, Google announced the “AI co-scientist,” an AI the company said was designed to aid scientists in creating hypotheses and research plans. Google pitched it as a way to uncover new knowledge, but experts think it — and tools like it — fall well short of PR promises.

“This preliminary tool, while interesting, doesn’t seem likely to be seriously used,” Sarah Beery, a computer vision researcher at MIT, told TechCrunch. “I’m not sure that there is demand for this type of hypothesis-generation system from the scientific community.”

Google is the latest tech giant to advance the notion that AI will dramatically speed up scientific research someday, particularly in literature-dense areas such as biomedicine. In an essay earlier this year, OpenAI CEO Sam Altman said that “superintelligent” AI tools could “massively accelerate scientific discovery and innovation.” Similarly, Anthropic CEO Dario Amodei has boldly predicted that AI could help formulate cures for most cancers.

But many researchers don’t consider AI today to be especially useful in guiding the scientific process. Applications like Google’s AI co-scientist appear to be more hype than anything, they say, unsupported by empirical data.

For example, in its blog post describing the AI co-scientist, Google said the tool had already demonstrated potential in areas such as drug repurposing for acute myeloid leukemia, a type of blood cancer that affects bone marrow. Yet the results are so vague that “no legitimate scientist would take [them] seriously,” said Favia Dubyk, a pathologist affiliated with Northwest Medical Center-Tucson in Arizona.

“This could be used as a good starting point for researchers, but […] the lack of detail is worrisome and doesn’t lend me to trust it,” Dubyk told TechCrunch. “The lack of information provided makes it really hard to understand if this can truly be helpful.”

It’s not the first time Google has been criticized by the scientific community for trumpeting a supposed AI breakthrough without providing a means to reproduce the results.

In 2020, Google claimed one of its AI systems trained to detect breast tumors achieved better results than human radiologists. Researchers from Harvard and Stanford published a rebuttal in the journal Nature, saying the lack of detailed methods and code in Google’s research “undermine[d] its scientific value.”

Scientists have also chided Google for glossing over the limitations of its AI tools aimed at scientific disciplines such as materials engineering. In 2023, the company said around 40 “new materials” had been synthesized with the help of one of its AI systems, called GNoME. Yet, an outside analysis found not a single one of the materials was, in fact, net new.

“We won’t truly understand the strengths and limitations of tools like Google’s ‘co-scientist’ until they undergo rigorous, independent evaluation across diverse scientific disciplines,” Ashique KhudaBukhsh, an assistant professor of software engineering at Rochester Institute of Technology, told TechCrunch. “AI often performs well in controlled environments but may fail when applied at scale.”

Complex processes

Part of the challenge in developing AI tools to aid in scientific discovery is anticipating the untold number of confounding factors. AI might come in handy in areas where broad exploration is needed, like narrowing down a vast list of possibilities. But it’s less clear whether AI is capable of the kind of out-of-the-box problem-solving that leads to scientific breakthroughs.

“We’ve seen throughout history that some of the most important scientific advancements, like the development of mRNA vaccines, were driven by human intuition and perseverance in the face of skepticism,” KhudaBukhsh said. “AI, as it stands today, may not be well-suited to replicate that.”

Lana Sinapayen, an AI researcher at Sony Computer Science Laboratories in Japan, believes that tools such as Google’s AI co-scientist focus on the wrong kind of scientific legwork.

Sinapayen sees a genuine value in AI that could automate technically difficult or tedious tasks, like summarizing new academic literature or formatting work to fit a grant application’s requirements. But there isn’t much demand within the scientific community for an AI co-scientist that generates hypotheses, she says — a task from which many researchers derive intellectual fulfillment.

“For many scientists, myself included, generating hypotheses is the most fun part of the job,” Sinapayen told TechCrunch. “Why would I want to outsource my fun to a computer, and then be left with only the hard work to do myself? In general, many generative AI researchers seem to misunderstand why humans do what they do, and we end up with proposals for products that automate the very part that we get joy from.”

Beery noted that often the hardest step in the scientific process is designing and implementing the studies and analyses to verify or disprove a hypothesis — which isn’t necessarily within reach of current AI systems. AI can’t use physical tools to carry out experiments, of course, and it often performs worse on problems for which extremely limited data exists.

“Most science isn’t possible to do entirely virtually — there is frequently a significant component of the scientific process that is physical, like collecting new data and conducting experiments in the lab,” Beery said. “One big limitation of systems [like Google’s AI co-scientist] relative to the actual scientific process, which definitely limits its usability, is context about the lab and researcher using the system and their specific research goals, their past work, their skillset, and the resources they have access to.”

AI risks

AI’s technical shortcomings and risks — such as its tendency to hallucinate — also make scientists wary of endorsing it for serious work.

KhudaBukhsh fears AI tools could simply end up generating noise in the scientific literature, not elevating progress.

It’s already a problem. A recent study found that AI-fabricated “junk science” is flooding Google Scholar, Google’s free search engine for scholarly literature.

“AI-generated research, if not carefully monitored, could flood the scientific field with lower-quality or even misleading studies, overwhelming the peer-review process,” KhudaBukhsh said. “An overwhelmed peer-review process is already a challenge in fields like computer science, where top conferences have seen an exponential rise in submissions.”

Even well-designed studies could end up being tainted by misbehaving AI, Sinapayen said. While she likes the idea of a tool that could assist with literature review and synthesis, Sinapayen said she wouldn’t trust AI today to execute that work reliably.

“Those are things that various existing tools are claiming to do, but those are not jobs that I would personally leave up to current AI,” Sinapayen said, adding that she takes issue with the way many AI systems are trained and the amount of energy they consume, as well. “Even if all the ethical issues […] were solved, current AI is just not reliable enough for me to base my work on their output one way or another.”

Technology

Pintarnya raises $16.7M to power jobs and financial services in Indonesia

Pintarnya, an Indonesian employment platform that goes beyond job matching by offering financial services along with full-time and side-gig opportunities, said it has raised a $16.7 million Series A round.

The funding was led by Square Peg with participation from existing investors Vertex Venture Southeast Asia & India and East Ventures.

Ghirish Pokardas, Nelly Nurmalasari, and Henry Hendrawan founded Pintarnya in 2022 to tackle two of the biggest challenges Indonesians face daily: earning enough and borrowing responsibly.

“Traditionally, mass workers in Indonesia find jobs offline through job fairs or word of mouth, with employers buried in paper applications and candidates rarely hearing back. For borrowing, their options are often limited to family/friend or predatory lenders with harsh collection practices,” Henry Hendrawan, co-founder of Pintarnya, told TechCrunch. “We digitize job matching with AI to make hiring faster and we provide workers with safer, healthier lending options — designed around what they can reasonably afford, rather than pushing them deeper into debt.”

Around 59% of Indonesia’s 150 million workforce is employed in the informal sector, highlighting the difficulties these workers encounter in accessing formal financial services because they lack verifiable income and official employment documentation.

Pintarnya tackles this challenge by partnering with asset-backed lenders to offer secured loans, using collateral such as gold, electronics, or vehicles, Hendrawan added.

Since its seed funding in 2022, the platform currently serves over 10 million job seeker users and 40,000 employers nationwide. Its revenue has increased almost fivefold year-over-year and expects to reach break-even by the end of the year, Hendrawn noted. Pintarnya primarily serves users aged 21 to 40, most of whom have a high school education or a diploma below university level. The startup aims to focus on this underserved segment, given the large population of blue-collar and informal workers in Indonesia.

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“Through the journey of building employment services, we discovered that our users needed more than just jobs — they needed access to financial services that traditional banks couldn’t provide,” said Hendrawan. “We digitize job matching with AI to make hiring faster and we provide workers with safer, healthier lending options — designed around what they can reasonably afford, rather than pushing them deeper into debt.”

image credits: Pintarnya (Founders of Pintarnya Ghirish Pokardas, Nelly Nurmalasari, and Henry Hendrawan)

While Indonesia already has job platforms like JobStreet, Kalibrr, and Glints, these primarily cater to white-collar roles, which represent only a small portion of the workforce, according to Hendrawan. Pintarnya’s platform is designed specifically for blue-collar workers, offering tailored experiences such as quick-apply options for walk-in interviews, affordable e-learning on relevant skills, in-app opportunities for supplemental income, and seamless connections to financial services like loans.

The same trend is evident in Indonesia’s fintech sector, which similarly caters to white-collar or upper-middle-class consumers. Conventional credit scoring models for loans, which rely on steady monthly income and bank account activity, often leave blue-collar workers overlooked by existing fintech providers, Hendrawan explained.

When asked about which fintech services are most in demand, Hendrawan mentioned, “Given their employment status, lending is the most in-demand financial service for Pintarnya’s users today. We are planning to ‘graduate’ them to micro-savings and investments down the road through innovative products with our partners.”

The new funding will enable Pintarnya to strengthen its platform technology and broaden its financial service offerings through strategic partnerships. With most Indonesian workers employed in blue-collar and informal sectors, the co-founders see substantial growth opportunities in the local market. Leveraging their extensive experience in managing businesses across Southeast Asia, they are also open to exploring regional expansion when the timing is right.

“Our vision is for Pintarnya to be the everyday companion that empowers Indonesians to not only make ends meet today, but also plan, grow, and upgrade their lives tomorrow … In five years, we see Pintarnya as the go-to super app for Indonesia’s workers, not just for earning income, but as a trusted partner throughout their life journey,” Hendrawan said. “We want to be the first stop when someone is looking for work, a place that helps them upgrade their skills, and a reliable guide as they make financial decisions.”

Technology

OpenAI warns against SPVs and other ‘unauthorized’ investments

In a new blog post, OpenAI warns against “unauthorized opportunities to gain exposure to OpenAI through a variety of means,” including special purpose vehicles, known as SPVs.

“We urge you to be careful if you are contacted by a firm that purports to have access to OpenAI, including through the sale of an SPV interest with exposure to OpenAI equity,” the company writes. The blog post acknowledges that “not every offer of OpenAI equity […] is problematic” but says firms may be “attempting to circumvent our transfer restrictions.”

“If so, the sale will not be recognized and carry no economic value to you,” OpenAI says.

Investors have increasingly used SPVs (which pool money for one-off investments) as a way to buy into hot AI startups, prompting other VCs to criticize them as a vehicle for “tourist chumps.”

Business Insider reports that OpenAI isn’t the only major AI company looking to crack down on SPVs, with Anthropic reportedly telling Menlo Ventures it must use its own capital, not an SPV, to invest in an upcoming round.

Technology

Meta partners with Midjourney on AI image and video models

Meta is partnering with Midjourney to license the startup’s AI image and video generation technology, Meta Chief AI Officer Alexandr Wang announced Friday in a post on Threads. Wang says Meta’s research teams will collaborate with Midjourney to bring its technology into future AI models and products.

“To ensure Meta is able to deliver the best possible products for people it will require taking an all-of-the-above approach,” Wang said. “This means world-class talent, ambitious compute roadmap, and working with the best players across the industry.”

The Midjourney partnership could help Meta develop products that compete with industry-leading AI image and video models, such as OpenAI’s Sora, Black Forest Lab’s Flux, and Google’s Veo. Last year, Meta rolled out its own AI image generation tool, Imagine, into several of its products, including Facebook, Instagram, and Messenger. Meta also has an AI video generation tool, Movie Gen, that allows users to create videos from prompts.

The licensing agreement with Midjourney marks Meta’s latest deal to get ahead in the AI race. Earlier this year, CEO Mark Zuckerberg went on a hiring spree for AI talent, offering some researchers compensation packages worth upwards of $100 million. The social media giant also invested $14 billion in Scale AI, and acquired the AI voice startup Play AI.

Meta has held talks with several other leading AI labs about other acquisitions, and Zuckerberg even spoke with Elon Musk about joining his $97 billion takeover bid of OpenAI (Meta ultimately did not join the offer, and OpenAI denied Musk’s bid).

While the terms of Meta’s deal with Midjourney remain unknown, the startup’s CEO, David Holz, said in a post on X that his company remains independent with no investors; Midjourney is one of the few leading AI model developers that has never taken on outside funding. At one point, Meta talked with Midjourney about acquiring the startup, according to Upstarts Media.

Midjourney was founded in 2022 and quickly became a leader in the AI image generation space for its realistic, unique style. By 2023, the startup was reportedly on pace to generate $200 million in revenue. The startup sells subscriptions starting at $10 per month. It offers pricier tiers, which offer more AI image generations, that cost as much as $120 per month. In June, the startup released its first AI video model, V1.

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Meta’s partnership with Midjourney comes just two months after the startup was sued by Disney and Universal, alleging that it trained AI image models on copyrighted works. Several AI model developers — including Meta — face similar allegations from copyright holders, however, recent court cases pertaining to AI training data have sided with tech companies.

Got a sensitive tip or confidential documents? We’re reporting on the inner workings of the AI industry — from the companies shaping its future to the people impacted by their decisions. Reach out to Rebecca Bellan at [email protected] and Maxwell Zeff at [email protected]. For secure communication, you can contact us via Signal at @rebeccabellan.491 and @mzeff.88.

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