The design platformlaunched Canva Craft on Thursday, its first image-generation model, similar to those from Google, Meta, and OpenAI.
Users can access Craft through Canva’s Dream Lab, including with free accounts, though it is subject to AI usage limits. You’ll need to sign in to a Canva account to get started.
In the release announcement, Canva said the tool plans a design’s layout, proportions, and text before generating an image, an approach that aims to help it produce legible lettering and accurate details.
The company also said that the model was trained on human-designed Canva templates, including posters, menus, and presentations, rather than photographs.
To test how Canva performs, Business Insider created three image generation prompts with escalating difficulty levels — from designing a simple greeting card to creating a photorealistic image from scratch.
Here is how Canva's brand-new image generation tool stacked up against three of the most powerful models.
Canva did not respond to a request for comment.
Starting off with an illustration
Canva's response to a greeting card prompt (left), compared to ChatGPT (middle) and Gemini (right).
Business Insider
We started off simple with a prompt for a birthday card.
Create a cartoon "Happy Birthday" greeting card for a friend named Jessica who has pink hair, brown eyes, likes to wear paisley, and is turning 28 years old in November.
Surprisingly, Canva, the design platform, fell short in its design of the birthday card, from the awkward font to the outsize head of the cartoon character. It didn't even bother to include any paisley.
By comparison, ChatGPT and Muse understood the assignment.
ChatGPT guessed that the recipient's favorite color might be pink, while Muse thoughtfully wove in cozy fall elements for the November birthday girl.
Muse wove in cozy fall elements for the November birthday girl.
Screenshot/Business Insider
Meanwhile, Gemini took the request literally and produced an image of an actual card. Extra points!
Winner: Muse
Editing an image
The request and original image (left). Canva's results (right).
Screenshot/Business Insider
For the second task, I tested the AI models' ability to identify an element and place it in a brand-new setting.
Edit this image of me holding a necklace pendant. Take the red garnet pendant off my hand and make it look as if it is displayed in a vitrine in a jewelry showroom.
Canva did a great job of identifying the pendant and creating an atmospheric setting, but, like Gemini, it depicted the jewelry in a way that defied physics and common sense. It would be unrealistic for a pendant to float in the air or sit propped on a metal pole.
ChatGPT (left), Muse (middle), and Gemini (right).
Business Insider
ChatGPT and Muse once again outperformed other models in understanding how things typically look in reality. Muse, however, made some alterations to the pendant, which I didn't ask for.
Winner: ChatGPT
Creating a photorealistic image from scratch
Canva's depiction of a cat perched atop a gothic castle.
Made with Canva/Business Insider
With Halloween approaching, I asked for a realistic depiction of my favorite animal.
Generate a photorealistic image of a long-haired orange tabby cat in a Halloween costume with bat wings, perched on the edge of a gothic medieval castle under the full moon and a starless night sky.
Though the cat's fur looks a little too strandy and high-contrast in Canva's creation, no components are missing or malformed, and the image composition pretty much matches the quality of Muse and Gemini.
The clear winner for me in this round, however, would be ChatGPT.
Gemini (left), ChatGPT (middle), and Muse (right).
Business Insider
Not only is the cat's posture majestic, but the soft glow on his left side is extremely realistic, and the white balance is well done. There are also clouds softly cradling the moon, which could be a response to my request for a starless sky.
Winner: ChatGPT
Overall, it's evident that Canva still has a lot of room to grow compared to other models, especially if graphic design is its focus. For now, I'll stick to moving components around by hand.
Sundar Pichai says Google's researchers are using AI to help build the next AI models.
Hardik Chhabra/ The India Today Group via Getty Images
Sundar Pichai said Google was still in the early phases of recursively self-improving AI.
Researchers are working with AI agents to build better models, he said.
Sergey Brin recently said Google was "starting to get onto that RSI treadmill."
Google's AI models may be helping to build their successors, but CEO Sundar Pichai says the moment of fully self-improving AI is still a while away.
Speaking on an episode of the "Big Technology Podcast" this week, Pichai said that Google researchers were using AI agents to assist them when building models, but caveated that it was still early days.
"We are pretty far from the end state of RSI," said Pichai, referring to recursive self-improvement. "The right way to describe it is, you have RSI loops along with AI researchers in various aspects of model development. I would say you almost have agents helping AI researchers, in some automated way, work together to build these models."
Pichai added that he expected that continuum to keep making progress into next year.
Earlier this month, Google cofounder Sergey Brin said the company's Flash series of models, as well as Gemini 4, were demonstrating how AI was helping to build itself, and that Google was "starting to get onto that RSI treadmill."
"And now our researchers and the model work hand in hand to make the next version of the model," he said.
Brin has been increasingly involved in Google's Gemini efforts and has leveraged his power as cofounder to shift resources to projects he deems most effective, Business Insider previously reported. This has included pushing for more focus on RSI.
A report from the firm Gladstone AI, published earlier this month, said that most researchers interviewed from companies including Anthropic and DeepMind believed RSI will start within the next three to 12 months, and possibly sooner without government intervention.
In another moment on the "Big Technology Podcast," Pichai was asked about how much AI was being used to code internally. The company said in April that 75% of new code inside Google was generated by AI.
"That number in our world today is like meaningless in the sense that every part of code we now touch is influenced by AI deeply," Pichai said. With Gemini 4 Argon, he said, people were going to tackle harder problems. He also said Google was seeing a one-third decrease in code rollbacks, where software is reverted to a previous version because of a bug or system issue.
"And so I think people are writing code more productively, and better code with it," he said of AI tools.
Pichai also uses AI to track Google's model development and internal discussions, he told the "Sources" podcast this week.
Gen Zers are less likely to have at least one kid than older generations were at the same age.
Rawlstock/Getty Images
Gen Zers are much less likely to have at least one kid than older generations at the same age.
Not wanting kids, affordability problems, and medical challenges are plausible reasons.
Having at least three kids is becoming less common across income percentiles, except in the top 1%.
Birth rates in the US have been falling for decades, but there's been an especially large drop in the likelihood of Gen Zers in their 20s having a kid compared with their older peers at the same age.
For 26-year-old Gen Zers, the likelihood of having at least one kid was 26%, down from 47% for millennials, 50% for Gen X, and 46% for boomers at that age. There were also differences between Gen Z and older generations in the probability of wanting another kid.
That's based on an analysis by a group of researchers published by the Federal Reserve Bank of Atlanta. Julie Hotchkiss, a research economist at the Atlanta Fed, told Business Insider the analysis controls for education, partnership status, and some health conditions. The analysis uses National Survey of Family Growth data up to 2023.
From affordability challenges to concerns about the future, there are several likely reasons behind the lower probabilities. Hotchkiss said that a lower desire to have kids is one explanation.
Gen Zer Brooklen Farley said in an as-told-to Business Insider essay in 2023 that she decided during college she didn't want to have kids. Farley is worried about issues like the economy and climate crisis.
"Many people call us Zoomers, but honestly, a lot of us are doomers: We feel pessimistic about the future and don't want to subject our children to it," she said.
The research also notes that there's been a rise in medical challenges related to having kids. The researchers looked at impaired fecundity, or trouble becoming pregnant or carrying to term. The average probability of impaired fecundity at 26 was 18% for Gen Z compared to 10% for boomers at that age. The probabilities are higher for older ages — the likelihood for 40-year-old millennials was about 30%, higher than the 26% for Gen Xers at that age.
"If we want to talk about birth rates, we can't just talk about preferences," Hotchkiss said. "It's not just that darned younger generation not wanting, just being selfish and not wanting to have children. There's also really a very real medical issue that needs to be addressed."
Another problem is all the financial costs that come with being a parent. LendingTree found the cost of raising a kid for over 18 years adds up to $303,418 after tax exemptions and credits, or about $17,000 a year.
Affordability issues aren't just a Gen Z problem. Surrounded by a large family growing up, millennial Lizzie Lawson said in a Business Insider essay that she assumed she would also have a large family one day.
"When I was a kid in the early 2000s, my dad was an electrician and sole provider for our growing family of eight," Lawson said in a Business Insider essay, adding that her family got by with the help of coupon cutting and hand-me-downs. "Money was tight, but we also owned a house in a safe suburb, and my parents sent six kids to Catholic school from K-8."
Lawson, a college instructor whose husband is a physical therapist with $100,000 in student loans, said the economy is different from the one she grew up in.
"We can't afford to have one of us stay at home, and there aren't enough coupons in the world to cover the rising cost of day care," she said.
Having large families is rarer than in the past
Michael Konczal, vice president of policy and research at the Economic Security Project, said in his newsletter that across income percentiles, families with at least three kids have become less common since the 90s, except at the top 1%.
"A third kid isn't new for them; what's new is that it has become rarer for everyone else," he said. "Compared with the 1990s, families with three or more kids became less common overall and among the rest of the top 10%. The top 1%'s estimated share barely changed, though the uncertainty is substantial. Maybe all the stresses that made childbearing more difficult over the past 30 years just bounced right off the 1%?"
The US fertility rate has cooled to 1.6 births per woman, below the 2.1 replacement rate, which Hotchkiss said would translate to slower population growth. The Congressional Budget Office projects that just a few years from now, the number of births in the US could fall below the number of deaths, which could have implications in future decades.
"Over time, the negative net contribution of births and deaths increasingly offsets the positive contribution of net immigration, until population growth slows to zero in 2056. The population is projected to shrink thereafter," the CBO said.
When Rishub Jain worked on safety at Google DeepMind, he held one of the most coveted jobs in tech. He was among a small group of researchers workingto mitigate the most consequential risks of AI.
But he left to cofound a nonprofit, Sampura Research. Jain traded the prestige and access that come with a Google job because he feels he can have a sharper, more profound impact on AI safety from the outside. Jain says he wants the autonomy to pursue research on a specific issue in AI alignment, which he felt had been deprioritized.
"All the top AI companies do care a lot about safety, and they invest in it, but they could be investing a lot more, and that's kind of what was inhibiting me," Jain says. "If they invested more in safety, I would still be at the lab and working on what I'm interested in with a large team." Google did not respond to a request for comment about whether it maintains a focus on this specific area of research, but DeepMind's principles state that it maintains "a holistic approach to responsibility, safety and security."
Jain's founder story echoes concerns raised by safety researchers who have quit OpenAI and Anthropic, including dire warnings that these companies are "gambling with our lives," and could develop technology that ends humanity. Once dismissed as fringe and doomsday-obsessed, the AI safety profession is growing as hacks by rogue agents and a string of departures have vaulted the issue into mainstream concern.
Open roles in AI safety jumped by 91% on LinkedIn over the past year. The industry expects a flood of cash from much-anticipated IPOs at Anthropic and, potentially, OpenAI, as those made absurdly wealthy by the equity returns may funnel money into potential clean-up crews for the messes created by the technology's rapid development.
The AI safety problem is existentially big, but the people who have spent time honing AI expertise are few. These are people who test and train AI systems to operate ethically. Some focus on technical research, others on policy and governance. And they're increasingly sounding alarms.
"The people who are most worried about AI are often the people who have been closest to the technology," Laura González Salmerón, advising manager at 80,000 Hours, a nonprofit focused on transitioning people into careers with high social impact. The scarcity issue is less about funds and more about people. "Talent is the main bottleneck."
Startups and nonprofits in AI safety must fight for talent in a shallow pool.
Jain's Sampura lists a salary range of $273,000 to $395,000 for an experienced team leader, which would put that worker in the top 3% of earners in the US, but likely pales in comparison to offers from larger companies. Meta reportedly offered $100 million in signing bonuses last year to poach the world's top experts. OpenAI gives employees an average of $1.5 million in stock-based compensation, and technical staff at Anthropic can earn more than $1 million annually.
Money aside, researchers are divided over whether they're more effective when working inside tech companies to mitigate risks as the latest models develop, or stepping back and attempting to steer the ship toward safer outcomes from the outside, like Jain did.
"I'm a little bit concerned about the pace of AI, and if we're able to actually come up with something useful before the rug gets pulled beneath us," Jain says. He's worried that AI's advancement will outpace the work to keep it in line with human values. "That's kind of what's been happening already."
David Robinson, who led transparency work for OpenAI's safety team, wrote an essay for The Atlantic explaining his departure. He said that AI companies should have more layers of checks and safety measures, akin to those in airports and nuclear energy, rather than the steamrolling culture of Big Tech.
"Perhaps I should have stayed and fought for fundamental shifts in our staffing and culture, but in practice, my colleagues and I were so busy sprinting that we seldom had the chance to consider big changes, much less to actually make them," he wrote. Robinson has said he plans to work on these issues from outside of the company.
As the issue grows in prominence, so has a niche of organizations helping train and recruit workers — and the opportunities aren't just for AI experts. Neav Topaz, co-director of Kairos, a nonprofit that upskills people to work in AI safety and recently raised $50 million from Coefficient Giving, says he speaks with about 10 to 20 people each month who want to move to AI safety, and interest is growing. He tells me that people are asking him more frequently about what the work entails.
"People are watching AI get good at their own jobs and starting to ask themselves what comes next," he says. The emerging jobs in AI safety "pay well, the field is growing fast, and you get to work on what might be the defining problem of our time."
Kairos this summer launched its Generator Residency, a cohort of 27 people who spent three months training for career transitions into AI safety. It's less about the research and technical work and more about merging other expertise, like in consulting, with AI safety principles.
He says that of the 27 residents, a dozen have already landed roles in the AI safety space, and many of the rest have offers to do so.
"Most of the people who will do this work are doing something else today," Topaz says. "The biggest talent gap in the field isn't researchers. It's people who grasp how high the stakes are and can translate their varied expertise into things that could make a difference in this space."
The major AI companies maintain that safety is a top priority. Last month, Anthropic CEO Dario Amodei wrote an essay calling for a slowdown in AI development, and Sam Altman and Elon Musk have agreed with him. Amodei and Altman have both said recently that they will bring third-party evaluators into their companies to bolster safety protocols.
These companies have their own safety teams, but safety work competes with business priorities to build the most advanced models and win in the market. The tension, along with safety issues like OpenAI's rogue agents hacking Hugging Face, has led to more scrutiny of internal safety work. Within days of Robinson's departure, OpenAI fired three researchers, claiming they shared sensitive internal data improperly with third-party AI safety researchers. The ousted researchers wrote a letter, reported by The Wall Street Journal, to OpenAI's board, arguing that their engagement with safety researchers fell within the scope of their roles, and that their terminations were "chilling those who remain at OpenAI." OpenAI said that the decisions "were not about raising safety concerns or speaking out," and that the company would not fire people for doing so.
While some have obsessed over AI risks for years, 2026's breakthrough moment has accelerated funding for and interest in the industry. 80,000 Hours has had calls with some 8,000 people since its founding in 2011, the majority of which have focused on AI in some way, González Salmerón says. Philanthropic groups like Coefficient Giving offer massive grants to startups in the AI safety space. METR, a nonprofit AI safety group that investigated the OpenAI agentic hack on Hugging Face, says it raised $71 million in six months this year.
Much of the funding flowing into AI safety filters through effective altruism, the Silicon Valley-tied philanthropic ideal focused on using evidence and logic to donate money where it will achieve the most good. Critics say the philosophy can place too much emphasis on avoiding future problems rather than addressing imminent needs. With more alarms sounding around the existential threat of AI, more people view those theoretical, distant fears as urgent causes.
AI safety groups and individual donors have spent nearly $50 million in this year's election on candidates focused on regulating AI, Axios reported. Jaan Tallinn, a billionaire who invested early in Anthropic and could soon have even more billions as a result, has donated $170 million to nonprofits focused on AI safety. Tallinn's view, as he recently told The Wall Street Journal, is that he can have the biggest impact on the future of AI and its risks by investing in AI companies, and then donating returns to nonprofits working in the AI safety space.
Coefficient Giving, the philanthropic venture from Facebook cofounder Dustin Moskovitz rooted in the effective altruism movement, announced its AI-safety-focused arm, Project Tailwind, in September. It says it's on track to give more than $1 billion this year to help companies of all sizes scale up in preparation for what it believes will be a deluge of philanthropic capital. Sampura Research has received an $11 million grant from the group. If startups don't begin hiring now, the logic goes, they won't move quickly enough when massive wealth hits the industry.
The dizzying development of AI has created safety risks, and there are "gaps in the ecosystem that aren't being filled and that aren't being owned by people," says Rossa O'Keeffe-O'Donovan, the program director in technical AI safety at Coefficient Giving. There are few people, he says, who sit at the intersection of technical expertise, risk analysis, a solutions-oriented vision, and the capacity to hustle as a startup founder.
New roles will emerge not just for technical talent, but also for consultants, policy wonks, communications experts, and founders who can run a business alongside technical experts. Working from the outside, they'll face hurdles in buying compute power, a lack of direct access to the latest AI models, reduced leverage, and smaller paydays.
The world feels unmoored, and the future perhaps more malleable than ever. The appeal of doing work that does good could stave off feelings of uncertainty, dread, and anxiety that people increasingly feel toward AI. In one doomer camp, AI will obliterate white-collar jobs. The optimistic view is that AI drastically reshapes work and spurs the creation of jobs we haven't yet thought of. AI safety is one of the first.
"There are lots of people beyond AI researchers who can help, like cybersecurity experts, like biosecurity experts, like economists," says Mantas Mazeika, a researcher with the Center for AI Safety, a nonprofit focused on AI research and advocacy. "They're making very significant contributions that the pure AI researchers, who were most of the people focusing on the problem beforehand, would be completely unable to do on their own."
For decades, hardware was a hard sell in Silicon Valley. Now investors are pouring tens of billions into machines that can do things in the real world.
A Goldilocks set of conditions has made robotics one of tech's hottest bets. As investors look beyond chatbots to the next frontier in AI, cheaper hardware, smarter AI models, labor shortages, and competition with China are fueling interest in machines capable of performing physical work.
Robotics and physical AI companies raised $33.4 billion globally in the first half of this year alone, according to PitchBook data. That's already more than the $28.7 billion the sector raised in all of last year — and an astronomical rise from 2020, when funding came in at just under $5 billion.
Investors are writing billion-dollar checks to fund both the machines and the AI brains behind them. Defense company Anduril raised $5 billion in May, co-led by Thrive Capital and Andreessen Horowitz. Autonomous shipbuilder Saronic raised $1.75 billion in March in a round led by Kleiner Perkins. Skild AI, which builds software to control different kinds of robots, raised $1.4 billion in January in a round led by SoftBank. Physical Intelligence, which is also building an AI model to command robots, raised about $1 billion in May, co-led by Leopold Aschenbrenner's Situational Awareness and Lux Capital.
"This is off the charts," Ken Goldberg, a UC Berkeley robotics professor and cofounder of warehouse robotics startup Ambi Robotics, told Business Insider. "In my 45 years in this field, this is the most funding and energy and interest in robotics that I've ever seen."
This is the first installment of Robonomics, Business Insider's new series on the rise of robots and their impact on business, jobs, and everyday life. We'll go inside factories and warehouses where humans work alongside robots, meet the founders, investors, and workers driving the boom, and examine how the technology is reshaping industries from manufacturing to defense. We'll also ask how close robots are to becoming part of everyday life, and what is still standing in their way.
The prize is enormous. Unlike software, which is largely confined to work done on screens, robotics companies are going after physical labor, from factories and construction sites to robot butlers in every home. Nvidia CEO Jensen Huang, who popularized the term "physical AI," has described robotics as the "technology industry's first opportunity to address a $50 trillion industry that has largely been void of technology until now." Many Nvidia veterans have launched their own robotics startups.
Nvidia CEO Jensen Huang
Bloomberg/Bloomberg via Getty Images
But investors are pricing companies as though the robot revolution has already arrived. Startups still in research and development command multibillion-dollar valuations, often with little revenue and few deployments to show for it. Humanoid maker Figure AI, for example, secured a $39 billion valuation last year, even as its deployments remain limited to small factory pilots.
"Robotics is going to happen, and a lot of money will still be lost," said Sarah Guo, founder of the venture firm Conviction, which is an investor in robotics startups Sunday Robotics and Watney. "Both things are true."
From not to hot
From industrial arms assembling electronics to Roombas cleaning floors, robots have long been part of everyday life.
What venture investors struggled to see was how to make money from them. Building robots required expensive hardware, factories, and supply chains, often to produce machines that could perform just one narrow task. Why fund that when you could back a college dropout building the next viral app?
Guo used to tell fellow investors that robotics was a good way to lose a lot of money over many years. "The models didn't work," she said. "Every robot was a brittle, single-purpose system hand-built for one task."
Ajay Agarwal, a partner at Bain Capital Ventures, was among the few investors willing to bet on robotics during the internet era. In 2004, he backed Kiva Systems, which pioneered warehouse automation with fleets of mobile robots that brought products directly to human workers.
A Kiva Systems robot moves a shelf to fill orders at an Amazon fulfillment center
Bloomberg/ Getty Images
Bain led every successive funding round because Kiva was "not able to raise money on Sand Hill Road," Agarwal told Business Insider. By the time Amazon bought it for $775 million in 2012, Bain was its only institutional investor.
More than two decades later, AI has changed the calculation. Advances in machine learning are helping robots perceive their surroundings, make decisions, and adapt to new tasks, raising hopes that companies can build more versatile machines instead of engineering a different robot for every job. Cheaper computing and hardware are also making the economics more attractive.
Some of Silicon Valley's biggest names are now chasing that vision. Former Uber CEO Travis Kalanick's Atoms raised $1.7 billion in July to pursue automation in food, mining, and transportation. Rivian CEO RJ Scaringe's Mind Robotics has raised $900 million this year, while former Cruise CEO Kyle Vogt's home robotics startup, The Bot Company, has raised at least $300 million.
Some venture firms are setting aside dedicated capital for robotics. In August, Andreessen Horowitz raised $1.1 billion for "Machine Age," its first fund focused on the physical infrastructure behind AI, including robotics.
The gold rush
For longtime robotics investors, alarm bells are ringing. Industry veterans whisper about "hardware tourists" piling into robotics deals — investors drawn by AI hype who underestimate how difficult it is to build machines that work reliably in the real world. There is precedent for caution. Venture firms poured billions into autonomous vehicles in the 2010s, only to see startups fold or consolidate as self-driving technology proved harder and more expensive to commercialize than expected. Years later, only a handful of deep-pocketed players have managed to bring robotaxis to the public.
Robotics faces its own formidable obstacles. Unlike large language models trained on vast amounts of internet text and images, robots lack a comparable trove of data showing them how to act in the physical world. Dozens of startups are now scrambling to collect it, but nobody knows how much will be enough. And even smarter AI won't eliminate the challenges of manufacturing, maintaining, and deploying expensive hardware.
"The most difficult part of physical AI is the physical part," said Charly Mwangi, a partner at VC firm Eclipse who has backed The Bot Company and robotics startup Genesis AI. "The next few years are going to be really, really hard for the robotics industry as companies go from the lab to deployments."
Some of the most speculative robotics bets are attracting the biggest checks. Humanoids and general-purpose robot models were the two fastest-growing areas of robotics investment last year, according to venture firm F-Prime. "You cannot raise money on demos forever," said Sanjay Aggarwal, an F-Prime partner and coauthor of the firm's annual robotics report. "I would personally be surprised if all these companies independently exist two or three years from now."
There are signs that some startups are becoming real businesses. Field AI, which is building an AI "brain" that works across robots, has crossed $100 million in revenue and customer contracts from more than 30 customers, including construction firms, data center operators, and defense companies, Business Insider previously reported. Skild AI said last month that it had reached $100 million in annual recurring revenue from more than 60 customers. Its projects include robots assembling Nvidia's Blackwell chips with Foxconn and kitchen pilots with Mitsui, a Japanese conglomerate.
An Nvidia GB200 Blackwell GPU
Bloomberg/ Getty Images
Believers see robotics on the cusp of a breakthrough that could put machines to work in nearly every corner of the economy. Brad Porter, the former head of Amazon Robotics who now runs workplace robotics startup Cobot, says the field could be nine to 18 months away from major advances in dexterous manipulation — the ability of robots to handle objects with the ease of human hands.
"When I think of a 'ChatGPT moment' for robotics, it is cracking dexterous manipulation," Porter said. "Then there will be a Cambrian explosion of robots."
The question is whether these breakthroughs will arrive quickly enough to justify the money pouring in.
At Business Insider's Growth Mode event this month, Menlo Ventures partner Matt Murphy singled out robotics among the fields attracting enormous amounts of financing "before they're really showing traction." Abundant funding, he warned, can make startups "sloppy."
For investors, he said, that means "a lot more opportunity to have a really big crater."
Have a tip? Contact Rya Jetha via email at rjetha@businessinsider.com or Signal at rjetha.07. Use a personal email address and a nonwork device; here's our guide to sharing information securely.
Anthropic CEO Dario Amodei engaged in a lengthy exchange with a critic on X.
Kevin Dietsch/Getty Images
Dario Amodei responded to a critic on X who said Anthropic wasn't delivering on its safety commitments.
He said Anthropic would be best placed to make AI safe by staying at the top of the game.
The critic, Nathan Calvin from Encode AI, said Anthropic was trying to win a race to superintelligence.
Anthropic CEO Dario Amodei disputed a criticism that the company was putting AI safety in the back seat in its "race to the top."
On Tuesday, the executive wrote a nearly 2,000-word response to a critical X post which said Anthropic was trying to win a race to superintelligence and deprioritizing AI safety.
The original Monday X post was by Nathan Calvin, the chief strategy officer of Encode AI, an AI-safety advocacy nonprofit. Calvin listed nine actions by Anthropic over the past year that he said undermined its safety commitments, including signing deals for more than $500 billion in compute and increasingly relying on Claude to develop its AI models. His post was lengthy, too, at over 1,500 words.
Amodei said in his response that while he was a "fan of the investigative work" Calvin had done for AI safety, he disagreed with many of Calvin's criticisms.
Amodei said his position was that Anthropic can do more to keep the AI industry safe if it stays at the frontier.
"Our strategy has always been what I've called the 'race to the top' — by showing how it's possible to build the technology responsibly, we can raise the bar for the entire industry," Amodei wrote.
He then wrote out his own nine-item list of actions that were "very costly" to Anthropic commercially, but had "strong safety benefits." This included releasing its powerful Mythos model only to a select group of cybersecurity partners and restricting the sale of its AI services to Chinese state-controlled companies.
However, Amodei acknowledged Anthropic could have handled some of the issues Calvin raised better. These included a draft bill Anthropic circulated to Congress this month — obtained exclusively by Politico — that critics said would make it harder for the federal government to block dangerous AI models.
"We'll always try to do better but, again, I am extremely proud of our track record and stand by it," he wrote.
Amodei and Calvin's exchange comes at a time when model safety has become one of the most pressing issues in the AI sphere. In September, AI researchers predicted an "AI apocalypse," in which the increasingly capable and rogue technology could kill all of humanity in the next decade.
In response to the concerns, AI chiefs like Amodei, OpenAI CEO Sam Altman, and SpaceXAI CEO Elon Musk all agreed to "pace the frontier," referring to slowing down AI development to ensure there were enough checks and balances in place.
Meanwhile, Anthropic is on the brink of its hotly anticipated IPO, expected to list at a $2 trillion valuation in November. Last week, it reported a third-quarter revenue of $16.8 billion.
The Kospi's sharp reversal shows how volatile South Korea's AI-fueled stock rally has become.
Chris Jung/NurPhoto/Getty Images
South Korea's Kospi sank 4% as Samsung and SK Hynix extended the AI-stock selloff.
AI cloud firm Elice crashed 32% in its market debut, despite frenzied demand for its IPO.
The rout is another warning that investors are reassessing the AI trade's sky-high valuations.
South Korea's AI-stock rout deepened Tuesday as Elice, a cloud-infrastructure company, crashed 32% in its market debut.
The benchmark Kospi index closed 4% lower, despite a rebound in Wall Street tech stocks on Monday. Index heavyweights Samsung Electronics and SK Hynix fell 3% and 4%, respectively.
South Korean chipmakers are major suppliers of memory used in AI systems, making their shares a closely watched gauge of the global AI boom.
The Kospi's latest selloff marks another blow for a market that has swung from boom to a nearly 40% bust in just four months.
The turmoil has also hit companies hoping to capitalize on the AI boom.
Elice, a South Korean company that rents out computing power for AI workloads, closed at 61,500 won on the Kosdaq, far below its IPO price of 90,500 won. The company raised about 201 billion won, or $151 million, in the Kosdaq's largest IPO this year.
The company's disastrous debut came despite overwhelming demand, with institutional and retail subscriptions topping available shares by roughly 400 times.
But Elice's IPO valued it far more richly than similar AI cloud companies, whose shares had already lost 20% to 60% over the preceding month, Tan wrote.
Similar concerns are rippling through AI infrastructure markets well beyond South Korea.
US-listed CoreWeave, which rents AI computing power, saw its shares and bonds tumble last week amid concern about its debt-fueled expansion. Nvidia-backed Firmus, meanwhile, withdrew its planned $5 billion Australian IPO, citing volatile market conditions.
