Robot Startup Figure Reveals an AI Breakthrough Called Helix

Figure, a Silicon Valley-based startup that builds humanoid robots, has announced Helix, a proprietary AI model that the company says enables robots to more easily pick up objects, collaborate on tasks with other robots, and smoothly control their entire upper bodies. The launch comes weeks after Figure publicly ended a deal to collaborate on AI models with OpenAI, and after the company raised $1.5 billion. In a blog post, Figure wrote that Helix’s launch marks a major step in bringing humanoid robots into “everyday home environments.” Unlike previous versions of its models, Figure says that Helix doesn’t need to be fine-tuned or trained on hundreds of demonstrations in order to perform specific tasks. Instead, a new design allows the robots to interact with objects that they’ve never encountered before. The company says that Figure robots equipped with Helix “can pick up virtually any small household object.” In a video example of Helix’s capabilities, a man asks two Figure-made robots, both equipped with the Helix AI model, to put away a bag of groceries. After looking around, the robots slowly opened up a nearby fridge, put most of the food away, and placed an apple in a serving bowl. Up until recently, Figure had been in a partnership with OpenAI in which the two firms collaborated to customize OpenAI’s models so they could be used by Figure’s robots. But on February 4, Figure founder Brett Adcock announced on X that he had decided to pull his company out of the agreement. He said that Figure had “made a major breakthrough on fully end-to-end robot AI, built entirely in-house,” a clear nod to Helix’s imminent reveal. Adcock said that the company has been working on Helix for over a year. It’s been a busy month for Figure. On February 14, Bloomberg reported that the company was discussing a new funding round with firms Align Venture and Parkway Venture Capital that would raise $1.5 billion at a $39.5 billion valuation. Bloomberg said that much of investors’ enthusiasm comes from a recent breakthrough that has sped up Figure’s timeline for penetrating the home market with its robots, implying that investors got an early look at Helix. Figure isn’t the only company trying to make humanoid robots happen. Just last week, Reuters reported that Meta is establishing a new division that will create AI-powered humanoid robots, and humanoid robotics startup Apptronik raised $350 million, with one of its investors and partners being Google DeepMind. Also in the race is Tesla, with its “Optimus” robots, and rumors are swirling that Apple could enter the fray as well. BY BEN SHERRY @BENLUCASSHERRY

How Google’s New AI Co-Scientist Tool Gives Us a Taste of Tomorrow’s Workplace

Google, like many other big tech names, has already released numerous AI tools — some more generic, designed to help with a wide range of tasks, some of which are tailored to the specific needs of specialized users. Its latest effort is definitely in the latter category: the new “AI co-scientist” system, built on its Gemini 2.0 AI model, is specifically designed to “aid scientists in creating novel hypotheses and research plans.” That sounds like a very niche market—and it is. But it’s also likely to be the tip of the AI-as-coworker iceberg. In a post announcing the tool, Google explained how the new system would be used in a research setting. Essentially a scientist who has a specific topic to investigate — like, say, discovering a new drug to tackle a particular disease — would input that into the tool using natural language. The AI would then reply, much like any other chatbot, with a useful output — in this case a hypothesis that the scientist can then test to either validate or invalidate their theory. The tool also does some of the work that goes into starting a new experiment by summarizing published literature about the topic, and suggesting an experimental approach. Google’s blog post explains that the tool is actually a “multi-agent” system, tapping into what many think may be the next big thing in AI innovations. Using Gemini’s ability to reason, synthesize data and perform long-term planning, the tool roughly models the actual intellectual process scientists use when tackling a novel problem—the scientific method. In this case Google’s system uses four AI agents called Generation, Reflection, Ranking and Evolution, refining its answers over and over in what Google calls a “self-improving cycle of increasingly high-quality and novel outputs.” Essentially the tool cycles through lots of different ideas, checking how good they are and then spitting out what it thinks is the best output. Google is very careful to note that the tool is designed to be a scientific collaborator, to “help experts gather research and refine their work,” and it’s not meant to “automate the scientific process.” What this means is that the AI co-scientist isn’t designed to replace scientists, but instead may inspire researchers with novel ideas or otherwise speed up the process of investigating a thorny physics problem, or tackling a biological issue like antimicrobial resistance. The pros and cons of AI-assisted science In a previous career I worked with plenty of high-tech scientific machinery, from particle accelerators to complex computer-controlled lab equipment, using everything at my disposal to help advance my research. From this experience I can say Google’s AI tool would’ve been invaluable, saving me hours of time looking up material online and in physical texts, as well as when it came to thinking up clever ways to “break in” to a particular physics problem — the hypothesis formation and testing process at the core of scientific progress. It also seems likely other scientists will race to adopt a tool like this, because it would free up valuable time to do actual real-world experiments. For now the AI science tool is only available through a Google-led pilot testing program, to help “evaluate its strengths and limitations in science and biomedicine more broadly,” before a wider launch. I can foresee a couple of issues that may hold some researchers back, however. More traditional scientists may be reluctant to trust such a revolutionary new model for carrying out research — even if it comes at the expense of seeing their rivals embrace the technology. Scientists whose research is politically sensitive, or perhaps secret, may not trust an AI, simply because of the known issues of AI data “leakage,” where information put into the AI as queries can then emerge later on when a different user types in a prompt. Some researchers may thus be forbidden from using this sort of AI at all. Scientific research is also a highly creative process that requires feats of imagination and insight to create original work. Some researchers may be reluctant to hand over this part of the process to a machine. Nevertheless, if Google’s tool really works (and the blog post includes several examples where it’s been tested out in the real world, including helping researchers looking at liver fibrosis) scientists of all stripes may embrace having an AI coworker in the lab. In this way, the tool gives us a hint of how AI may penetrate into many different types of workplace over time, as more and more specialized AI systems are developed by Google and rival firms. The idea even taps into the thorny “will AI steal my job?” issue — with this particular example suggesting that no, it won’t: instead AI will kind of ride along with you as you work through your day, helping you as you need it to. BY KIT EATON @KITEATON

This Chinese AI Bet Has Outperformed Magnificent Seven Names Like Meta and Google This Year

Magnificent Seven who? That’s what some U.S. investors might be asking themselves after Alibaba’s stellar earnings pushed its U.S.-listed stock more than 8 percent higher Thursday, bringing its year-to-date gains above 60 percent. The strong showing reinforced its position among investors’ favorite AI bets, particularly in comparison to its U.S. peers like Meta, Alphabet and Microsoft. It’s worth highlighting, too, that intrigue around Alibaba has spiked this week, with its co-founder Jack Ma making a rare public appearance Monday with Chinese President Xi Jinping. Here’s how the company performed in the quarter to December 31: Earnings per share: $2.93, above expectations for $2.66 Revenue: 280.15 billion yuan, above LSEG expectations for 279.34 billion yuan Net income: 48.945 billion yuan, above LSEG expectations for 40.6 billion Notably, Alibaba’s Cloud Intelligence Group saw its sales growth surge 13% compared to a year ago to 31.742 billion yuan. “This quarter’s results demonstrated substantial progress in our ‘user first, AI-driven’ strategies and the re-accelerated growth of our core businesses,” Alibaba chief executive Eddie Wu said in a statement. Alibaba has proven largely unaffected by the recent DeepSeek scare that throttled US tech stocks. Only a week ago, Alibaba announced a partnership with Apple to integrate Alibaba’s AI into iPhones in China. Indeed, Bloomberg Intelligence analysts wrote in a note Thursday that the company’s decisions through 2024 have panned out as effective strategies to fend off domestic competition too from Huawei, Tencent, Baidu and others. It’s also better positioned than its rivals, in Bloomberg Intelligence’s view, to withstand headwinds from a potential US-China trade war. That said, Alibaba’s margins could see weaker growth should the company continue to push for revenue growth. “Alibaba is likely to sacrifice some margin growth in e-commerce, logistics and cloud through the fiscal year ending March 2026 as it focuses on driving up revenue,” said Bloomberg Intelligence senior industry analyst Catherine Lim. “The company aims to enlarge the market shares of these businesses, in mainland China and overseas. That could fuel up-front expenditure on technology-led process upgrades, AI-related development and logistics solutions in the next 13 months.” As a side note, the Wall Street Journal reported Thursday that GameStop’s billionaire CEO Ryan Cohen raised his personal stake in Alibaba to roughly 7 million shares worth about $1 billion. Cohen, who is steering a company with a rising stock in his own right, seems to like the AI play. “The AI era presents a clear and massive demand for infrastructure,” Alibaba’s Wu said on the earnings call. “We will aggressively invest in AI infrastructure. Our planned investment in cloud and AI infrastructure over the next three years is set to exceed what we have spent over the past decade.” BY PHIL ROSEN, CO-FOUNDER AND EDITOR, OPENING BELL DAILY @PHILROSENN

The AI Energy Score attempts to quantify the environmental harm of AI models. Here’s how to use it.

Artificial Intelligence is notoriously energy-hungry. Now, a new tool from Salesforce is attempting to quantify the toll AI models are taking on the planet. “AI models vary dramatically in terms of their environmental impact, from big general models to small domain-specific models,” says Boris Gamazaychikov, head of AI sustainability at Salesforce. “What the AI Energy Score is aiming for is to develop a standardized way to actually start measuring these things.” AI Energy Score, launched Monday by Salesforce in collaboration with Hugging Face, Cohere, and Carnegie Mellon University, assigns a star rating to assess the energy efficiency of various AI models relative to other models. The ratings are available for 10 distinct tasks, from text and image generation to summarization and automatic speech recognition. A five-star rating is the most energy-efficient, whereas a one-star rating is least. It also shares a number estimate of the quantity of GPU energy consumed per 1,000 search queries in Watt hours. As of Feb. 10, DistilGPT2, a model developed by Hugging Face that is meant to be a “faster, lighter version of GPT-2,” was the most energy efficient, consuming 1.31 Wh of energy per 1,000 text generation queries. Alongside an interactive landing page with more information, the site also has a label generator that Gamazaychikov hopes developers will use to showcase their scores and drive awareness. For now, the tool only rates open-source models hosted on Hugging Face, a platform for data scientists, AI developers, and enthusiasts. That includes some models from Meta, Microsoft, Mistral, and StabilityAI. Salesforce also submitted its own open- and closed-source models for rating. Closed-source, or proprietary models, which include many big names such as OpenAI’s GPT-3 and 4, Anthropic’s Claude, and Google’s Gemini, are not readily available. But Gamazaychikov says there is a way for developers of these proprietary models to conduct the analysis securely and then submit it if they so choose. “By making a really easy to use, clear, standardized approach, we’re reducing any kind of friction between those leading companies being able to not do this,” Gamazaychikov says, adding that the tool was developed using feedback from academia, government officials, and AI companies. “We’re also hoping to increase some pressure, showing that it’s important to disclose this type of information.” Although Salesforce welcomes individuals to experiment with the tool to get a sense of how their use of AI models could take a toll on the environment, Gamazaychikov says “the primary audience is probably enterprises, or those that are integrating these types of models within their products.” Furthermore, a tool like AI Energy Score could help companies calculate indirect greenhouse gas emissions throughout the value chain. The launch of AI Energy Score follows Salesforce’s debut last fall of Agentforce, which allows customers to build or customize AI-powered agents that automate tasks or augment employee labor. Gamazaychikov says Salesforce also hopes to inspire greater change by giving regulators a jumping-off point for assessing the emissions-load of AI. He also envisions promoting the use of smaller, local models at a time when large reasoning models, which could consume even more energy than current large language models, are in development. A 2024 study from Goldman Sachs found that AI could drive up the energy demand of data centers by 160 percent by the end of the decade, even as the growing global population exerts additional pressures on the world’s energy needs. This comes at a time when leaders are contending with the worsening effects of climate change, prompting big tech companies to look to renewable energy and innovations in nuclear power to satisfy the growing demand. Want to try it out? Head on over to huggingface.co/AIEnergyScore and give it a whirl. BY CHLOE AIELLO @CHLOBO_ILO

Microsoft Says Workers are Already Using AI to Lighten Workloads, at Risk to Their Own Brainpower

Eccentric science-fiction author and technophile Douglas Adams once wrote about how tech was taking an effort-saving role in people’s lives: “Dishwashers washed tedious dishes for you, thus saving you the bother of washing them yourself,” he explained, and “video recorders watched tedious television for you” for much the same reason. But we’re in the AI era now, and a new Microsoft study suggests that Adams’s metaphor still applies: AI is able to take on much of that “tedious thinking” for you, saving you all the bother of actually working while at work. The new study actually warns that some knowledge workers are risking becoming overly reliant on generative AI, and their “problem-solving skills may decline as a result,” technology news site The Register says. The study acknowledges that people have objected to the impact of various technologies on the human mind since forever—from writing (a fundamental, ancient form of technology) all the way up to the internet. It also agrees that these worries are “not unfounded.” “Used improperly, technologies can and do result in the deterioration of cognitive faculties that ought to be preserved,” the authors write, noting that any type of automation can deprive people of chances to practice using their minds. In the survey, they asked 319 knowledge workers who used generative AI at least every week if they turn on their brains and apply “critical thinking” when they use tools like ChatGPT or Microsoft Copilot. The findings were stark. Survey respondents said that when they had high confidence that an AI tool would do well at a particular task, they felt less need to apply their own critical thinking. On the other hand, when a worker had high confidence in their own skills and less in the AI’s, they felt good about putting effort into evaluating the output the AI gave them and then improving it. AI is redefining how we see work It all boils down to the fact that when knowledge workers use AI tools, it shifts the way they think about performing activities like analysis, synthesis, and evaluation of information. The AI moves a worker’s focus from information gathering to information verification when using an AI to help try to understand something, and when using an AI for problem solving, the shift is away from carrying out the actual solving process to task stewardship. Think of it like this: When aircraft didn’t have an autopilot, fliers had to concentrate the whole time on operating the airplane—navigating, controlling, reacting to technical challenges, and feeling the way the wind was blowing. Modern day jetliner pilots have a very different job. They have to be able to fly the plane manually in case of unexpected problems, but minute to minute, what they’re often doing is monitoring the aircraft as it automatically flies itself to make sure it’s doing the right thing. Microsoft’s new research suggests that when people use an AI to help them solve work tasks, they’re doing the same thing—offloading the boring, slow, or difficult bits of the work to the AI and then managing the AI tool to get the desired output. The worry here is that over time people who used to hone their critical thinking skills all the time at work may lose some of that ability. One reassuring piece of pro-human info from the survey was that workers in high stakes workplaces or situations (like seeking medical advice from an AI) were conscious of the risk of over-relying on AI outputs that could be problematic, flawed, or flat-out wrong. Those respondents said they used their own thinking skills more. So what should we do about this? Should you worry that your workforce is going to become dimmer over time, human drudges merely shoveling data mindlessly into and out of an AI system? Not at all. The researchers suggest that one trick would be to design AI tools so they’ve got systems built into them that support worker skill development over the long term, The Register explains. And AIs should encourage workers to reflect on what’s happening when they’re interacting with AI outputs and even help the workers in this action—essentially keeping their minds focused, not blindly trusting the AI. It’s also possible that, as a good employer, you could give your staff tasks that keep their brains ticking over—ones that don’t need an AI boost. BY KIT EATON @KITEATON

Why do so many products that don’t seem to need AI integration still feel the need to include it?

The world didn’t ask for an AI-designed shoe. Nor did young parents across the country clamor for AI-enabled baby changing pads. It’s almost certain that your cat doesn’t know the difference between doing its business in a regular litter box versus one that is AI-enhanced. Yet all of these products exist, because the drumbeat of supposed progress in Silicon Valley is immutable. Super Bowl commercials were a prime example of AI’s societal stranglehold. OpenAI, Salesforce, and Google all aired commercials for their AI products during Sunday’s big game. How did we get here? We obviously didn’t start out with AI-powered teddy bears and a chatbot that specializes in erectile dysfunction. For the past couple of years, startups and tech giants alike have anchored themselves to AI hype because it’s been the easiest way to stay relevant or appear like the kind of mold-breaker that can turn the heads of almighty venture capitalists. After all, neither sovereign wealth funds nor institutional investors like stagnation. It’s been a trickle-down dynamic between AI’s influence on the tech world and consumer products in general. It started with OpenAI: Two years after initially getting bankrolled by Microsoft, the ChatGPT maker is closing a $40 billion investment from SoftBank, the Japanese investment titan. AI euphoria—or contagion—has swept Silicon Valley, and startups have the best chance of getting funding if they develop AI or otherwise integrate it into a product that’s apparently crying out for an overdue disruption—like the humble litter box. Last year, AI-related companies welcomed $100 billion in VC investment globally, which is roughly one-third of the $314 billion spent on all tech startups, a Crunchbase analysis shows. Undergirding all of this is a belief called “technological determinism,” Arun Sundararajan, a professor of technology, operations and statistics at New York University told Inc. last month. “If technology can do it, then it will happen,” Sundararajan explained. “As soon as the technological capability comes along, somehow, magically, it will enter our reality.” The latter half of that statement is undeniable. AI is being shoehorned into products that seemingly do not need to be enhanced by machine learning or large language models. Even in the summer of 2023, experts were warning that superfluous products riding the AI wave are eerily reminiscent of the firms that collapsed and caused market chaos in the dot-com crash. Last summer, Gayle Jennings-O’Byrne, CEO and general partner at VC firm Wocstar, said basically the same thing: “The mindset of VCs, versus the reality of what these business models and companies are going to look like, [is] just going to propel what we’re calling a bubble,” she told Inc. But inevitably, there will be more expansion, iteration, and pursuit of growth. Despite losing $5 billion last year, OpenAI is eyeing locations for new data centers in 16 different states, and Meta says it will allocate $65 billion for AI development this year. Even though ChatGPT has 200 million monthly users, it’s unclear to some experts whether the product adds anything of value to productivity and economic output. The worry is that consumer tools that can generate images and text on demand are more of a parlor trick than a new form of electricity. “We know ChatGPT has about 200 million unique monthly users, but the question is how many of them are using it in a way that will lead to significant productivity improvements/cost reductions. I don’t really know the answer to that question,” Daron Acemoglu, an economist at the Massachusetts Institute of Technology told NPR last October. BY SAM BLUM @SAMMBLUM

These Are the Jobs AI Will Replace

Question: Do you have a job that could be replaced by AI? OK, that was a trick question. Everyone’s job could be replaced by AI. That’s how they frightened CEOs into buying it. Better question: How do you know if your job will be replaced by AI? Let me answer that this way. There are two kinds of salespeople in this world. One kind is people who are good at selling and the other kind is people who are Salesforce Wizards. Similarly, there are two kinds of marketing people in this world. People who are good at marketing and HubSpot Gurus. You see where I’m going? To answer the question, we need to talk about the difference between expendable knowledge workers and irreplaceable knowledgeable workers. I’m Not Trying to Scare Anyone There’s actually a little bit of hope to talk about. For once. See, way back in the olden days—a.k.a., the summer of 2023—I predicted which jobs were most likely to be replaced by the coming Generative AI wave. TL;DR: I was one of the progenitors of early generative AI, in 2010, building a platform that enterprises like Yahoo and the Associated Press used to write insightful, informative narratives from nothing but raw data. Even back then, we knew that what we were doing would eliminate jobs. But everyone around us was confused as to which jobs our story-writing computers were going to eliminate. In 2010, we weren’t going to replace journalists or writers, at least not the good ones. Our tech was going to eliminate a new breed of “data scientists,” and only the sketchy ones. Those data scientists were knowledge workers. They knew how to use databases and SQL and R and Python to get insights out of the data. But it took the journalists, the knowledgeable workers, to make those insights make sense in context for the reader. I Was Right! Fast-forward to today and that battle is still going on. The threat has multiplied, of course, but not exponentially, because even today’s agentic AI is certainly not a font of unlimited contextual knowledge. What I learned back in 2010 and what still holds true today is that technical evolution has a way of calling out the rote-task knowledge workers in any industry. Back then, it was Johnny-come-lately data scientists. Today, it’s Salesforce Wizards and HubSpot Gurus. And AI does the calling out almost instantaneously, in a way that’s obvious when it’s not hallucinating. As I said in that 2023 article, it was only a matter of time before early-2023 generative AI was going to hit the knowledge economy, at which point, those rote-task knowledge workers should start worrying about their jobs. To clarify which jobs were in peril, I believe I used the phrase, “any white-collar, butt-in-a-seat, pixel-pushing, spreadsheet-spelunking job that the influx of data wrought on the workforce.” But even back in 2010, those jobs were starting to disappear, thanks to the automation that was and still is part and parcel of Big AI (or whatever). Those rote-task jobs were only being used as stepping stones to turn those knowledge workers into knowledgeable workers. In 2023, I said that most knowledge workers had about five to 10 years before they became obsolete. I Was Wrong! Well, we’re only in year three, maybe four, and corporate America seems hell-bent on eliminating the jobs of both knowledge workers and knowledgeable workers and letting God sort it out. That’s a huge problem. It has everything to do with how AI was sold into the enterprise, (i.e., FOMO), and that has been my problem with AI the whole time. In trying to reach maximum productivity, we just went all-in on the promise of AI and redefined productivity to meet it. However, as with most overreach cycles in business, I believe that’s finally changing. It might be too little and too late, but the AI bills are starting to come due. AI Hype Meets Financial Reality Even back in early 2024, obvious leaks in the AI productivity dream bucket started becoming very public. For example, this article from a fellow Inc. writer (go team!) digging into a survey from Upwork notes that 96 percent of C-suite executives expect AI to increase productivity, while 77 percent of employees actually using the tools as they exist today experienced decreased productivity. This isn’t a bright red warning flag or anything, but it does at least show the chasm-like mismatch in expectations versus reality that’s been snowballing over the last year. Now, in late 2023, I also said that AI was coming for SaaS, and everyone laughed at me again. Well, I was both right and wrong there too. I was right about AI replacing SaaS knowledge workers—those solely responsible for knowing how to get useful insights out of platforms like Google Analytics… or Salesforce or HubSpot. I was wrong in assuming corporate America would respond to this technical evolution sensibly and with caution and care for its employees. Boy, was I wrong. As I said, companies threw all kinds of babies out with all kinds of bathwater. To their own detriment. And here we are. We’re Not Out of the Woods Yet We’re not at the end of the AI hype cycle, but I believe we’re beyond peak AI hype. So the answer, the real answer for how to become irreplaceable, is the same and as simple as it ever was. Become a knowledgeable worker. Be unbeatable at what you do. Let AI handle the rote-task drudgery like staring at HubSpot all day. Because, yes, AI can come up with code or creative work or even make the hiring and firing decisions for you. It just can’t do it completely in context, and those skills which separate great coders and marketers and salespeople and CEOs are the same skills they always were. Look—it’s not how good you are at AI. It’s how good you are at everything that AI should not be doing. Which is a lot. OK. So now it’s just a matter of hiring back all those knowledgeable workers we lost—and are still losing—in the AI enterprise coup. Let’s hope our hiring system isn’t broken beyond all recognition. EXPERT OPINION BY JOE PROCOPIO, FOUNDER, JOEPROCOPIO.COM @JPROCO

The AI Energy Score attempts to quantify the environmental harm of AI models. Here’s how to use it.

Artificial Intelligence is notoriously energy-hungry. Now, a new tool from Salesforce is attempting to quantify the toll AI models are taking on the planet. “AI models vary dramatically in terms of their environmental impact, from big general models to small domain-specific models,” says Boris Gamazaychikov, head of AI sustainability at Salesforce. “What the AI Energy Score is aiming for is to develop a standardized way to actually start measuring these things.” AI Energy Score, launched Monday by Salesforce in collaboration with Hugging Face, Cohere, and Carnegie Mellon University, assigns a star rating to assess the energy efficiency of various AI models relative to other models. The ratings are available for 10 distinct tasks, from text and image generation to summarization and automatic speech recognition. A five-star rating is the most energy-efficient, whereas a one-star rating is least. It also shares a number estimate of the quantity of GPU energy consumed per 1,000 search queries in Watt hours. As of Feb. 10, DistilGPT2, a model developed by Hugging Face that is meant to be a “faster, lighter version of GPT-2,” was the most energy efficient, consuming 1.31 Wh of energy per 1,000 text generation queries. Alongside an interactive landing page with more information, the site also has a label generator that Gamazaychikov hopes developers will use to showcase their scores and drive awareness. For now, the tool only rates open-source models hosted on Hugging Face, a platform for data scientists, AI developers, and enthusiasts. That includes some models from Meta, Microsoft, Mistral, and StabilityAI. Salesforce also submitted its own open- and closed-source models for rating. Closed-source, or proprietary models, which include many big names such as OpenAI’s GPT-3 and 4, Anthropic’s Claude, and Google’s Gemini, are not readily available. But Gamazaychikov says there is a way for developers of these proprietary models to conduct the analysis securely and then submit it if they so choose. “By making a really easy to use, clear, standardized approach, we’re reducing any kind of friction between those leading companies being able to not do this,” Gamazaychikov says, adding that the tool was developed using feedback from academia, government officials, and AI companies. “We’re also hoping to increase some pressure, showing that it’s important to disclose this type of information.” Although Salesforce welcomes individuals to experiment with the tool to get a sense of how their use of AI models could take a toll on the environment, Gamazaychikov says “the primary audience is probably enterprises, or those that are integrating these types of models within their products.” Furthermore, a tool like AI Energy Score could help companies calculate indirect greenhouse gas emissions throughout the value chain. The launch of AI Energy Score follows Salesforce’s debut last fall of Agentforce, which allows customers to build or customize AI-powered agents that automate tasks or augment employee labor. Gamazaychikov says Salesforce also hopes to inspire greater change by giving regulators a jumping-off point for assessing the emissions-load of AI. He also envisions promoting the use of smaller, local models at a time when large reasoning models, which could consume even more energy than current large language models, are in development. A 2024 study from Goldman Sachs found that AI could drive up the energy demand of data centers by 160 percent by the end of the decade, even as the growing global population exerts additional pressures on the world’s energy needs. This comes at a time when leaders are contending with the worsening effects of climate change, prompting big tech companies to look to renewable energy and innovations in nuclear power to satisfy the growing demand. Want to try it out? Head on over to huggingface.co/AIEnergyScore and give it a whirl. BY CHLOE AIELLO @CHLOBO_ILO

Are you anxious about AI? Nvidia’s co-founder and CEO has some tips for how to find your feet in this new landscape.

If, during the past few years, you’ve felt like the future is barreling towards you like an oncoming train, you are not alone. The shock (and stock market dip) that accompanied last week’s announcement of a cheaper AI model from Chinese company DeepSeek shows that even experts can be blindsided by how fast tech innovation is happening. No one can take away that feeling of whiplash completely. Change is happening too fast for that. But if you’re looking for a good guide to help you get a handle on our AI-filled future, entrepreneur Jensen Huang should probably be at the top of your list. Huang is the CEO of Nvidia, maker of the chips driving the current AI boom. He built Nvidia into a $3-billion juggernaut by spotting the imminent rise of AI before just about anyone else and betting his company on it. Other tech CEOs fawn over his vision. What does Huang see for the future? Perhaps more importantly for entrepreneurs, how does he recommend you prepare? Huang: AI is like the interstate highway system That was the topic of conversation when Huang appeared on Cleo Abram’s Huge Conversations podcast recently. It’s an hour-long discussion, and if you’re fascinated by AI, then the whole thing is worth a watch. (I’ve embedded the complete interview at the end of this column.) Perhaps the most immediately actionable insight was Huang’s advice for everyday people wondering how best to prepare themselves for the coming AI revolution. On the podcast, Huang likens the change to the shift that arrived when the U.S. built the interstate highway system. Fast roads were the essential new technology at the heart of this change, but a whole ecosystem of other possibilities quickly developed around it. “Suburbs start to be created and distribution of goods from east to west is no longer a concern. All of a sudden, gas stations are cropping up on highways. Fast food restaurants show up. Motels show up, because people are traveling across the state,” Huang says. AI will be similar. Machines that can do many tasks better and faster than humans will create ripple effects that change many aspects of how we do our jobs and live our lives. How can you try to peek around the corner and get a glimpse of what that might look like? Huang suggests you ask yourself two key questions. If the drudgery it takes to do my job disappears, what changes? Some people worry that AI might take away jobs, making many workers superfluous. Huang doesn’t share this fear. He believes human insight and creativity will still be important, but what we spend our time on will be different. AI will kill rote donkey work. “Suppose that your job continues to be important, but the effort by which you do it went from a week long to almost instantaneous, that the effort and drudgery basically goes to zero. What are the implications of that?” Huang asks. Imagine you have an AI software programmer in your pocket that can write any software program you dream up. Or consider how it would impact your work if you could describe a rough idea and an AI could quickly produce a prototype for you to interact with. Innovations like these, Huang insists, shouldn’t make us feel threatened. They should make us feel empowered and excited about all the higher-level thinking and problem solving we’ll be freed to do. “I think it’s going to be incredibly fun,” he says. How can I use AI to do my job better now? If Huang’s first question is designed to get you thinking about what your work might look like 10 years from now, his second nudges you to consider what you can do now to prepare for that future. Huang tells Abrams that he has an AI tutor with him at all times. It’s a practice he recommends to just about everyone. “The knowledge of almost any particular field, the barriers to that understanding have been reduced,” he says. “If there’s one thing I would encourage everybody to do, it’s go get an AI tutor right away.” But don’t stop there. Huang’s more general point is that the more you experiment with AI now, the better prepared you’ll be to use it to your advantage as it develops. “If I were a student today, the first thing I would do is learn AI,” he declares a bit later in the podcast. He doesn’t mean learn technical details of the math behind the machines—though if you’re into that, certainly have at it. He means playing around with current tools like ChatGPT and Gemini to get comfortable with how to prompt them effectively. “Learning to interact with AI is not unlike being someone who is really good at asking questions,” he claims. “Promoting AI is very similar.” It’s a skill that requires honing. The end goal for everyone should be to begin thinking though how AI can best assist you with your work. “If I were a student today,” Huang continues, “doesn’t matter what field of science I am going to go into or what profession I am, I am going to ask myself, how am I going to use AI to do my job better?” Other AI experts agree with Huang Huang’s two questions are a great place to begin if you want to start to get a handle on how AI is going to affect you. But he’s hardly the only expert weighing in. There is no shortage of books on AI you can read to try and wrap your head around the technology. Other CEOs, like OpenAI’s Sam Altman and Bill Gates, have also weighed in on what our AI-future may look like and how to prepare. Even experts are still trying to figure out the future of AI, so don’t feel bad if you’re overwhelmed too. But while technologists are still building the future, they all agree we shouldn’t let anxiety or uncertainty get in the way of experimentation. The time for all of us to start thinking about the future of AI and playing with these tools is now. EXPERT OPINION BY JESSICA STILLMAN @ENTRYLEVELREBEL https://www.youtube.com/watch?v=7ARBJQn6QkM

Here’s How OpenAI’s New Deep Research Tool Could Change Your Workplace

OpenAI continues to champion the rise of AI agents, and now may be pushing that promotion to another level. Agents are next-generation AI tools, capable of acting on their own in a digital environment, and they’re potentially much more useful than the question-and-response AI chatbot systems we’re all getting used to. Demonstrating exactly how transformative agents could be, OpenAI has just released a new tool for ChatGPT called Deep Research that seems like it can speedily tackle a critical business task that could normally eat up days or even weeks of a worker’s time: gathering data and synthesizing it into a report. In an FAQ page explaining what the tool can do, OpenAI explains it’s “perfect for people who do intense knowledge work in areas like finance, science, and law,” or “researchers and discerning shoppers who need thorough, precise, and reliable research.” It’s particularly good at “finding niche, non-intuitive information that would involve multiple steps across numerous websites,” OpenAI says. It works like this: You ask the tool to look for information on a particular topic, adding images, files, or extra data like PDFs or spreadsheets to add context and help explain your query, which could be useful in, say, a question about financial information. OpenAI says it will sometimes pop up a form to ask for specific information before it starts gathering data so it can create a “more focused and relevant” answer. The final report is “fully documented with clear citations to sources,” so you can make sure that the information it found is both relevant and correct—streamlining the important step of checking whether the AI has hallucinated the info or if it’s real. Speaking at an event in Washington D.C. to show off the new tool, OpenAI chief product officer Kevin Weil made some bold claims about the tech, the New York Times reported. It’ll be able to do “complex research tasks that might take a person anywhere from 30 minutes to 30 days,” Weil said, adding that the tool will complete these tasks in maybe five to 30 minutes. It’s also able to search recursively, meaning it can do a single search, then, when that leads to other data sources, it can look for those too. If this sounds a lot like the kind of data-gathering task you might set for an intern or a junior employee when you begin a new program at work or when you encounter a novel problem that’s holding up a big project, then you’re likely thinking along the right lines. These seem to be exactly the sort of use cases that OpenAI has in mind. Users on Reddit who have used the tool are singing its praises. One highlighted the “time differential between the time it takes to complete its work compared to a human,” noting that “by some OpenAI employee estimates, it seems to be roughly 15x at the moment,” meaning it can complete a research task about 15 times faster than a human. That raises the question of when Deep Research could become cheaper and more effective to use than tasking an expensive worker to tackle these workplace chores. The Redditor projects how this might play out: If we imagine more advanced AI models that “can perform all the tasks of a lower-skill office job, but complete 3 weeks of work in a single working day,” then it’s quite simple to imagine “the cost of labour rapidly approaching zero as certain job sectors become automated.” Another user summed it up even more clearly: “Pro user here, just tried out my first Deep Research prompt and holy moly was it good. The insights it provided frankly I think would have taken a person, not just a person, but an absolute expert at least an entire day of straight work and research to put together, probably more.” This new tool may reignite the “will AI steal my job?” debate, but it also has great potential to transform many office tasks. Since it can speedily perform research, your staff may have more work hours available to actually respond to the data delivered from the research task, versus spending time trawling for info. BY KIT EATON @KITEATON

OpenAI Just Released o3-mini, Its Most Cost-Efficient Model Yet

OpenAI just released o3-mini, a miniature version of its upcoming flagship AI model. The new model is the company’s first “small reasoning model,” capable of using a train-of-thought process to complete tasks more accurately. The model’s launch, now available both on ChatGPT and through OpenAI’s API, caps off a week that also saw the company strengthen its ties with the United States government in the form of announcements about ChatGPT Gov and a partnership with the U.S. National Laboratories. In a blog post today, OpenAI shared that it anticipates o3-mini will be particularly useful for tasks involving science, math, and coding. The company’s testing indicates that o3-mini outperforms its predecessor, o1-mini, across several math and coding benchmarks, and in some aspects even outperforms the full o1 model. Like o1, users will be able to determine how much effort o3-mini puts into its reasoning, which could help developers save money when building applications that don’t require full effort. Subscribers at ChatGPT’s $200 per month Pro tier will get unlimited access to o3-mini, while those who pay $20 for ChatGPT’s Plus tier will be allowed 150 messages to o3-mini per day. Free users will also get a chance to try the model, but it’s unclear for how long. Developers who want to use OpenAI’s API to create new applications with o3-mini will pay $1.10 per one million input tokens and $4.40 per million outputs tokens. (Tokens are grammar elements that have been converted into data that can be processed by an AI model.) The model’s launch comes just as OpenAI is wrapping up a big week. On Tuesday, the AI market leader announced ChatGPT Gov, a version of the LLM that’s been tailored “to provide U.S. government agencies with an additional way to access OpenAI’s frontier models.” In a blog post announcing ChatGPT Gov, OpenAI wrote that government agencies will be able to deploy the system in their own Microsoft Azure cloud environment. “Self-hosting ChatGPT Gov,” according to OpenAI, “enables agencies to more easily manage their own security, privacy, and compliance requirements, such as stringent cybersecurity frameworks.” The company added that it anticipates this new service will make it easier for government agencies to approve the analysis of “non-public sensitive data” by ChatGPT. ChatGPT Gov has a similar operating system to ChatGPT Enterprise, OpenAI’s business-focused product. OpenAI also shared that since 2024, “more than 90,000 users across more than 3,500 US federal, state, and local government agencies have sent over 18 million messages on ChatGPT to support their day-to-day work.” The Air Force Research Laboratory uses the tool for basic administrative support, and the Commonwealth of Pennsylvania is taking part in a pilot program with ChatGPT that OpenAI claims has reduced the time spent on routine tasks by “approximately 105 minutes per day on the days they used it.” ChatGPT has also been used for some time to enhance research at Los Alamos National Laboratory in New Mexico, the birthplace of the atomic bomb. But a major new deal means OpenAI will soon have an even larger presence there. The company announced on Thursday that it has agreed to deploy current and future flagship AI models on Venado, a supercomputer in Los Alamos built in collaboration with Nvidia. According to OpenAI’s blog post announcing the deal, the computer was designed to “drive scientific breakthroughs in materials science, renewable energy, astrophysics, and more,” and it will be a shared resource for researchers at Los Alamos, Lawrence Livermore, and Sandia National Labs. As for how the models will be used, OpenAI says researchers will probe the technology for its potential to identify new approaches for treating and preventing diseases, improve detection of national security threats, and unlock “the full potential of natural resources.” The models will also be used to support Los Alamos’ nuclear security program, but use cases will be carefully decided on an individual basis in consultation with government officials and OpenAI researchers with security clearances. In a post on Linkedin, OpenAI national security policy and partnerships lead Katrina Mulligan said that she joined OpenAI “because I believed that some of the most consequential national security decisions of the decade would be made at companies like this and I wanted a seat at that table. Today’s announcement of our partnership with the National Labs to advance the future of science is exactly the kind of game-changing decision I wanted to have a role in making.” BY BEN SHERRY @BENLUCASSHERRY

Doubling Lifespans and Superintelligence: AI CEOs Are Saying Some Wild Stuff. Is Any of It True?

The AI revolution has been awash in hype for years, but it’s now truly on the cusp of sparking global transformation—if you take recent CEO and investor statements as gospel. The World Economic Forum in Davos, Switzerland, has offered a platform for leaders of AI’s biggest startups to wax lyrical about the industry’s bright prospects. Anthropic CEO Dario Amoedi jumped at the chance to laud AI’s power far beyond chatbots, saying that AI could allow humans to double their lifespans within the next decade. “If you think about what we might expect humans to accomplish in an area like biology in 100 years, I think a doubling of the human lifespan is not at all crazy. And then if AI is able to accelerate that, we may be able to get that in five to 10 years,” he said at a panel last week called Technology in the World. Amoedi also told reporters that superintelligence, or artificial general intelligence (AGI), is a feasible prospect by 2027. For many AI evangelists, the idea of building AI that possesses more knowledge than the collective sum of humanity is a pet topic and holy grail. OpenAI CEO Sam Altman wrote in September that such a milestone is within the industry’s grasp: “It is possible that we will have superintelligence in a few thousand days,” Altman claimed. The World Economic Forum in Davos, Switzerland, has offered a platform for leaders of AI’s biggest startups to wax lyrical about the industry’s bright prospects. Anthropic CEO Dario Amoedi jumped at the chance to laud AI’s power far beyond chatbots, saying that AI could allow humans to double their lifespans within the next decade. “If you think about what we might expect humans to accomplish in an area like biology in 100 years, I think a doubling of the human lifespan is not at all crazy. And then if AI is able to accelerate that, we may be able to get that in five to 10 years,” he said at a panel last week called Technology in the World. Amoedi also told reporters that superintelligence, or artificial general intelligence (AGI), is a feasible prospect by 2027. For many AI evangelists, the idea of building AI that possesses more knowledge than the collective sum of humanity is a pet topic and holy grail. OpenAI CEO Sam Altman wrote in September that such a milestone is within the industry’s grasp: “It is possible that we will have superintelligence in a few thousand days,” Altman claimed. BY SAM BLUM @SAMMBLUM