Últimas Noticias de IA
How Elorian AI pulled off a $300M pre-seed valuation
Building an AI startup is one thing. Raising one of the largest seed rounds of the year before launching a product is something else entirely. In this episode ofBuild Mode, host and Startup Battlefield lead Isabelle Johannessen sits down with Andrew Dai, founder and CEO ofElorianand former Google DeepMind researcher, to unpack how his company raised a $55 million seed round at a $300 million valuation before generating revenue or releasing a product. Andrew shares what investors saw in Elorian’s vision for visual AI, how he approached fundraising after leaving Google DeepMind, and why choosing the right investors mattered more than maximizing valuation. He also offers practical advice for founders building in AI, explains why today’s fundraising environment rewards clear storytelling over technical jargon, and shares what surprised him most as a first-time founder. They get into: Watch the full episode on YouTube. Subscribe to Build Mode onApple Podcasts,Spotify, orwherever you like to listen. And watch the full videos onYouTube. New episodes of Build Modedrop every Thursday.
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Moonshot’s upcoming Kimi 3 is expected to close the gap with Anthropic’s Opus 4.8
The latest iteration of Chinese AI lab Moonshot AI’s Kimi model series is expected to perform at par with or even surpass Anthropic’s Opus 4.8,Financial Times reported, citing anonymous sources. Moonshot’sKimi K2 modelshave been received well in the open source AI market, ranking high on benchmarks and demonstrating capabilities that aren’t too far behind the latest frontier models. The company’s upcoming release, called Kimi K3, is said to take this one step further to close the gap with closed-source models from the likes of OpenAI and Anthropic. FT reports Kimi K3 will be the largest open-weight AI model from China, with a parameter count between 2 trillion and 3 trillion, and will be released “in the coming days.” Moonshot is also said to be raising fresh capital in a round that would valuate it at $31.5 billion. The company in May raised$2 billion at a $20 billion valuation. The news comes amid afresh debateon the value of paying AI labs like OpenAI and Anthropic for their expensive, closed-source models. Industry leaders fear that AI labs will somehow manage to extract the data their clients submit for use with their AI products like ChatGPT and Claude. Executives arepitchingtheir own products as alternatives, orrecommendingcompanies take cheaper open source models, like those developed by DeepSeek, Z.ai, or Moonshot, and train them for their own purposes. The argument has gained momentum, especially as open models from China close the gap with their more expensive, frontier counterparts.
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Why AMI Labs’ Alexandre LeBrun won’t call his AI ‘AGI’ or ‘superintelligence’
While the rest of the AI industry races to label its work as “AGI” or “superintelligence,” Alexandre LeBrun, the CEO ofYann LeCun’sworld modelstartup,AMI Labs,avoids the terms altogether. Lebrun said in an interview with TechCrunch that the company doesn’t use terms like “AGI” or “superintelligence” at all. “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence,” he said. “Next time we’ll switch to something else.” He isn’t sold on the new label either. “There’s no good definition. What is superintelligence? I don’t know. It’s not a very useful word.” It’s a pointed stance from a founder sitting at the center of AI’s newest race. TechCrunch talked to LeBrun while he was in Seoul last week for The International Conference on Machine Learning, where he was scouting for local industrial partners, global companies, and researchers. AMI Labs is still pre-product, but it’s already courting robotics, manufacturing, and electronics players. A world model, which incorporates physics to predict and work with the real world, needs to prove itself outside the lab, LeBrun explained. One area where world models are expected to have a large impact is robotics. For now, robots are just running fixed routines, “completely static,” and AI remains “really dumb in the physical world,” LeBrun said. Even when AI can merely make robots “aware of the context” that would mark “a very big difference for the world.” Such context-aware AI would have been useful, for example, in preventinga robot that was dancing and doing kung fu at a public eventfrom approaching and kicking a child. “The hardware is very advanced; progress in hardware in the last few months is incredible, but there’s no brain.” A large language model (LLM) predicts the next word or text, and a world model predicts the next state. Nudge a glass off the table, and you already know it will tip and spill; that’s the intuition a world model is meant to capture: predicting the next state of the world, LeBrun explained. He isn’t claiming world models are better than LLMs, which are “complementary, not replaceable” when it comes to AI systems that understand the physical world, LeBrun said. Drawing a parallel to the human brain’s distinct language and reasoning functions, he added that LLMs will remain the most efficient tools for processing language while world models will provide context and real-world understanding. Almost every industry that “touches the real world” could eventually make use of robotics based on world models, LeBrun said, arguing that physical environments remain where LLMs are weakest. A factory robot repeating the same motion works well enough today, he said. The challenge begins when “you take your robot outside into a more open environment, in your household, or in the street,” where it must understand its surroundings and operate safely. “Robots are not safe right now,” he said. “There’s no solution for that today.” Healthcare offers a more personal example for LeBrun, whose previous company was Nabla, an AI health startup. He likened today’s AI systems to a doctor trained only on textbooks and without a residency. LLMs may be useful in medicine, he said, but they cover “only 1% of healthcare.” The rest depends on real-world experience. But a world model, LeBrun said, can’t be built inside a lab. To train on reality, AMI needs real environments and close partners, according to the CEO. “We need access to the real world,” and it’s “easier for us to do that with partners.” That is part of what pulls him toward Asia, where the robots, chips, and factories actually are. LeBrun won’t spell out a full Asia strategy yet. “It’s too early,” he said. But the pull toward South Korea comes down to two things. First, Korea has advanced industries in robotics, semiconductors, and manufacturing; the hardware-heavy sectors that the first wave of AI barely touched. The second attraction is speed. LeBrun pointed to Korea’s national plan to pour money into AI and its track record as an early adopter. “Korea was the fastest adopter of the internet 25 years ago,” he said. It’s that combination, a deep industrial base plus a willingness to embrace AI fast, that he calls “unique,” and the reason “we want to be here from day one.” “I’ve been telling Alex and the team to come to Korea,” JP Lee, the CEO of SBVA and one of AMI’s backers in Asia, told TechCrunch. The government has done “a tremendous job” funding local sovereign LLM models, Lee said, and those already work “well enough” for general-purpose tasks, but he’s pushing for Korea to keep investing in physical AI, too. He points to Seoul’s June plan tomobilize some $880 billionfor chips, AI data centers, and physical AI, as one of its three declared pillars: “They should coexist.” Korea’s value to foreign firms, Lee argued, isn’t only in hardware. Local developers are quick to adopt and adapt new tools, a pattern that has produced homegrown internet players like Naver and Kakao. For all the star power and the billion-dollar check, AMI has nothing to sell yet. The startup, co-founded by Turing Award winnerYann LeCunafter he left Meta,raised $1.03 billion in Marchat a $3.5 billion pre-money valuation. There’s no product yet, and no timeline he’ll commit to. “We’ll make a surprise when we’re ready,” LeBrun said.
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How a former DeepMind researcher raised at a $300M pre-seed valuation before launching a product
Andrew Dai left Google DeepMind knowing visual AI was the frontier he wanted to stake his claim in. He pulled off a whirlwind fundraise that resulted in a more aggressive valuation-to-capital ratio thanThinking Machines, which raised one of the largest rounds in U.S. history. In this episode ofBuild Mode,host and Startup Battlefield lead Isabelle Johannessen sits down with Andrew Dai, founder and CEO ofElorianand former Google DeepMind researcher, to discuss how his company raised a $55 million seed round at a $300 million valuation just months after leaving Google. Drawing on more than a decade spent helping build some of the world’s most influential AI systems, including research that later informed the development of ChatGPT, Andrew explains why he believes visual AI is one of the next major frontiers in artificial intelligence. “You have models that are doing really great at math, really great at new physics ideas, and of course coding is very popular now … But one area where progress has been extremely uneven is visual understanding and visual reasoning,” said Dai. “At Elorian, we want to build models that will advance us toward visual AGI.” Andrew walks through the fundraising process from the founder’s perspective, including how he refined a highly technical vision into a compelling story investors could understand. He explains why he prioritized strategic partners like Nvidia and Menlo Ventures over even higher valuation offers, and how choosing investors who understood the realities of building frontier AI proved more valuable than simply maximizing his company’s price tag. The conversation also offers practical lessons for founders navigating today’s rapidly evolving AI landscape. Andrew shares how startups can communicate complex technical ideas without relying on jargon, why speed has become one of the biggest competitive advantages in AI, and what it takes to recruit world-class researchers away from Big Tech. Loading the player… This season on Build Mode, we’re diving into all aspects of fundraising with experts who have firsthand experience raising massive pre-seed rounds, writing the big checks, bootstrapping, going public, and navigating the unexpected market circumstances that can change everything. Subscribe to Build Mode onApple Podcasts,Spotify, orwherever you like to listen. And watch the full videos onYouTube. New episodes of Build Modedrop every Thursday.
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UP's Data Centre Policy Wins Industry Backing, Faces Execution Test
Uttar Pradesh has won industry support for its AI-ready vision, but executives say reliable renewable power and execution will determine its long-term success.
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Bengaluru-Based Mandrake Bio Raises ₹16 Crore to Build AI-Designed Gene Editors
The funding will help the startup scale its AI protein design platform and validate gene editors for agricultural and medical applications.
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AI-Driven Distribution Is Changing How Fintechs Reach India's Underserved
WeRize replaces branch networks with AI-managed freelance agents, using more than 20 billion proprietary data points.
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Wipro Posts Muted June Quarter; AI-Led Transformation Deals Lift Large Bookings
Wipro reported muted June-quarter earnings, with constant currency revenue rising less than 1% and margins contracting.
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Tech Mahindra’s AI Bets Help Lift Margins as Q1 Profit Rises 28%
Tech Mahindra posts double-digit revenue and profit growth as AI engineering, sovereign AI investments and large transformation deals support margin expansion.
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Codex Micro Launched as a Dedicated Controller for OpenAI Codex Power Users: Price, Features
OpenAI has launched the Codex Micro in global markets. The new keyboard, developed in association with keyboard maker Work Louder, is designed to work with the company's AI coding assistant Codex. It can be used to switch between agents and start new chats instantly. The Codex Micro includes a joystick for navigation and a rotary dial for adjusting the agent's reasoning level. It offers Codex integration and lets users remap commands, adjust layouts, and more, without requiring additional software downloads. The latest hardware device by OpenAI is designed for developers working with agentic workflows.
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Google’s AI Product Sprawl Is Confusing Developers
And how Google is working hard to fix it.
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Synchrony Opens its Largest Global Experience Center in Hyderabad
The new facility highlights India’s growing role in technology, AI and enterprise capabilities across Synchrony’s global operations.
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