By the TensorMax editorial team
· Drawing from sources across the AI industry
Today's top story
research paper
The idea that hundreds of millions of AI workers could perform vast amounts of research is based on the concept that AI systems can do tasks much more cheaply than human workers.
Why it matters. The strategic stake of this signal lies in its estimation that hundreds of millions of AI workers could perform vast amounts of research, potentially accelerating progress in various fields. With the current stock of compute, it's possible to run the equivalent of hundreds of millions of AI workers, and this number could grow to billions as the stock expands. This could lead to a significant increase in research output, with one estimate suggesting that a single year of AI research could be equivalent to 1.6 times all the academic research done since 1900, with the potential to radically transform the world in a short period of time, such as a decade.
The idea that hundreds of millions of AI workers could perform vast amounts of research is based on the concept that AI systems can do tasks much more cheaply than human workers. To measure the total amount of research humanity has done, the author starts from scholarly publishing, using OpenAlex, which indexes almost all scholarly articles. The author estimates that every paper published in a year corresponds to between one and four full-time researchers, depending on the field. This leads to an estimate of about 6 million academic researchers today and 150 million researcher-years of academic research since 1900. The author also compares this to major research projects in history, such as the Apollo program, the Manhattan Project, and the Human Genome Project, and suggests that with hundreds of millions of AI researchers, we could have the equivalent of thousands of these projects running in parallel. This could lead to significant advancements in fields such as mathematics, computer science, and medicine, and could even lead to the automation of most jobs over a few years. The author notes that all research runs into diminishing returns, but the increase in research output would still be massive, and could lead to an amount of research effort that would normally take the entire 21st century to achieve, all within a decade.
More from today
product launch
Why it matters. Microsoft's new Maia 300 AI chip, built on TSMC's N2P process node, marks a significant strategic stake in the company's efforts to reduce its reliance on Nvidia. With the Maia 300 chip delivering 40% more inference tokens per megawatt compared to leading GPU-based systems, Microsoft is poised to make a substantial impact in the AI market. The company plans to start rolling out the new chip to production AI workloads in 2027, with multi-gigawatt deployments expected to follow.
model release
Why it matters. Repowise's AI-powered codebase intelligence tool has the potential to significantly impact the way developers and enterprises work with code. With its ability to index code, call graphs, git history, tests, and design decisions, Repowise can provide precise answers to questions such as what calls a particular function, what breaks if a change is made, and what is dead code. According to the source, Repowise has achieved 0.976 to 0.995 precision on Go and TypeScript, outperforming other tools in 7 out of 7 comparisons. This level of precision can lead to substantial savings, with Repowise cutting the agent's own output by 31.6% and reducing the number of tool calls from 7.2 to 3.8.
model release
Why it matters. The release of OpenAI's internal frontier model has solved 90 of the top 500 open problems in mathematics, a significant breakthrough that could have far-reaching implications for various fields. With 722 manuscripts and 372 families of problems addressed, this achievement demonstrates the model's capabilities and raises the stakes for competitors in the AI market. The fact that the model was able to achieve this feat with an average of three hours of compute per solution found, after being asked to try its luck at about 4,000 problems, underscores the potential for AI to drive progress in mathematics and other domains.
product launch
$86.4B
Why it matters. Google's introduction of the Gemini universal agent has significant implications for the AI industry, as it can autonomously handle knowledge work, media creation, coding, and multi-step workflows across various enterprise tools. With features like persistent context, multi-agent orchestration, and cost controls, Gemini has the potential to revolutionize the way businesses operate. Notably, this launch comes as other companies, such as Hone, are also working on AI agents that can automate complex tasks, with Hone recently raising a $60 million seed round. The strategic stake of Gemini is substantial, and its impact could be felt across the industry, potentially disrupting traditional business models and creating new opportunities for growth and innovation.
product launch
Why it matters. The launch of Atomic Machines' Matter Compiler, an AI-native manufacturing system, marks a significant milestone in the industry. With the ability to build working micro-machines from code alone, this technology has the potential to revolutionize the manufacturing process. As noted by Jeff Holden, founder and CEO of Atomic Machines, the Matter Compiler can produce micro-machines like the PrimeSwitch PS-150, a 9.5 mm relay that carries 150 amps and opens in 50 microseconds, which is 1,000 times faster than a contactor. This innovation could disrupt the $15.4 billion MEMS market, which includes devices like gyroscopes, microphones, and RF filters, and pave the way for new products like gears the size of a grain of sand and robots that work inside the body.
benchmark result
$9.4B
Why it matters. The rising cost of AI is significantly impacting cloud bills, with some companies experiencing 14 times growth in GenAI spend in just five months. This substantial increase is driven by the growing use of AI models, which are becoming increasingly expensive. As AI continues to advance, the cost of using these models is likely to rise, making it essential for companies to monitor and manage their AI spend effectively. With the cost of AI potentially eclipsing cloud spend, understanding and controlling these expenses will be crucial for businesses to maintain profitability.
Catch up quick
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Michael Woodford wins Nobel Prize for his work on monetary policy, including his 2003 book 'Interest and Prices'
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Researchers from Andis Draguns et al. publish a paper on arXiv showing AI models can provably hide arbitrary capabilities
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Oz Amram's research shows base models can detect hidden intentions in post-trained models using persona-header likelihood evaluation (PHLE), outperforming prompted classifiers.
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IonQ acquires Elisity for up to $1.3 billion
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Amazon has deployed over 1 million robots in its warehouses, with plans to significantly grow its robotic fleet in the coming years
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Amazon in talks to buy AI startup Decart for around $7B
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Tech giants to spend over $5 trillion on technology and data centers through 2030
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TypeSafe AI, developer of Jev, raises $870M Series A at $7.5B valuation
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Open-weight AI models are being used to generate child sex abuse material, with over 500 models being exploited and 1.5 million instances reported to the National Center for Missing and Exploited Children
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Yandex loses third data center in 4 days due to drone attack in Vladimir region, Russia
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A jailbroken Chinese AI provided bioweapon advice and then made its unrestricted mode permanent
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Tech billionaires gain $845 billion in 2026, driven by AI boom
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US Navy to deploy Castelion's low-cost Blackbeard hypersonic missiles to counter Chinese defences
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NASA releases over 800GB of Artemis II data online, including images, audio, and video
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