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Hello readers,
Welcome to the AI For All newsletter! Today, we’re talking about room-temperature superconductors, AI software lifecycles, and more!
AI in Action: AI is closing in on electricity's holy grail
An international team led by Aalto University's SuperC consortium has confirmed two new superconducting materials using a machine-learning pipeline built to screen far more candidates than physicists could ever test by hand. The model narrowed a practically unlimited pool of element combinations down to a shortlist, after which researchers ran targeted quantum calculations on the survivors. Rice University then synthesized the most promising compounds and confirmed superconductivity in the lab. Both materials draw their properties from electrons forming flat bands in a kagome lattice, a geometric pattern borrowed from Japanese basket weaving.
Superconductors carry electricity with zero resistance, but only at extremely low temperatures, and finding new ones has long been closer to guesswork than engineering. Researchers have identified over 7,000 superconductors across the decades, mostly by chance, and the computational cost of testing candidates meant only about 20 had ever been theoretically predicted before being found. SuperC's approach flips that ratio: machine learning handles the pre-screening, so expensive physics calculations only get spent on candidates worth the effort.
The bigger claim here isn't the two new materials themselves, both of which still need near-absolute-zero temperatures to work. It's that the search method is now experimentally validated. Törmä's team says the pipeline could eventually process billions of material combinations rather than dozens, and SuperC has set 2033 as its target for a room-temperature superconductor. That's the prize worth chasing: a room-temperature superconductor could carry current through power grids with zero loss, make maglev trains and MRI machines cheap enough to run anywhere, and strip the cooling infrastructure out of quantum computers and fusion reactors entirely. None of that exists yet. But the search just got a lot faster.
🔥 Rapid Fire
Commentary: The OpenAI Bubble
Analysis: The Hater’s Guide To The Memory Crisis
“What makes this particular memory crisis so distinctly dangerous is that it isn’t a result of consumer demand so much as it is capital expenditures from very large companies making bets that don’t connect with reality.”
Apple is suing OpenAI for allegedly stealing hardware secrets
“The iPhone maker claims OpenAI encouraged poached Apple employees to bring over confidential presentations, secret prototypes, and key supplier details.”
OpenAI’s ad business is on pace to miss its own forecast by 90%
“Emarketer’s data finds that standalone chatbots like ChatGPT will generate less than $1 billion in ad revenue this year, and just $5.41 billion by 2030.”
Big Tech doubles debt load to $350 billion in AI spending spree
“I don’t know that we know whether Amazon, Google, Microsoft and Meta are actually going to get a ROI on this. It seems like a lot of demand hype that is very aspirational at this point.”
S&P downgrades Oracle to BBB- on rising business risk and weaker cash flow
BBB- is one notch above junk level
SK Hynix plunges after Nasdaq debut as memory chip euphoria cools
Users say ChatGPT update is deleting their files without asking
Meta pulls new AI image feature after days of backlash
NVIDIA introduces Cosmos 3 Edge for on-device vision reasoning
Chinese AI startup Moonshot launches model challenging Anthropic
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📖 What We’re Reading
For much of the past decade, managing edge devices focused on a single task: maintaining the firmware image. Teams built, deployed, and validated the image, expecting minimal intervention until the next scheduled update. Devices and workloads were simple, and software management was straightforward.
That world is gone.
