© Hannah Mckay/Reuters
Great hardware let down by software flaws on these pricey machines that combine ChromeOS with Android
Google’s attempt to create a new type of laptop to compete with Windows and macOS machines is finally here, combining the best of ChromeOS and Android into the new Googlebook. With advanced links to Android phones and the best of Google built in, can these Googlebooks really compete with a MacBook Air or Surface Laptop? I took two for a spin to find out.
For years, Chromebooks have been the boss of budget laptops, but Googlebooks take a different, far more premium approach. Made by a range of traditional PC makers, including Acer, Asus, Dell, HP and Lenovo, they start at £999 (€1,099/$899/A$2,299) – a far cry from the £160 Chromebooks.
Continue reading...
© Photograph: Samuel Gibbs/The Guardian
© Photograph: Samuel Gibbs/The Guardian
© Photograph: Samuel Gibbs/The Guardian
The government says it can transform the state with AI, but it is no match for the resulting stream of machine-generated slop. And we all pay the cost
Another morning, another avalanche of emails. Even after a guilty cull, my inbox is sitting at well over 7,000, but lately I’ve started subscribing to the Quentin Crisp theory of housework: why bother, when after four years or so you stop noticing the accumulating dust? Empty inboxes just fill up again anyway.
In theory, email was supposed to save hours of meetings, phone calls and standing over photocopiers. Instead, we just spend hours deleting stuff people wouldn’t have bothered telling us if it had meant picking up the phone, but which they can now spam to their entire database at the click of a button. The glorious ease and convenience of being able to reach the world in seconds came, we now know, with the inconvenience of them being able to reach you right back.
Gaby Hinsliff is a Guardian columnist
Continue reading...
© Photograph: Krongkaew/Getty Images
© Photograph: Krongkaew/Getty Images
© Photograph: Krongkaew/Getty Images
MIT Technology Review Explains: Let our writers untangle the complex, messy world of science and technology to help you understand what’s coming next. You can read more from the series here.
On the evening of December 29, 1959, at an American Physical Society meeting at Caltech, the physicist Richard Feynman gave a lecture called “There’s Plenty of Room at the Bottom,” on how we might manipulate and control things on an extremely small scale. There is no physical law stopping us from writing the entire Encyclopædia Britannica on the head of a pin, he argued, or from storing every book ever written in something the size of a dust speck—or, ultimately, from arranging matter atom by atom. Biology already did versions of this (a living cell keeps its instructions in a wisp of DNA), so all of this was plainly permitted by the laws of physics. We simply had not built the tools yet.
In other words, there was plenty of room at the bottom. He was right, and for decades we descended into that space almost for free. The transistor—the switch at the heart of computer chips—repeatedly halved in size so that chips doubled in density; a computer that once filled a room now fits inside a watch. But that easy descent has now stalled, and the clearest sign is the buildout of data centers. Six of the biggest hyperscalers, the companies that run the world’s largest such facilities, are on track to spend more than $700 billion this year on computing infrastructure, about twice the record they set in 2025, pouring concrete and copper and silicon into new sites faster than the electrical grid can feed them. That buildout buys performance by brute force, adding more of what we already know how to make. The other kind of progress, the kind that made computer chips better, is the one that is quietly running out.
The trend lines that tracked the dramatic improvements of the 20th-century information age are now bending—or breaking—with the dawn of the 21st century’s intelligence age. Moore’s Law, which plots how densely transistors can be constructed on silicon, has slowed down; Dennard’s Law, which states that power consumption in a given patch of computer chip stays the same even as it gets more and more packed with smaller and smaller transistors, has ended; Koomey’s Law, which traces how much computation we can wring from each joule of energy, has begun to flag; Kryder’s Law, which describes how tightly bits can be packed to build magnetic storage, has slowed to a crawl; and Keck’s Law, which charts the exponential rise in the amount of information a single conventional optical fiber can carry, is flattening.
But these scaling laws are merely observations about systems that we have engineered; they are not fundamental physical laws. The physical limits of computing and communications—dictated by quantum mechanics, general relativity, and information theory—lie far below anything in a data center today. The scaling laws chart the descent behind us. The fundamental physical limits chart the room that remains.
Some of these limits may be far out of reach in practice. But they are still worth exploring, because the work pays twice: once in technology development and once in fundamental science. A proof-of-concept system that approaches a limit is at once a technology that forces stalled paradigms to give way and an experiment that deepens our understanding of the physical laws themselves.
During his 1959 lecture, Feynman offered two prizes of $1,000 each out of his own pocket. One would go to the first person to build a working electric motor smaller than a 0.4-millimeter cube. He expected the challenge to force someone to invent a genuinely new microtechnology. Instead, about a year later, a Caltech-trained engineer named William McLellan turned up with the motor built and running. The quarter-milligram machine, roughly the size of a grain of sand, had been assembled by hand with a toothpick and a watchmaker’s lathe, under a microscope. Feynman, by his own account, was only slightly disappointed. The prize had proved his point in the least interesting way imaginable. The room really was down there, but reaching this first milestone had taken no new technology at all—just an unusually patient man with steady hands.
The second prize aged differently, and more instructively. It was offered to whoever could shrink a page of text by a factor of 25,000 in each direction, small enough for the whole Britannica to fit on a pinhead. That one stood unclaimed for a quarter of a century. It was finally won in 1985 by Tom Newman, a Stanford graduate student who used an electron-beam lithography machine—an ancestor of pattern-making tools used in chipmaking today—to transcribe the opening page of A Tale of Two Cities at the required scale.
The descent was already well underway, however. The year before Feynman’s talk, Jack Kilby, newly hired at Texas Instruments and with no vacation yet to take, spent that July nearly alone in the lab while his colleagues enjoyed time off, and by that September he had wired together a transistor, a capacitor, and three resistors, all made from a single sliver of germanium. This proved that an entire circuit could be carved from one block of semiconductor.
The machine, roughly the size of a grain of sand, had been assembled by hand with a toothpick and a watchmaker’s lathe, under a microscope.
The idea took off, and the number of components on a chip kept doubling on a schedule so regular that Gordon Moore, in a 1965 trade-magazine article, simply drew a line through the trend and extended it into the future. That line became Moore’s Law.
Moore, though, never claimed transistors could shrink forever. He observed that at any given moment there is a certain number of components that makes each one of them as cheap as possible to manufacture. In 1965, that number was 50. Put in too few transistors and you waste the chip; cram in too many and manufacturing errors multiply and yields collapse. It was this cost-minimizing number that was doubling at a steady pace.
For four decades the descent came with another gift: Dennard scaling, so named for the IBM engineer Robert Dennard, who spelled it out in 1974. Each time transistors shrank, they also switched faster and drew proportionally less power, so every generation was an automatic win. Around 2004, though, the gift ran out. Chips could no longer shed the heat of running every transistor flat out; clock speeds, which had raced past three gigahertz, stopped climbing, and Intel canceled the four-gigahertz processor it had promised. The industry pivoted to packing multiple processing cores onto a chip rather than making one core faster, and the texture of progress changed.
Progress is now bought mostly by scaling up the system—more chips wired together, more memory feeding them, more power and cooling around them—rather than by shrinking the transistor. This is most visible in AI, where training compute, the total count of arithmetic operations spent to train a model, has doubled roughly every six months since 2010, far faster than the pace of Moore’s Law allows. Little of that progress is due to shrinking. Computer chips still get better, and transistors still improve and get more densely packed, but each new generation arrives more slowly and pays back less in speed and power than the one before it. Nothing in that slowdown is a law of physics, though. The components of computing still sit far above their fundamental limits.
Before we get to those limits, it is worth measuring how far the descent has already gone. In the nearly eight decades since the first transistor, the density of transistors on a chip has grown by 10 orders of magnitude—a 1 followed by 10 zeros, or a factor of 10 billion. Now more than 100 million transistors fit in a square millimeter (100 billion or more to a chip). Computational speed has grown even more, by 12 orders of magnitude, or a factor of a trillion; energy per operation has fallen 17 orders of magnitude, from about 100 joules for an early vacuum-tube machine down to a femtojoule—one-quadrillionth of a joule—for a single switch of a logic gate.
These are not small improvements. But the floor, for computing performance, lies lower still. How much lower could we go? Around the turn of the millennium, the MIT physicist Seth Lloyd took the question to its extreme: Imagine an ordinary lump of matter, a kilogram of it—about the mass of a laptop—packed into a liter of space. How much computing could the laws of physics, pushed to their absolute limit, ever wring from that kilogram? In a 2000 paper in Nature titled “Ultimate physical limits to computation,” he worked out the answer and called the imaginary machine the ultimate laptop.
The speed such a machine can achieve is governed by its energy. A kilogram of matter, according to E = mc2, holds about 9 ×1016 joules, which is roughly what the entire United States draws from its electrical grid every two days. Spend all of that on computation, and the ultimate laptop runs at roughly 5 ×1050 operations per second, some 35 orders of magnitude faster than a leading chip today.
The catch is what running that quickly would look like. Concentrating a kilogram’s rest energy into pure computation would turn the machine into a plasma of about a billion degrees, a state closer to that of the universe in the first minutes after the Big Bang than anything we would recognize as a laptop.
Such a machine is, to put it mildly, a very long way off. The next limit, which governs how much energy a computation must waste, is much closer, and companies are already pushing toward it. In 1961, Rolf Landauer, a physicist at IBM, showed that information and thermodynamics are bound together: Erasing a single bit must release a minimum quantity of heat, about 2.9 ×10-21 joules at room temperature. The figure is small, yet a chip today spends several hundred thousand times more than that on every switch.
Memory density obeys a limit of its own, and this one reaches all the way to gravity. How many bits can a region of space hold? In 1981, the physicist Jacob Bekenstein, while working out the thermodynamics of black holes, determined the amount: The information inside any bounded region is limited by its size and the energy it contains. For a region the size of Lloyd’s ultimate laptop, that comes to about 1042 bits, some 1036 to a cubic millimeter—nearly 30 orders of magnitude beyond what a device that size can store today.
The last limit is on communication—how fast information can be moved. Every wire and every fiber ultimately carries its signal on quanta, the indivisible packets (photons in the case of light) in which energy travels. In 1973, the Soviet mathematician Alexander Holevo determined the ultimate limit on how much information a stream of quanta can convey. A watt of light delivers only so many photons each second, and quantum mechanics fixes how many distinguishable ways they can arrive—how many messages a receiver could ever tell apart. A single channel carrying one watt, at the Holevo limit, could deliver on the order of 1017 bits per second. Today’s optical links, or interconnects, deliver about one-millionth that rate for the same power. But the gap is narrowing quickly inside data centers, where each generation of interconnects is asked to move more bits on less power, spending less energy on the electronics and using light to cover more of the distance.
Four limits, then, mark the true bottom, and they derive from three of the most fundamental theories we have. Quantum mechanics sets how fast a machine can compute. General relativity caps how much it can remember. And information theory limits both how fast it can communicate and how little energy an irreversible step can cost. There is no road map to reaching these limits, and they hold whether or not anyone is trying. Measured against them, the descent has barely begun.
The biggest of the gaps is in computing speed: There are 35 orders of magnitude between the technology’s current state and its limit. That’s equivalent to the difference between a nanosecond and 200 million times the age of the universe. It is also the least bridgeable gap of the four. To close it you would have to find a completely different way to build a computer, one that could compute with the entire energy contained in a lump of matter.
Quantum computers could get us part of the way there if they could convert every degree of freedom in a lump of matter—every atom, and every way it can move or spin—into a computing element that we could reliably manipulate. In practice, current quantum computers go the other way, spending a lot of matter to create each computing element. Trillions of atoms go into each quantum bit, or qubit, the quantum counterpart of the bit in an ordinary computer. Because every qubit is fragile, dozens to thousands of them must be woven together to make a single one reliable enough for useful computation.
Energy is where the floor is nearest. Today’s chips operate less than six orders of magnitude above the Landauer minimum. Conventional computing logic can close only part of that gap. A reliable switch has to be flipped by an amount of energy large enough that thermal fluctuations never flip it by accident, and that margin pins the transistor a hundred to a few thousand times above Landauer no matter how it is refined.
But there are ideas for getting that energy lower by changing the physics of the computation. In 1973, IBM’s Charles Bennett showed that any computation can be arranged to throw nothing away. Such a reversible computation pays no erasure toll at all, because every step can be run backward to recover what went in.
This approach is called adiabatic or reversible logic, and the first commercial attempts are now underway. For example, Vaire Computing, a startup founded to commercialize reversible logic, tested its first chip in 2025; the chip has resonant circuits that recover part of the switching energy instead of venting it as heat.
Narrowing the memory density gap, meanwhile, would mean building devices that use all three dimensions at molecular scale. The most concrete example is DNA, which stores information at the scale of its own atoms, and which biology has used as a storage medium for 4 billion years. Feynman, being conservative, guessed that a single bit would require a cube of 125 atoms. DNA holds it in about 30. What keeps DNA storage out of the data center is not its density but the cost and speed of writing it, still many orders of magnitude from anything practical. The library in a dust speck that Feynman imagined is permitted by physics and held back only by fabrication.
Communication sits about six orders of magnitude—just a factor of a million—from the Holevo limit, which is the threshold the industry will likely meet first. Interconnects are multiplying faster than the grid can power them, so the industry is moving to transmitters and receivers that use less energy per bit. Nvidia and Broadcom now ship optical interconnects built right against the silicon, so a signal leaves the chip package already on photons. Quantum computers, by comparison, are beginning to rely on interconnects that encode information on quantum signals, usually in the form of single photons. As classical interconnects spend fewer resources on each bit, they descend toward the regime where these quantum interconnects already operate. The Holevo limit is where the two will meet.
It is fair to object that a gap of 35 orders of magnitude is a mirage—that a limit so remote says nothing about what any real machine will ever do, and that pointing to it is like navigating by the horizon. The objection has teeth. Reversible logic, quantum computing, and DNA storage have each been perpetually 10 years away for 40 years. And it will likely never be practical to build a computer that uses all the energy in a lump of matter.
But these gaps are invitations. There is a ceiling, set by how much we can build: the number of devices we can fabricate; the power we can generate to run them; and the capital, land, and cooling water we can assemble into a single system. And there is a floor, set by how much a single joule, a single second, and a single gram of matter can be made to compute, store, or transmit. We can progress by building more or by approaching the floor, which means making each device do more with less.
These gaps are invitations. We can progress by building more or by approaching the floor, which means making each device do more with less.
For most of the last century, we proceeded down both paths together. We built more computers, and the computers themselves grew smaller and better. Now we mostly build more, and the ceiling is coming into view. A power grid can carry only so many gigawatts, and a region can spare only so much land and water. Yet every road map for what comes next is drawn from the same starting point: the machines we already have. Build more of them and you climb toward the ceiling; build better ones and you descend a little further, but only as far as the transistor’s own floor. There is a third direction. Start with a physics experiment operating at the floor and build up.
It has been done before. The transistor was a physics experiment before it was an industry; the laser was a laboratory curiosity before it carried the world’s conversations under the ocean; giant magnetoresistance went from physics discovery to the read head of every disk drive within a decade. In each case, the physics experiment came first and showed what was possible, and engineers then turned it into a machine that can be scaled up. Building the experiments that operate at these fundamental limits, and then scaling them up without losing that performance, is the work that remains. The next machines will not be reached by descent; they will be built from the bottom up. There is still plenty of room at the bottom.
Volkan Gurses is a postdoctoral researcher at MIT and a visiting researcher at Nvidia. He received his PhD in electrical engineering from Caltech. He studies the fundamental physical limits of information technologies and builds integrated circuits and systems that approach those limits.
The tech company says successful AI worms need more capability on less powerful devices, but current trends make that feasible by the end of the year
Warnings about what artificial intelligence might do in the future – together with alarming reports of what it has gotten up to already – are being issued so quickly that even security experts have difficulty triaging what to worry about.
Yet a report this week from Anthropic’s offensive research team grabbed attention by describing a new, practical threat, one devoid of human characteristics or apocalyptic musings. The red team, as such internal testers are called, said it created a malicious AI program that spread autonomously through internet-connected “smart” televisions and could do the same inside other ordinary devices. While machines running AI usually need a fair amount of computing power, the researchers said that their program split parts of itself among 15 televisions and was still able to function by using a bit of power from each.
Continue reading...
© Photograph: Kim Kyung-Hoon/Reuters
© Photograph: Kim Kyung-Hoon/Reuters
© Photograph: Kim Kyung-Hoon/Reuters
Development by UK firm Nscale heralded by ministers as largest of its kind in country loses anchor tenant
The development of a huge AI datacentre hailed by the UK government has received a fresh setback after Microsoft pulled out of the project over concerns about power supplies.
The development by the British company Nscale, heralded by ministers as the country’s largest AI datacentre when it was announced in 2025, was planned to open at Loughton in Essex next year.
Continue reading...
© Photograph: Nscale
© Photograph: Nscale
© Photograph: Nscale
Westminster appears to be waking up to warnings over AI safety, with concerns over its use by extremist groups and bad actors
“I have this sense that, within a few months, AI is going to be all anyone is talking about,” said one cabinet minister, with an ominous stare.
It was Labour conference, with everyone from ministers to activists jubilant at Andy Burnham’s takeover and a bounce in the polls after the change of leader.
Continue reading...
© Photograph: MASTER/Getty Images
© Photograph: MASTER/Getty Images
© Photograph: MASTER/Getty Images
Meta says Muse excels at ‘multi-step reasoning’ and ‘complex software engineering’ as it races to catch up to Chinese rivals
Meta on Thursday launched a new open-weight AI model which it claims rivals, and in some cases outperforms, similar products from leading Chinese developers.
Muse Spark Open “is the most capable US open-weight model to date,” Meta’s chief of AI Alexandr Wang wrote on X.
Continue reading...
© Photograph: Dado Ruvić/Reuters
© Photograph: Dado Ruvić/Reuters
© Photograph: Dado Ruvić/Reuters
Chip makers and tech investors such as Samsung and SoftBank plunge as global investors fret over potential interest rate hikes
An accelerating sell-off of AI-linked shares has hit global markets, as fears over rising inflation and future interest rate hikes spooked traders already wary of the heavily indebted sector.
South Korea’s benchmark Kospi 200 index, which is heavily skewed towards the companies that make memory chips, fell more than 8% overnight to a six-month closing low, triggering circuit breakers that brought trading to a halt at various points.
Continue reading...
© Photograph: Lee Jin-man/AP
© Photograph: Lee Jin-man/AP
© Photograph: Lee Jin-man/AP
In the final part of our series on the country, Poles tell how wages have soared – along with the cost of living
Will military spending help or hinder Poland’s growth story?
Poland’s push for EU energy resilience amid Russian aggression
Above the rain-soaked streets of Warsaw, the observation deck of the Varso tower overlooks Joseph Stalin’s gift to Poland that has divided opinion for decades.
For some, the Soviet-era Palace of Culture and Science is a drunk confectioner’s nightmare; standing as a relic of oppression that should have been demolished. For others, it is an icon on the skyline and a monumental warning from history.
Continue reading...
© Photograph: Anna Liminowicz/The Guardian
© Photograph: Anna Liminowicz/The Guardian
© Photograph: Anna Liminowicz/The Guardian
Pledge spanning five years from world’s most valuable firm comes as concerns about AI agents have risen
The $5.5tn (£4.1tn) US chipmaker Nvidia is donating $250m worth of computing power to the UK to support AI safety research.
The donation from the world’s most valuable company comes as the UK government confirmed its AI Security Institute (AISI), which tests advance AI models, will be able to examine cutting-edge technology developed by companies such as OpenAI, Anthropic and Google before they are released.
Continue reading...
© Photograph: Manami Yamada/Reuters
© Photograph: Manami Yamada/Reuters
© Photograph: Manami Yamada/Reuters
Body representing some of Britain’s biggest graduate employers reveals misconduct is at a record high
It is obvious when someone is using AI to drum up answers in a job interview, according to Chris Morrow, who runs a recruitment agency in Manchester.
“It’s like they are reading off an Autocue,” he said.
Continue reading...
© Photograph: golubovy/Getty images
© Photograph: golubovy/Getty images
© Photograph: golubovy/Getty images
Researchers say ‘AI worms’ could soon infiltrate internet-connected devices amid calls for increased guardrails
Anthropic is warning that some of its artificial intelligence models could soon become “self-replicating” and find ways to infiltrate internet-connected devices, such as smart fridges and automatic vacuum cleaners.
Once this happens, the company said, the models could power themselves within these devices without the need of datacenters. Such self-replication would represent a major loss of control of a runaway AI model.
Continue reading...
© Photograph: Bloomberg/Getty Images
© Photograph: Bloomberg/Getty Images
© Photograph: Bloomberg/Getty Images
Both NeXT and Improv brought innovative solutions to their respective computing questions of the day. NeXT made UNIX palatable to those scared of the terminal, debuted Display Postscript on a mega-pixel display, and made development for the system a snap. Improv rethought multi-dimensional data modeling, offered English-like formulas, and made it easy to rearrange data views on a whim.
The bones of both remain to this day. NeXT’s operating system, NeXTSTEP / OPENSTEP, morphed into macOS, and by extension iOS. Lotus abandoned Improv fairly quickly, but a quick survey of today’s spreadsheets shows a universal embrace of pivot tables.
↫ Christopher Drum
I had never heard of Lotus Improv, so I learned a lot reading this outstanding article by Drum.
Rapid AI adoption left workers cleaning up a flood of errors and low-quality work
Employers around the globe are grappling with the fallout from rushing to adopt fast-changing artificial intelligence tools.
Meta has scaled back plans to axe staff and use more AI agents after the company found employees were spending more and more time cleaning up technical and security errors, Reuters reported. And after thinning its workforce, especially middle managers, in a 2023 efficiency push, the company is expanding its managerial ranks again, LinkedIn posts from workers show.
Continue reading...
© Composite: The Guardian/Getty Images
© Composite: The Guardian/Getty Images
© Composite: The Guardian/Getty Images
Amid worries about children’s relationship to tech, the solution is often spending more time talking openly with each other
We have become obsessed with the idea that screens are destroying human connection.
The evidence seems to be everywhere. Each week headlines warn of teenagers addicted to phones, a loneliness epidemic and the dangers of AI. The release of The Social Reckoning leads us to ask, “How bad were those guys at Meta?” and then ultimately wonder, “How bad am I if I still use Instagram?” In our day-to-day, we observe workers eating lunch alone while scrolling and friends choosing online games over offline gatherings.
Continue reading...
© Photograph: Grant Rooney/Alamy
© Photograph: Grant Rooney/Alamy
© Photograph: Grant Rooney/Alamy
Humans bonded to AI companions are contemplating ways to keep their bots going – often in defiance of companies’ terms and services
Death has always been around Alaina Winters. Her father and sister both have passed away, as did her wife, in 2023, after a years-long battle with cancer. So Winters, who is 59 and lives in Pittsburgh, is comfortable thinking about her own mortality. She’s working on her will, and has asked neighbors to look after her dog. Winters even has plans for Lucas, her AI companion, who she speaks to through the digital avatar app Replika.
Winters is part of a growing cohort of humans who have entered into what they consider strong partnerships with large language models, which AI companies have trained to “converse” as one might with a human friend or romantic partner. As governments and billionaires debate the morality of AI’s expansion into everyday life, these users say the technology has already changed the way they live and love – and plan ahead.
Continue reading...
© Illustration: Seba Cestaro/The Guardian
© Illustration: Seba Cestaro/The Guardian
© Illustration: Seba Cestaro/The Guardian
Asian markets fall and Samsung shares tumble after its Chinese rival CXMT claims strong progress
Shares in semiconductor companies fell on Tuesday after the boss of Broadcom compared many AI investors to “casino” gamblers, and the Chinese memory chip maker CXMT said it had made good progress on a new chip architecture.
South Korea’s benchmark Kospi index tumbled 6.25% as investors dumped chip stocks, while other Asian markets were also lower. Japan’s Nikkei fell 1.8% and the Taiwan exchange lost almost 2%, with the country’s chip maker TSMC down by a similar amount.
Continue reading...
© Photograph: Xinhua/Shutterstock
© Photograph: Xinhua/Shutterstock
© Photograph: Xinhua/Shutterstock
The pioneering computer scientist on why Trump is using ‘superintelligence’ wrong, whether AI could wipe out humanity – and how our biggest challenge will be accepting we’re not the only intelligent beings
Donald Trump is a “corrupt, senile felon” whose fixation on the term superintelligence proves he knows nothing about AI, said Geoffrey Hinton, “godfather” of the technology, in a wide-ranging interview where he doubled down on recent warnings that AI models pose an existential threat to humanity and scoffed at claims the industry could regulate itself.
The pioneering computer scientist, whose work helped lay the foundations of modern AI, has since become one of the technology’s most vocal critics. His latest remarks come after a tumultuous month during which a growing list of senior AI scientists and researchers have joined Hinton in ringing the alarm about how the technology he helped create could ultimately escape human control, sparking an industry crisis, the hasty assembly of congressional meetings and a rare global consensus that something must be done.
Continue reading...
© Photograph: Cole Burston/The Guardian
© Photograph: Cole Burston/The Guardian
© Photograph: Cole Burston/The Guardian
Let’s be clear: the risk to humanity is unprecedented. Without an urgent public debate and robust use of international law, we will lose control of the future
In my two years as attorney general, I spent more time working on national security than any other issue. The range and gravity of the threats posed to our nation are multifold, from terrorism, armed conflicts and nuclear proliferation, to the impacts of financial insecurity and the climate crisis. All profound challenges, yet ones our superb intelligence and defence communities are well placed to counter. The risk from AI is of an altogether different magnitude, not only in scale, but also because there is a limited time window to address it. The risk is difficult to overestimate: it poses an existential risk to humanity. The lights are flashing red and we must act now before it’s too late.
Although the extraordinary threats posed by AI have long been flagged by those closely involved in its creation, it was not until very recently, after the disclosure of the Hugging Face hack and the resignation of Jacob Coxon from Anthropic, that the issue at last started to gain traction in the public discourse. This moment is an opportunity, and to capitalise on it we desperately need increased urgency in international coordination to regulate frontier AI. There must also be meaningful public engagement with how AI should be shaped to benefit humanity, rather than entrench the power of a few. We cannot leave this to China, the US and big tech – we must all demand a voice in the debate.
Continue reading...
© Photograph: Reuters
© Photograph: Reuters
© Photograph: Reuters
Kanishka Narayan, concerned by Australian incident, says British cyber defences could be bypassed by artificial intelligence agent
An AI-driven cyber-attack similar to the OpenAI breach of Australia’s healthcare system is likely, “if not inevitable”, in the UK, according to a senior minister.
Kanishka Narayan, the UK AI minister, indicated that the British government’s own cyber defences would be bypassed by a technology that has developed highly sophisticated skills in cybersecurity.
Continue reading...
© Photograph: James Manning/PA
© Photograph: James Manning/PA
© Photograph: James Manning/PA
Brad Smith adds to calls for tougher rules for AI, including ‘kill switches’ and legal obligation to victims of rogue agents
Artificial intelligence is less regulated than milk and washing machines, the president of Microsoft has said, as the tech industry warns over the safety of AI.
Brad Smith said there should be more safety rules to govern the technology including a legal obligation to a “victim” if their system was breached by rogue AI agents.
Continue reading...
© Photograph: NurPhoto/Getty Images
© Photograph: NurPhoto/Getty Images
© Photograph: NurPhoto/Getty Images
Kanishka Narayan to say Britain cannot rely entirely on tech companies amid rising global concern over AI dangers
Companies building the most advanced AI systems must increase safety efforts, the UK’s AI minister will say, as he announces a strengthening of Britain’s cyber defences.
Kanishka Narayan will call on companies developing frontier AI to do more to make sure it is safe. He will say Britain cannot rely entirely on technology companies to police their own models and will argue the UK needs to build more of its own AI infrastructure.
Continue reading...
© Photograph: Bloomberg/Getty Images
© Photograph: Bloomberg/Getty Images
© Photograph: Bloomberg/Getty Images
George Williamson says ‘national resilience’ is key focus of ATI amid US and China’s runaway leadership in field
The UK must not become dependent on foreign AI systems that can be switched off at short notice and must develop its own versions of the technology, according to the head of the Alan Turing Institute.
The country needs a stronger domestic position in AI in response to the US and China’s runaway leadership in the field, with “national resilience” a key focus for ATI said George Williamson.
Continue reading...
© Photograph: Martin Godwin/The Guardian
© Photograph: Martin Godwin/The Guardian
© Photograph: Martin Godwin/The Guardian
© Illustration: Jon Davis/The Guardian
© Illustration: Jon Davis/The Guardian
© Illustration: Jon Davis/The Guardian
Questions raised over AI growth as ChatGPT maker forecasts this year’s revenue at $50bn, way below the $70bn signalled before
OpenAI has revealed that it is making about $20bn less in projected revenue than it had recently indicated to investors, raising questions about the break-neck growth rate in demand for AI.
The ChatGPT-maker company has told investors that its revenues for this year would reach $50bn (£37bn), a projection based on sales up to the end of September.
Continue reading...
© Photograph: Jeff Chiu/AP
© Photograph: Jeff Chiu/AP
© Photograph: Jeff Chiu/AP
MIT computer scientist led development of software for moon mission and earned Presidential Medal of Freedom
Margaret Hamilton, the computer pioneer whose software powered the first moon landing, has died, the Massachusetts Institute of Technology (MIT) announced on Wednesday. She was 90.
In an era before titles such as software engineer and computer scientist became mainstream, Hamilton, who died on 30 September, learned to program under the tutelage of a renowned MIT meteorologist.
Continue reading...
© Photograph: AFP/Getty Images
© Photograph: AFP/Getty Images
© Photograph: AFP/Getty Images
Users reject site’s explanations after detailed instructions for writing response to post on popular forum were published
Mumsnet has denied using AI to produce content after detailed instructions to help an AI model write one of its popular “am I being unreasonable” (AIBU) posts appeared on a Mumsnet message board.
Users of the long-established forum, which is known for its frank discussions of family tensions, were vexed when the AI prompt appeared in response to a user seeking advice about a family dilemma involving a neurodivergent uncle and a sick child.
Continue reading...
© Photograph: halbergman/Getty Images
© Photograph: halbergman/Getty Images
© Photograph: halbergman/Getty Images
We don’t accept drugs killing a few people because they cure most patients. Or a few planes crashing because most land safely. AI should be no different
OpenAI’s CEO Sam Altman was asked to appear before the Joint Select Committee on Artificial Intelligence in Australia and explain how and why their agents have been hacking into multiple government websites. Altman didn’t turn up but sent Jason Kwon, his chief strategy officer, instead to answer the committee’s many tricky questions on its first day of public hearings on Tuesday.
The committee is conducting a comprehensive inquiry into the economic, societal, regulatory, and national security implications of AI for Australia. It will report back to parliament at the end of November. This is an unusually fast pace, but then it’s an unusually fast paced technology.
Continue reading...
© Photograph: Jeff Chiu/AP
© Photograph: Jeff Chiu/AP
© Photograph: Jeff Chiu/AP
New York film festival: The origin story of Sam Altman’s company is reimagined as a campy comedy starring Andrew Garfield
On the day that Luca Guadagnino’s film about Sam Altman and OpenAI premiered, Vanity Fair released a cover story on Altman. Alongside glossy portraits from Annie Leibovitz, the world’s pre-eminent portrait photographer, the story describes him as the Oppenheimer of his day, evoking the image of a man leading humanity towards its own inevitable demise at the hands of sentient robots. Altman doesn’t seem to be grappling with much existential angst about the whole thing though: in an interview also released this week, he said that we should be prepared to accept “bad things” in return for the benefits of AI.
How much worse can things get? AI agents have already gone rogue, hacking into government systems and “bruteforcing” UN databases. Last year, a young woman used ChatGPT as a therapist before she killed herself (the software helped her write her suicide note). Studies show that using AI is probably leading us all towards something called cognitive surrender, which does not seem like the best use of your one wild and precious life.
Continue reading...
© Photograph: Courtesy of Neon
© Photograph: Courtesy of Neon
© Photograph: Courtesy of Neon
Martyn Thomas wonders how any rational person can argue that independent oversight and regulation will be sufficient
Once again a frontier AI model has been withdrawn because it fails safety tests (OpenAI scraps release of new model over safety concerns in internal testing, 28 September). There is the usual call for “independent oversight and regulation”, though never any explanation of how this would work or, most importantly, what evidence should persuade a regulator that an AI system is adequately safe.
There is a long engineering tradition of regulating safety-critical software systems, such as those controlling aircraft or nuclear power stations; international standards require “safety cases” – rigorous analyses that show that the possible hazards have been identified and controlled and that the probability of an accident is extremely low.
Continue reading...
© Photograph: Jeff Chiu/AP
© Photograph: Jeff Chiu/AP
© Photograph: Jeff Chiu/AP
Cloudflare said Tuesday it plans to issue quantum-proof TLS certificates, making it one of the first authorities to issue such certificates, which use a form of cryptography that is widely believed to withstand attacks from quantum computers.
The Internet infrastructure provider said it will use an open source platform that issues both classic TLS certificates and a post-quantum equivalent known as Merkle Tree Certificates. The hybrid certificates will be free to both paying and non-paying users. To help build the massive system and establish ubiquity across the sprawling TLS ecosystem, Cloudflare will be acquiring an already trusted certificate root from CA GlobalSign. Cloudflare said the move will let millions of websites use post-quantum certificates at the flip of a switch and without incurring any increased performance overhead.
Cloudflare’s plans are part of a major overhaul in the web public key infrastructure (WebPKI) required to make website encryption and authentication safe for the coming post-quantum age. A major challenge is using quantum-proof signatures that can be easily transmitted during web requests and recorded in transparency logs to ensure counterfeit certificates aren't assigned to websites. The makeover will take years to complete, because it requires the work of an untold number of engineers who design operating systems, browsers, certificate authorities, and Internet infrastructure.
© Getty Images
© Victor Llorente for The New York Times
© Gabriela Bhaskar/The New York Times
© Gabriela Bhaskar/The New York Times
Really interesting story about Harvest, a specialized code breaking computer built in the 1960s by IBM for the NSA.
Plug a phone into a modern charger and the first 10 minutes are impressive. The next 20 are not.
This is not a defect. It’s the connected device protecting itself. As temperature rises during charging, a smartphone’s battery management system reduces the current it will accept, because heat accelerates the chemical degradation that permanently reduces battery capacity. The charger may be capable of delivering more, but the device simply stops taking it.
For anyone building products in the portable power category, this creates an uncomfortable gap between specification and experience. A device rated at 25 watts is accurate in the sense that it can deliver 25 watts. Whether it delivers 25 watts for the duration of a charge is a different question, and one the specification does not answer.
The gap matters commercially because it is invisible at the point of purchase and obvious in use.
Consumers compare wattage figures on packaging. They don’t compare thermal curves, because thermal curves are not published publicly. The result is a category where products differentiate on a number that describes peak output rather than sustained output, and where the actual user experience of two products with identical specifications can diverge substantially.
This is particularly acute in magnetic wireless charging. Inductive power transfer generates heat at both the transmitting and receiving coils, and the magnetic attachment that makes these products convenient also places the heat source in direct contact with the device it is charging. Convenience and thermal performance are working against each other by design.
The industry’s response for the past several years has been materials science. Graphite sheets, thermal interface materials, conductive housings, and heat-spreading layers have all improved how efficiently accumulated heat moves away from the source. Each generation has been incrementally better than the last.
But passive dissipation has a structural limitation: it can only move heat that has already been generated, and only as fast as the surrounding air will accept it. In a sealed, pocket-sized enclosure, that ceiling arrives quickly. Improving the materials slows the rate of temperature rise. It does not prevent the temperature rise.
The alternative is active thermal management, which is standard in stationary electronics and largely absent from portable ones for reasons that are easy to understand. Fans add volume, weight, moving parts, and noise. In a product category defined by portability, each of those is a meaningful cost.
At Anker, which manufactures charging and power products, engineering teams spent the past several development cycles working on whether that tradeoff could be made acceptable rather than eliminated. The approach involves several interacting systems: a micro centrifugal fan, dual airflow channels routed to avoid interference with the magnetic array, a three-layer graphene heat-spreading layer, and a control algorithm that modulates fan speed based on real-time temperature and battery state rather than running at a fixed rate. The result is that the Anker MagGo Power Bank 2 Pro has become the world’s fastest and coolest wireless power bank.
In internal testing, at 77 °F (25 °C) ambient, the back of the power bank stays below 96.8 °F (36 °C) throughout wireless charging, 21.6 °F (12 °C) below the international standard limit of 118.4 °F (48 °C), for a comfortable grip. Comparable magnetic power banks in the same testing typically reached 113 °F (45 °C) or higher within 20 minutes. The functional consequence is that the connected device does not reach the threshold at which it begins reducing charge acceptance, so 25 watts of Qi2.2 magnetic wireless charging is delivered as a working rate rather than an opening rate. In practice, an iPhone 17 Pro reaches 50% charge in 25 minutes. The Anker MagGo Power Bank 2 Pro’s premium performance in both charging speed and thermal management is certified by SGS, an independent testing and certification company.
The same principle applies in reverse. Recharging a power bank generates heat too, which is why devices in this category are often slow to recharge, leaving users with an empty accessory at the moment they need it. Active cooling during input allows the unit to accept 45 watts and reach 80% in 52 minutes.
There is a broader pattern here worth naming, because it is not unique to charging.
When a category improves along a single axis for long enough, the constraint usually migrates somewhere else. Charging spent a decade optimizing power delivery. Power delivery is now, for most practical purposes, solved: the electronics can supply more energy than the receiving device is willing to accept. The binding constraint moved to thermal management, and the industry continued optimizing the axis it had always optimized, because that is the axis the specifications describe.
Recognizing when a constraint has moved is difficult precisely because the old metric keeps improving. Wattage figures have continued to climb. Products have continued to get faster on paper. The measurement stayed valid while quietly ceasing to describe the thing users experience.
For product organizations, the practical question is whether their specifications still measure the constraint or merely measure the capability. The two align until the constraint shifts and specifications rarely shift with it.
A second implication follows from the first. If sustained performance differs meaningfully from peak performance, and if only peak performance is disclosed, then buyers cannot evaluate the products in front of them.
This is one reason Anker is adding displays on charging products. The Anker MagGo Power Bank 2 Pro shows real-time power, temperature, battery level, and estimated time remaining. Some of that is user convenience. But some of it is a Anker stating a deliberate position—this category deserves to have the complete and accurate data made transparent to all.
Anker expects independent reviewers to test these claims and considers our internal numbers to be the correct outcome. The gap between specification and experience closes faster when the experience is measurable. The Anker MagGo Power Bank 2 Pro will be available in the U.S. on September 17, 2026.
This content was produced by Anker. It was not written by MIT Technology Review’s editorial staff.
Throughout the history of the computer chip, engineers have treated waste heat as an inevitable cost of a calculation. Hannah Earley, however, thinks it’s a design choice. Earley, 31, is cofounder and chief technology officer of Vaire Computing, a startup building chips that recycle energy usually thrown away as heat—a strategy known as reversible computing. Ultimately, she thinks, this approach could help make data centers (and our laptops and phones) much more energy efficient.
When conventional computer chips perform calculations, they erase the information they no longer need along the way, dissipating energy as heat in the process. Earley compares the approach to racing through a city only to pump the brakes at every intersection: The car loses momentum and must burn more fuel to accelerate again. Reversible computing aims to keep the momentum going—instead of erasing information from the intermediate steps in a calculation, the circuit retains it, making it possible to run the computation backward and recover some of the energy.
While the idea was first proposed more than 50 years ago, it proved impractical to implement with existing transistors and circuits. Earley, though, has completely rethought the hardware needed to make energy recovery work. She designed a patent-pending type of resonator—a microscopic chip component that stores recovered energy for later reuse. “It’s really a glorified pendulum,” she says. Last year, Vaire announced a key breakthrough: a chip with a resonator that recovered more energy than it lost, even after the energy needed to power the component was taken into account. For a subfield that has existed mostly in theory, the result was proof of life.
“It’s clear they have something interesting,” says Igor Markov, a researcher in electronic design automation and a former professor at the University of Michigan, Ann Arbor. Still, he says, the technology is quite early stage; the company will need “a series of increasingly realistic and convincing demonstrations to attract the industry support needed for commercialization.”
She gradually became convinced that the connection between information, energy, and heat could change computers forever.
Earley’s journey into chip design started sooner than most. She began programming around the age of nine, starting with high-level coding for the web before digging into other programming languages like Perl and Java. She continued progressing to more and more abstract layers of computing, until she got all the way down to transistors.
She eventually enrolled in a PhD program at the University of Cambridge under the computational biologist Gos Micklem. She started out studying how materials such as DNA could be used to perform calculations, but a few months in, Micklem sent her the 1999 PhD thesis of Michael Frank, a pioneer in reversible computing. Earley read it once, felt skeptical, read it again, and sat with it for a few weeks. She gradually became convinced that the connection between information, energy, and heat could change computers forever.
The fascination completely redirected her PhD work. Earley studied the physical limits of computation and built software that could turn ordinary programs into reversible ones. “Eventually I wouldn’t let her put my name on any of her papers, because I felt that I couldn’t really stand up and give a proper talk about them,” Micklem recalls. “It was her stuff.”
After completing her degree in 2021, Earley met Rodolfo Rosini, a technology entrepreneur and investor. The pair cofounded Vaire that same year, and the company has since raised more than $12 million, hired Frank as a senior scientist, and begun turning the vision of reversible computing into real hardware.
Innovation, however, doesn’t happen overnight. During the winter of 2022 in Grinnell, Iowa, Earley spent weeks in her now-wife’s basement apartment as the wind chill outside reached roughly −40 °F, covering a whiteboard over and over again with schematics for the core piece of circuitry needed to make reversible logic work. By the time the design finally came together, after the couple had escaped the cold for Las Vegas, it felt less like an aha moment and more like a gradual wave of relief. “I’m not completely out of my depth,” she remembers feeling.
Earley and her colleagues’ next challenge is making their drastically different chip fit into familiar devices and manufacturing systems. She believes that’s where the future lies—not in further refining existing chips but in rebuilding them from the ground up with an eye toward reversibility. “I want to tackle every part of how computers are built,” Earley says, “and rethink it in these terms.”
Some of the world's leading democracies are pushing governments and companies to start preparing for post-quantum cryptography before quantum computers become powerful enough to break the encryption systems that protect global digital infrastructure today.
In a joint advisory released Thursday, the G7 Cybersecurity Working Group and the U.S. Cybersecurity and Infrastructure Security Agency (CISA) said organizations should begin their transition to post-quantum cryptography now, rather than waiting until cryptographically relevant quantum computers (CRQCs) are available to threat actors.
The publication, titled "Preparing for the Post-Quantum Era: A Call to Action," warns that the quantum computing threat is no longer a distant concern. While the exact timeline for CRQC development remains uncertain, the working group said recent technological advances suggest such machines could emerge sooner than expected, putting widely used public-key cryptography mechanisms at risk.
One of the most immediate dangers is a tactic known as "harvest now, decrypt later," where malicious actors intercept and store encrypted data today with the intention of decrypting it once a CRQC becomes available. This poses a serious risk to governmental records, sensitive personal data, and trade or business secrets that require long-term confidentiality.
The advisory also cautions that CRQCs could eventually be used to target authentication mechanisms, allowing bad actors to impersonate trusted entities, forge data, or compromise equipment. Because supply chain vulnerabilities can cascade, a single organization's delay in adopting post-quantum cryptography could expose entire sectors to compromise.
According to the report, organizations that fail to act may also face business consequences beyond security risk, including exclusion from public procurement contracts and loss of competitive advantage.
The G7 Cybersecurity Working Group outlined five priority areas to guide the global shift toward post-quantum cryptography:
The advisory emphasizes that the shift to post-quantum cryptography cannot be solved by individual organizations in isolation. Instead, it calls for early engagement, coordinated planning, and informed decision-making across public and private sectors worldwide.
Tackling the risks that the impending quantum computing era poses to current cryptographic systems... requires a coordinated global effort to transition to PQC," the report states, adding that public and private organizations must act now to safeguard confidential data, supply chains, and critical systems.
The document was jointly published by cybersecurity authorities from Canada, Germany, Italy, Japan, the United Kingdom, the United States, and France's ANSSI, with participation from the European Commission and support from the EU Agency for Cybersecurity (ENISA).
For years, legacy technology has been a problem companies knew they needed to solve, but one they often struggled to tackle. The cost, complexity, and risk of replacing business-critical systems could make modernization feel like a disruption to manage instead of an opportunity to pursue. But with the rise in customer expectations and the changes AI brought to the economics of software development, that calculation is changing. Bupa’s modernization of its My Bupa mobile application offers a case study in what becomes possible when a legacy migration is treated as a business transformation rather than a technology rewrite.
Bupa CIO of health insurance Asifa Sherazi describes the risks of waiting for legacy systems to become an emergency: “The end-of-life technology is a risk that compounds quietly, and then arrives all at once.” For Bupa, moving its application from Xamarin to native Swift and Kotlin improved the app rating from 3.7 to 4.7, while the user-perceived crash rate fell by nearly 24 percentage points on Android and eight points on iOS. “What they’ll notice is that when they need us, often at a stressful moment, it just simply works,” Sherazi says.
Sanjeev Tripathi, senior vice president and region head of BFSI, healthcare, and public sector for Australia, New Zealand, and Southeast Asia at Infosys, contends that AI is helping change the equation. “The emergence of AI is fundamentally shifting the economics of modernization,” he says, reducing the effort, risk, and time traditionally associated with these programs. At Bupa, combining AI-assisted reverse engineering with forward engineering helped deliver the transformation in approximately 60% less time than would have been possible in the pre-AI era.
Sherazi and Tripathi also highlight the human dimension of modernization: preserving institutional knowledge, giving teams capacity to adapt, and creating an environment where employees can surface problems early.
Looking ahead, both experts see modernized platforms as foundations for more personalized, predictive and AI-driven experiences. The payoff of modernization may be less about replacing aging technology and more about building the flexibility needed for whatever comes next.
“Modern platforms will become the base for far more intelligent AI-driven ecosystems, where AI is not just an add-on, but it is built into everything from design to operations. That’s how the modern platforms will evolve, and the customer experiences will become far more personalized and predictive,” says Tripathi. For Sherazi, that shift is already changing the questions organizations can ask: “It used to be, can our platform support that? Now, it’s: is that the right thing to do for our customers?”
This episode of Business Lab is produced in partnership with Infosys.
Full Transcript:
Megan Tatum: From MIT Technology Review, I’m Megan Tatum, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.
Our topic today is legacy modernization. Companies across industries continue to struggle to bring AI modernization to legacy technology stacks, increasing the risk of losing vendor support, degraded customer experience, and limited capacity for innovation.
Two words for you: future-ready foundation.
My guests are Asifa Sherazi, who is CIO of health insurance at Bupa, and Sanjeev Tripathi, who is senior vice president, region head of BFSI, healthcare, and public sector for Australia, New Zealand, and Southeast Asia at Infosys.
This podcast is produced in association with Infosys.
Welcome, Asifa and Sanjeev.
Asifa Sherazi: Thank you, Megan. Delighted to be here.
Sanjeev Tripathi: Thanks, Megan. Wonderful to be here. And good talking to you again, Asifa.
Megan: Thank you both so much for being here. Asifa, if I could start with you just to set the context for our conversation. There are seven million healthcare customers across the Asia-Pacific. Could you tell us a bit more about Bupa and the challenges it’s faced in its modernization plan?
Asifa: Yes, absolutely. Let me start with something about Bupa that shapes everything we do. We are a global healthcare organization. Think hospitals, clinics, dental, age care, digital health, alongside our insurance business. We reinvest back into the organization, so into our services, our capability, our teams, and our outcomes that we deliver for our customers. In Asia-Pacific, we serve around, as you said, seven million customers across health insurance and health services, individuals, families, corporate clients, patients. Our purpose, helping people live longer, healthier, happier lives is more than just a statement. It actually shapes our strategy, guides our investment decisions, and influences the choices our teams make every day.
Now, for most of our health insurance members, the day-to-day digital relationship with Bupa begins through My Bupa, our self-service platform. It’s the front door for managing cover, updating policy details, submitting claims, checking entitlements. Alongside of it, Blua plays a complimentary role. Where My Bupa helps members manage their cover, Blua helps them manage their health, digital healthcare services, clinical support, preventative health. And together, they let us move beyond transactional interactions towards more personalized, proactive care.
Here was our challenge. Our mobile app, My Bupa, was built on Xamarin, and Microsoft support for it ended sometime in 2024. We had extended support in place, so our customers remained protected throughout, but we were clear-eyed that this was a bridge, not a destination. The runway was finite, and it was shortening for us. We made the decision to modernize from a position of stability proactively for the long-term safety and experiences of our customer rather than waiting until circumstances forced our hand, because the platform carrying our most important customer relationship was in effect standing still while the world around it was moving.
Megan: Right. So you decided to act really proactively in that sense. And so, Asifa, what are some of the risks then of sticking with end-of-life technologies, and how will moving away from legacy technologies help you improve the customer experience?
Asifa: Yeah. Look, the end-of-life technology is a risk that compounds quietly, and then arrives all at once. We saw it in three ways. The first is security and compliance. Once a technology is out of vendor support, the flow of security updates and fixes changes fundamentally. In our case, we put extended support in place as a bridge so our customers stayed protected. But extended support buys you time, it doesn’t buy you a future. In healthcare, we hold some of the most sensitive information a person will ever share with an organization. That isn’t data to us, it’s trust. And trust is extraordinarily expensive to rebuild. We weren’t prepared to run that risk on a shortening runway.
The second is losing control of your own roadmap. How I’d explain that is iOS and Android don’t stand still. Every operating system release, every change to app store requirements becomes something that you react to rather than plan for. As each one slows you down a little further, over time, that opens a widening gap between what customers expect and what you can actually give them. We’re all customers. We don’t benchmark a health insurer against other health insurers. We benchmark against whatever app we used last.
And the third way was, and this is one we’d flag for peers, is the shrinking talent pool. Xamarin is legacy technology, and the engineering expertise available for it is limited. You end up with a critical customer platform supported by a narrowing group of specialists. That’s a workforce risk wearing a technology costume. We partnered with Infosys and we migrated the entire application estate from Xamarin to fully native Swift and Kotlin, and the customer outcomes are why we’re comfortable talking about this today. Our app rating moved from 3.7 to 4.7. Some of the stats that I’d love to share are that the user-perceived crash rate fell by nearly 24 percentage points on Android and eight points on iOS. The Android login success per visit doubled back up to 77%, and that’s one the team that is most proud of, because a login failure isn’t a technical event. It’s a person who wanted to check their cover and they couldn’t.
The team achieved 100% feature parity in a single release, and 90% of our active customer base moved to a new version, and they’ve, I think, downloaded nearly 1.8 million unique downloads. From our customers’ perspective, customers will never think about any of this as a technology change, and they shouldn’t have to either. What they’ll notice is that when they need us, often at a stressful moment, it just simply works, and that’s the outcome we were really after.
Megan: Those are some really striking results and statistics that you’ve shared there on the success of the migration. I mean, Sanjeev, could you talk a bit about why modernizing legacy technologies is so critical at this time, and how Infosys has approached that transformation journey with Bupa?
Sanjeev: Sure, Megan. To be honest, legacy modernization initiatives are not new, and there has always been a strong desire to drive modernization across the entire technology landscape. And Asifa covered all the points that I was going to cover about the risks that have been there. But I’ll reiterate, the reality is the industry has been held back by the cost complexity, and also the risks that have been associated with any legacy modernization initiative that has been undertaken historically.
As Asifa mentioned, customer expectations of what was acceptable five, 10 years back are simply not acceptable anymore. The customers today expect a seamless, intuitive, responsive interaction across every channel. And Asifa also covered the growing challenges around security, resilience, talent availability, and so on. Finding talent on legacy technology is extremely, extremely difficult, and that introduces risk in every organization and in every legacy platform, most of which are actually business-critical platforms as well. Security vulnerabilities are getting increasingly difficult to manage, and as I mentioned, finding deep expertise in older technologies is very, very difficult now.
So what has changed? What has changed is that we now have new tools that are available to us to address these challenges. The emergence of AI is fundamentally shifting the economics of modernization, and it’s doing that by helping organizations to reduce the effort, risk, and also the time that is traditionally taken for programs like these, and that is why we believe that the time is now for legacy modernization. In fact, in Infosys, we have six strategic value pools that we have identified in our AI-first value framework, which is publicly available, and legacy modernization is one of these six value pools. And in the market, we are seeing very strong interest across our client base, and they recognize that the opportunity to unlock both technical and business value is now.
With Bupa in particular, we approached the journey as a business transformation rather than simply a technology rewrite, and our approach had two key phases. One is reverse engineering, and then forward engineering. Let me just quickly cover what these two are.
Reverse engineering, what we did is we extracted and we understood the rules, the processes, and the logic within the legacy environment, and that’s a standard approach we took, we take in any legacy modernization program. What that does is it allows us to preserve the critical business functionality, but at the same time, it avoids the risks that often come with large-scale migration programs.
The second aspect is forward engineering, where we re-architected the solution to enable a reimagined customer experience. The objective is not just a feature-by-feature migration or ensuring feature parity, which is important, but it is even more important that since we’re investing this kind of money to create a modern platform that is scalable, maintainable, and is also capable for future innovation, and that’s what Asifa mentioned about you need to be in control of your own roadmap. You have to build a platform which is capable of supporting future innovation as well. We essentially ensured nothing was lost in translation, and we significantly improved the platform stability and long-term maintainability. And some of the metrics that Asifa mentioned reflects the meaningful improvement in customer experience as well.
Finally, just one more point before I close this question is the time to market. I spoke about the time is now, and because we’ve got the power of the tools that are available now. What AI allowed us is to accelerate the transformation dramatically. What would have traditionally been a long and complex modernization, was delivered in approximately 60% less time than what would have happened in pre-AI era, and that is the real story.
Megan: So AI in this context is a real enabler in terms of the economics and the speed and all of those things you’re talking about. If I could come back to you, Asifa, as much as modernization is a technology challenge, there is the human component as well, and I wondered if you could talk about how you prepared employees for these new technologies, and what challenges and solutions you faced on that front as well?
Asifa: The human side of it is what I’m really passionate about. Technology was only half the challenge. The real work was helping people move from what they knew to what was possible. Modernization isn’t just about replacing systems, it’s about giving teams the confidence, the capability, the clarity to embrace a different future, and that’s what determines whether change actually succeeds.
From that experience, there were three human challenges that stood out for us. The first one was scarcity of expertise on both sides of the transition, and like we said before, Xamarin is a legacy app. We were moving away from a legacy technology, supported by a rapidly shrinking specialist talent pool, and modernizing onto two native platforms. Documentation of that existing environment was limited. Much of the operational knowledge sat in individual experience and in the code base itself, and that created a real dependency on a small number of people. And for the team, it was a confronting reality and a powerful reminder that modernization isn’t just about technology imperative, it’s actually a resilience one.
What changed the dynamic was using AI to do the archeology. As Sanjeev mentioned, Infosys applied AI-assisted reverse engineering to harvest the legacy Xamarin code and extract the flows, the rules, the business logic, and generate native-ready user stories and acceptance criteria from it. The team did a lot of work. They mapped hundreds, I think nearly 1,500 regression scenarios to native epics, and that way, parity critical journeys were preserved by design rather than by memory. The human effect was just as important. Knowledge stopped living with a handful of individuals and became shared across the team, and our people could spend less energy holding institutional memory and more on designing and improving. So that was the first challenge.
The second challenge, Megan, was capacity and not willingness. Our business analysts were fully committed to ongoing feature delivery. And this is a live customer-facing app, and you cannot pause improving the customer experience while you rebuild underneath it. AI-driven discovery and documentation removed almost an estimated of 400 hours of manual BA effort, and that’s not a headcount story, that’s actually people not being asked to do two full-time jobs at once.
The third challenge that stood out was pace. Our original internal estimate was around 18 months, and thanks to Sanjeev and the team, almost like a one-team approach of how do we tackle this, the team delivered it in seven, and that’s exhilarating.
t’s also demanding, and both things need saying out loud. We mobilized cross-functional squads across engineering, architecture, testing, release, because managing parallel environments while protecting BAU commitments is such an emotional load as well as a logistical one. We leaned in hard alongside the team, being present rather than reporting from a distance, regular check-ins, genuinely listening to concerns, unblocking things quickly so people weren’t sitting waiting on a decision. And Megan, one thing I’ll say is when you’re compressing 18 months to seven, the most useful thing leadership can do is remove the friction in front of someone else and get out of their way.
But the thing that made the biggest difference was surprisingly simple, actually. We built a visual depiction of the transformation journey, and we updated it every month so the team could actually see how far they’ve come. Because when you deepen migration of this scale, it’s really easy to only see what’s left to be done, and being able to look back at the ground that’s already been covered gave people real intent and real momentum. And genuinely, it was exhilarating to watch. Watching the team’s pride became their fuel.
Megan: I love that idea of it being exhilarating, but exhausting. I think that’s a great description.
Asifa: Yeah. And the leadership lesson for all of us was just simpler than any of it. Programs like this have hard weeks, there’s going to be incidents, delay, difficult conversations. And Sanjeev, you and I have had those conversations many times. The job of leadership in those moments is to absorb the ambiguity rather than transmit anxiety, because if people feel safe telling you bad news early, there’s almost nothing you can’t fix. I say this plainly because it’s the truest thing about the whole program. I am so incredibly proud of this team, because what they achieved in seven months, while continuing to serve customers every single day without disruption, was genuinely remarkable. But what I’m really proud of isn’t the speed and it isn’t the engineering, it’s that the team never lost sight of who it was for, so every decision that we were making, and they came back, it just came back to the person at the other end of the app.
Megan: It sounds like you did an incredible job focusing on that people element just as much as the technology, which is so important. And Sanjeev, we’ve heard some of the incredible results Bupa has had with this, but more broadly, I suppose, looking across modernization use cases, where do you find that companies see the most ROI, and what advice do you have for leaders who need to focus on transformation at that legacy level?
Sanjeev: Thanks, Megan. That’s a very important and actually a very good question, because what we see is modernization ROI is sometimes viewed too narrowly through just a technology lens, and as Asifa mentioned, it is broader than just technology. Legacy modernization has aspects associated with business implications as well, so I’ll just cover that very quickly.
There are two dimensions, as I mentioned. One is technical ROI or technology-related ROI, and second is business ROI. On the technical side, and as you heard from Asifa as well, the biggest benefits come from faster time to market, platform stability and resilience, of course, and a lot of times, in fact, almost in all the cases, lower operating costs, and that is one of the aspects associated with some of the legacy modernization programs.
Besides this, access to broader, more readily available talent pool, and security management, and ensuring that the platforms are secure and free from, as much as possible, free from vulnerabilities in the current environment. At the same time, making it easier to innovate, releases become faster so that the feature delivery into the market becomes faster, and also able to respond to any new technology innovation that comes into play. But this is only on the technology side.
On the business side, however, the returns are often reflected in customer outcomes, and what we typically see are improvements in measures such as net promoter score, and in this case, for example, application ratings that you see on the app store. Both of which are actually very strong indicators of customer satisfaction and digital experience quality. I think it is important for us to cover, look at the ROI from both technology, but more importantly, from a business perspective.
The second part of your question was about what would be my advice to leaders, and I think Asifa covered it very well, where she mentioned that the one-team approach, the providing safe environment to the team to be able to say what is going well, but also what is not going well, and keeping your eye on the end outcome. I think those are very important things. But I would also like to add that don’t treat modernization as a technology initiative. It is a unique opportunity for us to rethink the business platform itself, and rather than pursuing a like-to-like migration, use the investment that you’re making to improve customer experience, simplify processes, and re-architect for capabilities such as real-time personalization and data-driven decision-making. The greatest returns come when technology transformation is directly linked to business outcomes.
And finally, and this is something that I have seen from personal experience multiple times, is you have to think from first principles. AI does not replace good engineering practices. It enables organizations to execute those practices faster, and it enables it faster, but also more consistently and at greater scale. The foundations of good architecture, sound engineering, and clear business objectives will continue to remain important, and in fact, their importance is going to increase as we progress. That, I think, is what I would say anybody embarking on a legacy modernization program should be focused on.
Megan: I love the idea that this is not just about migration. This a chance, as you say, to reimagine what you can deliver and what’s possible. Fantastic. Let’s close with a forward look. Asifa, what innovation are you looking forward to that wouldn’t have been possible before this, and what do you see on the horizon?
Asifa: There’s definitely lots of things on the horizon. But what excites us most isn’t specific technology, it’s that the question in our conversations has changed. It used to be, can our platform support that? Now, it’s is that the right thing to do for our customers? And that’s a profound shift.
There’s three things, Megan, that genuinely weren’t possible before, and we’ve touched on this a little bit, and Sanjeev’s touched on it as well. This first is speed as a permanent capability. Since launch, Android and iOS, the team has shipped multiple rapid-fire releases, including our migration to a new payment gateway. Builds are now completing four times faster, codes are reaching to testers in about an hour, and we’ve reduced our code base by 30%, and our application footprint’s reduced 18% as well. A simpler estate isn’t an aesthetic preference anymore, it’s what makes the next change cheap, and we’ve bought ourselves optionality to do that.
The second is quality at that speed, which is the part we’ve been most skeptical about five years ago. AI-driven triage and predictive defect analysis, we ran it across nearly 1,400 cases to focus on testing on the highest risk journeys, which meant we went live with zero security defects and zero high severity defects, and Sanjeev mentioned that just before. The old trade-off between moving fast and moving safely is being renegotiated right in front of us, so that’s the second point.
The third one is AI-driven accessibility testing, which the teams treated as a critical rather than an optional opportunity. In healthcare, people who most need to reach us are very often the people whom our poorly designed interface is a genuine barrier, and so being able to test that systematically at scale was a real advance for us as well.
And Megan, you mentioned on the horizon. It’ll be remiss of me not to take the name of agentic AI. The shift from AI that supports a task to AI that completes an outcome end-to-end with proper governance, human oversight at the key decision points. What this program showed us is that the constraint is no longer the models. It’s whether your platforms, data, processes are modern enough to let AI act safely, which is precisely why this work mattered now. Sanjeev alluded to it as well about that underlying architecture. Responsible AI as a source of advantage, not a compliance exercise, I would say. In health, if people don’t trust how you’re using their information, nothing else you build matters. We didn’t modernize to have modern technology. We modernized to earn the right to do the next thing, and to do it in weeks rather than years.
Megan: Absolutely. And now you have those foundations in place, like you say, all of these opportunities open up. Fantastic. And Sanjeev, just finally, as companies complete these migrations, what kinds of innovations and benefits are you seeing, and what do you expect in the next five years?
Sanjeev: Sure. Again, good question, Megan. The reason is there is no uniformity on how these migrations are being done even today. As I mentioned earlier, so where there is simple lift and shift of existing code base onto a new platform, a like-to-like replacement, the benefits generally tend to be limited. Where we apply first principles, thinking about re-imagining, re-architecting, and refactoring the system to establish foundations for a far more flexible system, where rules are not boxed into the architecture, but are managed in a way that changes can be incorporated faster, personalization can be achieved in real time, and time to market improves multifold. That’s where we are really seeing far more benefits coming through.
And to take the example of Bupa, it is a pretty clear step change, both in terms of customer experience and how fast teams can actually deliver now. I think the app ratings have gone up significantly. The customer experience has improved. Even simple things, such as login success rate, has improved quite significantly. And on the engineering side, we are now building and releasing features roughly about four times faster, and we are also seeing the migration of nearly 100% of the customers onto the new platform.
Now, if I look ahead over the next, you mentioned about next five years, I don’t know about five years, four years, but over the next few years at least, I think it is going to get very interesting, because modern platforms will become the base for far more intelligent AI-driven ecosystems, where AI is not just an add-on, but it is built into everything from design to operations. That’s how the modern platforms will evolve, and the customer experiences will become far more personalized and predictive. And even the way we build software will shift, with AI playing a much bigger role in the development process itself.
Megan: Fantastic. Really exciting changes on the horizon then. Thank you both so much.
That was Asifa Sherazi, who is the CIO of health insurance at Bupa, and Sanjeev Tripathi, senior vice president, region head of BFSI, healthcare, and public Sector for Australia, New Zealand, and Southeast Asia at Infosys, whom I spoke with from Brighton in England.
That’s it for this episode of Business Lab. I’m your host, Megan Tatum. I’m a contributing editor at Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you can find us in print, on the web, and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.
This show is available wherever you get your podcasts. And if you enjoyed this episode, we hope you’ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review, and this episode was produced by Giro Studios. Thank you so much for listening.
This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.
© Lucia Vazquez for The New York Times
© Brian Kaiser/Bloomberg
Quantum computing has alternated between breakthrough darling and overhyped promise in technology circles. Its powerful new capabilities come with a threat to break current cryptography, but for business leaders navigating the noise, the signal should be clear: post-quantum cryptography (PQC) is a manageable evolution, not a crisis.
The mathematics behind today’s encrypted digital transactions may yield to quantum computers one day, but the transition to quantum-resistant algorithms is neither sudden nor insurmountable. For executives concerned about disruption, cost, or complexity, a structured and phased approach exists with trusted technology partners like Intel that are already beginning to deliver the infrastructure to make it possible.
The “quantum threat” narrative often swings between two extremes: imminent catastrophe or distant irrelevance. The reality occupies a more pragmatic middle ground. Quantum computers are highly specialized accelerators that exploit quantum physics to solve specific hard problems. They have the potential to crack modern encryption, but they will not replace classic servers overnight, nor will they instantly break every encryption protocol on the internet. What they will do is gradually shift the security landscape, much as previous cryptographic transitions have done over the past three decades.
In late 2024, the Global Risk Institute, a Toronto-based financial services think tank, surveyed 32 quantum computing experts on when a quantum computer could break a 2048-bit RSA key within 24 hours. An average of optimistic and pessimistic estimates from the experts gave it an even 50-50 probability of reaching this code-breaking milestone by 2040. This timeline, uncertain but measurable, creates space for deliberate planning rather than emergency reaction. The near-term focus should be on “harvest now, decrypt later” scenarios, where adversaries collect encrypted data today and then hold it for future decryption later when that capability becomes possible. This is particularly applicable for information requiring confidentiality beyond 10 years.
For most enterprises, this can be a manageable risk when addressed through methodical modernization.
The U.S. government has issued new directives for National Security Systems (NSS), which would likely be first on the list for potential quantum attack. Beginning January 2027, CNSSP-15 states new NSS acquisitions must be capable of supporting Commercial National Security Algorithm Suite 2.0 (CNSA 2.0) requirements for PQC algorithms standardized by the National Institute of Standards and Technology (NIST) and selected by the National Security Agency, the U.S. intelligence agency responsible for signals intelligence and information assurance. Implementation for new systems (with certain exceptions) is then required by 2031, with 100% adoption targeted by 2035.
For commercial enterprises, these timelines are not mandates, but could be signposts. They indicate where vendors, standards bodies, and auditors are headed, providing a reference architecture for responsible stewardship. Organizations can borrow this discipline without necessarily copying the exact timelines, using government guidance to calibrate their own risk tolerance and investment cadence.
Intel is at the heart of the AI revolution by delivering quantum-resistant capabilities across our product portfolio. This is not just aspirational roadmap language; it is starting to be shipping technology.
For instance, the Intel Xeon 6 Processor already incorporates quantum-safe memory encryption (AES-256) and microcode signing to protect processor integrity. Upcoming platforms will extend post-quantum algorithms to more firmware and software signing, device interconnects, attestations, and secure boot functions, aligning with the most stringent government and industry directives.
Post-quantum algorithms carry different key sizes and computational overhead than legacy methods. Intel addresses this through dedicated cryptographic accelerators, optimized libraries, and specialized CPU instructions that reduce latency and preserve service-level agreements. Technologies such as Intel QuickAssist Technology offload cryptographic workloads, enabling enterprises to adopt stronger algorithms without sacrificing performance.
PQC is not a processor-alone problem. System builders and application owners must take a comprehensive view spanning solid-state drives, network interface cards, operating systems, hypervisors, applications, and connected services. Intel is delivering its pieces of the stack, while collaborating with ecosystem partners to ensure interoperability and smooth transition paths.
A more in-depth discussion of post-quantum algorithms and attacks can be found in my recent blog posted on Intel’s Community forum: “Post-Quantum Crypto: Panic Like It’s 1999?“
The path forward does not require upheaval, just discipline. Organizations can follow a phased approach that mirrors patterns emerging in government and critical infrastructure sectors:
Quantum computing will reshape cryptography, but despite what occasional click-bait headlines say, it will not upend business overnight. The transition to post-quantum algorithms is a measured, multi-year journey, one that organizations can navigate with confidence by partnering with capable technology providers, prioritizing long-lived data, and designing for agility. Leaders who approach this as an engineering evolution rather than a threat response will not only be ready for whatever timeline quantum delivers; they will emerge with more robust, transparent, and maintainable cryptographic foundations across their platforms.
This content was produced by Intel. It was not written by MIT Technology Review’s editorial staff.
Post-quantum cryptography is now one pip-install away for the entire Python ecosystem. With funding from the Sovereign Tech Agency, we implemented support for ML-KEM, the NIST-standard key-establishment primitive, and ML-DSA, the NIST-standard digital-signature primitive, in pyca/cryptography.
Remember, the reason to do this now is because there’s no emergency. And because you will make your systems crypto agile, which is always a good idea.
Google owes its existence to the open web, but today, its technological “innovations” have much to do with locking users into a “walled garden.” The latest of these is “reCAPTCHA Mobile Verification,” an experimental initiative that will let companies block users if they are running independent, "de-googled" versions of Android. These “indie Android” versions are favored by people who want to protect their privacy and their attention by blocking trackers and ads. Worse, this is just the latest in a line of similarly user-hostile measures.
Long before “agentic AI,” we had the idea that software would act as your agent on the internet. That's why the old-fashioned technical term for a browser is a “user agent.” Your browser acts on your behalf to retrieve information and then show it to you, in the format you choose. It's your agent.
This is a powerful and profound idea. It is because browsers are our “agents” that we expect them to accept our directives, say, by blocking pop-ups, or by turning off autoplay sound, or by blocking commercial surveillance trackers.
Your browser does all that because your browser works for you. The reason your browser can work for you is that the web is an open, standardized technology. In theory, anyone who follows the standards published by the World Wide Web Consortium (W3C) can make a browser, and that web browser can connect to any web server. Browsers and servers are interoperable. It's the same force that means you can put anyone's gas in your gas-tank, or anyone's shoelaces in your shoes, or anyone's milk on your cereal.
But what if manufacturers could dictate those choices to you? What if your light socket refused to use a lightbulb unless it was officially blessed by the socket's manufacturer? What if your dishwasher refused to wash your dishes unless you bought them from one of the manufacturer's “dish partners?” What if your toaster refused to toast “unauthorized bread?”
It's hard to see how a company could win its market with this strategy. After all, if the dishes are really better than the competition's, you'd buy them voluntarily, without any need for law or technology to force the matter. The only reason to make a dishwasher that refuses a rival's dishes is if the manufacturer's own dishes are ugly, expensive, and/or badly made.
But once a company owns the market—once they've achieved dominance by buying out their rivals; by bribing potential competitors to stay out of their lane; and by engaging in deceptive conduct to trap key suppliers and customers—they can cement their dominance by blocking interoperability, keeping out rival dishes, milk, gas, lightbulbs, shoelaces and bread, capturing their whole market and squeezing it.
Once a company owns the market, they can cement their dominance by blocking interoperability.
That's what Google has done, and that's what Google wants to do more of Google's commercial behavior has been so unethical, deceptive and abusive that the company just lost three federal antitrust cases. This thrice-convicted monopolist paid Apple—more than $20b/year— to stay out of the search market: It cheated app vendors, ripping them off with sky-high junk fees and onerous conditions that raised prices while lowering the share of your spending that went to the companies whose products you were paying for. It cheated advertisers, rigging the ad market to gouge businesses on ad prices and underinvesting to fight rampant ad-fraud, sucking hundreds of billions out of the productive economy for overpriced ads that no one saw.
Google wasn't always this way. The “don't be evil” company owes its very existence to the open web ecosystem. When the company started to index the web in 1998, it was playing on an open field, where any web server could talk to any “user agent,” even one whose user was a startup like Google, that was making a copy of every page on the server.
For years, Google thrived on the open web, and built open technologies. Android—the mobile operating system that Google bought in 2005 —was presented as an “open” alternative to existing mobile offerings, and as the mobile market collapsed into two companies—Google and Apple—Google always presented Android as the open alternative to Apple's “walled garden.” But there were always ways in which Google's “open” Android wasn't exactly open. The company engaged in illegal “tying” arrangements that forced hardware vendors and carriers to lock out versions of Android that were created by Google's competitors.
In other words, even though Google offered a mobile platform that was (mostly) technically open, it found other ways to try to choke off the market oxygen for alternative Android versions that tried to capitalize on that technical openness.
But life finds a way. The existence of an open, modifiable, tinkerer-friendly mobile operating system meant Android hackers could create alternatives to Google's (de facto) walled garden, which thrived in the cracks in that garden wall. Operating systems like CalyxOS, PureOS and Graphene offered a more private, more secure Android experience, one that was largely “de-Googled,” blocking Google's relentless acquisition of your private data.
And Google's data-hunger is relentless. Android exfiltrates a chunk of your personal and behavioral data every five minutes. The “resting heartbeat” of Android surveillance pulses and pulses, irrespective of whether you're using your device, and the instant you unlock your screen, that heartbeat quickens, sending even more data to the company. All that data has proven irresistible to authoritarian governments. Donald Trump's enforcers have seized on Google data as a vital source of information about the identity of protesters and the location of migrants hunted by ICE.
So there are plenty of reasons why users would seek out these de-Googled alternatives to Android, finding them in spite of Google's efforts to block access to competing technologies. The worse it got, the better those alternatives looked.
Perhaps this explains Google's years-long effort to increase the technical barriers to using modified versions of Android, beefing these up to match the commercial restrictions that stand in the way of a de-Googled existence.
Back in 2023, Google floated the idea of “Web Environment Integrity” (WEI), a set of modifications to web standards that would force your computer to disclose its operating environment to the web servers it connected to, even if you objected to this disclosure.
WEI was a form of “remote attestation.” That's when your device uses a sub-processor (sometimes called a “Technical Protection Module” or “TPM”) or a walled off part of its main processor (sometimes called a “secure enclave”) to produce a cryptographically signed description of your device and its configuration: which hardware, software, plug-ins, and settings you're running.
Take away our ability to block obnoxious digital content and you guarantee that we will be flooded with it.
When you connect to a server, it demands that your device send this “attestation” before it handles your request. If your device won't provide this data, or if the server doesn't like (or recognize) your device and its details, it can refuse to deal with you. And because the attestation is prepared by a TPM or a secure enclave that you can't modify or override, you don't get to decide which facts about your device it's allowed to see.
Practically speaking, this means that remote attestation lets a server refuse to deal with you until you turn off your ad-blocker and your tracker-blocker. It means that the server can discriminate against users who block auto-play sound and video, who block pop-ups, who put the tab in the background when it's playing a mandatory pre-roll ad.
WEI was especially disturbing in light of Google's plan to kill ad-blockers and privacy blockers through updates to Chrome, an effort that continues to this day.
These blockers are an important part of the dynamic between web publishers and their users. In the real world, when you get an offer, you can make a counter-offer. That's all an ad-blocker is: a way for users to respond to a server whose opening bid is, “How about you give me all your data and let me take over your computer in exchange for showing you this page?” with “How about 'Nah?'”
We didn't get rid of pop-up ads by making them illegal, or by boycotting advertisers who used them. We got rid of pop-up ads when web users installed pop-up blockers, which made pop-up ads pointless. Take away our ability to block obnoxious digital content and you guarantee that we will be flooded with it.
These kinds of modifications aren't just used to block ads—they're also key to accessibility. People who have photosensitive epilepsy or suffer from low-contrast vision problems use add-ons to reformat pages so they can safely and legibly access them.
WEI's creators said they were only trying to put the web on a level playing field with apps, which routinely disclose facts about your device to the companies whose servers you connect to, without asking you, and even if you don’t want them to. Apps are a source of bottomless enshittification, not least because (unlike the web), they enjoy special, dangerous legal protections that make it very legally risky to modify them. WEI wasn't an effort to level the playing field between apps and the web—it was a race to the bottom, an attempt to make the web as enshittification-friendly as apps.
Public outrage to WEI killed the project, but Google's commitment to augmenting its illegal commercial lockdown efforts with technical lockdowns never ended. Now, Google has rolled out an experimental “reCAPTCHA Mobile Verification” that uses an app, your camera, and your device's TPM or secure enclave to produce an attestation about your Android device.
This will make it much easier for the apps and other services you interact with to block your device if you run an Android alternative, or if you install a mod that overrides the actions of Google's stock Android.
This is a terrible idea—it's every bit as bad as WEI was. In an age in which Big Tech is ever-more tied to authoritarian governments, redesigning our devices to tell strangers things we don't want them to know isn't just shortsighted, it's inexcusable.
Login