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Inventors of Quantum Cryptography Win Turing Award

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/03/inventors-of-quantum-cryptography-win-turing-award.html

Charles Bennett and Gilles Brassard have won the 2026 Turing Award for inventing quantum cryptography.

I am incredibly pleased to see them get this recognition. I have always thought the technology to be fantastic, even though I think it’s largely unnecessary. I wrote up my thoughts back in 2008, in an <a href+https://www.schneier.com/essays/archives/2008/10/quantum_cryptography.html”>essay titled “Quantum Cryptography: As Awesome As It Is Pointless.”

Back then, I wrote:

While I like the science of quantum cryptography—my undergraduate degree was in physics—I don’t see any commercial value in it. I don’t believe it solves any security problem that needs solving. I don’t believe that it’s worth paying for, and I can’t imagine anyone but a few technophiles buying and deploying it. Systems that use it don’t magically become unbreakable, because the quantum part doesn’t address the weak points of the system.

Security is a chain; it’s as strong as the weakest link. Mathematical cryptography, as bad as it sometimes is, is the strongest link in most security chains. Our symmetric and public-key algorithms are pretty good, even though they’re not based on much rigorous mathematical theory. The real problems are elsewhere: computer security, network security, user interface and so on.

Cryptography is the one area of security that we can get right. We already have good encryption algorithms, good authentication algorithms and good key-agreement protocols. Maybe quantum cryptography can make that link stronger, but why would anyone bother? There are far more serious security problems to worry about, and it makes much more sense to spend effort securing those.

As I’ve often said, it’s like defending yourself against an approaching attacker by putting a huge stake in the ground. It’s useless to argue about whether the stake should be 50 feet tall or 100 feet tall, because either way, the attacker is going to go around it. Even quantum cryptography doesn’t “solve” all of cryptography: The keys are exchanged with photons, but a conventional mathematical algorithm takes over for the actual encryption.

What about quantum computation? I’m not worried; the math is ahead of the physics. Reports of progress in that area are overblown. And if there’s a security crisis because of a quantum computation breakthrough, it’s because our systems aren’t crypto-agile.

Apple’s Camera Indicator Lights

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/03/apples-camera-indicator-lights.html

A thoughtful review of Apple’s system to alert users that the camera is on. It’s really well-designed, and important in a world where malware could surreptitiously start recording.

The reason it’s tempting to think that a dedicated camera indicator light is more secure than an on-display indicator is the fact that hardware is generally more secure than software, because it’s harder to tamper with. With hardware, a dedicated hardware indicator light can be connected to the camera hardware such that if the camera is accessed, the light must turn on, with no way for software running on the device, no matter its privileges, to change that. With an indicator light that is rendered on the display, it’s not foolish to worry that malicious software, with sufficient privileges, could draw over the pixels on the display where the camera indicator is rendered, disguising that the camera is in use.

If this were implemented simplistically, that concern would be completely valid. But Apple’s implementation of this is far from simplistic.

As the US Midterms Approach, AI Is Going to Emerge as a Key Issue Concerning Voters

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/03/as-the-us-midterms-approach-ai-is-going-to-emerge-as-a-key-issue-concerning-voters.html

In December, the Trump administration signed an executive order that neutered states’ ability to regulate AI by ordering his administration to both sue and withhold funds from states that try to do so. This action pointedly supported industry lobbyists keen to avoid any constraints and consequences on their deployment of AI, while undermining the efforts of consumers, advocates, and industry associations concerned about AI’s harms who have spent years pushing for state regulation.

Trump’s actions have clarified the ideological alignments around AI within America’s electoral factions. They set down lines on a new playing field for the midterm elections, prompting members of his party, the opposition, and all of us to consider where we stand in the debate over how and where to let AI transform our lives.

In a May 2025 survey of likely voters nationwide, more than 70% favored state and federal regulators having a hand in AI policy. A December 2025 poll by Navigator Research found similar results, with a massive net +48% favorability for more AI regulation. Yet despite the overwhelming preference of both voters and his party’s elected leaders—Congress was essentially unanimous in defeating a previous state AI regulation moratorium—Trump has delivered on a key priority of the industry. The order explicitly challenges the will of voters across blue and red states, from California to South Dakota, scrambling political positions around the technology and setting up a new ideological battleground in the upcoming race for Congress.

There are a number of ways that candidates and parties may try to capitalize on this emerging wedge issue before the midterms.

In 2025, much of the popular debate around AI was cast in terms of humans versus machines. Advances in AI and the companies it is associated with, it is said, come at the expense of humans. A new model release with greater capabilities for writing, teaching, or coding means more people in those disciplines losing their jobs.

This is a humanist debate. Making us talk to an AI customer-support agent is an affront to our dignity. Using AI to help generate media sacrifices authenticity. AI chatbots that persuade and manipulate assault our liberty. There is philosophical merit to these arguments, and yet they seem to have limited political salience.

Populism versus institutionalism is a better way to frame this debate in the context of US politics. The MAGA movement is widely understood to be a realignment of American party politics to ally the Republican party with populism, and the Democratic party with defenders of traditional institutions of American government and their democratic norms.

This frame is shattered by Trump’s AI order, which unabashedly serves economic elites at the expense of populist consumer protections. It is part of an ongoing courting process between MAGA and big tech, where the Trump political project sacrifices the interests of consumers and its populist credentials as it cozies up to tech moguls.

We are starting to see populist resistance to this government/big tech alignment emerge on the local scale. People in Maryland, Arizona, North Carolina, Michigan and many other states are vigorously opposing AI datacenters in their communities, based on environmental and energy-affordability impacts. These centers of opposition are politically diverse; both progressives and Trump-supporting voters are turning out in force, influencing their local elected officials to resist datacenter development.

This opposition to the physical infrastructure of corporate AI is so far staying local, but it may yet translate into a national and politically aligned movement that could divide the MAGA coalition.

Any policy discussions about AI should include the individual harms associated with job loss, as employers seek to replace laborers with machines. It should also include the systemic economic risks associated with concentrated and supercharged AI investment, the democratic risks associated with the increased power in monopolistic and politically influential tech companies, and the degradation of civic functions like journalism and education by AI. In order for our free market to function in the public interest, the companies amassing wealth and profiting from AI must be forced to take ownership of, and internalize, these costs.

The political salience of AI will grow to meet the staggering scale of financial investment and societal impact it is already commanding. There is an opportunity for enterprising candidates, of either political party, to take the mantle of opposing AI-linked harms in the midterm elections.

Political solutions start with organizing, and broadening the base of political engagement around these issues beyond the locally salient topic of datacenters. Movement leaders and elected officials in states that have taken action on AI regulation should mobilize around the blatant industry capture, wealth extraction, and corporate favoritism reflected in the Trump executive order. AI is no longer just a policy issue for governments to discuss: it is a political issue that voters must decide on and demand accountability on.

Sen. Wyden Warns of Another Section 702 Abuse

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/03/sen-wyden-warns-of-another-section-702-abuse.html

Sen. Ron Wyden is warning us of an abuse of Section 702:

Wyden took to the Senate floor to deliver a lengthy speech, ostensibly about the since approved (with support of many Democrats) nomination of Joshua Rudd to lead the NSA. Wyden was protesting that nomination, but in the context of Rudd being unwilling to agree to basic constitutional limitations on NSA surveillance. But that’s just a jumping off point ahead of Section 702’s upcoming reauthorization deadline. Buried in the speech is a passage that should set off every alarm bell:

There’s another example of secret law related to Section 702, one that directly affects the privacy rights of Americans. For years, I have asked various administrations to declassify this matter. Thus far they have all refused, although I am still waiting for a response from DNI Gabbard. I strongly believe that this matter can and should be declassified and that Congress needs to debate it openly before Section 702 is reauthorized. In fact, when it is eventually declassified, the American people will be stunned that it took so long and that Congress has been debating this authority with insufficient information.

Over the decades, we have learned to take Wyden’s warnings seriously.

Team Mirai and Democracy

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/03/team-mirai-and-democracy.html

Japan’s election last month and the rise of the country’s newest and most innovative political party, Team Mirai, illustrates the viability of a different way to do politics.

In this model, technology is used to make democratic processes stronger, instead of undermining them. It is harnessed to root out corruption, instead of serving as a cash cow for campaign donations.

Imagine an election where every voter has the opportunity to opine directly to politicians on precisely the issues they care about. They’re not expected to spend hours becoming policy experts. Instead, an AI Interviewer walks them through the subject, answering their questions, interrogating their experience, even challenging their thinking.

Voters get immediate feedback on how their individual point of view matches—or doesn’t—a party’s platform, and they can see whether and how the party adopts their feedback. This isn’t like an opinion poll that politicians use for calculating short-term electoral tactics. It’s a deliberative reasoning process that scales, engaging voters in defining policy and helping candidates to listen deeply to their constituents.

This is happening today in Japan. Constituents have spent about eight thousand hours engaging with Mirai’s AI Interviewer since 2025. The party’s gamified volunteer mobilization app, Action Board, captured about 100,000 organizer actions per day in the runup to last week’s election.

It’s how Team Mirai, which translates to ‘The Future Party,’ does politics. Its founder, Takahiro Anno, first ran for local office in 2024 as a 33 year old software engineer standing for Governor of Tokyo. He came in fifth out of 56 candidates, winning more than 150,000 votes as an unaffiliated political outsider. He won attention by taking a distinctive stance on the role of technology in democracy and using AI aggressively in voter engagement.

Last year, Anno ran again, this time for the Upper Chamber of the national legislature—the Diet—and won. Now the head of a new national party, Anno found himself with a platform for making his vision of a new way of doing politics a reality.

In this recent House of Representatives election, Team Mirai shot up to win nearly four million votes. In the lower chamber’s proportional representation system, that was good enough for eleven total seats—the party’s first ever representation in the Japanese House—and nearly three times what it achieved in last year’s Upper Chamber election.

Anno’s party stood for election without aligning itself on the traditional axes of left and right. Instead, Team Mirai, heavily associated with young, urban voters, sought to unite across the ideological spectrum by taking a radical position on a different axis: the status quo and the future. Anno told us that Team Mirai believes it can triple its representation in the Diet after the next elections in each chamber, an ostentatious goal that seems achievable given their rapid rise over the past year.

In the American context, the idea of a small party unifying voters across left and right sounds like a pipe dream. But there is evidence it worked in Japan. Team Mirai won an impressive 11% of proportional representation votes from unaffiliated voters, nearly twice the share of the larger electorate. The centerpiece of the party’s policy platform is not about the traditional hot button issues, it’s about democracy itself, and how it can be enhanced by embracing a futuristic vision of digital democracy.

Anno told us how his party arrived at its manifesto for this month’s elections, and why it looked different from other parties’ in important ways. Team Mirai collected more than 38,000 online questions and more than 6,000 discrete policy suggestions from voters using its AI Policy app, which is advertised as a ‘manifesto that speaks for itself.’

After factoring in all this feedback, Team Mirai maintained a contrarian position on the biggest issue of the election: the sales tax and affordability. Rather than running on a reduction of the national sales tax like the major parties, Team Mirai reviewed dozens of suggestions from the public and ultimately proposed to keep that tax level while providing support to families through a child tax credit and lowering the required contribution for social insurance. Anno described this as another future-facing strategy: less price relief in the short term, but sustained funding for essential programs.

Anno has always intended to build a different kind of party. After receiving roughly $1 million in public funding apportioned to Team Mirai based on its single seat in the Upper Chamber last year, Anno began hiring engineers to enhance his software tools for digital democracy.

Anno described Team Mirai to us as a ‘utility party;’ basic infrastructure for Japanese democracy that serves the broader polity rather than one faction. Their Gikai (‘assembly’) app illustrates the point. It provides a portal for constituents to research bills, using AI to generate summaries, to describe their impacts, to surfacing media reporting on the issue, and to answer users’ questions. Like all their software, it’s open source and free for anyone, in any party, to use.

After last week’s victory, Team Mirai now has about $5 million in public funding and ambitions to grow the influence of their digital democracy platform. Anno told us Team Mirai has secured an agreement with the LDP, Japan’s dominant ruling party, to begin using Team Mirai’s Gikai and corruption-fighting Mirumae financial transparency tool.

AI is the issue driving the most societal and economic change we will encounter in our lifetime, yet US political parties are largely silent. But AI and Big Tech companies and their owners are ramping up their political spending to influence the parties. To the extent that AI has shown up in our politics, it seems to be limited to the question of where to site the next generation of data centers and how to channel populist backlash to big tech.

Those are causes worthy of political organizing, but very few US politicians are leveraging the technology for public listening or other pro-democratic purposes. With the midterms still nine months away and with innovators like Team Mirai making products in the open for anyone to use, there is still plenty of time for an American politician to demonstrate what a new politics could look like.

This essay was written with Nathan E. Sanders, and originally appeared in Tech Policy Press.

Microsoft Xbox One Hacked

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/03/microsoft-xbox-hacked.html

It’s an impressive feat, over a decade after the box was released:

Since reset glitching wasn’t possible, Gaasedelen thought some voltage glitching could do the trick. So, instead of tinkering with the system rest pin(s) the hacker targeted the momentary collapse of the CPU voltage rail. This was quite a feat, as Gaasedelen couldn’t ‘see’ into the Xbox One, so had to develop new hardware introspection tools.

Eventually, the Bliss exploit was formulated, where two precise voltage glitches were made to land in succession. One skipped the loop where the ARM Cortex memory protection was setup. Then the Memcpy operation was targeted during the header read, allowing him to jump to the attacker-controlled data.

As a hardware attack against the boot ROM in silicon, Gaasedelen says the attack in unpatchable. Thus it is a complete compromise of the console allowing for loading unsigned code at every level, including the Hypervisor and OS. Moreover, Bliss allows access to the security processor so games, firmware, and so on can be decrypted.

Proton Mail Shared User Information with the Police

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/03/proton-mail-shared-user-information-with-the-police.html

404 Media has a story about Proton Mail giving subscriber data to the Swiss government, who passed the information to the FBI.

It’s metadata—payment information related to a particular account—but still important knowledge. This sort of thing happens, even to privacy-centric companies like Proton Mail.

Neoclouds Are Winning on Compute. Storage Shouldn’t Slow Them Down.

Post Syndicated from David Johnson original https://www.backblaze.com/blog/neoclouds-are-winning-on-compute-storage-shouldnt-slow-them-down/

A decorative image showing servers, the cloud, and drives.

Neoclouds are having a moment.

As demand for AI infrastructure keeps climbing, a new wave of providers is proving there’s real appetite for something other than the traditional hyperscaler model. They’re moving fast, specializing deeply, and building strong businesses around the layers that matter most to their customers: GPU access, high-performance compute, AI services, and developer experience.

That momentum is real, as is the next bottleneck. For many neoclouds, the challenge is no longer just how to deliver more compute. It’s how to deliver a more complete platform without taking on all the complexity of becoming a full-stack cloud provider. And that usually brings teams to the same question: Sshould we build our own storage layer?

Key points: Why should neoclouds care about specialized storage?

  • Neoclouds are capturing a major market opportunity by specializing in compute, AI, and high-performance infrastructure instead of trying to replicate the hyperscaler model. But without an independent, S3 compatible storage layer, many providers run into a split-stack problem: compute lives on the neocloud while data stays in a major cloud, bringing egress fees, friction, and architectural sprawl.
  • Teams that decide to build storage themselves often underestimate what that really means. Whether the path is open-source software like Ceph or purpose-built hardware, the result is often the same: Slower execution, more operational burden, and less focus on the product that actually differentiates the business.
  • The stronger strategy is to treat storage as a specialized tech stack layer and intentionally partner to solve the need, so internal teams can stay focused on compute, AI services, and customer experience.
  • Backblaze gives neoclouds an S3 compatible object storage backbone that can be integrated quickly, scaled immediately, and delivered without the overhead of building and operating storage from scratch.

The real neocloud opportunity is specialization

The shift toward neoclouds is really a shift toward specialization.

For years, the default assumption in cloud infrastructure was that the winning model looked like a hyperscaler: Build the entire stack, own every layer, and expand horizontally into as many services as possible. That model produced scale, but it also produced operational sprawl, complexity, and costs that many customers are increasingly motivated to avoid.

Neoclouds are succeeding because they’re taking the opposite path. Instead of trying to be everything to everyone, they’re building best-of-breed platforms around targeted workloads and high-value services. That’s especially true in AI, where performance, cost control, and speed matter more than a long menu of loosely related products.

But the closer a neocloud gets to becoming a full platform, the more pressure it faces to solve for storage.

The split-stack problem gets expensive

Without integrated object storage, customers often end up in a split-stack architecture. They run compute on a neocloud, but keep their data parked in a major cloud provider, which creates problems quickly.

For example: Large training datasets, model checkpoints, and output artifacts have to move across environments, costs become harder to predict, egress charges start to shape architecture decisions, and performance can suffer when storage and compute are no longer designed to work together.

At that point, storage becomes a core requirement for offering a platform that feels complete, efficient, and economically viable.

So teams ask the obvious question: should we build it ourselves?

Building storage usually means building a second company inside your company

This is where the conversation often gets framed too narrowly.

On paper, the decision can look straightforward: deploy open-source software such as Ceph, or design purpose-built hardware for tighter control over performance and economics.

In reality, both paths create the same strategic problem. They pull engineering focus away from your core platform and into a long-term storage business you never actually meant to start.

That matters because storage is not just infrastructure. It is an operating discipline. It comes with its own tuning, scaling, durability trade-offs, support burden, procurement risk, migration complexity, and day-two operational entropy.

Once you build it, you own all of it.

The software trap: Ceph is open source, not low overhead

Ceph is often the default option for teams exploring S3 compatible storage because it appears flexible, proven, and relatively accessible on commodity hardware.

And to be clear, Ceph can be powerful. But there’s a big difference between deploying Ceph and running it well at scale.

In production, Ceph demands specialized expertise. Teams have to manage CRUSH maps, OSD tuning, replication behavior, rebalancing events, and the network impact that comes with those changes. Those are not occasional tasks. They are part of the ongoing operational load.

That burden grows as environments get larger and more performance-sensitive.

For AI and high-performance compute use cases, generic Ceph deployments can also become throughput bottlenecks. When storage ceilings start constraining training jobs or data-intensive workflows, the problem is no longer confined to the storage team. It starts affecting the value of your core compute offering.

And migration is rarely simple. Because data is distributed across the cluster in ways that are optimized for internal resilience, moving out of a Ceph environment can become a resource-heavy extraction exercise that introduces risk to live workloads.

So while Ceph may reduce license costs up front, it can create a much more expensive operational reality over time.

The hardware trap: more control, more rigidity

For some neocloud teams, custom storage hardware feels like the more strategic answer.

The logic is easy to understand: if storage is critical, why not optimize the hardware and software stack together and get more predictable performance?

The issue is that custom storage hardware rarely stays clean and predictable for long.

Supply chains change. Drive capacities shift. Components become harder to source consistently. Architectures designed around one hardware profile suddenly have to absorb another. This dynamic can leave teams paying for density they can’t fully use or reworking systems to accommodate equipment that wasn’t part of the original plan.

Durability management adds another layer of complexity. As systems age, parity strategies and erasure coding decisions may need to change to maintain reliability. That can reduce usable capacity, increase cost per terabyte, and trigger compute-intensive re-encoding processes at exactly the wrong time.

Then there’s the networking layer. At scale, object storage is not just disks and nodes. It also depends on a traffic management architecture capable of handling massive ingress and egress flows without introducing opaque failure points. Whether you build around open source components or buy expensive hardware appliances, you’re signing up for another category of highly specialized infrastructure work.

And all of that comes with a capital model that is harder to unwind. Hardware investments lock teams into depreciation cycles and planning assumptions that may not match where the market is headed next.

The strategic shift: own differentiation, not every layer

The most important shift here is not technical. It’s organizational.

At a certain point, the storage question becomes a question of where your best people should spend their time.

Should your engineers be tuning replication policies, planning hardware refreshes, and troubleshooting storage network behavior?

Or should they be improving the platform features your customers actually choose you for?

For most neoclouds, the answer is clear.

Their advantage comes from how well they deliver compute, how quickly they adapt to AI demand, how smooth their developer experience feels, and how effectively they help customers run modern workloads. That is where focus compounds. That is where differentiation lives.

Storage matters enormously, but that does not mean it has to be built in-house.

Storage works better as a specialized utility

The neocloud ecosystem works best when providers can connect to open, specialized layers instead of rebuilding the entire stack themselves.

When storage is treated as a utility rather than an internal R&D project, teams can move faster and stay aligned with what the business actually needs. They avoid procurement cycles, reduce operational overhead, and eliminate a category of complexity that would otherwise keep expanding over time.

Equally importantly, they can offer customers a more complete and coherent platform without forcing data to remain trapped in legacy cloud environments.

How Backblaze helps neoclouds move faster

Backblaze gives neoclouds an independent, S3-compatible object storage backbone that can plug into existing compute, AI, and container workflows without requiring a storage buildout from scratch.

That means teams can:

  • Integrate with existing tooling: Use a drop-in, API-compatible storage layer that works with existing workflows, SDKs, CLIs, and infrastructure tools.
  • Reduce operational burden: Offload the complexity of durability engineering, bit-rot protection, fleet management, and storage operations.
  • Avoid punitive egress economics: In Backblaze-powered and colocated partner environments, move data between compute and storage without the cost friction that often comes with major cloud architectures.
  • Scale immediately: Go from terabytes to exabytes without waiting on hardware procurement, deployment schedules, or expansion projects.
  • Keep teams focused: Direct engineering effort toward the product roadmap instead of a second internal storage program.

Backblaze also brings the underlying scale and performance neoclouds need to support modern AI and data-intensive workloads, including up to 1Tbps aggregate throughput, 11 nines of annual durability, a 99.9% uptime SLA, and enterprise security and compliance capabilities.

Build what matters

Neoclouds are winning because they know where to specialize.

That focus is their strength. It is also their opportunity.

The fastest path to a stronger platform is not to recreate every layer of the cloud stack. It is to build the parts that make your business distinct, then connect them to the right partners for the rest.

Storage is too important to ignore, but it is also too easy to underestimate.

If you want to move faster, serve customers better, and keep your roadmap centered on what makes your platform valuable, don’t turn storage into a distraction.

Build what matters. Let Backblaze handle the storage.Interested in learning how Backblaze supports neocloud platforms? Explore B2 Neo or talk with our team about building a more open, AI-ready storage architecture.

The post Neoclouds Are Winning on Compute. Storage Shouldn’t Slow Them Down. appeared first on Backblaze Blog | Cloud Storage & Cloud Backup

South Korean Police Accidentally Post Cryptocurrency Wallet Password

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/03/south-korean-police-accidentally-post-cryptocurrency-wallet-password.html

An expensive mistake:

Someone jumped at the opportunity to steal $4.4 million in crypto assets after South Korea’s National Tax Service exposed publicly the mnemonic recovery phrase of a seized cryptocurrency wallet.

The funds were stored in a Ledger cold wallet seized in law enforcement raids at 124 high-value tax evaders that resulted in confiscating digital assets worth 8.1 billion won (currently approximately $5.6 million).

When announcing the success of the operation, the agency released photos of a Ledger device, a popular hardware wallet for crypto storage and management.

However, the images also showed a handwritten note of the wallet recovery phrase, which serves as the master key that allows restoring the assets to another device.

The authorities failed to redact that info, allowing anyone to transfer into their account the assets in the cold wallet.

Reportedly, shortly after the press release was published, 4 million Pre-Retogeum (PRTG) tokens, worth approximately $4.8 million at the time, were transferred out of the confiscated wallet to a new address.

Possible New Result in Quantum Factorization

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/03/possible-new-result-in-quantum-factorization.html

I’m skeptical about—and not qualified to review—this new result in factorization with a quantum computer, but if it’s true it’s a theoretical improvement in the speed of factoring large numbers with a quantum computer.

Upcoming Speaking Engagements

Post Syndicated from B. Schneier original https://www.schneier.com/blog/archives/2026/03/upcoming-speaking-engagements-54.html

This is a current list of where and when I am scheduled to speak:

The list is maintained on this page.

Friday Squid Blogging: Increased Squid Population in the Falklands

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/03/friday-squid-blogging-increased-squid-population-in-the-falklands.html

Some good news: squid stocks seem to be recovering in the waters off the Falkland Islands.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

Academia and the “AI Brain Drain”

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/03/academia-and-the-ai-brain-drain.html

In 2025, Google, Amazon, Microsoft and Meta collectively spent US$380 billion on building artificial-intelligence tools. That number is expected to surge still higher this year, to $650 billion, to fund the building of physical infrastructure, such as data centers (see go.nature.com/3lzf79q). Moreover, these firms are spending lavishly on one particular segment: top technical talent.

Meta reportedly offered a single AI researcher, who had cofounded a start-up firm focused on training AI agents to use computers, a compensation package of $250 million over four years (see go.nature.com/4qznsq1). Technology firms are also spending billions on “reverse-acquihires”—poaching the star staff members of start-ups without acquiring the companies themselves. Eyeing these generous payouts, technical experts earning more modest salaries might well reconsider their career choices.

Academia is already losing out. Since the launch of ChatGPT in 2022, concerns have grown in academia about an “AI brain drain.” Studies point to a sharp rise in university machine-learning and AI researchers moving to industry roles. A 2025 paper reported that this was especially true for young, highly cited scholars: researchers who were about five years into their careers and whose work ranked among the most cited were 100 times more likely to move to industry the following year than were ten-year veterans whose work received an average number of citations, according to a model based on data from nearly seven million papers.1

This outflow threatens the distinct roles of academic research in the scientific enterprise: innovation driven by curiosity rather than profit, as well as providing independent critique and ethical scrutiny. The fixation of “big tech” firms on skimming the very top talent also risks eroding the idea of science as a collaborative endeavor, in which teams—not individuals—do the most consequential work.

Here, we explore the broader implications for science and suggest alternative visions of the future.

Astronomical salaries for AI talent buy into a legend as old as the software industry: the 10x engineer. This is someone who is supposedly capable of ten times the impact of their peers. Why hire and manage an entire group of scientists or software engineers when one genius—or an AI agent—can outperform them?

That proposition is increasingly attractive to tech firms that are betting that a large number of entry-level and even mid-level engineering jobs will be replaced by AI. It’s no coincidence that Google’s Gemini 3 Pro AI model was launched with boasts of “PhD-level reasoning,” a marketing strategy that is appealing to executives seeking to replace people with AI.

But the lone-genius narrative is increasingly out of step with reality. Research backs up a fundamental truth: science is a team sport. A large-scale study of scientific publishing from 1900 to 2011 found that papers produced by larger collaborations consistently have greater impact than do those of smaller teams, even after accounting for self-citation.2 Analyses of the most highly cited scientists show a similar pattern: their highest-impact works tend to be those papers with many authors.3 A 2020 study of Nobel laureates reinforces this trend, revealing that—much like the wider scientific community—the average size of the teams that they publish with has steadily increased over time as scientific problems increase in scope and complexity.4

From the detection of gravitational waves, which are ripples in space-time caused by massive cosmic events, to CRISPR-based gene editing, a precise method for cutting and modifying DNA, to recent AI breakthroughs in protein-structure prediction, the most consequential advances in modern science have been collective achievements. Although these successes are often associated with prominent individuals—senior scientists, Nobel laureates, patent holders—the work itself was driven by teams ranging from dozens to thousands of people and was built on decades of open science: shared data, methods, software and accumulated insight.

Building strong institutions is a much more effective use of resources than is betting on any single individual. Examples demonstrating this include the LIGO Scientific Collaboration, the global team that first detected gravitational waves; the Broad Institute of MIT and Harvard in Cambridge, Massachusetts, a leading genomics and biomedical-research center behind many CRISPR advances; and even for-profit laboratories such as Google DeepMind in London, which drove advances in protein-structure prediction with its AlphaFold tool. If the aim of the tech giants and other AI firms that are spending lavishly on elite talent is to accelerate scientific progress, the current strategy is misguided.

By contrast, well-designed institutions amplify individual ability, sustain productivity beyond any one person’s career and endure long after any single contributor is gone.

Equally important, effective institutions distribute power in beneficial ways. Rather than vesting decision-making authority in the hands of one person, they have mechanisms for sharing control. Allocation committees decide how resources are used, scientific advisory boards set collective research priorities, and peer review determines which ideas enter the scientific record.

And although the term “innovation by committee” might sound disparaging, such an approach is crucial to make the scientific enterprise act in concert with the diverse needs of the broader public. This is especially true in science, which continues to suffer from pervasive inequalities across gender, race and socio-economic and cultural differences.5

Need for alternative vision

This is why scientists, academics and policymakers should pay more attention to how AI research is organized and led, especially as the technology becomes essential across scientific disciplines. Used well, AI can support a more equitable scientific enterprise by empowering junior researchers who currently have access to few resources.

Instead, some of today’s wealthiest scientific institutions might think that they can deploy the same strategies as the tech industry uses and compete for top talent on financial terms—perhaps by getting funding from the same billionaires who back big tech. Indeed, wage inequality has been steadily growing within academia for decades.6 But this is not a path that science should follow.

The ideal model for science is a broad, diverse ecosystem in which researchers can thrive at every level. Here are three strategies that universities and mission-driven labs should adopt instead of engaging in a compensation arms race.

First, universities and institutions should stay committed to the public interest. An excellent example of this approach can be found in Switzerland, where several institutions are coordinating to build AI as a public good rather than a private asset. Researchers at the Swiss Federal Institute of Technology in Lausanne (EPFL) and the Swiss Federal Institute of Technology (ETH) in Zurich, working with the Swiss National Supercomputing Centre, have built Apertus, a freely available large language model. Unlike the controversially-labelled “open source” models built by commercial labs—such as Meta’s LLaMa, which has been criticized for not complying with the open-source definition (see go.nature.com/3o56zd5)—Apertus is not only open in its source code and its weights (meaning its core parameters), but also in its data and development process. Crucially, Apertus is not designed to compete with “frontier” AI labs pursuing superintelligence at enormous cost and with little regard for data ownership. Instead, it adopts a more modest and sustainable goal: to make AI trustworthy for use in industry and public administration, strictly adhering to data-licensing restrictions and including local European languages.7

Principal investigators (PIs) at other institutions globally should follow this path, aligning public funding agencies and public institutions to produce a more sustainable alternative to corporate AI.

Second, universities should bolster networks of researchers from the undergraduate to senior-professor levels—not only because they make for effective innovation teams, but also because they serve a purpose beyond next quarter’s profits. The scientific enterprise galvanizes its members at all levels to contribute to the same projects, the same journals and the same open, international scientific literature—to perpetuate itself across generations and to distribute its impact throughout society.

Universities should take precisely the opposite hiring strategy to that of the big tech firms. Instead of lavishing top dollar on a select few researchers, they should equitably distribute salaries. They should raise graduate-student stipends and postdoc salaries and limit the growth of pay for high-profile PIs.

Third, universities should show that they can offer more than just financial benefits: they must offer distinctive intellectual and civic rewards. Although money is unquestionably a motivator, researchers also value intellectual freedom and the recognition of their work. Studies show that research roles in industry that allow publication attract talent at salaries roughly 20% lower than comparable positions that prohibit it (see go.nature.com/4cbjxzu).

Beyond the intellectual recognition of publications and citation counts, universities should recognize and reward the production of public goods. The tenure and promotion process at universities should reward academics who supply expertise to local and national governments, who communicate with and engage the public in research, who publish and maintain open-source software for public use and who provide services for non-profit groups.

Furthermore, institutions should demonstrate that they will defend the intellectual freedom of their researchers and shield them from corporate or political interference. In the United States today, we see a striking juxtaposition between big tech firms, which curry favour with the administration of US President Donald Trump to win regulatory and trade benefits, and higher-education institutions, which suffer massive losses of federal funding and threats of investigation and sanction. Unlike big tech firms, universities should invest in enquiry that challenges authority.

We urge leaders of scientific institutions to reject the growing pay inequality rampant in the upper echelons of AI research. Instead, they should compete for talent on a different dimension: the integrity of their missions and the equitableness of their institutions. These institutions should focus on building sustainable organizations with diverse staff members, rather than bestowing a bounty on science’s 1%.

References

  1. Jurowetzki, R., Hain, D. S., Wirtz, K. & Bianchini, S. AI Soc. 40, 4145–4152 (2025).
  2. Larivière, V., Gingras, Y., Sugimoto, C. R. & Tsou, A. J. Assoc. Inf. Sci. Technol. 66, 1323–1332 (2015).
  3. Aksnes, D. W. & Aagaard, K. J. Data Inf. Sci. 6, 41–66 (2021).
  4. Li, J., Yin, Y., Fortunato, S. & Wang, D. J. R. Soc. Interface 17, 20200135 (2020).
  5. Graves, J. L. Jr, Kearney, M., Barabino, G. & Malcom, S. Proc. Natl Acad. Sci. USA 119, e2117831119 (2022).
  6. Lok, C. Nature 537, 471–473 (2016).
  7. Project Apertus. Preprint at arXiv https://doi.org/10.48550/arXiv.2509.14233 (2025).

This essay was written with Nathan E. Sanders, and originally appeared in Nature.

iPhones and iPads Approved for NATO Classified Data

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/03/iphones-and-ipads-approved-for-nato-classified-data.html

Apple announcement:

…iPhone and iPad are the first and only consumer devices in compliance with the information assurance requirements of NATO nations. This enables iPhone and iPad to be used with classified information up to the NATO restricted level without requiring special software or settings—a level of government certification no other consumer mobile device has met.

This is out of the box, no modifications required.

Boing Boing post.

Canada Needs Nationalized, Public AI

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/03/canada-needs-nationalized-public-ai.html

Canada has a choice to make about its artificial intelligence future. The Carney administration is investing $2-billion over five years in its Sovereign AI Compute Strategy. Will any value generated by “sovereign AI” be captured in Canada, making a difference in the lives of Canadians, or is this just a passthrough to investment in American Big Tech?

Forcing the question is OpenAI, the company behind ChatGPT, which has been pushing an “OpenAI for Countries” initiative. It is not the only one eyeing its share of the $2-billion, but it appears to be the most aggressive. OpenAI’s top lobbyist in the region has met with Ottawa officials, including Artificial Intelligence Minister Evan Solomon.

All the while, OpenAI was less than open. The company had flagged the Tumbler Ridge, B.C., shooter’s ChatGPT interactions, which included gun-violence chats. Employees wanted to alert law enforcement but were rebuffed. Maybe there is a discussion to be had about users’ privacy. But even after the shooting, the OpenAI representative who met with the B.C. government said nothing.

When tech billionaires and corporations steer AI development, the resultant AI reflects their interests rather than those of the general public or ordinary consumers. Only after the meeting with the B.C. government did OpenAI alert law enforcement. Had it not been for the Wall Street Journal’s reporting, the public would not have known about this at all.

Moreover, OpenAI for Countries is explicitly described by the company as an initiative “in co-ordination with the U.S. government.” And it’s not just OpenAI: all the AI giants are for-profit American companies, operating in their private interests, and subject to United States law and increasingly bowing to U.S. President Donald Trump. Moving data centres into Canada under a proposal like OpenAI’s doesn’t change that. The current geopolitical reality means Canada should not be dependent on U.S. tech firms for essential services such as cloud computing and AI.

While there are Canadian AI companies, they remain for-profit enterprises, their interests not necessarily aligned with our collective good. The only real alternative is to be bold and invest in a wholly Canadian public AI: an AI model built and funded by Canada for Canadians, as public infrastructure. This would give Canadians access to the myriad of benefits from AI without having to depend on the U.S. or other countries. It would mean Canadian universities and public agencies building and operating AI models optimized not for global scale and corporate profit, but for practical use by Canadians.

Imagine AI embedded into health care, triaging radiology scans, flagging early cancer risks and assisting doctors with paperwork. Imagine an AI tutor trained on provincial curriculums, giving personalized coaching. Imagine systems that analyze job vacancies and sectoral and wage trends, then automatically match job seekers to government programs. Imagine using AI to optimize transit schedules, energy grids and zoning analysis. Imagine court processes, corporate decisions and customer service all sped up by AI.

We are already on our way to having AI become an inextricable part of society. To ensure stability and prosperity for this country, Canadian users and developers must be able to turn to AI models built, controlled, and operated publicly in Canada instead of building on corporate platforms, American or otherwise.

Switzerland has shown this to be possible. With funding from the federal government, a consortium of academic institutions—ETH Zurich, EPFL, and the Swiss National Supercomputing Centre—released the world’s most powerful and fully realized public AI model, Apertus, last September. Apertus leveraged renewable hydropower and existing Swiss scientific computing infrastructure. It also used no illegally pirated copyrighted material or poorly paid labour extracted from the Global South during training. The model’s performance stands at roughly a year or two behind the major corporate offerings, but that is more than adequate for the vast majority of applications. And it’s free for anyone to use and build on.

The significance of Apertus is more than technical. It demonstrates an alternative ownership structure for AI technology, one that allocates both decision-making authority and value to national public institutions rather than foreign corporations. This vision represents precisely the paradigm shift Canada should embrace: AI as public infrastructure, like systems for transportation, water, or electricity, rather than private commodity.

Apertus also demonstrates a far more sustainable economic framework for AI. Switzerland spent a tiny fraction of the billions of dollars that corporate AI labs invest annually, demonstrating that the frequent training runs with astronomical price tags pursued by tech companies are not actually necessary for practical AI development. They focused on making something broadly useful rather than bleeding edge—trying dubiously to create “superintelligence,” as with Silicon Valley—so they created a smaller model at much lower cost. Apertus’s training was at a scale (70 billion parameters) perhaps two orders of magnitude lower than the largest Big Tech offerings.

An ecosystem is now being developed on top of Apertus, using the model as a public good to power chatbots for free consumer use and to provide a development platform for companies prioritizing responsible AI use, and rigorous compliance with laws like the EU AI Act. Instead of routing queries from those users to Big Tech infrastructure, Apertus is deployed to data centres across national AI and computing initiatives of Switzerland, Australia, Germany, and Singapore and other partners.

The case for public AI rests on both democratic principles and practical benefits. Public AI systems can incorporate mechanisms for genuine public input and democratic oversight on critical ethical questions: how to handle copyrighted works in training data, how to mitigate bias, how to distribute access when demand outstrips capacity, and how to license use for sensitive applications like policing or medicine. Or how to handle a situation such as that of the Tumbler Ridge shooter. These decisions will profoundly shape society as AI becomes more pervasive, yet corporate AI makes them in secret.

By contrast, public AI developed by transparent, accountable agencies would allow democratic processes and political oversight to govern how these powerful systems function.

Canada already has many of the building blocks for public AI. The country has world-class AI research institutions, including the Vector Institute, Mila, and CIFAR, which pioneered much of the deep learning revolution. Canada’s $2-billion Sovereign AI Compute Strategy provides substantial funding.

What’s needed now is a reorientation away from viewing this as an opportunity to attract private capital, and toward a fully open public AI model.

This essay was written with Nathan E. Sanders, and originally appeared in The Globe and Mail.

За лекарите и пациентите

Post Syndicated from Григор original http://www.gatchev.info/blog/?p=2683

Това ми го разказа днес на улицата един състудент, сега шеф на болнично отделение. Промених идентифициращите подробности, за опазване на лекарската тайна (а нищо чудно и той да не ми е казал истинските). Ето разказа му, по памет:
—-
Когато ни докараха баба Гана, не ѝ обърнах особено внимание. Дребничка и съсухрена, 92 години, конгестивна кардиомиопатия, вероятно още не повече от месец живот дори с що-годе прилично лечение. Най-близки роднини – внуци на починала вече нейна сестра, някъде из чужбините, може дори да не знаят, че тя съществува. Някаква съседка разбрала, че тя вече няма сили да става от леглото, и звъннала на Бърза помощ.

– Човек не е тук завинаги, сине – каза ми тя, докато я преглеждах. – Пък аз стоях доста. От наборите ми на село един няма жив вече, всичките ме чакат горе. А за какво още да се бавя? Пущайте ме, да си ходя…

Ама работата ни е да спираме тези като нея, не да ги пускаме. Назначих ѝ лечение, отметнах другите грижи за деня… До края на смяната оставаше половин спокоен час. Забих тайно един фас в кабинета, да ми олекне малко, и тръгнах да видя пациентите.

Тя беше в последната стая. Като ме видя, ме изгледа с един пронизителен поглед и се усмихна беззъбо:

– Нещо угрижен ми се видиш, сине. Сподели, па току-виж ти олекне.

– То са си мои проблеми, бабо. Защо да те товаря с тях?

– Сине, аз вече ни да копам мога, ни сено и вода да сипя на добитъка. Ама да изслушам човек още ставам. Да има смисъл някакъв от мене. Па и споделена болка – половин болка. Думай.

Права беше. Пък и наистина имах нужда да споделя.

– Не се разбираме с жената. Уж двайсет и пет години заедно, деца изгледахме, ама някак… вече не сме същите. Изстинахме един към друг, мислим да се развеждаме…

– Че сте изстинали иди го кажи на някой млад, сине, не на мене. Ако си изстинал към нея, ще си намусен ли, че ще се развеждате, или ще се радваш?

Въздъхнах и приседнах до леглото ѝ.

– Така е, бабо Гано. Много хубаво мина между нас, тежи ми да се разделим. Ама някак не се разбираме вече…

– Видиш ли кожухчето ми? Ей там, на закачалката. Донеси ми го, да ти покажа нещо… Видиш ли това тук? И това?

– Ми… кръпки. Това кожухче на колко години е бе, бабо? То е кръпка до кръпка.

– Анджак, сине. Купи ми го Митю, преди вече повече от седемдесе години… Гледай го хубаво. Ще познаеш ли кое от него си е оттогава? Не мож, щото няма такова. И кожата му е подменяна цялата, един път това парче, друг път онова. И копчета съм губила и подменяла, и на джебовете плата… По мойто време не хвърляхме нещата като тръгнат да се късат, сине. Кърпехме ги и те стояха. Нищо старо да не остане, новото и по-добро ставаше. На туй кожата беше преди една тъничка, джиджава, за млада булка. Като почнаха да се трупат лазарниците, и кръпките почнаха да са от по-дебела кожа, по-топла. Вече не бях същата, та че кожухчето се е прокъсало беше добре дошло, и то да се промени с мен. И ми е хубаво, пази ме, другар ми е цял живот…

Изгледах я въпросително – какво искаше да ми каже? Бях карал нощна смяна преди дневната, едвам се държах от умора и не ми просветваше.

– И семейството е като дреха, сине. Няма как да го носиш и да не се протрива и къса. Ама не го ли запуснеш доде се разпадне съвсем, не е за хвъргане, нищо че у магазина има нови и лъскави. Кърпиш го и му слагаш кръпки според трябването. Ще се променяте и двамата, времето и камъка не жали. И че семейството ви се е протрило е добре, ако имате акъл да го закърпите, вместо да го хвърлите. Ще го пригодите по както сте се променили, да ви е сгодно сега. А като остареете още – пак. Доде ви има.

– То ако беше толкова лесно. И аз пробвам, пък ми се струва, че и жената пробва. Ама все нещо не ни се получава.

– Ми иди ѝ купи нещо, сине. Нещо само за нея, дето никой друг файда няма от него. Може да е дребно и евтино, ама без повод да е, ей така. Женско чадо е, хич да не ѝ личи, няма как да не се зарадва. – Тя се усмихна беззъбо. – Утре пак, или направи нещо за нея. Кога ти е трудно и късаш от твоето време, да види тя, че ти е скъпа… Първите пъти ще минават без следа, като шепа вода на съвсем пресъхнала земя. Ама сипваш ли пак и пак и пак, ще дойде време земята да омекне. Щом не сте истински изстинали един към друг, има в тая дреха още хляб, закърпете я. И я направете по каквито сте станали, не като кога сте били млади и щури. Сега не ви е сгодна, щото вие сте вече други. Пробвай…

Няколко дни по-късно пък една от сестрите, също напоследък угрижена и с ядове, направо за една нощ стана друг човек. Какво точно е било – не знам, може на жените да е казала, на мен не е. Ама разбрах, че си е говорила две нощни дежурства с баба Гана.

Още два дни по-късно Стефанов един ден дойде угрижен. Някакъв проблем с детето му, също не разбрах какъв точно, нали уж съм им шеф и пред мен не говорят много. Ама и той седя два часа при баба Гана и си говори с нея, нищо че не ѝ е лекуващият. И като излезе, му се бяха развеяли облаците от физиономията.

Съседката ѝ по стая също се промени. Млада жена, петдесетинагодишна, и проблемът ѝ не е наистина сериозен, ама се беше предала духом. Чудехме се какво да я правим. Ама и тя тръгна нагоре – придоби воля за живот, почна да се усмихва… Ще ми е странно причината да е различна от разговор с баба Гана.

Три седмици по-късно като че ли цялото отделение вече не беше същото. Персоналът ходеше по коридорите усмихнат и винаги готов да помогне с нещо. Бойка чистачката, дето преди се мусеше като ѝ кажеш да измие коридора, сама се хвана и изми прозорците. Болните също придобиха кураж и оптимизъм. То вярно че моите семейни неща като се пооправиха, и аз станах друг човек, и нали съм шефът, то се предава надолу. Пък и сигурно по виждам хубавото, като ми е по-леко на главата. Ама съм сигурен, че не е само от мен.

Та, миналата седмица влиза при мен Минков, той ѝ е лекуващият:

– Шефе, баба Гана нещо почва да потъва. Май ѝ се поизчерпват ресурсите вече. Можем ли да направим нещо?…

Сепнах се. Въпреки тежката диагноза, бях приел баба Гана като някаква даденост. А сега…

– Имаш ли предложение?

Минков ме изгледа малко колебливо, след това изстреля името на едно лекарство. Замислих се и кимнах:

– Не го покрива здравната каса, а е кошмарно скъпо. Но признавам, може би е подходящо за нейния случай… Как мислиш да го осигурим?

– Предлага се като (Минков назова търговската марка и фармацевтичната фирма). Шефе, ти нали имаше някакви връзки с тях? Дали биха отпуснали поне една-две опаковки – пробно, рекламно? Ще им направим каквато реклама е нужна… Или поне да го дадат с отстъпка? Пък аз ще дам колкото мога, и други в отделението ще дадат…

Грабнах телефона. От представителството на фирмата се опитаха да ме отсвирят, но изнахалствах и се свързах със зам-шефа им. Може би го помниш, Кирчо, беше в трети поток. Падна молене и обещания за услуги, но се нави да отпусне пет опаковки на промоционална цена, под половината на официалната на едро. Говорих след това със счетоводството ни, излъгах ги, че става дума за един от нас – пренасочиха тихомълком едни пари, останали от ремонта преди два месеца. Пак не стигаха, ама персоналът на отделението събрахме разликата.

Та, и сега отделението ни е слънчево. Ходим на работа с удоволствие. Май с повече удоволствие от всякога. Имаме си в една от стаите деветдесет и две годишно слънчице. Дето който е влязал при нея, всекиго е намерила как да стопли.

Още не е за нагоре. Лекарството я постегна прилично, а още не сме го свършили. Вече кроя схеми как да ѝ подсигуря още един курс. Няма да е лесно, де. Ама като разказах на жената историята, сама предложи ремонтът на банята да изчака. И тая нощ двамата… абе, сещаш се, за пръв път от сигурно две-три години вече. Май семейството ни ще го бъде.

Другите от персонала също се стегнаха, вече събират пари. Стефанов говорил с някакъв роднина от друго фармацевтично представителство – ако предложим добри условия, ще докарат едни тестове на лекарства да са при нас. Аз днес убеждавах шефа на болницата, да видим ще разреши ли. Успеем ли да ѝ изкараме още един курс, с малко късмет ще може да издържи ендоскопска подмяна на митралната клапа, това ще я позакрепи още малко. Стане ли, ще говоря с Националната кардиология, да видим ще ги убедя ли…

Та, лекуваме я баба Гана… ама май тя повече лекува нас.