n 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.

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iPhones and iPads Approved for NATO Classified Data

[2026.03.12] 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.

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Academia and the "AI Brain Drain"

[2026.03.13] 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
approa
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