[Starlink] Re: Data centers are racing to space — and regulation can’t keep up

Vint Cerf
2026-02-26T13:37:15Z

maybe this has already been addressed but things in orbit disappear over
the horizon. If you rely on store/forward relay to keep in touch with an
inferencing engine while in orbit, you will experience variable latency. I
don't see how you could easily instantiate the same inferencing on the next
data center to come into view. Seems to me this is a very different
computing and communication environment than ground-based data centers on
the terrestrial Internet.

What am I missing?

v

On Thu, Feb 26, 2026 at 6:55 AM Mark Handley via Starlink <
starlink@lists.bufferbloat.net> wrote:

> AI datacenters effectively split into training and inference, and it's
> worth optimizing for one or the other. For training, you want as much
> compute in one low latency cluster as possible. A single GB200 rack (72
> GPUs) is currently around 160kW, and a leading edge training cluster is now
> well north of 100,000 GPUs. A single VR200 rack will be ~250kW later this
> year. For inference, you typically neeed between a few and a few hundred
> GPUs (or equivalents - TPUs, Celebras, etc) interconnected.
>
> It's easy to see inference clusters, especially wafer-scale like Celebras,
> being capable of being put in orbit. But the problem there is low(ish)
> latency to customers is also a requirement, so that constrains the orbits
> you could use. And it's actually not hard to get terrestrial power if you
> can scatter large numbers of smaller inference clusters worldwide, which is
> what we do.
>
> It's really hard to see training clusters in orbit, not only for cooling
> reasons, but also because they have very high failure rates and require a
> lot of human maintenance. In our current supercomputers we're looking at
> more than a million optical links in one building, so there is a continuous
> rate of link failure and replacement. We are continuously replacing
> switches and GPU nodes. Now you can design for resilience, and we are
> already running a new network design that does this. But living on the
> leading edge of what's possible in compute and having low failure rates
> tend to be mutually incompatible. We are working with all our suppliers to
> reduce failure rates in the next-but-one generation by design - we'd love
> that because a non-trivial cause of failure is a technician fixing one
> fault and causing another. So there's a lot of hope to improve things, but
> there's nothing coming down the pipeline that would allow large training
> clusters to have leading edge performance and simultaneously run unmanned.
>
> Mark (currently doing supercomputer networks at OpenAI)
>
> On Thu, 26 Feb 2026, at 4:39 AM, David Lang via Starlink wrote:
> > a couple comments in response (specifically applying to SpaceX
> >
> > they are working to reduce launch costs between 10x and 100x
> >
> > they are not just looking at sun synchronous orbit, but also at
> launching from
> > the moon into a moon-size earth orbit or solar orbit
> >
> > re: chip vulnerability to radiation
> >
> > chips have gotten MUCH smaller over the years, which in part makes it
> more
> > likely for a cell that's hit to flip, but also means that (for a given
> > capability) it's much smaller, so is far less likely to be hit.
> >
> > probability based systems don't require every calculation to be perfect
> >
> > "AI" systems need to be validated anyway (since their behavior can't be
> > predicted), so if there are too many errors, it just will fail validation
> >
> > with enough processing capacity, you can re-run the calculations and
> compare
> > resuts.
> >
> >
> > One thing I haven't seen people talk about is that space-based systems
> > are NOT
> > going to be massive, coherent clusters the way current AI training
> > clusters are.
> > They will be many smaller clusters with relatively low bandwidth/high
> > latency
> > communications between them (you can't send data faster than the speed
> > of
> > light). The first posts about space datacenters were dense, massive
> > things
> > (comparable in size to ground based systems) with solar panels and
> > radiators
> > measured in square miles. Elon and SpaceX are talking about many small
> > satellites in the 100Kw range, similar in size to the starlink
> > satellites that
> > Starship can deploy.
> >
> >
> >
> > I fully expect that new training algorithms will be found that will
> > drastically
> > improve the efficiency, but I also expect that when they are found,
> > those
> > companies with lots of hardware and expertise in running it will be
> > able to make
> > better use of the new algorithms, if only to train more models doing
> > different
> > things at the same time. It still favors those companies that get ahead
> > (and
> > don't collapse in the process)
> >
> > every bubble over-builds infrastructure, as a lot of people who lose
> > their
> > shirts jump on board the new fad without being able to evaluate the
> > companies.
> > But those companies that fail generally get bought out by others,
> > cheap, and the
> > infrastructure that is built gets used by someone else with a more
> > realistic
> > business model. It may take years (see the massive overbuilding of
> > fiber in some
> > areas), but it will eventually be used.
> >
> > I think there is disagreement on if AI is going to 'hockey stick' or
> not, but
> > even if it doesn't, thee are a lot of good uses for the pattern matching
> > capability (just not at today's prices)
> >
> > David Lang
> >
> >
> > On Wed, 25 Feb 2026, Nick Matthews wrote:
> >
> >> Date: Wed, 25 Feb 2026 19:38:52 -0700
> >> From: Nick Matthews <matthnick@gmail.com>
> >> To: David Lang <david@lang.hm>
> >> Cc: Daniel AJ Sokolov <daniel@falco.ca>,
> >> Dave Taht via Starlink <starlink@lists.bufferbloat.net>
> >> Subject: Re: [Starlink] Re: Data centers are racing to space — and
> regulation
> >> can’t keep up
> >>
> >> The underlying theory here is if someone builds a model that can improve
> >> itself faster than humans, they win. Military, economy, future problems,
> >> etc. That could have a lot of real on-Earth impact. There's investments
> and
> >> races going on that support that theory.
> >>
> >> If the major limiting factor is how big and fast you can build power
> plants
> >> on earth, and assume the person with the most access to power wins, it
> >> starts to make more sense.
> >>
> >> However, there's also a giant list of technical assumptions that need
> to be
> >> true for those assumptions to fly (get it?). And those technical
> >> assumptions don't necessarily need to be true in order to cash the
> checks
> >> from people that either want to compete in that race or invest in
> someone
> >> that is.
> >>
> >> Some of the assumptions I've come to include:
> >> * Adding more power and data to models eventually gets you to the
> >> intelligence needed to hockey stick. (Versus solving this problem with a
> >> different approach, algorithms, or different kinds of data.)
> >> * The models, data, and underlying algorithms aren't easily replicated
> by
> >> others once they start exponentially increasing in ability. E.g. can
> >> someone like Deepseek just take the outputs of the first mover, and then
> >> not require the same power capacity and replicate a similar value. This
> >> would slow down the first mover velocity benefits.
> >> * AI eventually starts creating returns.
> >> * Launch costs go down significantly (x10?)
> >> * There's enough room in sun synchronous orbit to run at power scales
> not
> >> possible on earth without kicking off Kessler
> >> * A combination of very large solar panels, radiative cooling, fluid
> >> exchange between them, the computing, propulsion, and any necessary
> >> redundancy of these components is still economical.
> >> * Operational loss due to radiation, micro asteroids, and general
> >> component failure is tolerable.
> >> * Components like GPUs and RAM and underlying bus structures can be
> built
> >> to be more radiation tolerant.
> >> * Burning up new orders of magnitude of amounts of elements in the
> >> atmosphere can be managed (aluminum, silicon, etc.)
> >> * Or, there is some amount of in-orbit recycling and manufacturing
> without
> >> returning material back to Earth.
> >> * Bandwidth can be built for 1) intra cluster within a satellite, 2)
> cross
> >> cluster via OISl, 3) Back to Earth using RF or lasers.
> >> * Regulatory bodies agree with the risk versus reward and approve this
> >> kind of plan.
> >> * The smarter-than-human AI doesn't decide to destroy the human race in
> a
> >> move of self preservation because the AI companies didn't have time for
> >> boundaries.
> >>
> >> I think it's a neat thought experiment, even if it's a little
> terrifying in
> >> scale and impact if it's remotely possible.
> >>
> >> -nick
> >>
> >> On Wed, Feb 25, 2026, 6:33 PM David Lang via Starlink <
> >> starlink@lists.bufferbloat.net> wrote:
> >>
> >>> Daniel AJ Sokolov wrote:
> >>>
> >>>> Block spots in orbit
> >>>
> >>> at the scale that he operates, everyone else combined in in the noise.
> >>> Starlink
> >>> is already several times the number of other satellites in orbit
> combined.
> >>>
> >>> besides, in the long run, he's talking about launching from the moon
> into
> >>> solar
> >>> orbit, not earth orbit, but even if he was just talking about launching
> >>> into
> >>> earth orbit near the moon's orbit, it's not like there are very many
> >>> satellites
> >>> there to contend with.
> >>>
> >>>> From a technology point of view, this is bonkers.
> >>>
> >>> if you only look at technical details, you may be right, but if you add
> >>> the
> >>> regulatory burden and delays in building traditional datacenters, that
> may
> >>> be
> >>> enough to change the math.
> >>>
> >>> Now, if we could ease the regulations so that it's easier to build
> power
> >>> plants
> >>> and hook up to the grid (or get small next-gen nuclear power plants
> >>> operational
> >>> so they can be dropped at the datacenters), that could change the math
> >>> back.
> >>>
> >>> David Lang
> >>> _______________________________________________
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> >>>
> >>
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