If they turned it on for business orgs, that would blow up fast. The line between "helpful telemetry" and "silent corporate data mining" gets blurry once your team's repo is feeding the next Copilot.
People are weirdly willing to shrug when it's some solo coder getting fleeced instead of a company with lawyers and procurement people in the room. If an account tier is doing all the moral cleanup, the policy is bad.
The individual/corporate asymmetry you're describing is standard across B2B SaaS. Slack, Notion, and Figma all include ML training carve-outs in enterprise DPAs that free users don't get. GitHub isn't doing anything unusual here — they're just doing it with code, which feels more sensitive than documents or messages because it might literally be your employer's IP you're working on from a personal account.
The interesting nuance is the enforcement mechanism. martinwoodward clarified below that exclusion happens at the user level, not the repo level: if you're a member of a paid org, your interaction data is excluded even on a free personal Copilot account. That's actually more protective than I expected — it handles the contractor case where someone works across multiple repos of varying org types.
The remaining ambiguity is temporal: if someone leaves an org, do their historical interactions get retroactively included? Policy answers to that question are hard to verify and even harder to audit.
I loved Apple IIs at schools and libraries as a young child, fell in love with my Mac IIsi at home at the age of 7. Later, at 13, I had a Macintosh-evangelizing web site and mailing list that Guy Kawasaki (Apple's lead evangelist) even subscribed to.
I've been a primary Mac user through the 68k, PowerPC, Intel, and Apple Silicon days, from System 6.0.7 through today. Got an original iPhone and iPad, have upgraded my iPhone every few years since.
The technofeudalism, bugginess, and UI crappiness has me done and looking for the exits, to say nothing of the embrace of Trump. My next laptop won't be a Mac, and my next phone won't be an iPhone.
Yes, the actual LLM returns a probability distribution, which gets sampled to produce output tokens.
[Edit: but to be clear, for a pretrained model this probability means "what's my estimate of the conditional probability of this token occurring in the pretraining dataset?", not "how likely is this statement to be true?" And for a post-trained model, the probability really has no simple interpretation other than "this is the probability that I will output this token in this situation".]
It’s often very difficult (intractable) to come up with a probability distribution of an estimator, even when the probability distribution of the data is known.
Basically, you’d need a lot more computing power to come up with a distribution of the output of an LLM than to come up with a single answer.
In microgpt, there's no alignment. It's all pretraining (learning to predict the next token). But for production systems, models go through post-training, often with some sort of reinforcement learning which modifies the model so that it produces a different probability distribution over output tokens.
But the model "shape" and computation graph itself doesn't change as a result of post-training. All that changes is the weights in the matrices.
That's 4–6 months in the 18 months the trials lasted for, i.e. about a 30% slowdown of progression. The open-label extensions suggest this relative slowdown seems to continue at least to the 4-year mark (at which point it would have bought you over a year of time): https://www.alzforum.org/news/conference-coverage/signs-last...
Time will tell if the 30% slowdown continues beyond four years, and/or if earlier treatment with more effective amyloid clearance from newer drugs has greater effects. The science suggests it should.
A mistake in this critique is it assumes an exponential: a constant proportional rate of growth. It is true that, in some sense, an exponential always seems to be accelerating while infinity always remains equally far away.
But this is a bit of a straw man. Mathematical models of the technological singularity [1], along with the history of human economic growth [2], are super-exponential: the rate of growth is itself increasing over time, or at least has taken multiple discrete leaps [3] at the transitions to agriculture and industry, respectively. A true singularity/infinity can of course never be achieved for physical reasons (limited stuff within the cubically-expanding lightcone, plus inherent limits to technology itself), but the growth curve can look hyperbolic and traverse many orders of magnitude before those physical limits are encountered.
It can’t be infinitely fast, but after the point where we all collectively cease to be able to comprehend the rate of change, it’s effectively a discontinuity from our point of view.
Looks like not, but would it actually have been shadier, or are we just used to individual users being fucked over?
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