Our one dominant model of technology-driven economic progress is the industrial revolution. Manufacturing.
As Ai companies argue for market cap based on projected economic output... I'm increasingly thinking this model can be badly misleading.
It's very rare that the PC Revolution and or the internet Revolution are used as a primary model to explain technology and how it affects the economy.
Network enabled PCS are administrative powerhouses. They really did permeate all aspects of administration. But... The number of employees in administrative adjacent roles is higher, not lower. Accountants, university armin. HR. Project management. Etc.
It's very unclear how to quantify economic output/product. From this ambiguity , everything downstream is also vague.
The web also totally exploded in use. Web companies got huge revenue, even huger your profits.
It's very hard to draw lines, and apply economic reasoning that describes who gains what.
Users get to use Facebook, google and whatnot. Customers/advertisers get to advertize. The tech companies business model is based on network effects, momentum and whatnot.
What value is being created? Who is capturing how much of IT? These questions are almost philosophical. You just cannot apply reasoning like you would to the economics of mass produced cars.
Dopamine fracking , financial arbitrage racking, sales fracking... As a phenomenon, I think these occur in places where competition between firms is most intense over something that isn't correlated to external value.
Before advertising bands, cigarette companies were ad fracking. Tobacco is a commodity. Producing cigarettes is trivial. The only thing differentiating a billion dollars Tobacco Company from a million dollar Tobacco Company was the recognizability of their brand.
Government suppliers, or urban real estate can get to a point where the main driver of success, is lawyers.
A lot of industries went through a gradual process, as they matured... Where the domain of competition is decreasingly relevant to external value. The digital industries often start here or reach this point quickly.
The original sin is the idea that the profit motive on a free market will solve all our resource allocation problems, and that consumption demand should be the ultimate arbiter of social value. Markets are pretty freaking amazing things. But their efficiency relies on assumptions that knowledge economies and software break on pretty much every front. So, it's really no surprise that we're in this mess. I don't really know what would work better, though, in a way that can practically evolve from our existing systems.
Hey, I appreciate your insight. Especially your observation that when the underlying assumptions are wrong/broken then the model produces less reliable results.
Like you, I also don't know what would work better, nor do I believe any one individual can know.
But I do have some ideas for what would make a good framework for the evaluation?
If the idea is to allocate resources in a way that provides the most benefit to the most people, where most feel they are getting a 'fair deal' or something...
and we have social institutions that convert 'resources' to value (in quotes because time, attention, etc are 'resources'. The key principle is organizing human behavior over time to produce something humans value)...
Companies
Religion
Sports
Government
then think about what value each creates, how it is delivered, how it is captured, ... recognizing that each offers some unique strengths and unique limitations.
One of the easiest approache is torch.compile, it's the latest iteration of pytorch compiler (previous methods were : TorchScript and FX Tracing.)
You simply write
model = torch.compile(model)
"Across these 163 open-source models torch.compile works 93% of time, and the model runs 43% faster in training on an NVIDIA A100 GPU. At Float32 precision, it runs 21% faster on average and at AMP Precision it runs 51% faster on average."[1]
What google is trying to do, is to involve more people in the R&D of these kind of methods.
The near term promise is that you can use AMD, CUDA, TPUs, CPUs etc without explicit vendor support for the framework on which the model was developed.
Disclaimer: I will be very handwavey, reality is complex.
This is achieved by compiling the graph into some intermediate representation. And then implementing the right backend. For projects here, look at stableHLO, IREE, openXLA.
You can argue that Jax's jit compiler is a form of such compiler, mapping the traced operations down to XLA, which then does its own bit of magic to make it work on your backend.
It's transformations and abstractions all the way down.
Bodybuilders do this thing called "bulking and cutting." The best way to add muscle fast is to overeat. Work out lots. Sleep lots. Eat lots.
You get fat, but you also get muscular because food is never a limiting factor.
Then, they lose the extra fat with a crash diet.
Google, FB and such are such money machines that they never have to cut. They can just bulk. The others... they want some of that rapid growth potential too, but can't afford to add fat forever.
This analogy doesn’t seem to add any explanatory power.
Corporations can’t magically know the optimal number of employees to maximize profits. And even if they could this will change over time. So we should expect them to cut when they have too many and hire when they have too few.
>Google, FB and such are such money machines that they never have to cut.
Not sure what you are trying to achieve by saying something so blatantly untrue that a two-word query into any search engine can debunk it as fast as your browser loads a web page.
What makes this hard to read/follow is the grandiose moral vision... and the various levels of credulity it's met with.
If it's words from Ilya, Sam, the board... the words are all about alignment, benefiting humanity and such.
Meanwhile, all parties involved are super serious tycoons who are super serious about riding the AI wave, establishing moats, monopolies and the next AdWords, azure, etc.
These are such extreme opposite vocabularies that it's just hard to bridge. It's two conversations happening under totally different assumptions and everyone assumes at least someone is being totally disingenuous.
Meanwhile, "AI alignment" is such a charismatic topic. Meanwhile, the less futuristic but more applicable, "alignment questions" are about the alignment of msft, openai, other investors and consortium members.
If Ilya, Sam or any of them are actually worried about si alignment... They should at least give credence to the idea that we're all worried about their human alignment.
> the words are all about alignment, benefiting humanity and such.
that's why you only consider the actions taken, not the words spoken.
And in fact, i fail to believe that there are any real altruists out there. Esp. not rich ones. After all, if they're really altruistic, they would've given all their wealth away - before their supposed death (and even then, i doubt the money given to their "charitable foundations" count for real!).
Not necessarily. Money keeps you in the game. Giving it all away means you are at the bottom being not that effective. And you can donate money in your will.
One of those things that are widely true, but rarely admitted to. It seems to be very much a maturity thing. The older,larger and more governed an org is, the more likely such a pattern is.
Habits become precedents. Precedents become rules. A pattern emerges is everyone operates within rules. Staying within the ruleset, represents known safety. Even if something is dubious.. as long as it's within the rule set you are safe.
Is shaky principle tentatively applied once.. it doesn't have that kind of safety. That means it's less likely to be stretched and made absurd.
There is a logic to trimming unused budgets. not perfect, but it wouldn't surprise me if it worked well enough, often enough. If a department keeps going over budget, well.. they need more budget.. or maybe less work. Where is that budget going to come from? Departments without enough budget.
It's hard to get more legible, than last year's expenditure as the starting point for the next years budget. Nothing very notable about birthing this "principle."
I'm sure it makes sense, often. At least in the sense that it's the easiest, good enough method.
If there's a new management, using old methods is helpful. They don't know enough.. and this just gets the job done. If budgets become contentious, sticking to "principles" helps smooth things.
That is the point though.. whether it's a big-hype management method like agile.. or some unofficial budgetary principle that happened to work before these are principles. We like to be principled, especially when we don't really know what to do.
There's a literary trope of a bone casting seer. It takes a wise person to cast bones. It's an art and science. Sure, you have to know what all the bones mean. But, you also have to figure out it's a good idea to fight to fight this particular battle, build a town in that particular place... And also to understand the role bone casting plays in this particular case.
Bones must be cast, because we like external validation. It helps to bring everyone together, and calms underconfident, overwhelmed, or underunited leadership.
Knowing when to cast them, why, all the different implications.. how to define the question, how to approach the answer.. those are jobs for the seer, not the bones themselves.
Things are better when soldiers watch the stones, and chieftains watch the seer. If and when that flips, the paradigm is not at its best.
Sure, considering the platform-game paradigm different products sources cross subsidize one another a different stages of their life cycles. This is just how firms work, and is often mostly a matter of accounting.
Investors and executives.. everyone in 2023 is hyper focused on "Thiel Monopoly."
Platform, moat, aggregation theory, network effects, first mover advantages.. all those ways of thinking about it.
There's no point in being bing to Google's AdWords... So the big question is pathway to being the adWords. "Winning." That's the paradigm. This is where big returns will be.
However.. we should always remember, but the future is harder to see from the past. Post fact analysis, can often make things seem a lot simpler and more inevitable than they ever were.
It's not clear what a winner even is here. What are the bottlenecks to be controlled. What are the business models, revenue sources. What represents the "LLM Google," America online, Yahoo or a 90s dumb pipe.
FYIW I think all the big text have powerful plays available.. including keeping powder dry.
No doubt that proximity to openAI, control, influence, access to IP.. all strategic assets. That's why they're all invested an involved in the consortium.
That said assets or not strategies. It's hard to have strategies when strategic goals are unclear.
You can nominate a strategic goal from here, try to stay upstream, make exploratory investments and bets... There is no rush for the prize, unless the price is known.
Obviously, I'm assuming the prixe is not AGI and a solution to everything... That kind of abstraction is useful, but I do not think it's operative.
It's not a race currently, to see who's R&D lab turns on the first super intelligent consciousness.
Assuming I'm correct on that, we really have no idea which applications LLM capabilities companies are actually competing for.
As Ai companies argue for market cap based on projected economic output... I'm increasingly thinking this model can be badly misleading.
It's very rare that the PC Revolution and or the internet Revolution are used as a primary model to explain technology and how it affects the economy.
Network enabled PCS are administrative powerhouses. They really did permeate all aspects of administration. But... The number of employees in administrative adjacent roles is higher, not lower. Accountants, university armin. HR. Project management. Etc.
It's very unclear how to quantify economic output/product. From this ambiguity , everything downstream is also vague.
The web also totally exploded in use. Web companies got huge revenue, even huger your profits.
It's very hard to draw lines, and apply economic reasoning that describes who gains what.
Users get to use Facebook, google and whatnot. Customers/advertisers get to advertize. The tech companies business model is based on network effects, momentum and whatnot.
What value is being created? Who is capturing how much of IT? These questions are almost philosophical. You just cannot apply reasoning like you would to the economics of mass produced cars.
Dopamine fracking , financial arbitrage racking, sales fracking... As a phenomenon, I think these occur in places where competition between firms is most intense over something that isn't correlated to external value.
Before advertising bands, cigarette companies were ad fracking. Tobacco is a commodity. Producing cigarettes is trivial. The only thing differentiating a billion dollars Tobacco Company from a million dollar Tobacco Company was the recognizability of their brand.
Government suppliers, or urban real estate can get to a point where the main driver of success, is lawyers.
A lot of industries went through a gradual process, as they matured... Where the domain of competition is decreasingly relevant to external value. The digital industries often start here or reach this point quickly.
Is manufacturing actually the exception?
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