1. the training & inference costs have dropped to triple digits and a phone app, respectively.
2. given the preexisting codebase for distributed training, some non-negligible fraction of the billions of GPUs are going to be volunteered in an exascale fashion not unlike Folding@Home.
3. given that many business processes have already been articulated & opensourced in natural language, effectively any SME has the means to finetune their own nuances & SOPs to drastically lower training costs and turnover for new employees. this is a multimodal trend, any apprentice in the world can snap a photo of what they're doing and ask an LLM what to do next. eventually, it will be video if that modality can be inferred on mobile hardware.
4. admission to the bar and license might be the bottleneck for lawyers, but it is no longer the same bottleneck for incorporation and other legal services
5. given how much operational budget in hospitals goes to administrative work, I'm curious to see how the people deal with their medical bills in the next couple of years.
6. we haven't even confronted garage-tier sentiment analysis. I genuinely wonder how many markets get arbitraged due to this, starting with social media dogfooding.
7. what's the necessary cost of mainstream journalism to the general public? I'm sure you'd agree that should be weighed. same as 6), what's newsworthy & why should it be published by a corporate media company?
8. on the tail-end to this, legislature & lobbying costs just got profoundly cheaper. also cheaper to pick apart pork-barrel or other inconsistencies therein.

these are just a few downstream effects. and I'm leaving out the parallel gains in manufacturing automation, machine vision, crowdsourcing, etc.

no, I'm the guy saying that books can easily sell online because they're nonperishable, dense, and can be packaged in a garage. and regardless of the chatgpt hype, we're literally days after the discovery that someone can package LLMs that hit the same benchmarks from their garage.

are we absolutely sure that the average multiple during high rate environments is going to stabilize at a minimum, and during a zero-rate moment in the not-too-distant future, are average multiples going to reach a maximum because they must be dictated by retail speculation as the buyer of last resort? I'm willing to argue no to both. we're going to observe disruption that supercedes the macro sentiment.

precisely the point. but it's not a theoretical conjecture, there is a very practical connection between the job exposure paper, several sources of labor market truth, and the current capabilities of Langchain + Alpaca. everyone should be asking why the public should bear anywhere the same cost to necessitate the multiple that VCs/CEOs/EAs/consultants are compensated for, given how exposed these sectors are at present.

with all due respect, isn't it kind of a cliche to preface something with "not to be rude" with the self-awareness that you're about to state something condescending? and I'd argue the same point of originality or productivity when it comes to estimates of scale for displacement or acceleration. not going to belabor the point, but I'm just emphasizing that "AI replace jobs" is a red herring debate and a waste of time, the actual debate is whether the nature of the firm can be challenged, given the observed change to the market. "If you don't believe me or don't get it, I don't have time to try to convince you, sorry."

my point was that some jobs are more exposed to layoffs because of their nature. likewise, some aspects of labor economy are exposed in some way to future technological discoveries no matter what they are. I would say we are more than one inning in; there's OSS text-to-image and NLP that's trained at scale and inferred on retail hardware, with the corresponding backlash. the inning we're in is the battle over executive function, or the scale at which human workers opensource the standard operating procedures they're most familiar with. the takeaway should be that monolithic firms with tens of thousands of employees are volatile enough as it is, but the least exposed jobs within, with respect to AI, are going to want to compete as smaller, more insulated firms.

I'm going to push back on this. from what we know, certain jobs are being laid off more, and in tech of all sectors. this is not a story about how blue collar or service sector wages have risen to meet the cost of living (they likely never will). in those cases the robotics can conceptually replace the labor in a bespoke fashion, but the economics of scale are the limiting factor. what's being described in the jobs exposure paper are heavily routine, white-collar tasks that are being automated & scaled. it might not hit us today or tomorrow, but at some point this particular economy of scale is going dwarf the impact of outsourcing.

I think that LibreOffice & Collabora will stack nicely on Open Assistant. for every software that interfaces via natural language with the user, there is probably an opensource LLM and an open repo that acts as its client.

as far as subscription-based productivity software, I will refer back to this classic.

rational market actors recognize their own worth. either they're underpaid by the company and should become independent, or they're overpaid and are insulated from competing in the open market. in either case, the tragedy of the commons is that all firms compete to the extent that they can dispose of their profit margin, and ultimately the end consumer benefits from commodification.

OTOH, with respect to artisanal goods & services, it makes further sense for employees to not be commodified as labor by a larger firm if they're artisans. they should compete in their niche market. but that is not an acceptance that the larger public market should be captive to a firm, even if that firm sets a less efficient, higher price to offset the employment of commodified labor.

so long as the product is not a commodity. if the market errs towards an oligopoly, of course those firms have pricing power. that is definitely the present circumstance, however things like LLaMa, StableDiffusion, and Alpaca are demonstrating AI (the orchestrating element) can be a commodity. in other words, if your labor is so specialized that you control the pricing power, then it's further in your self-interest to be self-employed over time. if the firm employing you provides something that compels you to surrender your pricing power, then that is a bargaining cost that will likely shrink over time as it gets commodified.

100%, it makes engineering faster, not more real. the critical step outside of that is that AI makes the flow of ideation to execution more feasible if the cost of engineering is prohibitive enough to have made that flow infeasible in the past. this applies to RFCs as well. it's like the difference of having connected rooms in virtual reality because the environment suddenly upgraded to doors and opposable thumbs.

it doesn't take much for a layperson to hallucinate bad code via prompt right now, whereas the barrier for layperson to manifest any code used to be binary in the past. it's going to be even easier to subdivide an LLM prompt into chains of prompts. if one can load the respective codebase/docs as context (GPT-4 goes up to 32k tokens), the cost of hallucinating bad, but very relevant code, gets progressively cheaper.

right now, I expect any OSS community to progressively gain the ability to dogfood on whatever natural language the testers and powerusers are outputting. I think that major platforms, like social media, are quickly going to figure out that they can offer an experimental branch and not twiddle their thumbs around an unanswered user survey because of how easy it will be to transcribe sentiment & nuanced feedback from the comments.

point being, software doesn't impact the world because of how self-involved the team of a monolith is. software impacts the world when the modularity spikes (between many teams/firms and the larger market).

the sober outlook is that whatever commodity the company offers is going to produce a slimmer margin over time, especially if it's digital. on the other hand, I'm pretty confident that most firms (especially sole proprietorshisps) are more capable of affording their own optimization process. for any given employee, the important question is whether they understand the practice of what they're paid for, relative to the labor market. the followup is whether they are industrious enough to form their own firm and compete.

just keep in mind that it's humans who get to be overlords of silicon first. 😉

I would tend to agree that something resembling AGI might manifest. curious to hear your thoughts about the Alberta Plan and if any of the 12 steps sound like they might be possible within 1-5 years.

considering all the hype around ChatGPT, I felt like I should concretize some thoughts around ML at present and AI in the immediate future. the main point is that we're already crossing thresholds that indicate an accelerating path towards AGI and a much different looking society because of it. I link to several directions that ML can be studied in order to achieve AGI, but this is mostly an introductory post. may write again about this subject very soon.

look at the boston dynamics android vs the tesla optimus. creating a robot that can perform physical tasks is far from impossible. creating a fleet of robots to replace all the physical tasks done in a nationwide economy is economically infeasible.

the key distinction is which sector of labor is more scarce. if knowledge work in a digital world is scarce, there's more incentive (i.e. investment) to deploy software at scale. if robotics manufacturing in a physical world is scarce, there's more incentive to stockpile the limiting reagent (usually the chips & minerals).

in terms of timeframe, robotics intelligence is accelerating rapidly. look at RT-1, for example. it's clear that the public domain has already adopted the means to operate robotics, at a hobbyist level, on a global scale. there are a plethora of youtube videos demonstrating this. I suspect that RT will be iterated enough within 5 years to include the capability of self-reproduction. by that point it will not be economically infeasible for independent actors with enough capital to procure their own self-assembling factories with one ML model. the limiting factor will be the raw resources for manufacturing enough robotics to do the physical labor of a given niche.

within 10 years there will be a ML model that convincingly operates an android that instructs/outperforms humans in basic physical tasks. that's the trajectory we're on. it is likely that within 20 years, even specialized physical tasks will be done by android-operating models. before then, I suspect that there is a point of no return with androids playing televised sports. by then the form factor will be a nonissue for industrial purposes.

thanks, just tried it with some ps5.js samples

I would disagree with your point about how we answer questions, we optimize for comprehensively sound and valid answers, not for statistical adjacency. If someone says a whole bunch of techno-jargon or other word salad just to sound convincing, the wisdom of the crowds is already powerful enough to call that redundant. Likewise, the wisdom of the crowds can break GPTChat and there's already actively collected techniques to "jailbreak" the application.
My point is that a general conversational model is a gimmick at this point, and likewise GPT4 is already prescribed to have limitations like being text-centric and is not multimodal. It'll definitely being uncannily entertaining as a conversational homunculus, but a homunculus does not a singularity make.

maybe I'm misunderstanding, but if you don't expect state-of-the-output or, for lack of a better term, gain of function from the output of these current AI, how do you see our approach to the singularity being shortened based on the current consumer product. as far as the math olympiad reference, I'm assuming you're referencing Minerva or something at the same level. Again, it doesn't show completely error-free answers, it just shows a sequence of words & algorithms that are statistically adjacent enough to be convincing. it should be expected that if olympiad (or college level) question sets were available in the training data, then the bot can just recall the answers as complete chunks without "thinking".

no, if anything what I've observed with chatgpt, as well as the drama surround stablediffusion 2.0, the singularity will not be publicly noticeable or available in public consumer products. these applications are demonstrating a negative feedback where arbitrary limitations become more necessary for increasingly social (not technical) reasons. additionally, chatgpt is like a snapshot of everything that's been said in the past, and whatever it spits out sounds convincingly authoritative but has no certain accuracy for basic logic & reasoning (like incorrect math). I suspect that further iterations will be more convincing, perhaps even frighteningly "informative", but sussing out errors and inaccuracies will just get proportionately more demanding for the human domain experts. it does spit out a lot of code, but give it a complex enough prompt, and the code will abruptly end. there's might be a subscription service that matches the work being done to serve up output. I still suspect that the advances in AI will accelerate for quite a while, and only past a certain threshold (maybe 2030 or later) will a collection of humans procure a novel methodology that self-evidently produces all the necessary reasoning and self-awareness that an AGI would require. until then, there is 0% chance that we build an AI that builds an AI, so on and so forth, that would actually reach another stage of complexity. in all likelihood, AGI is closest to those that have the most scaled computational facilities with the most optimized ASICs and the widest distribution of feedback mechanisms. this does not 100% overlap with current academic work using AWS and other cloud compute.

agreed, but a post like this just makes the preconceived bias obvious. I think anyone that has made a point of steelmanning between solarpunk and collapse ideology has come across the lunarpunk and cypherpunk subsets, and yet it seems that the discussion has erred more to the figurative terms that describe the "other side" as misguided or wrong.

I wish there was more introspection on reddit about what forms bias can take.

nobody's in control if the approach to ML/AI is not fully known. we're still reinventing blackbox neural network architectures. training data is only curated to a finite degree. 5 years from now, all bets are off. 10 years from now, all bets made in 5 years are off.

I would be worried most about corporations like Tesla and Amazon that can afford the industrial scale to deploy robots for ML feedback and model refinement. here's a summary of what got covered at tesla AI day: https://www.youtube.com/watch?v=ABbDB6xri8o