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 [53][                    ]
 Cory Doctorow wearing "Ellsberg & Nikitin & Manning & Snowden" tshirt
 [55]Commentary [56]Cory Doctorow [57]Features [58]Slider 
 
 Cory Doctorow: What Kind of Bubble is AI?
 
 [59] December 18, 2023December 22, 2023 [60]locusmag [61] 46 Comments [62]Cory
 Doctorow
 [63][Issue11_Doctorow_200x30]Cory Doctorow (by Amelia Beamer)
 
 Of course AI is a bubble. It has all the hallmarks of a classic tech bubble.
 Pick up a rental car at SFO and drive in either direction on the 101 – north to
 San Francisco, south to Palo Alto – and every single billboard is advertising
 some kind of AI company. Every business plan has the word “AI” in it, even if
 the business itself has no AI in it. Even as two major, terrifying wars rage
 around the world, every newspaper has an above-the-fold AI headline and half
 the stories on Google News as I write this are about AI. I’ve had to make rule
 for my events: The first person to mention AI owes everyone else a drink.
 
 It’s a bubble.
 
 Tech bubbles come in two varieties: The ones that leave something behind, and
 the ones that leave nothing behind. Sometimes, it can be hard to guess what
 kind of bubble you’re living through until it pops and you find out the hard
 way.
 
 When the dotcom bubble burst, it left a lot behind. Walking through San
 Francisco’s Mission District one day in 2001, I happened upon a startup founder
 who was standing on the sidewalk, selling off a fleet of factory-wrapped
 Steelcase Leap chairs ($50 each!) and a dozen racks of servers with as much of
 his customers’ data as I wanted ($250 per server or $1000 for a rack).
 Companies that were locked into sky-high commercial leases scram­bled to sublet
 their spaces at bargain-basement prices. Craigslist was glutted with foosball
 tables and Razor scooters, and failed dotcom T-shirts were up for the taking,
 by the crateful.
 
 But the most important residue after the bubble popped was the mil­lions of
 young people who’d been lured into dropping out of university in order to take
 dotcom jobs where they got all-expenses paid crash courses in HTML, Perl, and
 Python. This army of technologists was unique in that they were drawn from all
 sorts of backgrounds – art-school dropouts, hu­manities dropouts, dropouts from
 earth science and bioscience programs and other disciplines that had
 historically been consumers of technology, not producers of it.
 
 This created a weird and often wonderful dynamic in the Bay Area, a brief
 respite between the go-go days of Bubble 1.0 and Bubble 2.0, a time when the
 cost of living plummeted in the Bay Area, as did the cost of office space, as
 did the cost of servers. People started making technology because it served a
 need, or because it delighted them, or both. Technologists briefly operated
 without the goad of VCs’ growth-at-all-costs spurs.
 
 The bubble was terrible. VCs and scammers scooped up billions from pension
 funds and other institutional investors and wasted it on obviously doomed
 startups. But after all that “irrational exuberance” burned away, the ashes
 proved a fertile ground for new growth.
 
 Contrast that bubble with, say, cryptocurrency/NFTs, or the complex financial
 derivatives that led up to the 2008 financial crisis. These crises left behind
 very little reusable residue. The expensively retrained physicists whom the
 finance sector taught to generate wildly defective risk-hedging algorithms were
 not able to apply that knowledge to create successor algo­rithms that were
 useful. The fraud of the cryptocurrency bubble was far more pervasive than the
 fraud in the dotcom bubble, so much so that without the fraud, there’s almost
 nothing left. A few programmers were trained in Rust, a very secure programming
 language that is broadly applicable elsewhere. But otherwise, the residue from
 crypto is a lot of bad digital art and worse Austrian economics.
 
 AI is a bubble, and it’s full of fraud, but that doesn’t automatically mean
 there’ll be nothing of value left behind when the bubble bursts. World­Com was
 a gigantic fraud and it kicked off a fiber-optic bubble, but when WorldCom
 cratered, it left behind a lot of fiber that’s either in use today or waiting
 to be lit up. On balance, the world would have been better off without the
 WorldCom fraud, but at least something could be salvaged from the wreckage.
 
 That’s unlike, say, the Enron scam or the Uber scam, both of which left the
 world worse off than they found it in every way. Uber burned $31 billion in
 investor cash, mostly from the Saudi royal family, to create the illusion of a
 viable business. Not only did that fraud end up screwing over the retail
 investors who made the Saudis and the other early investors a pile of money
 after the company’s IPO – but it also destroyed the legitimate taxi business
 and convinced cities all over the world to starve their transit systems of
 investment because Uber seemed so much cheaper. Uber continues to hemorrhage
 money, resorting to cheap accounting tricks to make it seem like they’re
 finally turning it around, even as they double the price of rides and halve
 driver pay (and still lose money on every ride). The market can remain
 irrational longer than any of us can stay solvent, but when Uber runs out of
 suckers, it will go the way of other pump-and-dumps like WeWork.
 
 What kind of bubble is AI?
 
 Like Uber, the massive investor subsidies for AI have produced a sugar high of
 temporarily satisfied users. Fooling around feeding prompts to an image
 genera­tor or a large language model can be fun, and playful communities have
 sprung up around these subsidized, free-to-use tools (less savory communities
 have also come together to produce nonconsensual pornography, fraud materials,
 and hoaxes).
 
 The largest of these models are incredibly expensive. They’re expensive to
 make, with billions spent acquir­ing training data, labelling it, and running
 it through massive computing arrays to turn it into models.
 
 Even more important, these models are expensive to run. Even if a bankrupt AI
 company’s model and servers could be acquired for pennies on the dollar, even
 if the new owners could be shorn of any overhanging legal liability from
 looming copyright cases, even if the eye-watering salaries commanded by AI
 engineers collapsed, the electricity bill for each query – to power the servers
 and their chillers – would still make running these giant models very 
 expensive.
 
 Do the potential paying customers for these large models add up to enough money
 to keep the servers on? That’s the 13 trillion dollar question, and the answer
 is the difference between WorldCom and Enron, or dotcoms and cryptocurrency.
 
 Though I don’t have a certain answer to this question, I am skeptical. AI
 decision support is potentially valuable to practitioners. Accountants might
 value an AI tool’s ability to draft a tax return. Radiologists might value the
 AI’s guess about whether an X-ray suggests a cancerous mass. But with AIs’
 tendency to “hallucinate” and confabulate, there’s an increasing recognition
 that these AI judgments require a “human in the loop” to carefully review their
 judgments.
 
 In other words, an AI-supported radiologist should spend exactly the same
 amount of time considering your X-ray, and then see if the AI agrees with their
 judgment, and, if not, they should take a closer look. AI should make radiology
 more expensive, in order to make it more accurate.
 
 But that’s not the AI business model. AI pitchmen are explicit on this score:
 The purpose of AI, the source of its value, is its capacity to increase
 productivity, which is to say, it should allow workers to do more, which will
 allow their bosses to fire some of them, or get each one to do more work in the
 same time, or both. The entire investor case for AI is “companies will buy our
 products so they can do more with less.” It’s not “business custom­ers will buy
 our products so their products will cost more to make, but will be of higher
 quality.”
 
 AI companies are implicitly betting that their customers will buy AI for highly
 consequential automation, fire workers, and cause physical, mental and economic
 harm to their own customers as a result, somehow escaping liability for these
 harms. Early indicators are that this bet won’t pay off. Cruise, the
 “self-driving car” startup that was just forced to pull its cars off the
 streets of San Francisco, pays 1.5 staffers to supervise every car on the road.
 In other words, their AI replaces a single low-waged driver with 1.5 more
 expensive remote supervisors – and their cars still kill people.
 
 If Cruise is a bellwether for the future of the AI regulatory environment, then
 the pool of AI applications shrinks to a puddle. There just aren’t that many
 customers for a product that makes their own high-stakes projects bet­ter, but
 more expensive. There are many low-stakes applications – say, selling kids
 access to a cheap subscription that generates pictures of their RPG characters
 in action – but they don’t pay much. The universe of low-stakes, high-dollar
 applications for AI is so small that I can’t think of anything that belongs in
 it.
 
 Add up all the money that users with low-stakes/fault-tolerant applications are
 willing to pay; combine it with all the money that risk-tolerant, high-stakes
 users are willing to spend; add in all the money that high-stakes users who are
 willing to make their products more expen­sive in order to keep them running
 are willing to spend. If that all sums up to less than it takes to keep the
 servers running, to acquire, clean and label new data, and to process it into
 new models, then that’s it for the commercial Big AI sector.
 
 Just take one step back and look at the hype through this lens. All the big,
 exciting uses for AI are either low-dollar (helping kids cheat on their
 homework, generating stock art for bottom-feeding publications) or high-stakes
 and fault-intolerant (self-driving cars, radiology, hiring, etc.).
 
 Every bubble pops eventually. When this one goes, what will be left behind?
 
 Well, there will be little models – Hugging Face, Llama, etc – that run on
 commodity hardware. The people who are learning to “prompt engineer” these “toy
 models” have gotten far more out of them than even their makers imagined
 possible. They will continue to eke out new marginal gains from these little
 models, possibly enough to satisfy most of those low-stakes, low-dollar
 ap­plications. But these little models were spun out of big models, and without
 stupid bubble money and/or a viable business case, those big models won’t
 survive the bubble and be available to make more capable little models.
 
 There are some promising avenues, like “feder­ated learning,” that
 hypothetically combine a lot of commodity consumer hardware to replicate some
 of the features of those big, capital-intensive models from the bubble’s
 beneficiaries. It may be that – as with the interregnum after the dotcom bust –
 AI practitioners will use their all-expenses-paid education in PyTorch and
 TensorFlow (AI’s answer to Perl and Python) to push the limits on federated
 learning and small-scale AI models to new places, driven by playfulness,
 scientific curiosity, and a desire to solve real problems.
 
 There will also be a lot more people who un­derstand statistical analysis at
 scale and how to wrangle large amounts of data. There will be a lot of people
 who know PyTorch and TensorFlow, too – both of these are “open source”
 projects, but are effectively controlled by Meta and Google, respectively.
 Perhaps they’ll be wrestled away from their corporate owners, forked and made
 more broadly applicable, after those corporate behemoths move on from their
 money-losing Big AI bets.
 
 Our policymakers are putting a lot of energy into thinking about what they’ll
 do if the AI bubble doesn’t pop – wrangling about “AI ethics” and “AI safety.”
 But – as with all the previous tech bubbles – very few people are talking about
 what we’ll be able to salvage when the bubble is over.
 
 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 
 Cory Doctorow is the author of Walkaway, Little Brother, and Information
 Doesn’t Want to Be Free (among many others); he is the co-owner of Boing Boing,
 a special consultant to the Electronic Frontier Foundation, a visiting
 professor of Computer Science at the Open University and an MIT Media Lab
 Research Affiliate.
 
 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 
 All opinions expressed by commentators are solely their own and do not reflect
 the opinions of Locus.
 
 [INS::INS]
 
 This article and more like it in the [64]December and January 2023 issue of 
 Locus.
 
 [65]Locus Magazine, Science Fiction FantasyWhile you are here, please take a
 moment to support Locus with a one-time or recurring donation. We rely on
 reader donations to keep the magazine and site going, and would like to keep
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 ©Locus Magazine. Copyrighted material may not be republished without permission
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 46 thoughts on “Cory Doctorow: What Kind of Bubble is AI?”
 
   • [ede5d74f]
     [87]Vienna Mike
     December 19, 2023 at 3:01 pm
     [88]Permalink
 
     Good article, but I’m not sure that I 100% agree. For radiology, the BEST
     review would indeed be the more expensive mix of human plus AI (and some
     places are now doing this and charging a surcharge for use of AI). However
     there is at least some studies that suggest AI is not better at reading
     mammograms than a human radiologist. So the best, most expensive option is
     human + AI, the 2nd best and cheapest is AI, and the worst and 2nd most
     expensive is human (only). If the business model for AI + human doesn’t
     fly, I can see AI only working fine.
 
     On automated vehicles, Cruise is a good example of what can go wrong, but
     Waymo is having significantly better results. I think the jury is still out
     on that one.
 
     On LLM, I agree with you. I do ask two questions (and it’s an ask, I’m
     ignorant in this area): 1) are the newly emerging small models fully
     derivatives from the massive models, or might that continue even if the
     massive models are a bubble? 2) There are a lot of applications where
     training is done on massive data sets and hardware, but the resulting
     trained models can then run at far lower cost, on MUCH smaller machines. Is
     this also a possible continued path if LLM’s are a bubble that burst?
 
     [89]Reply
   • [c3c3135a]
     Dymitr
     December 19, 2023 at 3:33 pm
     [90]Permalink
 
     Curious that author does not dwell more on the fact, that use cases like
     GitHub copilot will actually make any programming faster and easier, also
     lower the entry threshold for new workforce which will be most likely very
     positive.
 
     [91]Reply
       □ [1b19293b]
         PiRX
         December 28, 2023 at 12:49 am
         [92]Permalink
 
         As a programmer with some experience under my belt – copilot doesn’t
         make “real” (outside demos) programming much faster, it gives maybe a
         few percent gain. A lot of that could also be gained just by improving
         libraries and frameworks used to reduce repetitive and/or boilerplate
         code.
 
         [93]Reply
           ☆ [e222c1b0]
             khlorghaal
             December 31, 2023 at 3:51 am
             [94]Permalink
 
             Utility for biolerplate and glue is EXTREMELY useful, since those
             are the demoralizing activities humans dont want to concern
             ourselves with, that take significant time.
             Its also a substitute for reading a full manual when you only want
             to use a singular feature of something for a specific purpose.
 
             [95]Reply
               ○ [9eb71600]
                 Curt J. Sampson
                 January 16, 2024 at 7:53 pm
                 [96]Permalink
 
                 But we already have _much_ better techniques for getting rid of
                 boilerplate code: better languages, better libraries,
                 refactorings, and so on.
 
                 Keep in mind that a lot more time is spent _reading_ code than
                 writing it; you can’t modify existing code unless you can read
                 it and be sure what it does and what the effects of your
                 changes will be. So generating more boilerplate code might fix
                 a few problems in the very short term (over a few weeks, or
                 often even just days), but creates a larger problem (often
                 referred to as “technical debt”) over the long term.
 
                 I have tried using ChatGPT to give me hints on how to write
                 code, but I’ve inevitably found that, while it’s been useful
                 for giving me (almost invariably incorrect) rough draughts in
                 languages I’m less familiar with, it works only because I’m
                 already a good programmer in many other languages, and so I can
                 fairly easily edit that draught into useful and correct code.
 
                 [97]Reply
       □ [2528bf2e]
         Mike
         December 30, 2023 at 12:22 pm
         [98]Permalink
 
         I’m not finding Copilot to be that useful. It makes lots of wrong
         suggestions, too.
 
         [99]Reply
   • [e32f7298]
     Tuna
     December 19, 2023 at 3:42 pm
     [100]Permalink
 
     AI will probably prove to be a bad solution (at least for now) for a lot of
     the domains where it’s being attempted, for the simple reason that a lot of
     the output is bullshit.
 
     But there is a lot of money in products and services where “it’s bullshit”
     isn’t a show-stopper. Mostly entertainment, but there is a lot of money in
     that. I recently participated in a D&D campaign where the DM made very
     liberal use of AI, both for text/running encounters, and for image
     generation. The AI clearly wasn’t yet good enough to do the whole thing on
     it’s own, but it was much better and more useful than expected, and clearly
     reduced the workload on the DM a lot, while also greatly improving
     immersion.
 
     What I’m saying is that in a couple of years the experience of videogaming
     will change a lot, and this will probably produce enough revenue to keep
     the bubble going, at least for a while.
 
     [101]Reply
       □ [d4cec217]
         Vera
         February 26, 2024 at 3:28 am
         [102]Permalink
 
         They want, so bad, for it to replace the GM too but that is such a
         complicated job that I suspect they might find self driving cars
         easier. Those GM emulators are fun for a bit but rapidly get repetitive
         or just lose the thread and they are terrible at knitting together an
         overarching narrative out of pieces and chaos like a good human GM can
         do.
 
         [103]Reply
   • [d4d10da0]
     Eli Langer
     December 19, 2023 at 5:51 pm
     [104]Permalink
 
     Cory Doctorow’s piece on AI as a bubble provides a nuanced perspective on
     the current AI landscape.
 
     He argues convincingly that AI is in a bubble phase, akin to previous tech
     bubbles, where hype and investment overshadow practical utility and
     sustainable business models. Doctorow draws parallels with the dotcom
     bubble, where despite the burst, valuable skills and infrastructure
     remained.
 
     He points out the distinct lack of tangible, beneficial remnants from other
     bubbles, like cryptocurrency/NFTs and the 2008 financial crisis
     derivatives.
 
     Validating Doctorow’s Opinions:
 
     1. AI Overhype: Doctorow’s assertion that AI is overhyped and likened to a
     bubble seems valid. The ubiquity of AI in business plans, news headlines,
     and advertising, regardless of their actual AI integration, mirrors the
     classic signs of a technology bubble.
 
     2. Historical Bubble Analysis: His analysis of different bubbles, like the
     dotcom and cryptocurrency bubbles, and their aftermaths is insightful. The
     contrast between bubbles that leave behind valuable assets, skills, or
     infrastructure (dotcom) and those that don’t (cryptocurrency/NFTs) is a
     valuable framework to assess the potential impact of the current AI bubble.
 
     3. Skepticism About AI’s Business Model: Doctorow’s skepticism about the AI
     business model’s sustainability and its potential to deliver value is
     grounded. He highlights the mismatch between the promise of increased
     productivity and the real need for human oversight in AI applications,
     which raises questions about the long-term viability of these technologies.
 
     Countering Doctorow’s Opinions:
     1. Underestimating AI’s Potential: While Doctorow rightly points out the
     overhype and potential for fraud in AI, he might underestimate the
     technology’s transformative potential. AI’s capacity for data analysis,
     pattern recognition, and decision support can revolutionize fields like
     healthcare, finance, and more, beyond just being a productivity tool.
 
     2. Broad Generalization of AI: Doctorow’s argument, at times, seems to
     broadly generalize AI technologies, not fully acknowledging the diversity
     within the field. Not all AI applications are equivalent; some, like
     machine learning models in healthcare and environmental sciences, show
     substantial promise and utility.
 
     3. Overlooking Positive Use Cases: His focus on the negative aspects, while
     crucial, might overshadow the positive, real-world applications of AI that
     are already making an impact. For instance, AI’s role in medical
     diagnostics, climate modeling, and even creative arts, though in their
     nascent stages, demonstrate a constructive trajectory.
 
     In conclusion, Doctorow’s opinions on the AI bubble are largely
     well-founded, especially his critique of the overhyped nature of AI and the
     parallels with historical tech bubbles. However, his perspective might
     benefit from a more nuanced acknowledgment of the positive, transformative
     potential of AI in various sectors.
 
     [105]Reply
       □ [e77ea68c]
         Jonathan
         December 22, 2023 at 8:17 am
         [106]Permalink
 
         Thanks, ChatGPT, that was a good summary of what I just read.
 
         [107]Reply
           ☆ [d4d10da0]
             Eli Langer
             December 22, 2023 at 8:39 am
             [108]Permalink
 
             Dang, you got me.
 
             [109]Reply
               ○ [8260abb9]
                 Sebastian
                 December 25, 2023 at 2:23 am
                 [110]Permalink
 
                 Even in the countering points it suggests no low stakes high
                 value use cases and only doubles down on the point of medical
                 use cases, which the author already covered. This barren
                 absence of use cases that are more than making a complete
                 amateur, 1 rudimentary subject in any knowledge area better, is
                 exactly the point of what is making this a bubble.
 
                 [111]Reply
                   ■ [3431255b]
                     Matt
                     January 6, 2024 at 2:28 am
                     [112]Permalink
 
                     Exactly. It was a regurgitation of the same talking points
                     we’ve all been hearing for months now… with the word
                     “nuanced” thrown in. I guess ChatGPT interpreted that Cory
                     was coming across a little too direct? lol
 
               ○ [cc121394]
                 Ormond Otvos
                 January 7, 2024 at 7:28 pm
                 [113]Permalink
 
                 It was obvious, as are many comments.
                 AI requires editing to remove the bland exposition of all
                 points of view.
                 Be great for writing a paper on the morality of intersex
                 dysphoria treatment.
 
                 [114]Reply
       □ [dfdaf90f]
         Anatoly
         December 22, 2023 at 9:51 am
         [115]Permalink
 
         Did you submit this to your English 101 class?
 
         [116]Reply
   • [4f9ace9e]
     Alvin Starr
     December 19, 2023 at 7:34 pm
     [117]Permalink
 
     I lived through the 2000 tech bubble burst.
     I noticed that it started about 97-98.
     At times it seemed that if you stuck “on the internet” with anything on a
     napkin in a bar you could get financed.
     I have a feeling that AI will go that way where lots of businesses go bust
     and lots of investors lose their shirts but the world will be forever
     changed but not the way people pre-bubble thought.
 
     If you could not copyright anything generated by AI what would that do to
     those who want to create and own content.
     There would not be much point in using AI to create that blockbuster movie
     script if you could not claim you own it.
 
     [118]Reply
   • [ede98f50]
     Ian Holmes
     December 20, 2023 at 8:15 am
     [119]Permalink
 
     This is a nice comparison to previous bubbles. However, it leaves out the
     thing this bubble was driven by, other bubbles less so: a vast accumulating
     body of academic research in neural network architectures, deep learning
     theory, and domain-specific applications. There is a LOT that will be left
     when the bubble pops. In some ways this can be compared to the development
     of web based technology that accompanied the dotcom boom, but it is much
     more substantial. Consider for example AlphaFold, the solution to a 50 year
     fundamental problem in protein bioinformatics. That’s a whole lot more than
     just some CSS and JavaScript frameworks and the Model-View-Controller
     paradigm.
 
     [120]Reply
       □ [07d0ed93]
         Amy
         December 23, 2023 at 2:36 pm
         [121]Permalink
 
         Yes, and AlphaFold is not based on generative AI. AI was everywhere
         before November 2022, and it’s not going anywhere. GenAI and LLMs are
         the bubble.
 
         [122]Reply
   • [b41d7825]
     Maub Nesor
     December 22, 2023 at 3:34 pm
     [123]Permalink
 
     Interesting article, but very static analysis in a very dynamic field. The
     hardware cost issues will most likely be address by some combination of
     Moore’s Law and Application Specific Integrated Circuits (ASICs), so that
     is a short term hardware issue.
 
     The software also has a lot of room for optimization. Particularly with
     zero weights. It may also turn out that the saying about Neural Nets, that
     they are the technology of the future and always will be, is true. Who
     creates a system that mucking large with no instrumentation? That’s eng101.
 
     As for accuracy, they need to start training with a None Of The Above
     (NOTA) category. We used to use it all the time in the days before NNs when
     simple feature vectors and convolution filters were used. Once a calc falls
     below a confidence level, it gets flagged. So with the radio example, the
     AI picks off all the easy ones. The ones that are obviously clean or
     cancerous. The hard ones get flagged for a second look. Using multiple AIs
     and voting is also an effective approach. But what ever they do they must
     instrument the mucking things. It has to be able to explain why it made
     this choice. IDing every spot on the X-ray and labeling it, is a start.
 
     The current products are absurdly bad. Ask it anything it doesn’t already
     know, and it looses it’s mind. Even things it should know, are not immune.
     Again, Eng101, before you do anything with it, ask it questions you already
     know the answers to. Don’t even trust the fact check. I’ve seen cases where
     the model was right and the fact check got it wrong. You just can’t make
     this spit up.
 
     As always, just my $0.02 worth. It may all just be an AI hallucination.
 
     [124]Reply
   • [013e47ac]
     Justin
     December 22, 2023 at 4:02 pm
     [125]Permalink
 
     Doctorow is being pretty reductive here – LLMs (specifically the
     transformer algo) are being used for so many interesting things I can’t
     imagine we’re going to run out of giant data sets ripe for mining for
     meaningful discoveries any time soon. That’s the real application of
     transformer AI – making sense of enormous amounts of data.
 
     For instance – protein folding – Deepmind’s Alphafold basically
     short-cutted the human race in that area by about 20 years. Same thing for
     material science – they used the same technique to discover a couple of
     hundred thousand different material compositions that we’re working with
     now to find the useful ones.
 
     Then of course there’s DNA interaction, all of the medical records in the
     world, deep space scan research, etc…. there’s sooooo much data out there
     just waiting to be devoured by an LLM there’s got to be a crapload of money
     on the table.
 
     [126]Reply
   • [f2d06772]
     Ephemerality
     December 23, 2023 at 2:20 am
     [127]Permalink
 
     I’m not sure AI is a bubble, but mainly because I’m hopeful that by
     learning from AI and machine learning we can learn about human
     intelligence. New research of AI discovering a new class of antibiotics (
     [128]https://www.scientificamerican.com/article/
     new-class-of-antibiotics-discovered-using-ai/) or helping to control
     nuclear fusion ([129]https://www.wired.com/story/deepmind-ai-nuclear-fusion
     /) to the point where ignition has now been achieved multiple times makes
     me think AI or at least some version of it is here to stay.
 
     [130]Reply
   • [de743c9c]
     Davide
     December 23, 2023 at 6:45 am
     [131]Permalink
 
     I don’t think the whole article is correctly set. There’s a lot of talk
     about AI but it’s misleading since we should talk about Generative AI which
     has gotten mainstream this year, while AI as a general technology has
     already in use for decades. Talking about GenAI, there’s certainly a lot of
     hype around it but it is a great assistive tool to enhance several fields
     of human work, even if we are still in early stages. A lot of improvement
     could and should be made but it is a promising direction and I don’t see
     the reason to criticize too early, but I agree on reasoning and discuss
     about it.
 
     [132]Reply
   • [37108fa8]
     JB
     December 23, 2023 at 7:13 am
     [133]Permalink
 
     Whoever says ‘bubble’ first after this comment buys the comment section
     drinks.
 
     [134]Reply
   • [7311ebb8]
     [135]Jeff Hecht
     December 23, 2023 at 8:23 am
     [136]Permalink
 
     Good analysis. I was deeply immersed in the fiber-optic portion of the
     dot.com bubble, and the fundamental problem there were that the technology
     got a decade or more ahead of the demand for bandwidth. That, in turn, was
     powered by Worldcom’s claim that internet traffic was doubling every three
     months — which may have been true for one company for quarter around 1995,
     but was not sustained. No carriers were willing to release data on actual
     transmission growth because they all considered it proprietary, so the
     hustlers made up numbers, and the market wanted to believe.
 
     I remember worrying about the actually fiber capacity and demand back
     around 1999, and thinking thinking this irrational exuberance could not
     sustain itself, but the dot-com crash didn’t spread to fiber until late in
     2000. What happened was that nobody looked down. It was like the laws of
     cartoon physics; Wyle E Coyote’s legs kept churning after he ran beyond the
     edge of the cliff and the law of gravity did not take hold until he looked
     down. And when the market did look down and see how far they were above the
     ground, the market dropped like a rock, and big tech stocks dropped to
     pennies on the dollar.
 
     [137]Reply
   • [e10c8a21]
     Bob
     December 23, 2023 at 11:32 am
     [138]Permalink
 
     I think the large scale AI providers trying to corner the AI market is
     definitely a bubble and the large scale AI can solve it fantasy is
     currently a fantasy. However even they are making an immediate and
     fundamental changes. All of our developers are absolutely dependent on AI
     for bug checking and finding incremental solutions much faster as they
     provided the creativity to what they are building.
 
     As someone who specifically works adjacent to Radiologists working in AI
     and Deep Learning they have been working on imaging specific AI since long
     before the current bubble. It is about carefully training rigorous models
     on controlled data sets to improve accuracy and efficiency. They presume
     multiple layers of QA and project better overall outcomes. Dealing with PHI
     absolutely limits how and what kind of third parties are involved. Having
     dedicated experts working on refining specific closed AI systems is
     required to avoid poisoning.
 
     My point being that AI is more than just the current bubble and those more
     focused incremental change areas will have long term impact. The current AI
     hype and fraud you discuss will also have negative long term impacts but
     that is no different than any other corporate fraud bubble.
 
     [139]Reply
   • [ed66d318]
     Mark
     December 24, 2023 at 12:40 pm
     [140]Permalink
 
     The answer I suspect is that there will be a limited capacity regardless of
     cost effectiveness so only the most profitable will even be supportable.
     National Security seems to be the most likely beneficiary with the NSA, FBI
     and DoD using it for things like threat analysis, early warnings, missile
     targeting, radar enhancement, etc. Places where the scope can be defined
     sufficiently that accuracy and reliability are substantial. The other place
     I would guess is agriculture as rising chemical costs drive farmers to need
     tools to identify soil conditions, plant health, weed growth and water
     needs in real time. Another set of data than can be relatively simply built
     and operated.
 
     Replacing US workers writ large is going to take AGI and that’s not even
     conceptually related to these LLM programs.
 
     [141]Reply
   • [ff29b880]
     ape
     December 24, 2023 at 6:07 pm
     [142]Permalink
 
     Over 20 years ago, I was told that the AI embedded in speech recognition
     software would replace human court stenographers and closed captioners “any
     day now.”
     I’m still waiting.
 
     [143]Reply
       □ [c2364a84]
         dude
         December 29, 2023 at 7:34 am
         [144]Permalink
 
         This one example proves technology improvement never happens.
 
         [145]Reply
           ☆ [ede5d74f]
             [146]ViennaMike
             December 29, 2023 at 2:23 pm
             [147]Permalink
 
             Sarcasm don’t? Because we may not have those particular
             applications doesn’t mean that AI hasn’t improved voice recognition
             by multiple orders of magnitude, leading to new applications,
             including smart speakers, automated translation that is good enough
             for many use cases, and more.
 
             [148]Reply
           ☆ [ff29b880]
             ape
             December 29, 2023 at 2:25 pm
             [149]Permalink
 
             That’s not quite the logical leap I would make, but you do you.
             Court reporters being replaced by technology just happens to be my
             personal litmus test for whether or not I start taking recent
             claims about AI seriously.
             Keep in mind that court reporters in many venues make 6-figure
             salaries.
 
             [150]Reply
               ○ [ede5d74f]
                 [151]ViennaMike
                 December 29, 2023 at 2:35 pm
                 [152]Permalink
 
                 There’s a huge difference between “technology never improves”
                 and “AI is currently over hyped.” The first is demonstrably
                 false, including for AI. The latter is hard to disagree with,
                 and I certainly wouldn’t.
 
                 AI’s abilities has grown by multiple orders of magnitude and is
                 now routinely used in many new applications AND, at the same
                 time, it is greatly over hyped.
 
                 [153]Reply
   • [1dc6eb0a]
     terrymac
     December 24, 2023 at 6:36 pm
     [154]Permalink
 
     Are we investing more in AI than its potential worth? I would agree that
     many of today’s firms will go bust, especially those who think that an AI
     version of Petsmart will dominate the world.
 
     But I wonder: NVIDIA became a trillion-dollar firm, but it wasn’t entirely
     hype-based. They have been “eating their own dogfood” by using stacks of
     their hardware to speed up the design and validation of subsequent versions
     of their chippery. A particularly notable example is the development of
     CuLitho, which accelerated computational lithography forty-fold. I can cite
     many other instances where AI has already led to vast improvements in
     productivity.
 
     Today’s chatty LLMs are rife with problems, but they’re not the Omega of
     the process, but the leading edge. Many other approaches are designed to be
     truthful and transparent and useful; from my survey of arxiv preprints, a
     great deal of substantial improvements are in the wind.
 
     [155]Reply
   • [9cfe0d38]
     Hadoom
     December 25, 2023 at 9:23 am
     [156]Permalink
 
     The author conflates overinvestment bubbles with other types of crises /
     bubbles such as accounting fraud (Enron), or debt crisis (2008 / 2009).
     This apples and pears comparison makes that part of the analysis invalid.
 
     Overinvestment bubbles usually leave behind the assets that were
     overinvested in. The first / most famous case was the US railways in the
     1800’s. The fiber optic overinvestment mentioned is another great example.
     One could argue that all government infrastructure programs are in some
     ways like that, not justified by economic calculations within the normal
     time horizons but accretive over the long term nevertheless.
 
     So in that way of course something will be left behind.
 
     A useful part of the analysis is the 2×2 on monetizability (!) vs tolerance
     to errors. I agree the venn diagram is narrow. The supporters of the AI
     case usually point to the rate of improvement and to the fact that we
     barely started exploring the use cases.
 
     AI seems to work for well bounded problems and less well for open problems.
 
     If AI drops the cost of coding software by an order of magnitude it will
     have already paid for itself.
 
     [157]Reply
   • [3878c71d]
     Adam
     December 28, 2023 at 6:25 pm
     [158]Permalink
 
     You people imagining that LLMs will make “coding” easier or faster
     (laughably “drop the cost of coding software by an order of magnitude”!)
     probably don’t have much experience in software development. Writing the
     code is a very small part. Putting it together, testing it, maintaining it,
     enhancing it and above all finding and fixing bugs takes far longer. Right
     now LLMs are best at creating bugs – what they write always looks plausible
     but usually isn’t 100% right. LLMs don’t just take the best and most
     expert-reviewed code as their learning, they take ALL the code. Garbage in,
     garbage out. I hope my competitors rely on “AI” code, I really do.
 
     [159]Reply
       □ [ede5d74f]
         [160]ViennaMike
         December 29, 2023 at 2:30 pm
         [161]Permalink
 
         I’ve been playing around with ChatGPT to aid in writing and debugging
         software and I agree 100% with your assessment of the current state of
         the art.
 
         The question is can and will this change? Personally I think it’s an
         open question.
 
         [162]Reply
       □ [e222c1b0]
         khlorghaal
         December 31, 2023 at 4:00 am
         [163]Permalink
 
         6 months ago artists were mocking midjourney for not being able to draw
         hands.
         the state of the art ones draw hands quite well.
 
         [164]Reply
   • [ff66ca8c]
     EPL
     December 30, 2023 at 12:30 pm
     [165]Permalink
 
     Personally, I expect at least benefits in communicating to and from
     automated systems.
 
     But, setting everything else aside, let’s remember the energy cost of
     generative AI. The next few years are critical for our energy transition
     and the planet needs the help from all of us. I think we need to hold cloud
     services accountable to transition to fully renewable energy, with an extra
     emphasis in not negatively impacting other services and industries
     transitions to renewables on our (shared in most cases) grid.
 
     [166]Reply
   • [4223749f]
     [167]RM
     January 8, 2024 at 4:55 pm
     [168]Permalink
 
     The way programmers are talking about this right now reminds me of various
     earlier fads that ended up leaving massive amounts of cleanup and
     administrative work for decades in their wake. I have absolutely no
     programming skills myself so I can’t comment further on that.
 
     I think both sides of this argument have clear, ulterior motives, but
     basically only Doctorow, his comrades in arms, and maybe one or two of the
     pro “AI” set are being honest about what they are. Doctorow’s is, while
     technically no less ulterior, in the end really much better served by
     honesty. if he gets this wrong, for example, it will be much more lucrative
     for him to market his work, as having been improved by learning from the
     failure than if he simply asserts to have always been right. That kind of
     stuff might fly in certain political circles online, but they aren’t the
     ones he has clear incentives to work. while other ulterior motives on
     display in the comment thread, like desiring prolonged control of a budget,
     actually would be quite threatened from having been demonstrated wrong. It
     is hardly a coincidence that these are exactly the people claiming that the
     bubble tech in question is actually intelligent in some meaningful way such
     that calling it AI isn’t a lie.
 
     Now we are talking political linguistics, which is more of my wheelhouse.
     while the article does also use the buzzword AI, the truth of that name is
     really outside the scope of the piece. This is less appreciably the case
     for certain arguments in the comments, which speak of it as currently
     measurable and significant intelligence we should not hasten to distrust.
     That only raises more questions for me.
 
     [169]Reply
       □ [4223749f]
         [170]RM
         January 8, 2024 at 5:00 pm
         [171]Permalink
 
         Case in point: after a relatively painless transcription with apple
         speech to text, the compulsory “AI” rewrite forced me to intervene in
         every sentence multiple times to fix errors added after I had visually
         checked the transcription and still missed several. While I don’t get
         Perl, I have never made grammatical or stylistic errors like those in
         my life. I am a disabled person currently being coerced to manually
         enter text, at slight but real cost to my mental and physical health-
         if it were literally any more difficult I wouldn’t have bothered- to
         clean up after “AI” from this little upstart called APPLE COMPUTERS.
 
         And this is supposed to get BETTER after the bubble pops?
 
         [172]Reply
   • [9588b9c1]
     Nishan Stepak
     January 12, 2024 at 2:43 pm
     [173]Permalink
 
     The point of AI in this article like most of what is being written misses
     the boat completely. Where AI will make a difference is incredible fast,
     automated scientific discovery. Google is already developing hundreds of
     new materials using AI, accelerating protein folding, and advancing fusion.
     Apparently Deepmind has an algorithm to stabilize fusion. Microsoft just
     made a bet with Helion energy with a power purchasing agreement for fusion
     energy eight years from now. There is a prediction, that in the next 10
     years, there will be 50 to 100 years of scientific progress in automated
     research done by AI. A lot of what is in the news misses the boat
     completely. The bubble will pop and we will find ourselves with a whole lot
     of new scientific breakthroughs in energy and materials.
 
     [174]Reply
   • [37a0695f]
     Andrew Dabrowski
     January 28, 2024 at 6:32 pm
     [175]Permalink
 
     This reminds me of Michael Kinsley’s confident prediction in the early
     1980s that the AIDS kerfuffle would blow over when people got bored with
     it. But diseases don’t go away due to boredom.
 
     But with medical development and safe sex, AIDS did weaken as a menace over
     the years. But AI won’t weaken, it will just get stronger.
 
     [176]Reply
   • [d3c8efce]
     Glen
     February 1, 2024 at 4:51 pm
     [177]Permalink
 
     Many people seem to be missing the point.
     It’s not whether these algorithms work or not, or are even fit to purpose
     in some cases. That’s not really what defines a bubble. Bubbles are built
     on useful stuff, like the 2008 housing bubble. Houses are good, far better
     than living in a tent. The bubble was on people throwing more money at the
     houses and loans paying for those houses than those things would ever be
     worth.
     These kinds of algorithms have been used for many years already for all
     kinds of useful stuff. Scanning handwritten text into usable text in a
     computer. Or speech recognition. Good stuff that takes horrible drudge work
     from humans.
     A bubble is when the current investment fad is tossing money into something
     where there’s no clear return on investment, and instead of backing off
     when concerns are expressed, doubling down and throwing more money at it.
     And when too much money is flying around the scammers and grifters start
     showing up to skim a little (or a lot) of that money for themselves.
     For many people, to admit the initial investment, or the obvious scam,
     they’ve just thrown millions of dollars into might be an error is more
     difficult than throwing more money at it and ignoring, or attacking, any
     criticism of the thing.
     What Cory is saying here, and what many other critics of so called AI are
     saying, is the cost of running LLMs is far in excess of what anyone will
     pay for them, and there’s a limit to their capabilities that means you
     still need experienced professionals to do complex and difficult jobs. That
     even if Waymo figured out how to get their self-driving cars to be
     monitored by .75 humans and don’t kill anyone they’d still be losing money
     and go out of business eventually; the investors will lose their shirts
     while the VC’s and founders and scammers will come out of it all with bags
     full of money.
     I can’t tell if the article is saying there will be useful stuff left over
     when the bubble pops or not. Personaly, I think the AI mania has already
     caused too much destruction. The greenhouse gases barfed out to build and
     power the server farms alone is too high a cost.
 
     [178]Reply
       □ [37a0695f]
         Andrew Dabrowski
         February 2, 2024 at 9:45 am
         [179]Permalink
 
         The technology will improve enormously and prices will come down
         correspondingly. AI is guaranteed to make some people fortunes. Of
         course you’re correct that it will also probably ruin many more.
 
         [180]Reply
       □ [9eb71600]
         Curt J. Sampson
         February 2, 2024 at 6:32 pm
         [181]Permalink
 
         Bubbles are not always built on useful stuff. Consider the 17th century
         Dutch tulip mania. A tulip bulb, while not entirely useless, was not a
         new invention, and was not adding any significant utility to society
         that it had not already been adding for centuries before the bubble.
 
         [182]Reply
   • [0678c148]
     Rick
     February 10, 2024 at 9:47 am
     [183]Permalink
 
     Gen-AI has a strong tendency to hallucinate… traditional (non-gen) AI does
     not.
 
     [184]Reply
   • [307ba98d]
     Zac Sims
     February 27, 2024 at 3:42 pm
     [185]Permalink
 
     “There will be a lot of people who know PyTorch and TensorFlow, too – both
     of these are “open source” projects, but are effectively controlled by Meta
     and Google, respectively.”
 
     PyTorch is now governed by the PyTorch Foundation, an independent
     organization within the Linux Foundation, so Meta does not control it
     anymore.
 
     [186]Reply
 
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