Money bubble
tbray.org
tbray.org
As someone who actually had to deal with the government recently in the US I disagree. It was impossible to reach a human or otherwise get an answer to my likely not too unusual question. If they had an even half decent LLM then I'd have probably had my answer and action items for me to do within 30 seconds. Instead I've wasted days in various attempts to get some type of answer.
I recently needed to fix some issues in something I filled with the government. Email support used to exist but probably cut due to budgets. Chat support used to exist but probably cut due to budgets. Phone support has no waiting queue and require 1 minute of entering numbers to hit the disconnect point (due to not available agents). Physical mail seems an option but I don't know the format or address. Etc.
And you can't even do that with Social Security anymore.
If it is something you could be legally liable for, I'd at least send a certified letter to whatever address you can find, so that if it becomes a problem later you can at least show you tried.
They do try to discourage it sometimes. The local passport office has a sign on the door that says "by appointment only." The first thing you hear upon walking in is "if you don't have an appointment get into line B." If you have an urgent mater they will take care of it without an appointment. I wonder how many people turned around upon seeing that sign on the door. Dark patterns left and right to make it harder to get anywhere.
it has been, and remains to be, the case that the main purpose of certain parts of public services is to give people employment. there is rarely any meritocracy at scale once you get the job.
the reason why we get poor service cannot be completely put down to getting understaffed or lack of budget. while the UK govt has a better public service experience online than many developed countries, this approach I feel is missing the forest for the trees.
That does not sound right. This could be partially true in old days (here in Poland during communists rule) but nowadays all public service has stated purpose that has nothing to do with employment. The purpose can be total b*s of course but almost always has nothing to do with just providing jobs.
I doubt solving people's problems is even 10% of the time government customer service spends talking to people. Letting them spend 50% actually solving people's problems would improve everyone's lives.
If the switch to LLMs is largely a cost-cutting measure for organizations, I could see that the human operators—though downsized—would continue to receive the same compensation as before. In short, they will be paid the same to do more and harder work. If their performance metrics are based on how quickly they can close a case, these cases will never receive the amount of effort they need to get properly resolved. That is bad for the customer, who can't get a strange but pressing problem solved, and it is bad for the employee, who has to work harder at the same rate as before. The only person who comes out ahead is the capital owners.
I've sat with help-line operators for a medium-sized consumer tech company. It seems like 80% of their time is spent troubleshooting very niche issues, with the simple ones sprinkled in for levity. People need wins in order to feel good about their jobs. If it's all difficult problems—at bad pay, then that's just torture.
The bigger problem with LLMs as they currently stand is that one can easily bully them into breaking outside their normal operation parameters.
With all that said, what makes you hope any government LLM will escape the whims of budget cuts? I'd rather walk in and wait for hours than share a 500 token/sec chatbot with thousands of other users and never get a resolution.
During every other hype boom I have been through that ultimately failed, those regular Joe types either hadn't even heard of the tech, simply didn't care or were actively hostile to it. Comparatively, with the new generative AI people are talking about how much they love it, how they use it every day, etc.
Even the Internet had a bubble that popped (back in the Pets.com days, circa 2001 [1]) and this short-term AI bubble will pop too. I expect the same pattern as the early Internet: an early pop followed by a recovery that leads to massive growth.
Nvidia is lucky though, a lot of big companies will want their GPTs in-house to ensure their secrets won't be used to train someone else's GPT, and that means buying a lot of hardware (could be on a cloud data center too, but, same result for Nvidia)
In fact, there are few Internet companies from the early 2000s that made it up until now intact. The same may end up true for this crop of AI.
But anyone who was alive during that time and working should ask themselves: would your career have been better or worse if you started getting familiar with Web Technologies in the early 2000s. What if you saw the impending dot com crash and you decided that the entire Internet was not going to live up to the hype?
I don't have a crystal ball but my gut is telling me that 20+ years from now we'll see any short-term market correction around AI as a blip.
Worse. The only time there was any real money to be made with web technologies was prior to 2000. Plain old boring RPC, serving interesting data to lowly web developers, is where the money has been made since.
But I would wager many people are like me, having made a comfortable income on the back of tcp/ip, dns, bgp, http, html, javascript et al. and it is hard for me to think of very many jobs in the TC range of FAANG companies where familiarity with those technologies is not a requirement.
My point wasn't: you should have been a web developer in 2000. It was: in early 2000s the Internet was the most important advancement in tech and you should have been learning all about it. I am arguing that the same holds for the recent surge in AI technology.
My reaction when I hear this is that those people are being paid entirely too much money if an LLM can do their job. I think this is where the real economic impact will come from: when managers realize it's just LLMs generating emails to be summarized by LLMs and it's just bots spamming each other with busy work all day. At some point companies will realize it's all pointless and start trimming these pointless jobs, leaving a lot of people without any actual skills.
That feels like such an unnecessarily cynical view to me. First, parent comment didn't say they are using LLMs to "do their jobs". Frankly, I feel that if you're a knowledge worker and aren't using LLMs at least part of the time, you're likely being inefficient. E.g. LLMs don't replace my skill as a software developer, but they sure make it faster to learn new libraries/technologies faster.
I like that it can figure out my boilerplate, but I wouldn’t trust any info it spits out.
I’ve had it write incorrect SQL many times, and when it is correct it’s not often the best query, so I only have it write sql for one off queries.
Not the greatest example. LLMs fundamentally cannot replace software developers. At the end of the day an LLM is just an interpreter, much like python, but using a different programming language. Any input to an LLM is developing software.
Perhaps the previous comment would be more understandable if phrased as:
"My reaction when I hear this is that those people are being paid entirely too much money if software developers can do their job."
The author said that AI was and has been obviously useful, but there’s a lot of dumb money flying around in AI land (to try building things like AGI)
What companies are making a ton of money on AI? Nvidia? Nvidia makes money selling chips to large, massively profitable companies that are in an arms race to capture as much market share as possible. Or they are selling to smaller companies trying to make a name. But none of those companies are making any money from the AI services they sell. All of them are spending massive amounts of capital.
What happens when we reach an equilibrium point where AI services are ‘good enough’? I’ll tell you what will happen, it will become another cost center for the big companies and all further development will cease once they have eliminated the competition.
Want an example? Smart home speakers. Do you think that Alexa and Google home are the best that they could do? Do you ever wonder why Alexa came out and made a splash and then Google frantically made one of their own, but once market share was evenly split between both companies all new development ground to a halt? It is because they were only going to spend enough to keep the other company from dominating then stop spending. Because there is no way to monetize it. Not really. You can charge for the hardware, but that is a pittance to them. Can you charge for the service? I use my google home all the time for some things, but if they told me they were going to start charging me would I continue using it? Probably not. There is a reason Amazon recently RIF’d a bunch of people on the alexa team.
People say that this is not like pets.com because profits are real, but are they? Or is it some crazy ponzi-like thing where the amazing profits being had by companies like Nvidia are going to dry up eventually. I say it more like Cisco of 2000, where they were making tons of money selling hardware to all those pets.com companies. Follow the money. Once you get to the person/company paying for the service, there is none. Not on this scale at least. I think there will definitely be companies that pay for an AI service, but the amount of total market spend will be somewhat less than the total spend for something like cell phone service or streaming services. You know, like those things that everyone you know, from technical to luddite, from rich to poor, all pay for. I don’t see AI reaching that level of ubiquity. Do you pay for email service? I know that the people on this site do, to some extent. Email providers charging for their services are a niche market. AI is destined to be the same.
Just to be clear, I agree with you that this will be like the internet was. It will change the world. But it is without a doubt a bubble, just like the early internet. And for the reasons that you say - everyone can easily see that it is ‘something’ just by using it. The barrier to entry is very low, just like opening a browser and going to a website was. It does not take a genius to see the potential. Which is all the more reason that dumb money is flowing like water into this bubble.
And this will not just be in government, it will be everywhere. The scariest part is that as people start to spend less time developing a skill set, and instead deferring to AI answers, you will cross a point where this problem can't be fixed (because nobody has the skills to fix it and the AI is trained on the outputs of previous generations of humans).
For the "olds" who already have a skillset, this will be incredibly lucrative (as those who can afford to pay to fix it will handsomely). But the potential for this to—at best—plateau humanity and at worst, make it regress, is significant.
The dark humor in all this: we thought AI would get us the Terminator, but instead it's going to get us rapid degeneration.
---
Edit: an addendum, the overall point I'm making is well encapsulated in this talk https://www.youtube.com/watch?v=ZSRHeXYDLko
The government could then save money and provide better service for menial tasks such as "what permit do I need to do such and such"
You're still wrong on the merits, though. To pick one example, the folks who work at my local MVA ("DMV" in most states) office do not work for me, though they're paid out of my taxes and those of everyone else who earns in Maryland. They don't report to me.
Nor should they, because if they did, they would also report to that freak who drives a car plastered with QAnon garbage around Perry Hall.
My city councilman and my General Assembly representatives work for me, but the people employed to deliver services managed by the state and city government do not.
I presume you mean to imply that civil servants are paid by your taxes, but that's not true either if we're talking about a sovereign state - a moments consideration would show that spending must preceed taxation, which is true as a matter of accounting.
Really you'd do better to note that state employees allow you to get money enabling you to pay your taxes (but that's also not a very helpful way to look at it).
This comes back to bite California every time there is a major tax revenue crunch for whatever reason.
If it means "they cannot make payments on bonds and cannot issue new ones because nobody wants them" then the Feds will have to step in, or they'll have to liquidate state assets (including privatizing various governmental functions, selling land and leasing it back, etc), or raise taxes to balance the budget. They literally cannot print money.
This cycle has already destroyed a few cities (usually the city gets swallowed by the county).
There's a step where they issue "warrants" like CA did a few times: https://taxfoundation.org/blog/california-issuing-state-warr...
https://reason.com/2019/03/01/companies-should-avoid-states-...
Edit: here's my offer for the UK case: https://www.ucl.ac.uk/bartlett/public-purpose/publications/2...
If you want to argue something that directly contradicts that analysis, I await with anticipation.
However, I suggest they probably should care given how much policy is guided by an incorrect understanding of the monetary system. The whole concern about deficits and sovereign "debt" is the obvious one.
In addition, a good understanding of why taxation is necessary helps to understand which taxes might be useful and which are not.
Finally (for now!), policy options open up when you understand this stuff properly that make no sense at all through the state-as-a-household view.
Politicians need to be held to account and an ignorant population is not able to do that.
Put another way, go ask some African nation that got "bailed out" by the IMF if their "deficits didn't matter."
Where's the mechanism for inflation?
It's government spending that doesn't cause a deficit that is potentially inflationary, not the deficit spending part.
That is, the ability of a state to provision itself is driven through its currency which in turn is driven through taxation.
The US Treasury website provides a wealth of information about how the Government collects and spends its revenue.
Here's a more complete analysis of the US: https://www.jstor.org/stable/43905834?seq=21
Edit having read your edit: that paper doesn't have the rest of the circuit so can't explain the source of the money.
If you haven't taken an accounting course already, I would highly recommend it!
When you have the whole model, you'll see that money creation (triggered by spending) has to preceed destruction (from taxation for the most part), otherwise nothing can flow.
A critical point to take from this is to note that all the entities in the circuit need to be in balance (for the double entries to be correct). That means that for the private sector to have net savings and the foreign sector to be in surplus (i.e. a historic current account deficit), the government sector (which includes the central bank) must be in deficit.
This is super important! It means that deficits are not just not a problem, but that they are a necessary part of the system. It also shows that governments are not financially constrained (there's no limit on how big the numbers can be), so taxation is not needed for getting the money to pay for things. [2]
Steve keen has a blog post that goes through much of the accounts behind this:
https://profstevekeen.substack.com/p/money-from-nothing
[1] receipts and outlays is interesting terminology vs income and expenditure - perhaps noting that the government is not a business?
[2] however, governments very much _are_ resource constrained, so they must use their infinite buying power very wisely, otherwise inflation ensues. This shows one of the main purposes of taxation, which is to induce the private sector to provide real resources that the government can purchase.
I’m not going down the MMT rabbit hole with you; it is a waste of time.
Can you elaborate on that? I can think of numerous examples from history for how governments bootstrap themselves. If your point is as simple as who pays the tax collector, the tax collector can be paid on commission, debt, or with plunder.
The general case is more or less the same as the UK with only the details varying. As noted elsewhere, this doesn't apply to non sovereign states.
However, I'm sure that's unacceptable to some folks and they'll soon crapify it.
Yes, but apply Murphy's Law. They could also automate something like appeals to eminent domain claims and make it impossible for you to fight. Imagine being told the family farm that's been passed down over 5 generations is now going to be claimed by the government and turned into a parking lot for a new "justice center."
When you go to appeal, the hyper-efficient but devoid-of-empathy AI bot just says "sorry, Dave, I'm afraid I can't do that."
Who will be held accountable when these promises evaporate? My problem is not with innovation, it is with falsehoods and lack of accountability for those falsehoods.
Edit: As PheonixPharts says in another comment:
> All these jobs being "replaced by AI" are simply being eliminated with the consequences of them being eliminated ignored. Customer service jobs aren't being replaced by AI, companies, like Klarna, are just giving up on customer service and using AI to increase their perceived value rather than reducing it.
https://news.ycombinator.com/item?id=39554367
You don't need an LLM to do that. You can ignore your customers just fine without it. Cut out the performance art, go straight to zero without it. It is still mostly a powerful search engine backed by the equivalent of a knowledgeable, not a replacement for human support. If the human is not providing what is needed, that is a system failure, not a human failure. This tech augments the human, it does not replace the human.
Unless and until the executive and the legislature can seriously threaten the financiers, journalists, academics, lobbyists, judges, bureaucrats, etc. again, like FDR could, you can't expect accountability. The politicians are just the faces that implement other people's decisions, and those other people don't even have elections to lose.
I'm willing to wager that a govt. employee processing, let's say renovation permits, sees no more five applications in their entire service history that are textbook as in can be approved without any required corrections.
Extend that to any other application, and you'll quickly see the value of an experienced government employee helping you navigate the bureaucracy. If you haven't yet, then you are likely very young and/or your parents have taken care of everything for you to date. And before you blame the civil servant for the byzantine rulebooks, I want to rush to remind you that those civil servants only interpret the laws/rules. They have as much hand in creating them as you or I.
If the "olds" learnt a skillset at some point, the data they used to learn the skill is presumably available to the AI too. Why can't the AI learn it too?
(Not talking about physical labour which clearly has way less potential to be replaced than knowledge work)
Better creators, not learners. AI can't create, it can only remix what's already been produced by humans. Human progress is created, not learned. The olds who are conditioned to try new things when an existing solution doesn't work still have the capacity to create something new (wholly new, not just remixed new).
Unfathomably grim even if the alternative is rigid low skill bureaucrats.
I find LLM's extremely fascinating but if this is the end game i really hope AI free zones will emerge.
You can already see Gen Z being obsessed with face ranking filters, "looksmaxing" from data points and using filters day to day. It's dark.
All these jobs being "replaced by AI" are simply being eliminated with the consequences of them being eliminated ignored. Customer service jobs aren't being replaced by AI, companies, like Klarna, are just giving up on customer service and using AI to increase their perceived value rather than reducing it.
Did it, or is it like the automated elevator? The automated elevator didn't reduce the number of elevator operators, it substantially increased the number of elevator operators.
The technology is clearly flawed. Regardless, a lower cost option (AI) is replacing a higher priced option (human labor). Ten years ago these tasks would have been handled by human employees or specialists.
There is certainly a history of this in the corporate world, when expensive union labor in Detroit is replaced by lower cost workers in Mississippi, manufacturing experts are forced to train their replacements in lower-cost countries, or entire engineering and customer service departments are shifted overseas.
And the thing is, the more the risk the less likely an LLM is going to be trusted to just make a decision.
For example, do you think an insurance agency would want an LLM to decide, on it's own, claim approvals? Can you imagine the headache for the insurance agency if the LLM approves the wrong claims or denies the wrong claims?
Or for a doctors office, Imagine AI diagnostics without a human. Can you imagine the headache for a hospital when an AI misses cancer? Or diagnoses a false cancer? It's bad enough when humans get that wrong, but now you have to explain to your legal team "We just left up the practice to the stats gods!"
You say this, but I sometimes worry that these issues are hand-waved away by decision-makers with, "oh we'll just have another LLM doing frontline claim support to verify these issues." It's the whole XML/violence thing, where the solution to XML-induced pain often ends up being more XML.
To be slightly pedantic, the Web killed travel agencies for individuals who were mostly interested in booking flights, cruises, and big city hotels once making those bookings became easy on an individual level.
Companies absolutely still use corporate travel sites, for reasons good and bad.
And there are various types of specialist tour operators, arrangers of private trips, etc. who have on-the-ground knowledge of specific locations--and often won't even book things like air travel for you.
(More broadly, the Web forced travel agents of various types to add value above and beyond what a travel portal or a random travel agent could do because they were gatekeepers to the needed systems.)
(From time to time I have some trivial questions, but not enough hundred valued bills, to ask actual doctor)
I agree with your point though, before starting on chemotherapy I would definitely shell out some money for opinion of a live doctor.
I’m not calling to check my balance or get directions. I’m calling because your system did something wrong and needs to be overridden
Half of the jobs will be replaced with AI soon. Writers? No need to have huge team, one senior is enough. Developers? Lawyers? Illustrators? Lay off half and replace them with AI tools!
My experience, having grown up when all customer service reps were people, is exactly what I stated above: this is just giving up on customer service.
Anyone who has ever called automated support knows this. When you reduce 10 people to 2 people and some chatbots now you simply have to wait 5x as long for customer support.
I worked at startup a few years back that refused to scale customer support so they could be forced to "automate" the process. The result? Customers got completely screwed over, but those customers weren't investors so who cares.
I can't recall a single time in my life were automated customer support solved my problem, it just kept me busy so that the 5x wait doesn't seem as long since I'm trying to navigate the labyrinth of a customer support decision tree to get my problem solved.
- The AI assistant has had 2.3 million conversations, two-thirds of Klarna’s customer service chats
- It is doing the equivalent work of 700 full-time agents
- It is on par with human agents in regard to customer satisfaction score
- It is more accurate in errand resolution, leading to a 25% drop in repeat inquiries
- Customers now resolve their errands in less than 2 mins compared to 11 mins previously
- It’s available in 23 markets, 24/7 and communicates in more than 35 languages
- It’s estimated to drive a $40 million USD in profit improvement to Klarna in 2024
Apparently it affected a Call-Center's (Teleperformance) stock [1].
[0] https://www.klarna.com/international/press/klarna-ai-assista...
[1] https://live.euronext.com/en/product/equities/fr0000051807-x...
If AI costs were reaching very high levels perhaps they would try to make non-AI flows for standard processes. But I think that is unlikely given how cheap AI is vs smart people wages.
The cost of using generative AI to answer questions is orders of magnitude more expensive than using flows. Plucking a company out of thin air - Landbot [1] offers both flow and generative chats. For $100 per month you can have 2,500 flow chats, or 30 "AI" chats. That's nearly a 100x difference in cost. The risks are much higher too - with the flow builders if there's a sudden policy change or whatever then someone can just go into the system and edit it - with AI you'd have to retrain the model somehow. There's also no risk of hallucination with a flow based builder.
I'm not saying that Gen AI customer service chatbots don't have a use - what I'm trying to say is that in the real world, business would probably be better served day-to-day with just setting up decent flows in rules based bots. That's unsexy though - it doesn't attract tech talent, it doesn't get people promoted and it doesn't get shouted about in the press. It is, however, probably much better for the environment and the company's P&L (but possibly not their valuation if they're trying to ride the hype train).
However, I've encountered some pretty weird interactions with customer support over the years, including reportedly "The iMac can't do anything except browse the internet" when the demo unit on display behind them was running Nanosaur (a game); "we only support Microsoft Internet Explorer" when the customer support team didn't have that installed on their computers; «You need a Windows PC and an Android phone» from the German PostIdent people despite it being obvious they could talk to us while we used a Mac and that they knew this because they raised the issue spontaneously; and "yes, we will get your internet connection running by the end of tomorrow" from BT (it took them a month or two, by which time I had already cancelled; apparently someone put the wires in back to front).
It only took me explaining it 3 times, then telling him to "get a fucking person on the phone that understands tech", which he did and it was processed in minutes.
There's poor training, then there's just plain stupid.
Clarification: I'd given him the Service Tag, so he knew what device it was. He was insisting that I run the diagnostics and report the results, which is even dumber, in the end.
Also known as IPoV (IP over voice).
I think the subtle point is that not all humans will replaced -- it's just that a human and AI will be able to do the work of a few humans. Same work, less people.
People think AI technologies improve in a linear fashion. But there is nothing restricting this area of technology from non-linear progress.
Consider the failing prophesies of AGI as a precient example of what’s happening:
- It was 2015, and I’m reading articles about AGI being here in 2050 at least.
- It’s 2018 and everyone is talking about a few new research papers but secure in their predictions but maybe feeling like it is skewing towards the bottom end of that range (except for a few inspired nerds — my people — who boldly claim 2035).
- It’s 2022 and suddenly an AI is blowing people’s minds and we have the fastest growing technical product in the history of the world. Predictions are now 2030 for an AGI.
- It’s 2024 and people are debating if AGI has already happened and debating about the definition and many people are calling for an “advanced level” AGI in the next 2-3 years.
My point is that predictions of this technology have been terrible. Just like the worst. People have been off on every prediction by orders of magnitude.
So now every major tech company is blowing all their money towards AI, realigning their business towards hardware and software solutions and we’re in the middle of an arms race the size of Jupiter towards AI technology, and it’s happening across the world but definitely in both China and the USA where the stock market is going crazy and 25% of the entire markets growth is just nvidia’s massive growth (and those gains are basically powered by the leading ai training solutions).
So, the idea that somehow the technology isn’t going to replace people is asinine. This is the biggest, fastest tech wave I have ever seen, it’s growing geometrically, it’s funded by insane amounts of money and has most of the western and eastern world’s technology research focused on it, and has been wildly ahead of predictions from its inception.
Let’s get real about this. This is a bomb going off in slow motion and is set to interrupt employment and radically reshape society in a time scale that almost no humans can physically comprehend.
But more importantly the trend is accelerating and like most parabolic markets that don’t have physical limitations holding them back (like input materials for a gold rush) it could accelerate to literally crazy levels. There’s no restriction but breakthroughs here.
I’m aware of the current reality of the software systems and I know what I’m saying is futurist, but from a trend perspective we are way, way ahead of where we thought we were going to be and the trends point to railgun speed acceleration from here.
If AGI is achieved, it will almost certainly be a surprise rather than be intentional.
While I'm really impressed with ChatGPT, and am one of the people who regards it as meeting my prior definition of AGI[0], I can still see its current flaws, and do wonder if this is a similar case, where the first 90% needed a major breakthrough but once that was invented anyone could do it and many wanted in on the economic opportunity… but the second 90% turned out to be just as hard, and so was the third, … and you need at least six nines[1] to really replace humans in these roles.
[0] All three letters mean different things to different people. To me: it's artificial, it's much too general to count as a narrow AI, and I count it as intelligent because the things it can do were the things I grew up thinking were signs of intelligence, like speak Latin, do algebra, and answer trivia questions, and also things I added later like 'write code' and 'pass medical and law exams'; even though it gets the answers wrong sometimes, I don't think my standard was ever "must be perfect" because nobody ever scored 100% on exams at school either.
At its best (and it's weird that it even has a best and a worst), the free version of ChatGPT has given me better code than one specific real human I've had to work with, more if you also add in the students. (And at its worst, it gives me stuff that doesn't compile and wouldn't do what I asked even if I fixed the compiler errors).
[1] Assuming a driver is making 1 decision per second that has a serious wrong answer, it would take eight nines to have just under one serious accident in a lifetime of 1-hour-each-way commutes, 5 days a week, 50 weeks a year for 40 years. I suspect the actual time between opportunities for serious mistakes is less than that, but probably at least once per minute even on an empty road.
For an LLM, I don't know exactly how good they'll really need to be, all I can guess at is that they're not going to have more than one opportunity to seriously mess up per token.
But the question I would ask you is whether you are any barrier to simply scaling up the tokens on the existing technology? I don’t. I see us at the beginning of a ladder where the only input is capacity just like when Intel was young.
While breakthroughs can change the path, the truth is we have a fairly predictable step-by-step to much more capable systems without one just by scaling the size of the hardware.
Consider this argument:
1) I mean at core the crux of our learning is that predicting the next word may be what human thinking is generally about, and how our brain works, because that’s largely the innovation here.
2) Becoming better at doing that is entirely predictable and we can scale profoundly from current levels with hardware that we are putting into production right now and that we have already invented.
3) Therefore the path to next generation capabilities is relatively (and that’s important I admit) linear.
So prior to breakthrough, we have a simple path forward to what would be at peast fairly advanced capabilities of language and media prediction and manipulation.
Now your argument about progress is right. Predicting material progress of technology breakthroughs tends to be unpredictable and inherently dangerous, but we are in an accelerating trend and most of the time (big statement here, right?) the appearance of an acceleration trend tends to extend to continuance of the trend in a certain timeframe. At least that’s been the case with the “waves” of technology breakthrough since the Industrial Revolution according. I mean to invoke Smihula's theory of waves in this argument, since I know you’ll understand that.
Those last two arguments are statistically supported and quite logical. How smart does it need to be and how statistically probable are excellent points.
As an aside…
For me, one of the areas I am focused on and thinking about a lot is self-organizing AI agents.
Having worked a lot with large scale networks, agents and task specialized networked AI systems get me excited. My brain considers it a blue ocean opportunity.
The parallels between human civilization density and current learning about density of population in demographics driving essential human progress makes me believe there will be parallels in AI. The more, the more they will self organize into network effects, and the outcome of this, like human civilization, will be high quality and rapid progress. I am not a believer in one gigantic AI, but networks of networks self organized in a way where they self-optimize around goals and outcomes, and we are really just at the beginning of exploring this direction of the technology.
The biggest limiting factor to AI technology at this point is human input and the need for human oversight.
While that oversight is definitely necessary, once AI becomes self organizing and self-creating, progress should be profound.
Anyone who doesn’t think that’s going to happen needs to understand the nature of intelligence and realize it’s just a matter of time. You can’t go down this path in a meaningful way and repress only certain aspects of digital intelligence in the long term.
> But the question I would ask you is whether you are any barrier to simply scaling up the tokens on the existing technology? I don’t. I see us at the beginning of a ladder where the only input is capacity just like when Intel was young.
My expectation is that we need algorithmic improvements rather than scaling; AI can read approximately all of the internet, but current models need to actually do so just to reach the level of intern or fresh graduate. While this makes them superhuman in the breadth of skills they can perform, they need something else to improve the maximum quality in any given skill — in some cases, we can already train them on synthetic data or self-play, e.g. chess, though I don't know how broad an impact that would have.
But I do expect such algorithmic improvements, so in effect we are in agreement, if not in the details of how.
When it comes to hardware improvements, I'm not sure how that particular landscape will change over the next decade. Transistors are close enough to atomic scale they can't go on much longer, and Dennard scaling has long since stopped, but that doesn't mean nobody cares or that nobody is working on the energy efficiency. And if — just if, it isn't necessarily true — if human level intelligence needs a network with as many free parameters as there are synapses in a human brain, we're around 3-4 orders of magnitude away from that at present.
> I mean to invoke Smihula's theory of waves in this argument, since I know you’ll understand that.
Thanks, I was unfamiliar with it: https://en.wikipedia.org/wiki/Smihula_waves
> Having worked a lot with large scale networks, agents and task specialized networked AI systems get me excited. My brain considers it a blue ocean opportunity.
I think you're correct. The current zeitgeist is do-everything models, and the only blue ocean opportunities are found when you zig when everyone else is zagging, and vice-versa.
Although, be quick; if my cursory reading of Smihula's theory of waves was correct, you don't have much time before the current market reaches saturation, and moves on to the next thing.
Forget working for anyone, just make the machines do what you want.
This is creating a ridiculous wealth disparity and deincentivizing a whole generation to get good with a skillset. I already heard from a lot of young people that working is not worth it, hard to disagree with them when even a basic thing like a piece of land or a house looks out of reach for a regular person.
But as you put it unsustainable things are not sustainable, society will regress until the equilibrium is found again. But things didn't need to be like this.
> “We can say without exaggeration that the present national ambition of the United States is unemployment. People live for quitting time, for weekends, for vacations, and for retirement; moreover, this ambition seems to be classless, as true in the executive suites as on the assembly lines. One works not because the work is necessary, valuable, useful to a desirable end, or because one loves to do it, but only to be able to quit - a condition that a saner time would regard as infernal, a condemnation.”
> - Wendell Berry
Look, I'm no Bernie Sanders, but you have to be honest about the morality of it, and the feasibility of it. I don't see the current system lasting.
This linen of thought is just so lacking gratitude for the time and place you were actually born.
You basically won the lottery in the grand scheme of things but still complain.
Would it have been better to be born in 1950? Certainly not if you happen to be born in China.
How about in 1910 so you hit your 20s right as the depression hits. Or 1920 so you grow up in the depression then go fight in WW2.
How about Cambodia in 1970?
Yea life would be better if I was 6'4, strikingly handsome with a dead rich uncle that left me all his money too.
I pointed out a structural issue of the modern world and you came up with a pointless counterpoint about being born in an unfavourable condition in the past.
I guess I'm being trolled.
Letting citizens deal with their bureaucratic errands with an online form or portal instead of with a civil servant in their office has been an enormous benefit, in the places that offer this. An AI will fuck things up, being an AI, but it will not necessarily treat people with a hostile attitude and lie to clients to spite them. Unless it's programmed by civil servants, that is.
Well, Gemini just proved that if you're white, you're the ethnicity the AI is told to hate.
That's the thing, because it can be programmed by humans means at some point, it will be abused to do something nefarious. And because it only knows what humans tell it about reality, it will always "think" within the context it's been given (never in the abstract).
The loss of knowledge/skills was a key bit of Foundation which itself was a retelling of the fall of the Roman Empire.
As key skills become rarer, the price goes up.. until you can't hire for those skills at any price.
I think that would require AI development to approximately halt at close to the current level for over a lifetime.
Conditional on development halting, I'd agree with you. By analogy, there's this single, very useful, very powerful, set of "hidden methods that can be used to win all games, get rich, find love, determine the limits of thought itself!" — mathematics[0]. Do people like learning it? They do not. Calculator much easier. What a calculator does is none of that, calculators are merely arithmetic, but most people can't tell the difference between mathematics and arithmetic.
I think LLMs have the same effect on anything that can be expressed in words, and all the various image generator models have this effect on graphical arts. One must be extremely motivated to get past the "but the computer is better than me" hump.
However, I don't expect AI development to even approximately halt at anything close to the current level. There's a lot of room for self-play in domains like maths and computing where the proofs can be verified, and probably a lot of room for anything that can be RLHF'd, too. And that's also assuming we don't get any brain uploads; regardless of the question of "is such an upload of a human capable of consciousness", which absolutely matters, it may still be relevant to the economic issues of AI depending on the cost of running one depending on all the details of such an upload that I can't even begin to guess at at this point (last I heard, https://openworm.org was not actually measuring synaptic weights directly, but rather neural activity? I may be out of date, not my field).
Whatever happens, however good it does or doesn't get, I do expect something to go very weird before I reach the current state pension age — close enough that, if that something is "the machines break" or "society breaks", then there will still be plenty who remember the before times.
What I'm getting at isn't AI development halting, but human knowledge/creativity halting [1]. Because the AI is and can only be trained on human knowledge, it's knowledge of reality has an upper bound (whereas, theoretically, humans can know anything or make new discoveries that don't exist in our current knowledge set).
If you don't tell the AI that strawberries are a thing/reality, it will never conceive of a strawberry on its own. And arguably, it's not fair to call these things "AI" until they can do so.
[1] Charlie Munger said "show me the incentives and I'll show you the outcome." Well, in this case, the incentives to use AI > spending the time to learn and develop skills. The outcome here is clear: humans will stop producing new knowledge and by extension, the AI will stop receiving new knowledge to learn.
AI do not have such a limitation; they can be trained on anything, including to design and then perform their own experiments in laboratories, e.g. this one: https://engineering.cmu.edu/news-events/news/2023/12/20-ai-c...
> If you don't tell the AI that strawberries are a thing/reality, it will never conceive of a strawberry on its own. And arguably, it's not fair to call these things "AI" until they can do so.
I have far too many projects on my plate right now, but that is kinda one of them, has been since… wow, only December, these last few months have felt like years: https://benwheatley.github.io/blog/2023/12/12.html
Yes, I think it is way overhyped, but on the other hand, actual people are using ChatGPTs. I've used it for simple code to get started with an unfamiliar (but popular) library. I talked with a non-technical friend recently who was using it for relationship advice (with predictably unhelpful responses, it can't tell you the issues you are unaware of, but still).
If there's an AI bubble, it's in the early stages. In my mind the over-priced aspect of the market is the complete denial that stock prices at 5% interest rates should not be higher than at 1%, all things being equal. At least not if value = profit / costOfCapital as it is supposed to.
[Edited - added Tim's name to help future searchers]
Also editor of the JSON RFCs:
I started my tech career around the turn of the century, and made the mistake of putting a ton of money (at least for me, at the time) into Global Crossing. My thought was that while there were all these "fluffy" doomed dot coms at the time, Global Crossing had billions in real, physical infrastructure they built. Obviously I didn't quite understand debt at the time, never mind the actual fraud that Global Crossing committed (I remember thinking "Wow, stocks really can go to zero and never come back.")
Sure, you could argue I made every newbie investor mistake in the book, but the worse consequence for me was that it "spooked" me early in my investing career, such that I became very reticent to want to invest in things when I felt they were overvalued. E.g. I was one of those people who thought there was a giant tech bubble when Facebook bought Instagram for a billion dollars - in 2012...
So sure, you may think I'm an idiot, but I can quite guarantee I was far from alone. It was only at the point where I really, truly believed "I'm definitely not smarter than anyone else in the market" (and hardly anyone is) that I just put my money in index funds, did regular rebalancing, and otherwise forgot about it.
We may be in an AI bubble, we may not, but I've seen way too many "vastly overvalued" companies continue to be "vastly overvalued" for over a decade (and then only briefly coming down before shooting back up again) to think that Tim Bray has any special insight here.
I suppose it's not uncommon for people to have this kind of experience, so I'm just glad I had it young.
I say this as someone who lost money in the 12DailyPro / eGold fiasco of 2005/2006. (read: I am very, very dumb.)
Investing in Bitcoin = investing, though.
This is the way.
Dropping money into an index fund is generally the right way of going about things. My suspicion is that those who talk about clearing N million on NVidia big bets either had enough money that they could go long with 1MM on a single stock with Y thousands or just got very lucky in their first trading experiences.
If someone gets 1/100 luck three times in a row - then they can easily get to 1-10MM portfolios from a ~10k starting point. You'd expect around 1 in one million traders to do this.
“The price of X index fund/asset/real estate will be lower at future date Y than today”.
And while I think this line of thinking is still more correct than not, I wonder how much I (and a lot of other folks in the US) are discounting the possibility of a prolonged period without growth.
Despite shocks like in 2000 and 2008, the S&P has spent very little time "underwater" over the past 50 years. But that's not the case if you look at something like the Nikkei, which took until this year to get back to its 1990 peak.
1. You wouldn't want to dump 100% of your money in an S&P 500 index fund. There is a reason to diversify.
2. The point of dollar cost averaging is essentially to reduce the risk of dumping all of your money in (or out) at a bad time. Taking your Nikkei example, I'd be curious to see if you looked at, say, investing the same amount of money on the first of the month over a 2 or 3 year period. The amount of time you'd be under water over the past 4 decades would be much less than just looking at any single instance in time.
S&P:
once dca_2yr dca_5yr
count 25.0 25.0 25.0
mean 0.7 0.8 0.7
std 1.8 1.5 1.1
min 0.0 0.0 0.0
max 8.0 6.0 3.0
Nikkei:
once dca_2yr dca_5yr
count 25.0 25.0 25.0
mean 11.4 11.5 11.8
std 9.1 9.5 9.4
min 0.0 0.0 0.0
max 29.0 29.0 28.0
I did not - adding an optimistic 2% dividend didn't change much for the S&P, but slightly reduced the underwater counts for the Nikkei (max 23, average ~7.5)
Vanguard Research actually wrote a paper about this called 'Dollar-cost averaging just means taking risk later' [0].
Or if you would like more recent research the paper 'Dollar Cost Averaging v.s. Lump Sum Investing' by Ben Felix [1] is worth a read imho.
0: https://www.passiveinvestingaustralia.com/wp-content/uploads...
1: https://www.pwlcapital.com/wp-content/uploads/2020/07/Dollar...
Edit: Formatting
1. Choose 4 different funds to represent each of those classes (e.g. an S&P 500 index fund, an MSCI EAFE fund, etc.). You want to be sure to reinvest dividends.
2. On a specific time period (i.e. once a quarter) you rebalance your portfolio - if anything has gone above 25%, you sell it so that you can buy anything that has fallen below 25%.
Many investment platforms let you essentially do this automatically these days.
After 1 year, you look at your portfolio, and because of market movements, your portfolio is now 81% NASDAQ and 19% S&P.
So you sell some NASDAQ and buy some S&P to rebalance to 75% / 25%.
Rebalancing can be any mix of securities or assets (or both). You decide how you want your wealth distributed, and you rebalance to stay within those levels.
Every few months they would check the ratio and “rebalance”
They all seem to be hyping GenAI a ridiculous amount, prompting this question. And it makes sense for them to ride the hype train and get something out of it. But it also makes me wonder if that only makes the eventual drop even larger.
- LLM's provide functionality that was very difficult to implement until 2 years ago. - We can decode natural language statements relatively well and relatively easily. - We have an approximate common sense knowledge base. - We can encode statements into human readable text flexibly. (this was never so much of a problem as the first two - but it's still useful).
But, these are not magic boxes that can tell our fortunes.
So we can do good things if we engineer things well, and there is a lot of synergy with other AI tech that's been evolving in the last ten years. STT and object recognition are both very useful, end to end differentiable reasoners are coming in now as well. ML was becoming important in 2019, 2023 created an inflection and some hysteria, but there's substantial value to be had.
For instance, Bray considers the adage "The CIO is the last to know". From the 90s until now, developers have always snuck new technology in without management approval. You put Apache on a forgotten Linux box in the corner because it's easy and fun, and a few months later the whole company relies on it. Developers are not rushing to deploy skunkworks generative AI solutions, so, the argument goes, probably generative AI isn't that good.
There's a couple of problems with this.
1. Not everything that is good can be deployed skunkworks-style.
It might be that AI is only really good with incredibly high up-front costs and extremely specialized developers. Like launching a satellite. You can't do it yourself with stuff you have lying around, and even if you had the money to do it you probably don't have the expertise to do it safely. But it's still extremely valuable!
2. Sometimes we are using this technology to hack up solutions to personal problems!
I had a video which I wanted my hearing-impaired father to watch. I could have paid a human or AI-powered service to generate subtitles, but I found that I could do it myself with OpenAI's Whisper, on an old laptop, and then munging text files together in the usual way. I was a little shocked that this worked offline. I could have done it on a plane. This absolutely fits into a hacker workflow.
I got asked about onboarding someone to a fund online and it occurred to me to check out current services before responding - 20 seconds with google document AI made it clear to me that things have moved decisively in the last couple of years.
That's not what the article says, it's about processing responses not responding to people. I don't think there's anything about responding to citizens.
And it also doesn't say LLM it says AI.
1000000 documents -> 1000000000 embeddings Citizen question -> GPT normalised question -> 'embeddings match 'embeddings <-> embeddings recover document fragments with matched embeddings use GPT to create answer from document fragments
The question is how successfully this process creates the answers required. Who knows? But, I would not be surprised if it worked pretty well and it might boost productivity to the point where there's a massive saving to be had.
Maybe - but that's the fun!
The article mentions generating answers based on sources like Hansard, and dealing with large numbers of consultation responses. Frankly it's shocking they haven't done any ai stuff with consultation responses, lots of freeform text is where you usually want to start doing clustering and analysis.
BTW - I used to do lots of clustering and analysis on freeform text, but first SVD and then embeddings have just changed the game so much that it's the starting point now.
If it's "backend tool to more easily find relevant sources" rather than what I originally thought which was "automated FOI responses with minimal/no human oversight" then I could see that more.
The examples in the article were a backend tool for searching hansard and one for what sounds like clustering and summarising consultation responses. I think the latter was misunderstood by the author of the submission to mean responding to citizens rather than processing citizen responses.
> BTW - I used to do lots of clustering and analysis on freeform text, but first SVD and then embeddings have just changed the game so much that it's the starting point now.
I agree, also with things like naming clusters and summarising what a cluster is about is a huge improvement IMO to results.
When the valuation requires "they will continue to grow sales X% a year" is when it quickly becomes impossible, and for a much smaller X than you might realize.
Short Nvidia only if you hedge with some call options. Or other financial instrument that mitigates your losses.
Well, it looks like he lost a lot of money
Taking money off the table when it is enough for you is the way you guarantee that you make any money...
better to risk it going down from $450 to $400 before selling than to miss out on the ride to $800
Failure to maximize returns is not losing money.
>Ever since Amazon was in its early days, he has said that Amazon was overvalued and he has always been wrong because Amazon has always found new verticals to build and create more value with.
Amazon has been overvalued multiple times.
Amazon stock had negative return 10 years between 1999 - 2009.
Jan 5, 1999 to Dec 28, 2009, AMZN had an 8.9% annual return.
Jan 5, 1999 to Dec 28, 2008 was -0.52% annual return.
Jan 5, 2000 to Dec 28, 2009 was 6.88% annual return.
But why give a crap about returns during a specific 10 year period? Almost nobody is buying something today to liquidate all of it at a single point in time in the future.
It did not. You will have to cherry pick very specific days in this time frame (top of the dot com bubble and bottom of the GFC) to get negative returns. But how about 1998-2008 or 2000-2010?
Here is how $10K invested in AMZN performed in 10 years [1]:
1998 - 2008 $102,134
1999 - 2009 $25,124
2000 - 2010 $23,625
2001 - 2011 $111,229
[1] https://www.portfoliovisualizer.com/backtest-portfolio?s=y&s...
To say it is clueless would be too nice.
* https://en.wikipedia.org/wiki/Technological_Revolutions_and_...
* Via Ben Felix: https://www.pwlcapital.com/investing-technological-revolutio...
That's basically art. So AI's only really good at producing art. So we're safe... but now I feel bad for the artists.
That is always the case so I wouldn’t over index on that.
The recency of 2008 has really warped people's brains. 2008 was the 2nd worst financial crisis of all time (maybe it would have been the worst if our fiscal and monetary tools were still at 1929 levels of sophistication). You should be extremely hesitant to declare that anything will even come close to it.
Not saying this current bubble won't pop eventually, but the scale would be on a completely different order of magnitude. It's not likely that 170-year-old banks will collapse and be sold off for pennies on the dollar (Lehman Brothers) if this bubble ends.
But I don't really understand how AI being hyped, and NVIDIA's stock being overvalued by extension, could result in a 2008-like market crash.
If that just doesn't seem unsustainable I don't think what is... AI is not pulling up traditional firms just a very small number of tech stocks.
So with how much is concentrated on a few tech stocks, downturn in tech could lead to significant correction.
Ok, but none of the major tech companies other than Nvidia are AI companies. Sure, some of the pop to MSFT's stock is probably because of the OpenAI deal, AWS, GCP, and Azure is riding some of the wave of new AI investment money coming in, but none of them are first and foremost AI companies selling AI.
This. LLMs are not the path to AGI. At best they’re one of many ingredients.
That's not by accident and it's been at the expense of non-investor world for a long time.
What we're seeing is finance capitalism suck surplus value from every piece of the Earth it can. We're still burning more fossil fuels than ever before [0] despite the now visible risk of climate catastrophe. I believe we're likely to see investors continue to become irrationally wealthy while increasing larger and larger pools of people a driven into ever increasing situations of hardship.
I see no signs of the madness stopping until both human and planetary resources start to buckle under the pressure and refuse to give yields they once did. The article repeatedly mentions crypto as though it were an obvious bubble, but bitcoin is near record highs and even Sam Altman's bizarre world coin is at extreme record highs, COIN is up 200% in the past year.
The bubble won't "burst", it's just that increasingly less people will be invited in.
It really is like that paperclip game where at the beginning you can click once to get 1 paperclip and at the end you're using the resources of a galaxy to generate gazillion paperclips, except on this planet the number we're all irrationally obsessed with wanting to make go up is our bank account total.
Money is a very "efficient" market in this sense because each individual can decide do you want to hold currency X or currency Y or currency Z? And there are pros and cons to each that lead to price discovery between currencies. And I think as time goes on it is becoming more and more apparent that many central banks do not take the management of their countries currency seriously and so people choose alternatives. This is what well-functioning markets look like. Consumer choice and incentives.
We've had crypto for how many years? More than a decade, yes?
Can it be used as replacement for money yet? Anywhere on the planet? Does it look like it ever will?
Most investors have been conditioned by many popular talking heads to immediately dismiss the idea of successful market timing - and for the most part, the talking heads are correct. For the average investor, successful market timing is nearly impossible.
However, we have many counter-examples of successful market timers over the long term. James Simons' Medallion fund has returned 50%+ CAGR over a multi-decade period and stomping the market, creating many centimillionaires and billionaires in the process.
I set out thinking, what's so different about Simons and his crew at RenTec? Why is it so difficult for their success to be replicated? Not one to easily back down from a challenge, I began working on my own algorithms to successfully hedge against market downturns and provide superior absolute and risk-adjusted returns compared to the S&P 500. While I haven't yet seen Simons-level success in live trading, since launching Grizzly Bulls (https://grizzlybulls.com) in January 2022, 6 of our 7 models have outperformed the market on an unleveraged basis:
SPX (benchmark): +7%
VIX-TA-Macro-MP Extreme: +39.98%
VIX-TA-Macro Advanced: +34.38%
VIX-TA Advanced: +12.92%
VIX Advanced: +9.91%
Vix Basic: +5.76%
TA - Mean Reversion: +15.46%
TA - Trend: +12.97%
Of course two years of outperformance also doesn't yet stand the test of time of Simons' remarkable run, but I'm confident that we've discovered alpha here.
“If you don't find a way to make money while you sleep, you will work until you die.” ― Warren Buffett
> VIX-TA-Macro Advanced: +34.38%
I'm not sure how to reconcile that with the numbers shown on https://grizzlybulls.com/models/vix-ta-macro-advanced
When 2022 is selected as the starting year, it shows an increase from 16,911,242 to 17,881,510 which is an increase of 5.7% in 26 months.
Anyway, here's to think about AI. Intelligence is the most precious commodity of all of humanity. Intelligence captured in bits is easily distributed and scaled.
We pay $1000 / hour for intelligent agents and it's easy to see, how a super intelligent system can capture 50% margins on that. (Volume of Data and Proprietary hooks will make switching difficult).
But, wait, there is more.
Intelligence begets more Intelligence. Every artifact an AI produces, we need more AI to maintain, enhance, distribute it. So, for the first time we have an entity whose demand creates it's own demand spiraling into a vicious positive growth rate.
All this means is our AI demand may at 0.000001% of what the demand in 20 years would look like, which makes AI enablers incredibly cheap. I could be wrong, but to dismiss the possibility is exactly what's wrong with today's "skeptical liberal (with a decel mindset) engineer's" framework / vision.
Build your own mental model
This is a common take, it feels like a not-cynical-actually-smart counter to the hype train.
I think that take is missing something -- that this is how capitalism pays for fast learning.
You have a space entirely unexploited, you give million pioneering fortune seekers shovels and ignite exploration across the whole unexplored surface area. Most will quickly discover they're unsuited to exploring, or picking at dirt with there's nothing to find here, some will find fools gold and labor over it until they realize it won't buy land, and a couple will strike oil instead of gold and build an entirely new economy generating unfathomable wealth.
On the whole, no money was "lost", even without mentioning overcoming costs of delay since this got the exploration done the fastest.
I'm not saying this is more efficient than centrally planned 5 year programs (though in practice it probably is), but it does seem more effective at learning a new thing fast ...
... and getting from “exploration to exploitation” the fastest.
1) The stock market is in a bubble due to a decade of low interest rates and tax slashing by “right wing” governments.
2) Big tech in particular has been doing well but this is not sustainable.
3) AI is in a bubble. People are pinning their hopes on it to keep tech and I presume big tech growing.
4) A bunch of references to academic papers from 2000 about why AI is hard.
5) Gen AI requires a lot of compute which generates a lot of carbon and is bad for the environment.
Thus his statement: “ I think I’m probably going to lose quite a lot of money in the next year or two. It’s partly AI’s fault, but not mostly. ”
Which I disagree with. Because A) I think in the long term (5+ years) the investment in AI will be a positive ROI. B) if the stock market crashes in the short term it’s likely going to be for non AI reasons. 3) His arguments as to why AI isn’t going to pan out long term are a bit weak.
Having lived in the Bay Area for over 13 year's, I’ve seen a few cycles: social, mobile, cloud, gig economy etc.
The cycle pattern is always the same: a) a big new exciting tech idea comes along. b) investors pile in money. c) 95% or more of the companies they invest in go bust and if the space has legs some companies do really well.
How is this any different with the current wave of AI companies?
Today the big winners in AI are the incumbents, some examples:
Microsoft: is making money being the hyperscaler of choice for AI companies (on prem ChatGPT, mistral, etc), it’s co pilot lines and enterprise subscription products.
Nvidia is making bank being the current standard on which all of these companies run their models. They have some recent competition from Groq but are still likely going to be crushing it for the next year or two. Mainly due to precommits from the hyperscaleralers.
Meta: seem to have been able to leverage AI to claw back advertising revenue due to Apples crack down by improving targeting.
As someone who has raised venture capital to do an AI startup I’d say yes there is a lot of hype in this space. Yes a lot of these startups are going to go out of business but it’s also early days.
I also think working AI into this poorly written article about how the stock market is going to crash is a bit of stretch.
I’m concerned about a market crash myself but I am more worried about it being caused by a combo of a) the upcoming US election. B) the war in the Ukraine. C) conflict with Iran. D) interest rates in the USA being high.