Jevons paradox
en.wikipedia.org
en.wikipedia.org
This felt false to me, LED bulbs are generally 10x more efficient, so the average number of lights would have had to increase 10 fold, and this is ignoring the savings from not having AC cool those other 50 watts a bulb that went to heat.
So I took a look at the source, which doesn't actually have a source and just links to another white paper that claims light pollution increased, but is unclear about how much and due to what. Perhaps that whitepaper links to another whitepaper, but this is where I stopped.
Kind of makes me question how often I blindly trust Wikipedia, though this point in particular is pretty minor.
> This felt false to me, LED bulbs are generally 10x more efficient, so the average number of lights would have had to increase 10 fold, and this is ignoring the savings from not having AC cool those other 50 watts a bulb that went to heat.
The first 5W also went into heat. Perhaps with the exception of light escaping through a window, *every* watt that goes into a lightbulb comes out as heat for your AC to cool.
I would not be surprised if there were a 10x increase in use of lighting with LEDs.
Not taking Wikipedia (or any source) on blind trust is always wise. More often than not, when you kick tires, you come back to folklore. It's fine for big things, but details are often wrong. Ditto for newspapers, for that matter.
I said 50 because I was afraid someone would go 'ACTUALLY LED's only save 50-55w', I didn't expect someone to argue that the 5w led bulb is also outputting heat. You are of course right, 2.5w of that 5w is likely heat being cooled by the AC.
>I would not be surprised if there were a 10x increase in use of lighting with LEDs.
Quite frankly I don't care either way, I just wanted a source. I'm happy you feel that way though.
Quite frankly I don't care either way, I just wanted a source. I'm happy you feel that way though.
As a first step in looking at this, I’ve taken the data from the analysis and plotted it on a log scale. This shows clearly how consumption has increased as population and incomes (measured by per capita GDP) have risen and prices have fallen. The central role played by efficiency is indicated by the dashed line, which shows the number of kWh needed to produce a unit of light (1/efficiency). Comparing this with the solid blue price line showing price indicates that the 100 fold fall in price per unit of light in the 20th century was due to increased efficiency, mainly as electricity replaced gas. Yet demand rose strongly over the period, increasing more than 100 fold. This challenges the view that increases in efficiency translate into reductions in demand, and thus emissions.
Has the average homeowner added more ceiling lights or lamps because of different bulbs?
Has the average office added more lighting because of LEDs? Factories?
In order for an overall 10x increase in usage, there needs to have been some MASSIVE new usage or expansion of lighting, somewhere, to make up for all of these enormous use cases (residential, commercial) where there was probably close to zero increase.
So, do you have any pet theories ?!!
As much as I appreciate the snark and sarcasm, at least where I live, the equivalent is happening.
Old home and commercial construction would typically have one central light per room. Homes would have a single light fixture with 1-3 lightbulbs at the center of each ceiling and were pretty dimly lit. Commercial would be similar with fluorescents. You would turn it off when you left a room.
New home and commercial construction has accent lighting all over the place, some turned on 24/7. Cabinets have under-cabinet-lights. Closets have closet lights. We have rows of LEDs lining all sorts of accents.
If you compare 80's-style construction to 2020's-style, the amount of new light seems to be about that much of an increase.
I am describing where I live.
If you don't accept that a place like this might exist, without so much as a follow-up question, says more about you than anything else.
Fast forward to today, the last time I bought lightbulbs for my house was over 5 years ago and that was to upgrade from regular led lamps to Philips hue.
My sister just did a home remodeling and she added must have been 50 LED recessed lights in the ceiling. There are automated LED lights in the outdoor pathways, lights hanging on the balcony rails, lights under kitchen cabinets, lights inside closets, lights inside pantries, solar powered LEDs in backyard, etc.
Her appliances also have more indicator lights than ever before.
I’m not even counting the countless LED screens such as phones, iPads, TVs.
In total, she may every well have 10x more lights than before the LED boom.
Time Square in NYC was famous for having many lights and screens. Walk around any large Asian city in 2025 and they all look like time square with countless lights and screens.
Thats said, there is a limit to lighting demand because we still need darkness to sleep. I think the upper bound for intelligence is far higher than LED lights.
I just watched Veritasium’s video on how LEDs were indented and the companies making LEDs saw exponential increase in revenue. https://youtu.be/AF8d72mA41M
Basic Google search tells me the lighting industry has experienced high growth due to the invention of LED lighting.
People generally don't like if it is "too bright" in the room, and the extra lights in closets, pantries etc. will only be used marginally, for a few minutes a day.
It helps that LED lifetime isn't degraded by frequent on-and-off cycling.
Light bulb sales? Total lumen output? Total lighting revenue?
I did find that the total consumption of light increased 100,000x between 1700s and 2000 in Uk.
Chart: The consumption of light in the United Kingdom increased over 100,000 fold between 1700 and 2000.
Also keep in mind that better lighting efficiency enables people in poor countries to have lights where they couldn’t before. It’s not just wealthy counties. You have to count them as well. It's not just counting a rich person's house before and after LEDs.
My grandma (born 1926, died 2016) was born in Eastern Slovakia in the pre-electric era, the region she lived in only got connected to the grid in 1945.
In her youth, the whole family would simply have one kerosene lamp and gather around it after dark.
This felt false to me, LED bulbs are generally 10x more efficient, so the average number of lights would have had to increase 10 fold, and this is ignoring the savings from not having AC cool those other 50 watts a bulb that went to heat.
With that said, it would not surprise me if there is a 10x increase in light sources (not total lumens) since LEDs.
Comparing this with the solid blue price line showing price indicates that the 100 fold fall in price per unit of light in the 20th century was due to increased efficiency, mainly as electricity replaced gas. Yet demand rose strongly over the period, increasing more than 100 fold. This challenges the view that increases in efficiency translate into reductions in demand, and thus emissions.
Another confounder to Jevons' paradox is that the price of the input (electricity) can also change, independent of changes in efficiency. If electricity prices were to go up ten-fold during the switch to LED lighting, then we wouldn't expect much change in illumination (probably a net decrease, as people would be hurting so much from other electricity expenditures that they'd be hunting for any savings on their energy bill). That's of course a fictional scenario, but in the case of GPUs, well, they have been getting quite expensive.
Related: Jenson's Paradox: The more you buy, The more you save
Or are people just applying this as everyone and their mothers are going to start training competing models to carve out their piece of the market?
Yes. For example Google has just made Gemini a standard part of Workspace, before that it cost $36 or something per month per user. That was too much for many SMBs to experiment with (you and I understand that the potential efficiency gains are way higher but for an SMB, paying 4x more for your office suite sounds bad).
1. You can't tune them to your needs; they have restraining bolts and the training data is a generic corpus
2. You don't own your interactions with them; your data transits a network and is processed by third-party servers
3. They waste an immense amount of power relative to the usefulness of their output
4. Their responses tend toward uncanny simulacra and hallucination
Are people (or companies) locked out of training LLMs due to price?
I don't know the immediate answer. I really expect it to be a resounding "yes" on the long term because different LLMs should be good for different things. But in any way, this is not about the people adding a cloud client to their software.
Consumers of AI do have sensitivity to pricing. Many OpenAI customers "ration" their usage. I imagine lower costs open up new demand for these people.
On the Service side: it seems to have reduced the cost of operating a "commercially viable" (something people will actually pay money for) LLM. But even beyond that, "self hosted" models are also far more affordable now, which means models that target specific niches can be viable to build or buy.
However, this won't be an overnight phenomena. In the short-term, it will seem like demand drops, but in the long-term demand will go up. Big caveat: that demand may not be concentrated on the current incumbent players.
And finally, the elephant in the room: AI still needs to become more useful to reach its full potential. Easily a decade+ more needed here.
Running either Grok2 or DeepSeek or even Llama405b requires nearly 400-500gb of memory.
Buying a tinybox with enough gpu memory costs $15k-25k. Or equivalently the same if you build your own.
A distributed Mac cluster costs about the same, if not more, if you’re buying 2-3 M2 Ultra each with 192gb of memory.
So people are absolutely constrained by price/supply here. Every engineer, analyst, scientist would be far more untethered by rules & regulations or policies & terms-of-service nitty gritties if they can trust that LLM they use is completely local, without-telemetry or tracking and is licensed fairly for commercial use (perhaps this excludes llama).
Not a lot of people can afford $15k-30k in spending for a computer (that can run this sota llms). But you can a billion will buy one when it’s $1k
I think so. When you start using a good model, you soon learn that it can make a difference. If you have domain knowledge, you can treat LLMs like a design partner. With DeepSeek, what would have cost 50 dollars to use Sonnet is now 5 dollars. Having to only spend 5 dollars a month can be a real game changer for many.
Why do you think this is a specific case of induced demand (as opposed to induced demand being a specific case of Jevons paradox, or the two being different words for the same thing?)
This seems like splitting a hair but it is a more consistent intellectual framework for understanding the dynamics of a particular market.
Should a city spend $500,000 to install lighting in the park downtown? After all nobody uses it at night because its too dangerous! Induced demand is central to the answer but isn't a first thought for many because the problem space doesn't look like a traditional market.
* there is the additional concept that the long term availability of a good or service at a lower cost changes consumer behavior in a durable way. Building a new train station realizes latent demand but also creates new demand as it causes denser housing to be constructed near it**.
** Whether you want to model that as just more realized latent demand or as a consequence that's technically distinct from induced demand is IMO where a lot of the confusion comes from. If you ask me this should probably have a distinct name as it is a long-term, interrelated process that doesn't map well to sliding around supply and demand curves. Generated demand is what Bloomberg calls it but that has insane overlap with a marketing technique.
"Induced demand": The highway is expanded such that it only takes me 15 minutes to get downtown instead of 45, so I go downtown more.
Jevson paradox: The highway is expanded such that it only takes 15 minutes to get from the suburbs to my office instead of 45, so I move to the suburbs.
So one is movement along the demand curve, while the other is a movement of the demand curve.
https://www.reddit.com/r/badeconomics/comments/oo2s48/that_o...
[0] https://www.toppr.com/guides/business-economics/theory-of-de...
You say drilling more oil is something different than building more efficient cars.
I would say in both cases supply of oil increased so there is no real difference.
There are meaningful differences in terms of pricing strategies, anticipating demand, etc.
The concept of "Induced Demand" is easily explained by the default state of the downward sloping demand curve, and upward sloping supply curve.
In basic economic theory, if you reduce the cost of a good, more people will consume it.
e.g. the classic building a highway example; there was always demand for cheap housing with accessibility to downtowns, but the supply of cheap housing with accessibility to downtowns did not exist prior to building the highway there. The "demand curve" doesn't shift at all, you're just moving along it as perception of cost changes.
Has there ever been a case where a highway was run through the middle of nowhere and traffic didn't increase? It's intuitive why
It is easily explained by that because that is literally the definition of induced demand (see sentence one of wikipedia). The concept has a name because its important to discuss the externalities and long term implications of that additional consumption.
w.r.t your highway example, how you define the market is extremely important and is sensitive to context. The market for "transportation between suburb X and city Y" experiences a durable change in the demand curve as a result of the construction of all that cheap housing. Both market definitions are valid but if your concern is e.g. urban sprawl then contextually one is a lot more relevant than the other. All that said, you can think of the change in the demand curve of market B not as induced demand itself, but as a consequence of realized latent demand (i.e. induced demand) in market A (cheap housing with accessibility to downtown). Alternative solutions to realizing latent demand in Market A (public transit, denser housing, etc.) have different and potentially preferable externalities which is why considering induced demand and its consequences are important.
There is no shift. I am addressing this common misunderstanding, not debating the wording in the Wikipedia article.
"Induced Demand" is a misnomer, as the demand was always there at the given price. It is not induced, just realized. If I offer you a gold bar for $0, did I induce your demand to accept it?
Most people will always have demand for goods offered below their perceived intrinsic value.
Ultimately it's semantics around definitions, but the thinking of lay people around this concept is typically more of the shifting demand curve, not realization along the existing curve
That's not necessarily true. Suppose you're the government and you produce food for free, and every year people eat everything you make, and everyone is well-fed. You decide you want to prepare for a famine, so this year you start more farms such that next year you make 20% more food. The first two months you're able to save, but when people see that there's more food available, they change their habits and start doing even more exercise than before, and so they eat more until they eat all the food every month again.
My assumptions is that we in the late 1940s through the 1950s had achieved a reasonable standard of living, but perhaps not available to all. Even if we assume that everyone in a country has access to the same, high, late 1950s standard of living, but applied modern technology, like improved insulation, district heating, EVs, energy efficient light bulbs and appliances, then how much would that lower (or raise) our energy usage? More of the energy would surely be moved to electricity, from coal, gas and oil delivered directly to our home, but is it enough chance the environmental impact?
https://www.cam.ac.uk/research/news/insulation-only-provides...
although maybe i'd get bored and run the heater and the AC at the same time
.. I suspect this is wildly less good than most people would expect, along with forgetting about how widely distributed it was or wasn't.
Just look at the first chart here: https://www.bbc.co.uk/news/uk-42182497
If you could get people back to 1950s level of travel, whether in 1950s vehicles or (way better!) modern EVs, that would make a huge difference to the environmental impact. But of course if you actually suggest measures to force that people (correctly) notice it would reduce their standard of living.
I guess people are different, the less I have to travel the higher my quality of life. Excluding the US, where things are just further apart, I do wonder why people need to move around so much. More and more jobs barely require you to leave your house. I know that the Danish government is proud that Denmark has a highly mobile workforce, meaning that we're willing to transport ourselves relatively far for work, which is a weird thing to be proud of, knowing how much that pollutes.
Jevons Paradox and DeepSeek AI
The Jevons Paradox, an economic principle stating that increases in efficiency often lead to higher overall consumption, has been observed in the context of DeepSeek AI. DeepSeek, a Chinese AI startup, recently introduced its R1 model, which achieves comparable performance to leading AI systems like OpenAI's ChatGPT while requiring significantly less computational power and energy. This efficiency has made the technology more accessible and affordable, spurring wider adoption across industries.[30] As Microsoft CEO Satya Nadella noted, the increased accessibility and efficiency of AI tools like DeepSeek's R1 model could lead to a surge in applications and use cases. This expanded usage may result in higher energy consumption overall, despite individual efficiencies. For example, while DeepSeek's model uses fewer resources per task, the growing demand for AI-driven solutions—ranging from chatbots to complex reasoning models—could increase the total energy requirements of the global AI infrastructure.[31]
I guess we'll see what happens with the stock price, but long term I'm pretty confident in this Jevon's Paradox effect on AI use. I'm praying for more negativity hype out among those who maybe don't know as much about tech as the HN crowd. Because those people collectively, can move stock prices.
Both at the tactical level and strategic level that paper means we will see _more_ nvidia gpus fly off the shelves. If anything it's showing why the h800 is a decent card and everyone in China should buy one (dozen thousands).
https://news.ycombinator.com/item?id=42856652
I have to agree. I'm not sure the architectural and engineering changes here don't put NVDA further ahead. Some stuff all gpu makers can do. Like quantize on transfer. But NVDA is so far ahead in other areas and DeepSeek leans heavily on that industry leading tech. At least at the low level.
But I'm betting the average investor doesn't have the level of facility with ML to properly analyze the information that's come out. Heck, I'm betting most of the people working in AI are really just TF or PyTorch monkeys. The real AI experts have probably been hovered up by MS or FB or AMZN or OpenAI or what have you. So it feels like we could get lucky here and the AI related market could drop like a rock taking the good with the bad. Which I think would be awesome for most people in a position to wait for it to happen.
What gets me about the deepseek work is that it's neither novel or theoretically difficult.
I've been using a similar synthetic data pipeline to generate examples for sota bert embedding models.
That low-hanging fruits like this are left unexplored by meta or alphabet - and if we're to be believed lead to crisis meetings for a week straight - makes one wonder wtf the engineers there are doing?
There's already some pretty impressive work being done with folks using just a pair of M2 Ultras with r1 in a "home lab" context that goes way beyond what you could previously do with llama.
There are things, like doc strings for functions, that llama models give better results for.
You can't pay me to use AMD cards. I have work to do. I don't have time to write drivers for cards that no one supports.
The point of the deepseek r1 paper is that the more synthetic data you generate by over trained models the better the resulting model. That means more GPUs. There is no reason why you can't train a dozen models like their nameless logic model in other domains and then use them for synthetic data augmentation.
So yes, an increase need for ever better thinking models.
On the inference side, why does one need Nvidia GPUs over Intel and AMD GPUs?
Not to mention there’s little real world evidence that AMD is better at inference speed not cost/inference over Nvidia.
Comparing AMZN and NVDA is like comparing apples and oranges. Amazon has significantly diversified from its origins as an online bookseller and started when the internet, and later mobile devices, were not yet mainstream. NVDA, on the other hand, is an entirely different kind of company.
It doesn't matter whether it's AMZN or NVDA, you should try to distinguish between the company's fundamental value and the speculative component, as both are reflected in the price.
At $95b in op income, you're looking at ~33 times op income for NVDA for 2025, or lower. I don't consider that a bargain but it's also not crazy expensive given the stock market valuation context + their growth + their market dominance in a critical field.
https://en.m.wikipedia.org/wiki/Rebound_effect_(conservation...
The rebound effect is common (people have something more efficient and use it more, so the efficiency improvement is somewhat offset by more use), but Jevon's is relatively uncommon (people have something more efficient and the increase in use is large enough to offset the efficiency improvement and then some).
I think the situation is fairly clear with some understanding of finance / equity markets. My analysis of the market and competitive situation:
Deepseek's cheaper LLM services + providing open models for other hosts to provide
=> overall prices for using LLM services will fall due to competition (lower prices + more hosts entering the market); AI users won't pay so much for LLM services
=> the major LLM hosts/providers won't be able to project such high revenues or even have the funds to purchase as many GPUs (they will receive less capital investment to buy GPUs since revenues per dollar invested are lower; their stock valuations will fall)
=> corporate demand for Nvidia cards will fall (AND consumer demand will not rise as much to counteract it for multiple reasons)
On the basis of this possible logic, portfolio managers and algorithms project lower growth/revenue for Nvidia and sell off its stock, setting off the usual chain reaction as other managers notice the downward price action and follow suit in order to stop further losses.
Now, if the new hosts entering the market with lower prices attract significantly more users (instead of merely competing away margins) as we might expect by the paradox, then demand for GPUs might grow instead of shrink instead. If more portfolio managers think this is likely, then it's likely for NVDA's price to recover, and it would be wise to get back into the stock once the worst of the fall is over.
So the way Jevon's paradox could apply is that if an AI model only costs 10% of the energy+GPU to train (or run), then we might not get 90% energy+GPU savings... we might actually wind up consuming more energy+GPU on the whole because the models become so much cheaper, we use them more than 10x as much.
Obviously this will affect Nvidia's price, but this principle absolutely does not apply to stock prices directly.
If Nvidia announced a new chip that was 10x more effective at the same price, the market would be like WOW they're going to sell so many of those!
But a software advance that achieves the same effect somehow causes the opposite reaction.
« In 1873, greed, speculation and overinvestment in railroads sparked a financial crisis that sank the U.S. into more than five years of misery »
https://www.smithsonianmag.com/history/robber-baron-gamble-r...
You can still lose money in the greed-fear cycle even if the road leads up to unimaginable heights.
For example laptops are getting faster every year but most people I know are not clamoring for the latest laptop, same with phones, good enough is good enough and people would rather save the money. If a laptop came out that was 50x faster for the same price people wouldn’t buy 50x more laptops, they would wait even longer to upgrade.
new method was 1,000,000x more efficient would there still be more total money spent on GPUs because of it
On the assumption that we’d want super intelligence to do interesting things, consider that we currently have many very smart humans and society is not maximizing their use, quite the opposite.
Do you have a use case for your opinion?
Obviously yes; 6 orders of magnitude would result in insane amounts of money swooping in. The market has currently been going crazy over relatively minor improvements compared to that.
> If a laptop came out that was 50x faster for the same price people wouldn’t buy 50x more laptops, they would wait even longer to upgrade.
That would be equivalent to decades of improvement in the tech. The last time we did that (over the last few decades) we saw massive increases in spending on computers.
Yeah, it would be super impressive, perhaps another point SMT solvers algorithms have improved faster than the hardware for a very long time and are incredibly useful. But we are not spending infinite money on SMT solvers, quite the opposite.
But that’s not how it always was, it was a core part of Bill Gates world domination plan during the ‘foundation’ era (2008). Good ones used to be super expensive, then they were sold as excel plugins, and now Z3 is given away for free. And despite being free it’s still not used nearly as often as it should be, it’s not the algorithm or price holding back adoption it’s the people. It was assumed that there was this huge untapped demand curve at the long end of the tail before finding out that there really was not. Z3 was originally released as academic use only (2012) on the assumption that it would drive demand for commercial use of Z3 or another commercial solution. Once it became clear that wasn’t going to make any real money they allowed it to be released for commercial use as well (2015).
As an aside people still do pay for SMT solvers so the market hasn’t completely disappeared but it’s clearly not what it was thought to be.
My guess is that you're mistake is thinking something like [demand = 1/price]. That was never the prediction, the prediction is when efficiency improves, demand increases which is a pretty basic observation for most in-practice supply and demand curves (something like a Veblen good would be different). You're not talking about changes in demand and you're barely looking at efficiency beyond noticing that the price was positive and is now 0.
> And despite being free it’s still not used nearly as often as it should be ...
Case in point, if this wasn't obvious then you need to modify your expectations. Jevons' paradox predicts exactly this, because there are prices below free (ie, paying someone to use the thing) and we'd expect they eventuated people would find new uses for SMT solvers due to Jevons' paradox in context of a price signal that they were getting more efficient. That implies even at $0 in software costs we aren't going to be using them everywhere we could.
If RL and synthetic data creation are all we need for self-improving AI, the large labs have an internal use case to utilize all available compute, regardless of efficiency gains.
If they have to go external and deploy in the broader market, time to deploy would make the GPUs worthless before they are fully utilized.
Let’s assume you were buying the GPUs, how would you plan on paying for it? What’s the business case?
What happens to a holistic system doesn't necessarily apply to every player in that system.
nvidia's heights are largely because of a few super capitalized players dumping enormous amounts of money into data centre GPUs, letting nvidia enjoy outrageous profit margins selling rapidly obsoleted, rapidly iterating products to mega-money players who are all counting on this giving them such a differentiation that it's financially worthwhile. If AI is just some free thing that has little differentiation between the players -- commoditized, so to speak -- the market for $200,000 GPUs probably isn't going to be as big. People won't be rushing to build $500B buildouts.
Another argument I keep seeing is smaller models and edge compute. NVDA has almost no stake in that, and there is no reason to believe they're going to have any particular advantage if they tried harder there, any more than Intel could leverage PCs to Smartphones. The core operations of running a model are well known by all of the silicon makers, and they can all make massively scalable FMA matrix operators, etc.
This "paradox" is being cited everywhere, and while it might apply to AI, people are wrongly using it to justify why nvidia should have a $3.6T or greater valuation.
Nvidia being worth $3.6T is a different topic. People think DeepSeek should have reinforced that valuation or even increased it, not lower it.
There may only be 10 people willing to pay $100 for something, but 1000 willing to pay $10 for it, so lower price results in more revenue.
Another counter argument is that’s Nvidia will sell less high margin advanced GPUs that require expensive networking equipment.
Problem with that argument is that every competitor gets the benefit of higher efficiency and the baseline will reset back to 0.
Nvidia has products to serve lower end as well. There’s no reason why Nvidia can’t dominate in the commodity sector.
And if Nvidia can’t keep their high margins, they will make it up with significantly more volume (Jevons Paradox in play).
My guess is that is that what passes as "AGI" will also become commoditized, maybe even free to consumers (funded by advertising, business referrals to restaurants, etc).
Where was Stargate gonna get $500B? How was Altman ever going to raise.. $7T? (~1/3 of the entire GDP of US)
Does a 10x or even 40x decrease in compute cost just mean they can actually raise & spend the capital that their compute requires?
People seem to have forgotten that there’s a time element involved, not a perfectly elastic and instant demand.
Thinking about it another way, if it turned out it was going to cost 30x more compute to train the next generation of models than we thought due to some scaling law, would the chip share prices be expected to rise, stay the same or go down? Probably rise.
In that case, if it was 30x cheaper to do what we need today would we expect option a, b or c?
Assuming that there would be less short / mid term sales, that impacts their bottom line and flattens off their (ie nvda) demand curve. Slower growth, lower share price. Slower growth, longer opportunity for competition. Therefore the speculative element of the chip leader’s share price’s should come down.
I don’t have any long or short positions in the chip sector.
it's like we are spending 500bn for x ai needs, but suddenly, the cost fell to 1/20, now we must convince everybody there'll be x20 needs, so that the 500bn will not go missing
that is what Satya Nadella and Sam Altman are trying to achieve
the real problem:
1. will there be 20x needs for ai? 2. how much ai needs are priced now? x? 5x? or, 20x?
If training efficiency is 30x more, then Anthropic, Google, OpenAI, Mistral, Qwen, etc all get the benefit. It resets the baseline. They will not stop competing against each other just because there is a 30x efficiency improvement that everyone has access to.
The only thing relevant, is the large sale that Nancy Pelosi did before the crash. Another incredible coincidence again of course, by one of he most successful hedge managers in history...
https://www.newsweek.com/nancy-pelosi-sells-nvidia-stock-wee...
but I dont think its insider trading, just informed and reactive trading - not eoy profit trading either though
I'm just calling out that "last year" wasn't really "last year" if you follow that argument.
deepseek v2 showed effeciency months ago, and nothing happened, there's also nothing happened after v3 and r1 release
the insider info is about when will Wall Street stop pushing and squeezing the market and start harvesting, and, IMO, this will happen anytime after rotation of ruling parties
i don't know if the similar scene happened before in the US, but this happened elsewhere, the rulling party with connected market makers and puppet central bank, push the market to highest and let it freely fall after rotation
nancy definetly know what the timing is
Just some nonsense, but if I’m right, it won’t take too long to witness it happen.
"Nancy Pelosi Outperformed Nearly Every Hedge Fund In 2024" - https://finance.yahoo.com/news/nancy-pelosi-outperformed-nea...