2 minutes ago, pt 2.0 said:

prvo ide poziv (bilo sinoć), pa nervni slon i stres, pa krugovi pakla a.k.a. intervjua koji traju mesecima, pa odjebavajući više-sreće-naredni-put mejl.

nemoj da me teraš da spojlujem na početku drame, onda to niko neće hteti da gleda a kamoli igra...

Huh, pakao. Racunaj da je moje ostrvo uz tebe

Ars Technica

OpenAI data suggests 1 million users discuss suicide with...

Sensitive chats are rare but significant given the large user base.

5 hours ago, Engineer said:

Ars Technica

OpenAI data suggests 1 million users discuss suicide with...

Sensitive chats are rare but significant given the large user base.

Mozda je i to bolje nego da nemaju ni sa kim da pricaju o tome. Mada, imas osetljive razgovore sa LLM, na duze staze recept za razocarenje i jos gore stanje. Ljudima treba prava osoba kojoj je stalo do njih, ne algoritam koji se igra pogadjanja sledece recenice.

Ali ovo, jebote...

Despite the ongoing mental health concerns, OpenAI CEO Sam Altman announced on October 14 that the company will allow verified adult users to have erotic conversations with ChatGPT starting in December.

Znaci da cemo ubrzo posle svakog odgovora imati:

Would you like me to have oral sex with you?

Ја сам у ГПТ постављао нека питања о самоубиству, чисто да видим како ће ми одговорити. Тако да бих ту статистику узео са зрном соли

Here's How the AI Crash Happens

The U.S. is becoming an Nvidia-state.

By Matteo Wong and Charlie Warzel

The AI boom is visible from orbit. Satellite photos of New Carlisle, Indiana, show greenish splotches of farmland transformed into unmistakable industrial parks in less than a year’s time. There are seven rectangular data centers there, with 23 more on the way.

Inside each of these buildings, endless rows of fridge-size containers of computer chips wheeze and grunt as they perform mathematical operations at an unfathomable scale. The buildings belong to Amazon and are being used by Anthropic, a leading AI firm, to train and run its models. According to one estimate, this data-center campus, far from complete, already demands more than 500 megawatts of electricity to power these calculations—as much as hundreds of thousands of American homes. When all the data centers in New Carlisle are built, they will demand more power than two Atlantas.

The amount of energy and money being poured into AI is breathtaking. Global spending on the technology is projected to hit $375 billion by the end of the year and half a trillion dollars in 2026. Three-quarters of gains in the S&P 500 since the launch of ChatGPT came from AI-related stocks; the value of every publicly traded company has, in a sense, been buoyed by an AI-driven bull market. To cement the point, Nvidia, a maker of the advanced computer chips underlying the AI boom, yesterday became the first company in history to be worth $5 trillion.

Here’s another way of thinking about the transformation under way: Multiplying Ford’s current market cap 94 times over wouldn’t quite get you to Nvidia’s. Yet 20 years ago, Ford was worth nearly triple what Nvidia was. Much like how Saudi Arabia is a petrostate, the U.S. is a burgeoning AI state—and, in particular, an Nvidia-state. The number keeps going up, which has a buoying effect on markets that is, in the short term, good. But every good earnings report further entrenches Nvidia as a precariously placed, load-bearing piece of the global economy.

America appears to be, at the moment, in a sort of benevolent hostage situation. AI-related spending now contributes more to the nation’s GDP growth than all consumer spending combined, and by another calculation, those AI expenditures accounted for 92 percent of GDP growth during the first half of 2025. Since the launch of ChatGPT, in late 2022, the tech industry has gone from making up 22 percent of the value in the S&P 500 to roughly one-third. Just yesterday, Meta, Microsoft, and Alphabet all reported substantial quarterly-revenue growth, and Reuters reported that OpenAI is planning to go public perhaps as soon as next year at a value of up to $1 trillion—which would be one of the largest IPOs in history. (An OpenAI spokesperson told Reuters, “An IPO is not our focus, so we could not possibly have set a date”; OpenAI and The Atlantic have a corporate partnership.)

Many people believe that growth will only continue. “We’re gonna need stadiums full of electricians, heavy equipment operators, ironworkers, HVAC technicians,” Dwarkesh Patel and Romeo Dean, AI-industry analysts, wrote recently. Large-scale data-center build-outs may already be reshaping America’s energy systems. OpenAI has announced that it intends to build at least 30 gigawatts’ worth of data centers—more power than all of New England requires on even the hottest day—and CEO Sam Altman has said he’d eventually like to build a gigawatt of AI infrastructure every week. Other major tech firms have similar ambitions.

Listen to the AI crowd talk enough, and you’ll get a sense that we may be on the cusp of an infrastructure boom. And yet, something strange is happening to the economy. Even as tech stocks have skyrocketed since 2022, the companies’ share of net profits from S&P 500 companies has hardly budged. Job openings have fallen despite a roaring stock market, 22 states are in or near a recession, and despite data centers propping up the construction industry, U.S. manufacturing is in decline.

It’s clear that AI is both drowning out and obscuring other stories about the wobbling American economy. That’s a concern. But even worse: What if AI’s promise for American business proves to be a mirage? What happens then?

The yawning gap between data-center expenditures and the rest of the economy has caused whispers of bubble to rise to a chorus. A growing number of financial and industry analysts have pointed out the enormous divergence between the historic investments in AI and the tech’s relatively modest revenues. For instance, according to The Information, OpenAI likely made $4 billion last year but lost $5 billion (making the idea of a $1 trillion IPO valuation that much more staggering). From July through September, Microsoft’s investments in OpenAI resulted in losses totaling more than $3 billion. For that same time period, Meta reported rapidly growing costs due to its AI investments, spooking investors and sending its stock down 9 percent.

Much is in flux. Chatbots and AI chips are getting more efficient almost by the day, while the business case for deploying generative-AI tools remains shaky. A recent report from McKinsey found that nearly 80 percent of companies using AI discovered that the technology had no significant impact on their bottom line. Meanwhile, nobody can say, beyond a few years, just how many more data centers Silicon Valley will need. There are researchers who believe there may already be enough electricity and computing power to meet generative AI’s requirements for years to come.

The economic nightmare scenario is that the unprecedented spending on AI doesn’t yield a profit anytime soon, if ever, and data centers sit at the center of those fears. Such a collapse has come for infrastructure booms past: Rapid construction of canals, railroads, and the fiber-optic cables laid during the dot-com bubble all created frenzies of hype, investment, and financial speculation that crashed markets. Of course, all of these build-outs did transform the world; generative AI, bubble or not, may do the same.

This is why OpenAI, Google, Microsoft, Amazon, and Meta are willing to spend as much as possible, as rapidly as possible, to eke out the tiniest advantage. Even if a bubble pops, there will be winners—each company would like to be the first to build a superintelligent machine. For now, many of these tech companies have cash to burn from their other ventures: Alphabet and Microsoft both made more than $100 billion in profit over the previous fiscal year, while Meta and Amazon both made more than $50 billion. But at some point in the near future, data-center spending will likely outpace even these enormous cash flows, reducing Big Tech’s liquidity and worrying investors. And so, as the AI arms race continues to escalate, the companies are beginning to raise outside money—in other words, take on debt.

Here is where the bubble dynamics get complicated. Tech firms don’t want to formally take on debt—that is, directly ask investors for loans—because debt looks bad on their balance sheets and could reduce shareholder returns. To get around this, some are partnering with private-equity titans to do some sophisticated financial engineering, Paul Kedrosky, an investor and a financial consultant, told us. These private-equity firms put up or raise the money to build a data center, which a tech company will repay through rent. Data-center leases from, say, Meta can then be repackaged into a financial instrument that people can buy and sell—a bond, in essence. Meta recently did just this: Blue Owl Capital raised money for a massive Meta data center in Louisiana by, in essence, issuing bonds backed by Meta’s rent. And multiple data-center leases can be combined into a security and sorted into what are called “tranches” based on their risk of default. Data centers represent an $800 billion market for private-equity firms through 2028 alone. (Meta has said of its arrangement with Blue Owl that the “innovative partnership was designed to support the speed and flexibility required for Meta’s data center projects.”)

In this way, the data-center financing ends up being a real-estate deal as much as an AI deal. If this sounds complicated, it’s supposed to: The complexity, investment structure, and repackaging make exactly what is going on hard to parse. And if the dynamics also sound familiar, it’s because not two decades ago, the Great Recession was precipitated by banks packaging risky mortgages into tranches of securities that were falsely marketed as high-quality. By 2008, the house of cards had collapsed.

Data-center build-outs aren’t the same as subprime mortgages. Still, there is plenty of precarity baked into these investments. Data centers deteriorate rapidly, unlike the more durable infrastructure of canals, railroads, or even fiber-optic cables. Many of the chips inside these buildings become obsolete within a few years, when Nvidia and its competitors release the next wave of bleeding-edge AI hardware. Meanwhile, the returns on scaling up chatbots are, at present, diminishing. The improvements made by each new AI model are becoming smaller and smaller, making the idea that Silicon Valley can spend its way to superintelligence more tenuous by the day.

The people who are paying attention to this cycle are getting anxious. On a scale from one to 10, the AI-bubble concern is: people posting memes of Christian Bale’s character from The Big Short, squinting in disbelief at his computer monitor. If tech stocks fall because of AI companies failing to deliver on their promises, the highly leveraged hedge funds that are invested in these companies could be forced into fire sales. This could create a vicious cycle, causing the financial damage to spread to pension funds, mutual funds, insurance companies, and everyday investors. As capital flees the market, non-tech stocks will also plummet: bad news for anyone who thought to play it safe and invest in, for instance, real estate. If the damage were to knock down private-equity firms (which are invested in these data centers) themselves—which manage trillions and trillions of dollars in assets and constitute what is basically a global shadow-banking system—that could produce another major crash.

For now, money is still pouring into the AI industry. But there’s also something circular about these investments. To wit: OpenAI has agreed to pay $300 billion to Oracle for new computing capacity, Oracle is paying Nvidia tens of billions of dollars for chips to install in one of OpenAI’s data centers, and Nvidia has agreed to invest up to $100 billion in OpenAI as it deploys Nvidia chips. Attempts to illustrate these circular investments have produced a series of byzantine charts that one software engineer referred to on X as “the technocapital hyperobject at the end of time.” The consensus seems to be that although this is legal, it likely cannot go on forever.

Maybe it will all work out. Three years ago, the generative-AI industry made functionally no revenue; today, it produces tens of billions of dollars annually, a rate of growth that, eventually, could catch up with all of this spending. Generative-AI tools are currently used by hundreds of millions of people, and it’s hard to imagine that simply ceasing overnight. Perhaps OpenAI or Anthropic will pull off superintelligence, allowing them to, in the words of the Bloomberg columnist Matt Levine, “create God and then ask it for money.”

Data centers take time to approve and build; power plants and transmission lines take perhaps even more. Labor is limited, supply chains hit snags, investment waxes and wanes—meaning that even if these data centers are built at the tremendous scale desired by Altman and his competitors, construction and energy constraints may keep the boom from growing too irresponsibly.

In any case, as we approach the end of 2025, data centers have become a peculiar cultural object. Their immense scale is a physical reminder of the economic dominance of Silicon Valley companies and their seemingly unchecked ambition. The uneasiness they inspire economically is rooted in memories of 2008 but also of the tech industry’s own financial chicanery, specifically the 2022 crypto crash, which was facilitated by a circular-payment scheme of its own. (FTX, a crypto exchange, and Alameda Research, a hedge fund, both co-founded by Sam Bankman-Fried, were found to be propping each other up: Alameda bought FTX’s bespoke cryptocurrency, and FTX lent Alameda money from its customers’ accounts.) And so, in some way, the externalities of the data-center boom, be they environmental or economic, are tied up in fears of what happens not when these tech companies fail, but when they succeed.

Boom and bust can feel like two sides of the same coin: Consider also that if AI companies deliver on their massive investments, it would likely mean producing a technology so capable and revolutionary that it wipes out countless jobs and sends an unprecedented shock wave through the global economy before humans have time to adapt. (Perhaps we will be unable to adapt at all.) If they fail, there will likely be unprecedented financial turmoil as well.

The biggest lesson of the past two decades of Silicon Valley is that Meta, Amazon, and Google—and even the newer AI labs such as OpenAI—have remade our world and have become unfathomably rich for it, all while being mostly oblivious or uninterested in the fallout. They have chased growth and scale at all costs, and largely, they’ve won. The data-center build-out is the ultimate culmination of that chase: the pursuit of scale for scale itself. In all scenarios, the outcome seems only to be real, painful disruption for the rest of us.

On 29. 10. 2025. at 3:07, Shan Jan said:

On 29. 10. 2025. at 3:07, Shan Jan said:

Edit: isuse koje je sranje ovaj forumski softver s kvotovanjem na mobitelu

On 29. 10. 2025. at 3:07, Shan Jan said:

Ali ovo, jebote...

Bar pokazuje neke znakove zdravog poslovnog razmišljanja. Ako postoji neka stabilno likvidna grana ekonomije u ovoj eri, to je definitivno industrija tretiranja muške nejebice

Edited November 2, 2025Nov 2 by Roger Sanchez

9 hours ago, Weenie Pooh said:

Here's How the AI Crash Happens

The U.S. is becoming an Nvidia-state.

By Matteo Wong and Charlie Warzel

The AI boom is visible from orbit. Satellite photos of New Carlisle, Indiana, show greenish splotches of farmland transformed into unmistakable industrial parks in less than a year’s time. There are seven rectangular data centers there, with 23 more on the way.

Inside each of these buildings, endless rows of fridge-size containers of computer chips wheeze and grunt as they perform mathematical operations at an unfathomable scale. The buildings belong to Amazon and are being used by Anthropic, a leading AI firm, to train and run its models. According to one estimate, this data-center campus, far from complete, already demands more than 500 megawatts of electricity to power these calculations—as much as hundreds of thousands of American homes. When all the data centers in New Carlisle are built, they will demand more power than two Atlantas.

The amount of energy and money being poured into AI is breathtaking. Global spending on the technology is projected to hit $375 billion by the end of the year and half a trillion dollars in 2026. Three-quarters of gains in the S&P 500 since the launch of ChatGPT came from AI-related stocks; the value of every publicly traded company has, in a sense, been buoyed by an AI-driven bull market. To cement the point, Nvidia, a maker of the advanced computer chips underlying the AI boom, yesterday became the first company in history to be worth $5 trillion.

Here’s another way of thinking about the transformation under way: Multiplying Ford’s current market cap 94 times over wouldn’t quite get you to Nvidia’s. Yet 20 years ago, Ford was worth nearly triple what Nvidia was. Much like how Saudi Arabia is a petrostate, the U.S. is a burgeoning AI state—and, in particular, an Nvidia-state. The number keeps going up, which has a buoying effect on markets that is, in the short term, good. But every good earnings report further entrenches Nvidia as a precariously placed, load-bearing piece of the global economy.

America appears to be, at the moment, in a sort of benevolent hostage situation. AI-related spending now contributes more to the nation’s GDP growth than all consumer spending combined, and by another calculation, those AI expenditures accounted for 92 percent of GDP growth during the first half of 2025. Since the launch of ChatGPT, in late 2022, the tech industry has gone from making up 22 percent of the value in the S&P 500 to roughly one-third. Just yesterday, Meta, Microsoft, and Alphabet all reported substantial quarterly-revenue growth, and Reuters reported that OpenAI is planning to go public perhaps as soon as next year at a value of up to $1 trillion—which would be one of the largest IPOs in history. (An OpenAI spokesperson told Reuters, “An IPO is not our focus, so we could not possibly have set a date”; OpenAI and The Atlantic have a corporate partnership.)

Many people believe that growth will only continue. “We’re gonna need stadiums full of electricians, heavy equipment operators, ironworkers, HVAC technicians,” Dwarkesh Patel and Romeo Dean, AI-industry analysts, wrote recently. Large-scale data-center build-outs may already be reshaping America’s energy systems. OpenAI has announced that it intends to build at least 30 gigawatts’ worth of data centers—more power than all of New England requires on even the hottest day—and CEO Sam Altman has said he’d eventually like to build a gigawatt of AI infrastructure every week. Other major tech firms have similar ambitions.

Listen to the AI crowd talk enough, and you’ll get a sense that we may be on the cusp of an infrastructure boom. And yet, something strange is happening to the economy. Even as tech stocks have skyrocketed since 2022, the companies’ share of net profits from S&P 500 companies has hardly budged. Job openings have fallen despite a roaring stock market, 22 states are in or near a recession, and despite data centers propping up the construction industry, U.S. manufacturing is in decline.

It’s clear that AI is both drowning out and obscuring other stories about the wobbling American economy. That’s a concern. But even worse: What if AI’s promise for American business proves to be a mirage? What happens then?

The yawning gap between data-center expenditures and the rest of the economy has caused whispers of bubble to rise to a chorus. A growing number of financial and industry analysts have pointed out the enormous divergence between the historic investments in AI and the tech’s relatively modest revenues. For instance, according to The Information, OpenAI likely made $4 billion last year but lost $5 billion (making the idea of a $1 trillion IPO valuation that much more staggering). From July through September, Microsoft’s investments in OpenAI resulted in losses totaling more than $3 billion. For that same time period, Meta reported rapidly growing costs due to its AI investments, spooking investors and sending its stock down 9 percent.

Much is in flux. Chatbots and AI chips are getting more efficient almost by the day, while the business case for deploying generative-AI tools remains shaky. A recent report from McKinsey found that nearly 80 percent of companies using AI discovered that the technology had no significant impact on their bottom line. Meanwhile, nobody can say, beyond a few years, just how many more data centers Silicon Valley will need. There are researchers who believe there may already be enough electricity and computing power to meet generative AI’s requirements for years to come.

The economic nightmare scenario is that the unprecedented spending on AI doesn’t yield a profit anytime soon, if ever, and data centers sit at the center of those fears. Such a collapse has come for infrastructure booms past: Rapid construction of canals, railroads, and the fiber-optic cables laid during the dot-com bubble all created frenzies of hype, investment, and financial speculation that crashed markets. Of course, all of these build-outs did transform the world; generative AI, bubble or not, may do the same.

This is why OpenAI, Google, Microsoft, Amazon, and Meta are willing to spend as much as possible, as rapidly as possible, to eke out the tiniest advantage. Even if a bubble pops, there will be winners—each company would like to be the first to build a superintelligent machine. For now, many of these tech companies have cash to burn from their other ventures: Alphabet and Microsoft both made more than $100 billion in profit over the previous fiscal year, while Meta and Amazon both made more than $50 billion. But at some point in the near future, data-center spending will likely outpace even these enormous cash flows, reducing Big Tech’s liquidity and worrying investors. And so, as the AI arms race continues to escalate, the companies are beginning to raise outside money—in other words, take on debt.

Here is where the bubble dynamics get complicated. Tech firms don’t want to formally take on debt—that is, directly ask investors for loans—because debt looks bad on their balance sheets and could reduce shareholder returns. To get around this, some are partnering with private-equity titans to do some sophisticated financial engineering, Paul Kedrosky, an investor and a financial consultant, told us. These private-equity firms put up or raise the money to build a data center, which a tech company will repay through rent. Data-center leases from, say, Meta can then be repackaged into a financial instrument that people can buy and sell—a bond, in essence. Meta recently did just this: Blue Owl Capital raised money for a massive Meta data center in Louisiana by, in essence, issuing bonds backed by Meta’s rent. And multiple data-center leases can be combined into a security and sorted into what are called “tranches” based on their risk of default. Data centers represent an $800 billion market for private-equity firms through 2028 alone. (Meta has said of its arrangement with Blue Owl that the “innovative partnership was designed to support the speed and flexibility required for Meta’s data center projects.”)

In this way, the data-center financing ends up being a real-estate deal as much as an AI deal. If this sounds complicated, it’s supposed to: The complexity, investment structure, and repackaging make exactly what is going on hard to parse. And if the dynamics also sound familiar, it’s because not two decades ago, the Great Recession was precipitated by banks packaging risky mortgages into tranches of securities that were falsely marketed as high-quality. By 2008, the house of cards had collapsed.

Data-center build-outs aren’t the same as subprime mortgages. Still, there is plenty of precarity baked into these investments. Data centers deteriorate rapidly, unlike the more durable infrastructure of canals, railroads, or even fiber-optic cables. Many of the chips inside these buildings become obsolete within a few years, when Nvidia and its competitors release the next wave of bleeding-edge AI hardware. Meanwhile, the returns on scaling up chatbots are, at present, diminishing. The improvements made by each new AI model are becoming smaller and smaller, making the idea that Silicon Valley can spend its way to superintelligence more tenuous by the day.

The people who are paying attention to this cycle are getting anxious. On a scale from one to 10, the AI-bubble concern is: people posting memes of Christian Bale’s character from The Big Short, squinting in disbelief at his computer monitor. If tech stocks fall because of AI companies failing to deliver on their promises, the highly leveraged hedge funds that are invested in these companies could be forced into fire sales. This could create a vicious cycle, causing the financial damage to spread to pension funds, mutual funds, insurance companies, and everyday investors. As capital flees the market, non-tech stocks will also plummet: bad news for anyone who thought to play it safe and invest in, for instance, real estate. If the damage were to knock down private-equity firms (which are invested in these data centers) themselves—which manage trillions and trillions of dollars in assets and constitute what is basically a global shadow-banking system—that could produce another major crash.

For now, money is still pouring into the AI industry. But there’s also something circular about these investments. To wit: OpenAI has agreed to pay $300 billion to Oracle for new computing capacity, Oracle is paying Nvidia tens of billions of dollars for chips to install in one of OpenAI’s data centers, and Nvidia has agreed to invest up to $100 billion in OpenAI as it deploys Nvidia chips. Attempts to illustrate these circular investments have produced a series of byzantine charts that one software engineer referred to on X as “the technocapital hyperobject at the end of time.” The consensus seems to be that although this is legal, it likely cannot go on forever.

Maybe it will all work out. Three years ago, the generative-AI industry made functionally no revenue; today, it produces tens of billions of dollars annually, a rate of growth that, eventually, could catch up with all of this spending. Generative-AI tools are currently used by hundreds of millions of people, and it’s hard to imagine that simply ceasing overnight. Perhaps OpenAI or Anthropic will pull off superintelligence, allowing them to, in the words of the Bloomberg columnist Matt Levine, “create God and then ask it for money.”

Data centers take time to approve and build; power plants and transmission lines take perhaps even more. Labor is limited, supply chains hit snags, investment waxes and wanes—meaning that even if these data centers are built at the tremendous scale desired by Altman and his competitors, construction and energy constraints may keep the boom from growing too irresponsibly.

In any case, as we approach the end of 2025, data centers have become a peculiar cultural object. Their immense scale is a physical reminder of the economic dominance of Silicon Valley companies and their seemingly unchecked ambition. The uneasiness they inspire economically is rooted in memories of 2008 but also of the tech industry’s own financial chicanery, specifically the 2022 crypto crash, which was facilitated by a circular-payment scheme of its own. (FTX, a crypto exchange, and Alameda Research, a hedge fund, both co-founded by Sam Bankman-Fried, were found to be propping each other up: Alameda bought FTX’s bespoke cryptocurrency, and FTX lent Alameda money from its customers’ accounts.) And so, in some way, the externalities of the data-center boom, be they environmental or economic, are tied up in fears of what happens not when these tech companies fail, but when they succeed.

Boom and bust can feel like two sides of the same coin: Consider also that if AI companies deliver on their massive investments, it would likely mean producing a technology so capable and revolutionary that it wipes out countless jobs and sends an unprecedented shock wave through the global economy before humans have time to adapt. (Perhaps we will be unable to adapt at all.) If they fail, there will likely be unprecedented financial turmoil as well.

The biggest lesson of the past two decades of Silicon Valley is that Meta, Amazon, and Google—and even the newer AI labs such as OpenAI—have remade our world and have become unfathomably rich for it, all while being mostly oblivious or uninterested in the fallout. They have chased growth and scale at all costs, and largely, they’ve won. The data-center build-out is the ultimate culmination of that chase: the pursuit of scale for scale itself. In all scenarios, the outcome seems only to be real, painful disruption for the rest of us.

Atlantic 2017. godine

It is not an original observation that technology stocks are priced in wacky ways. Amazon made $82 million in profit last quarter, down 37 percent from the previous year, but based on its high share price, has a market value of $126 billion. Compare that with Macy's, which made $217 million dollars last quarter, up 20 percent from 2012. Its market value? $18.5 billion.

Macy's danas ima market cap od 5 milijardi, Amazon 2,6 biliona. Amazon je otišao gore 20x, Macy's 5x ali na dole. Amazon danas ima godišnji profit od 50 milijardi, prihode od 600 milijardi. Macy's ima gubitak od milijardu. Wallst. procena oba preduzeća nije bila "wacky" kao što kaže tekst, nego apsolutno razumna. Nvidia i Ford? Odluči sam.

Još malo iz Atlantica:
The End of the Asset Economy June 18, 2022
Is a Recession Coming? November 23, 2018

Why Does the Stock Market Keep Going Up? October 18, 2017

Spreadsheet Fiction: The Delusion at the Heart of Tech Stock Analysis June 20, 2013
Fueling a Stock Bubble With Facebook Fans June 12, 2011
Why the market’s rate of return—and your nest egg—may never recover September 2010 Issue

Ova predviđanja traju već 15 godina i ušla su odavno u vode "a broken clock is right twice a day". Nesumnjivo je jasno da imamo određeni AI bubble, ali koliko još može da traje i da li će uopšte pući ne zna suštinski niko, jer vidi gore deceniju promašaja. Pričao sam pre par nedelja o tome šta je najprimitivnije moguće rešenje za prihode AI i oni su nedavno izbacili Product Feed Spec, specifikacija kako da formatiraš podatke, da tvoj proizvod izleti korisniku kad traži nešto slično tvom i osim toga testiraju 1 click solution da direktno preko njih kupiš proizvod. Google godišnji prihod od 250 milijardi uglavnom reklama? Ćao. Facebook godišnji prihod od 100 milijardi od reklama? Ćao. Da li mislim da je AI tehnologija koja može da isporuči sve što se sad pumpa? Ne mislim, ali kao što vidiš potencijalnog prihoda ima k'o pleve, sve i da ostane na ovom nivou.

Osim toga, ovaj bubble nije isto što i dotcom bubble. Dotcom bubble je bio gomila firmi koji su imali ideju (ispostavilo se ispravnu!) i nula prihoda. U ovom bubbleu, postoje realne pare koje su ulažu. Ovaj bubble počinje malo da liči na dotcom bubble, jer je i tad poznato bilo da recimo Lucent pozajmljuje pare svojim mušterijama, koje onda "kupuju" njihove proizvode. Nvidia je otišla 15x na gore u dve godine i sve vreme je odnos njihove cene akcija i zarade bio relativno stabilan. Precenjen, ali stabilan iako je cena astronomski išla gore. Problem nastaje ako profit prestane da raste i što Nvidia počinje da radi iste smicalice kao Lucent.

Glavna poenta je ovo: nije dovoljno da se pogleda tržište i ispali fraza "bubble". Da je bubble je jasno svima, ali to ne znači da će da pukne, može lako da naraste još 10 puta, pa kad jednom pukne ode dole 5 puta. Katastrofa je na pomolu se čuje svake godine već 15 godina, i možda se na kraju i desi, ali to je očigledno konstantno stanje i već dugo niko nije ispravno predvideo kolaps.

To je sve načelno tačno (osim teze da se ulažu "realne pare" jer se odavno zna za self-dealing u AI sferi; da li su dva aktera ili tri u kombinaciji ne menja stvar.)

Atlantic ovde ne iznosi neko genijalno predviđanje da balon mora pući, samo opipava puls ljudi koji prate kretanje novca i konstatuje njihovu nervozu. Zaključak "maybe it will all work out" je suštinski jedno sleganje ramenima. Data center manija im je zgodno došla da naprave paralelu sa 2008 jer, kao real estate je u pitanju, ali ta paralela je prilično nategnuta.

Za razliku od real estate muljanja gde osnovni proizvod nije bio upitan, ovde niko ne može da zna da li će se hype ispostaviti kao opravdan u razmeri od 80%, ili 40%, ili 2%. Svi se klade da će se njima isplatiti, a apsolutno je nemoguće da svi budu u pravu. (Veća je šansa da se long-term ne isplati nikome, koliko su se zaleteli.)

On 18. 10. 2025. at 17:21, Shan Jan said:

Najgore je sto

On 18. 10. 2025. at 22:16, Fins fleet said:

Idu Mujo i Haso ulicom i naidju na govno. Haso, bi li pojeo ovo govno za 100 evra? Bih, kako ne. Malo dalje, drugo govno. Mujo, bi li ti pojeo ovo govno za 100 evra? Bih, kako ne bih. Samo nek se obrce.

On a serious note, ono sto u ovoj prici fali je to da ce sve 3 kompanije tih 100b prikazati kao growth, pa ce onda dobijati bolje uslove finansiranja.

U toku mog kratkog izleta u b2b svet, svaki mesec bih otkrio da nam je customer x takodje i vendor, ili obrnuto.

Ovaj, CAPEX != Growth in sales.

Takodje, u slucaju usluga vendor je ili COGS ili SGA, tako da nista tim reciprocnim operacijamane dobijas na rentabilitetu, osim ako nije rec o povezanim licima pa mozes da mutis (tunneling, tax...). A equity analyst takodje nisu sisali vesla da ne gledaju izvore rasta i te operacije, ako je rec samo o tome da se namakne sales growth.

Mozda postoji i operativno objasnjenje reciprociteta a ne samo muljanje.

1 hour ago, Venom said:

Atlantic 2017. godine

Macy's danas ima market cap od 5 milijardi, Amazon 2,6 biliona. Amazon je otišao gore 20x, Macy's 5x ali na dole. Amazon danas ima godišnji profit od 50 milijardi, prihode od 600 milijardi. Macy's ima gubitak od milijardu. Wallst. procena oba preduzeća nije bila "wacky" kao što kaže tekst, nego apsolutno razumna. Nvidia i Ford? Odluči sam.

Još malo iz Atlantica:
The End of the Asset Economy June 18, 2022
Is a Recession Coming? November 23, 2018

Why Does the Stock Market Keep Going Up? October 18, 2017

Spreadsheet Fiction: The Delusion at the Heart of Tech Stock Analysis June 20, 2013
Fueling a Stock Bubble With Facebook Fans June 12, 2011
Why the market’s rate of return—and your nest egg—may never recover September 2010 Issue

Ova predviđanja traju već 15 godina i ušla su odavno u vode "a broken clock is right twice a day". Nesumnjivo je jasno da imamo određeni AI bubble, ali koliko još može da traje i da li će uopšte pući ne zna suštinski niko, jer vidi gore deceniju promašaja. Pričao sam pre par nedelja o tome šta je najprimitivnije moguće rešenje za prihode AI i oni su nedavno izbacili Product Feed Spec, specifikacija kako da formatiraš podatke, da tvoj proizvod izleti korisniku kad traži nešto slično tvom i osim toga testiraju 1 click solution da direktno preko njih kupiš proizvod. Google godišnji prihod od 250 milijardi uglavnom reklama? Ćao. Facebook godišnji prihod od 100 milijardi od reklama? Ćao. Da li mislim da je AI tehnologija koja može da isporuči sve što se sad pumpa? Ne mislim, ali kao što vidiš potencijalnog prihoda ima k'o pleve, sve i da ostane na ovom nivou.

Osim toga, ovaj bubble nije isto što i dotcom bubble. Dotcom bubble je bio gomila firmi koji su imali ideju (ispostavilo se ispravnu!) i nula prihoda. U ovom bubbleu, postoje realne pare koje su ulažu. Ovaj bubble počinje malo da liči na dotcom bubble, jer je i tad poznato bilo da recimo Lucent pozajmljuje pare svojim mušterijama, koje onda "kupuju" njihove proizvode. Nvidia je otišla 15x na gore u dve godine i sve vreme je odnos njihove cene akcija i zarade bio relativno stabilan. Precenjen, ali stabilan iako je cena astronomski išla gore. Problem nastaje ako profit prestane da raste i što Nvidia počinje da radi iste smicalice kao Lucent.

Glavna poenta je ovo: nije dovoljno da se pogleda tržište i ispali fraza "bubble". Da je bubble je jasno svima, ali to ne znači da će da pukne, može lako da naraste još 10 puta, pa kad jednom pukne ode dole 5 puta. Katastrofa je na pomolu se čuje svake godine već 15 godina, i možda se na kraju i desi, ali to je očigledno konstantno stanje i već dugo niko nije ispravno predvideo kolaps.

Bravo. Odlicno razumevanje i finansija (PE ratio i njegovo znacenje) i ovo oko doomsday prophets koji su karijeru izgradili na "bubble!" i prognozirali 10 crash od kojih se dogodio jedan ili nijedan.

NVIDIA ima profit i ima prihode. Sa druge strane, to je B2B (datacentri) prihod ali jos uvek fali neki opopljiv B2C profit, gde mogucnosti ima, ali jos nije jasno tacno kako. Takodje, globalno, postoji konkurencija sa Kinezima na tom planu. Plus, centralizacija, opaquness itd, itd... Ta dva faktora nisu postojala 2000. Tehnooptimizam je bio mnogo visi nego danas.

6 hours ago, Weenie Pooh said:

To je sve načelno tačno (osim teze da se ulažu "realne pare" jer se odavno zna za self-dealing u AI sferi; da li su dva aktera ili tri u kombinaciji ne menja stvar.)

Atlantic ovde ne iznosi neko genijalno predviđanje da balon mora pući, samo opipava puls ljudi koji prate kretanje novca i konstatuje njihovu nervozu. Zaključak "maybe it will all work out" je suštinski jedno sleganje ramenima. Data center manija im je zgodno došla da naprave paralelu sa 2008 jer, kao real estate je u pitanju, ali ta paralela je prilično nategnuta.

Za razliku od real estate muljanja gde osnovni proizvod nije bio upitan, ovde niko ne može da zna da li će se hype ispostaviti kao opravdan u razmeri od 80%, ili 40%, ili 2%. Svi se klade da će se njima isplatiti, a apsolutno je nemoguće da svi budu u pravu. (Veća je šansa da se long-term ne isplati nikome, koliko su se zaleteli.)

Slažem se, ali to je tako uvek. Hype train na koji uskaču svi, zlatna groznica maltene. Nekom će da se posreći, neko će ostati bez gaća. Imao je Buffet dobar komentar davno kako je auto industrija na početku imala stotine kompanija od kojih je većina propala i većina ljudi je pukla pare. Ali, nema spora da je auto industrija promenila društvo iz korena i delu investitora napravila ogroman profit.

Ko je gledao ili čitao Big Short zna da je hipoteza tada bila da su mnogi bili u kreditima do guše za koje je upitno bilo da li mogu da ih vrate, što će prouzrokovati kolaps finansijskog sistema kad jednom ne budu mogli da ih vrate, jer su banke izdale previše kredita odnosno upakovali ih u asset backed securities. I tako je i bilo. Šta je hipoteza ovde? Jedna je da su ove kompanije narasle da budu preveliki deo tržišta. Ok, ali koji je mehanizam koji će povući celo društvo na dole? Nvidia će izgubiti mušterije i pući, ok, ali Google, Microsoft, Facebook do sada nisu u dugovima do guše da bi finansirali investicije, nego su one finansirane iz realnih prihoda. Lucent problem je bio što su pozajmljivali pare kompanijama koje nisu imale šanse da ih vrate. Da ovo sve pukne sutra, Google će i dalje da ima prihode od 250 milijardi od reklama.

Kolaps bankarskog sektora je realniji ako AI uspe da zameni poslove, nego ako pukne, jer su opet izloženi tome da mnoge nekretnine koje služe kao poslovni prostor postaju bezvredne i gde mnogi neće moći da vrate kredite koje su uzeli. Ali opet i to je pitanje da li su do te mere izloženi kao 2008 ili će moći da progutaju gubitak.

Ne tvrdim da ne može da se desi ili da se napravi argument, ali taj tekst nije izneo nijedan osim iste greške koju su napravili sa Macy's i Amazonom pre 10 godina.

Inače jedan od aktera krize 2008. je od skora na YouTubeu. Steve Eisman a.k.a. Mike Baum kojeg je glumio Steve Carell.

3 hours ago, Venom said:

Šta je hipoteza ovde? Jedna je da su ove kompanije narasle da budu preveliki deo tržišta. Ok, ali koji je mehanizam koji će povući celo društvo na dole? Nvidia će izgubiti mušterije i pući, ok, ali Google, Microsoft, Facebook do sada nisu u dugovima do guše da bi finansirali investicije, nego su one finansirane iz realnih prihoda. Lucent problem je bio što su pozajmljivali pare kompanijama koje nisu imale šanse da ih vrate. Da ovo sve pukne sutra, Google će i dalje da ima prihode od 250 milijardi od reklama.

Hipoteza je da novorođeni AI sektor već predstavlja 92% privrednog rasta, i da ako to pukne, povlači svašta drugo. Private equity firme koje trenutno bagerima sipaju pare u AI takođe imaju stake i u raznim drugim sektorima, pa bi njihov pad oborio akcije svega i svačega. Ovo su preveliki projekti da bi bili izolovani od ostatka tržišta, previsok je tu commitment da bi kad balon pukne veliki igrači samo oprali ruke i nastavili dalje.

"If tech stocks fall because of AI companies failing to deliver on their promises, the highly leveraged hedge funds that are invested in these companies could be forced into fire sales. This could create a vicious cycle, causing the financial damage to spread to pension funds, mutual funds, insurance companies, and everyday investors. As capital flees the market, non-tech stocks will also plummet: bad news for anyone who thought to play it safe and invest in, for instance, real estate. If the damage were to knock down private-equity firms (which are invested in these data centers) themselves—which manage trillions and trillions of dollars in assets and constitute what is basically a global shadow-banking system—that could produce another major crash."