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Their reasoning is straightforward: These companies have decided that the best way to make generative AI better is to build bigger AI models. And that is really, really expensive, requiring resources on the scale of moon missions and the interstate-highway system to fund the data centers and related infrastructure that generative AI depends on

“If we’re going to justify a trillion or more dollars of investment, [AI] needs to solve complex problems and enable us to do things we haven’t been able to do before.” Today’s flagship AI models, he said, largely cannot.

Now a number of voices in the finance world are beginning to ask whether all of this investment can pay off. OpenAI, for its part, may lose up to $5 billion this year, almost 10 times more than what the company lost in 2022,

Dario Amodei, the CEO of the rival start-up Anthropic, has predicted that a single AI model (such as, say, GPT-6) could cost $100 billion to train by 2027. The global data-center buildup over the next few years could require trillions of dollars from tech companies, utilities, and other industries, according to a July report from Moody’s Ratings.

Over the past few weeks, analysts and investors at some of the world’s most influential financial institutions—including Goldman Sachs, Sequoia Capital, Moody’s, and Barclays—have issued reports that raise doubts about whether the enormous investments in generative AI will be profitable.

generative AI has already done extraordinary things, of course—advancing drug development, solving challenging math problems, generating stunning video clips. But exactly what uses of the technology can actually make money remains unclear

At present, AI is generally good at doing existing tasks—writing blog posts, coding, translating—faster and cheaper than humans can. But efficiency gains can provide only so much value, boosting the current economy but not creating a new one.

Right now, Silicon Valley might just functionally be replacing some jobs, such as customer service and form-processing work, with historically expensive software, which is not a recipe for widespread economic transformation.

McKinsey has estimated that generative AI could eventually add almost $8 trillion to the global economy every year

Tony Kim, the head of technology investment at BlackRock, the world’s largest money manager, told me he believes that AI will trigger one of the most significant technological upheavals ever. “Prior industrial revolutions were never about intelligence,”

“Here, we can manufacture intelligence.”

this future is not guaranteed. Many of the productivity gains expected from AI could be both greatly overestimated and very premature, Daron Acemoglu, an economist at MIT, has found

AI products’ key flaws, such as a tendency to invent false information, could make them unusable, or deployable only under strict human oversight, in certain settings—courts, hospitals, government agencies, schools

AI as a truly epoch-shifting technology, it may well be more akin to blockchain, a very expensive tool destined to fall short of promises to fundamentally transform society and the economy.

Researchers at Barclays recently calculated that tech companies are collectively paying for enough AI-computing infrastructure to eventually power 12,000 different ChatGPTs. Silicon Valley could very well produce a whole host of hit generative-AI products like ChatGPT, “but probably not 12,000 of them,

even if it did, there would be nowhere enough demand to use all those apps and actually turn a profit.

Some of the largest tech companies’ current spending on AI data centers will require roughly $600 billion of annual revenue to break even, of which they are currently about $500 billion short.

Tech proponents have responded to the criticism that the industry is spending too much, too fast, with something like religious dogma. “I don’t care” how much we spend, Altman has said. “I genuinely don’t.

the industry is asking the world to engage in something like a trillion-dollar tautology: AI’s world-transformative potential justifies spending any amount of resources, because its evangelists will spend any amount to make AI transform the world.

in the AI era in particular, a lack of clear evidence for a healthy return on investment may not even matter. Unlike the companies that went bust in the dot-com bubble in the early 2000s, Big Tech can spend exorbitant sums of money and be largely fine

perhaps even more important in Silicon Valley than a messianic belief in AI is a terrible fear of missing out. “In the tech industry, what drives part of this is nobody wants to be left behind. Nobody wants to be seen as lagging,

Go all in on AI, the thinking goes, or someone else will. Their actions evince “a sense of desperation,” Cahn writes. “If you do not move now, you will never get another chance.” Enormous sums of money are likely to continue flowing into AI for the foreseeable future, driven by a mix of unshakable confidence and all-consuming fear.

But with the web, “all of the sudden we began moving to a market where first mover advantage became enormous,” he said. Connection speeds were growing faster, Americans were starting to shop online, and multiplying e-commerce sites fought gladiatorial battles to control most every area of spending.

If the dawn of the start-up era meant that consumer-oriented ideas were becoming more important than proprietary technology, he asked himself aloud, shouldn’t more women have flooded in?

“The notion that diversity in an early team is important or good is completely wrong,” he added. “The more diverse the early group, the harder it is for people to find common ground.”

David Sacks, on the other hand, was unmarried and unencumbered, and in 1999 he left politics, his law degree and a job at the consulting firm McKinsey & Company to join his Stanford Review friends at a technology start-up, because of “the desire to live on the edge, to fight an epic battle, to experience in a very diluted way what previous generations must have felt as they prepared to go to war,” he wrote at the time. For his generation, he wrote, “instead of violence, unbridled capitalism has become the preferred vehicle for channeling their energy, intellect and aggression.”

his lack of social grace became an asset, according to Mr. Thiel and other former colleagues. He did not waste time on meetings that seemed pointless, and he bluntly insisted that the engineers whittle an eight-page PayPal registration process down to one.

But those debates did a great deal for Mr. Sacks. After graduation, he and Mr. Thiel published “The Diversity Myth,” a book-length critique of Stanford’s efforts. Within a few more years, he, Mr. Thiel, Mr. Rabois and others had transformed themselves into a close-knit network of technology entrepreneurs — innovators who created billion-dollar business after billion-dollar business, using the ideas, ethos and group bonds they had honed at The Stanford Review.

he and Mr. Thiel now had a setting in which to try out their ideas about diversity and meritocracy. “In the start-up crucible, performing is all that matters,” Mr. Sacks wrote about that time. He wanted to give all job applicants tests of cognitive ability, according to his colleague Keith Rabois, and when the company searched for a new chief executive, one of the requirements was an I.Q. of 160 — genius level.

intentionally or not, he stated something many people quietly believed: The same thing that made Silicon Valley phenomenally successful also kept it homogeneous, and start-ups had an almost inevitable like-with-like quality.

The kind of common ground shared by the early PayPal leaders “is always the critical ingredient on the founding teams,” Mr. Thiel said in an interview. “You have these great friendships that were built over some period of time. Silicon Valley flows out of deep relationships that people have built. That’s the structural reality.”

Another woman from the class of 1994 was quoted in the Fortune article: Trae Vassallo, who was Traci Neist when she built the taco-eating machine all those years ago, attended Stanford Business School with Ms. Herrin and Ms. Bianchini, co-founded a mobile device company, and then joined Kleiner Perkins, a premier venture capital firm.

The success of the struggle to create PayPal, and its eventual sale price, gave the men a new power: the knowledge to create new companies and the ability to fund their own and one another’s. Billion-dollar start-ups had been rare. But in the next few years, the so-called PayPal Mafia went on to found seven companies that reached blockbuster scale, including YouTube, LinkedIn, Yelp and a business-messaging service called Yammer, founded by Mr. Sacks and sold a few years later to Microsoft for $1.2 billion.

Since 1999, the number of female partners in venture capital has declined by nearly half, from 10 percent to 6 percent, according to a recent Babson College study.

in early 2014, Ms. Vassallo was quietly let go. The firm was downsizing over all, especially in green technology, one of Ms. Vassallo’s specialties, and men were shown the exit as well. But in interviews, several former colleagues said it was far from an easy environment for women, with all-male outings and fierce internal competition for who got which board seat — meaning internal credit — for each company, not to mention a sexual discrimination lawsuit filed by a female junior partner, scheduled for trial in early 2015.

They also said that Ms. Vassallo, earnest and so technical that she started a robotics program at a local girls’ school, had not been as forceful, or as adept a politician, as some of her male peers.

Less than 10 years after graduation, he and Mr. Thiel had been transformed from outcasts into favorites with a reputation for seeing the future. Far from the only libertarians in Silicon Valley, they had finally found an environment that meshed perfectly with their desire for unfettered competition and freedom from constraints. The money they made seemed like vindication of their ideas.

As classmates started conversations with greetings like “How’s your fund?” some of those who did not work in technology joked that they felt like chumps. The Stanford campus had gone computer science crazy, with the majority of students taking programming courses. A career in technology didn’t feel like a risk anymore — it felt like a wise bet, said Jennifer Widom, a programming professor turned engineering dean. Computer science “is a degree that guarantees you a future, regardless of what form you decide to take it in,” she said.

The nature of start-ups was shifting again, too, this time largely in women’s favor. From servers onward, many components could be inexpensively licensed instead of custom-built. Founders could turn to a multiplying array of investment sources, meaning they no longer had to be supplicants at a handful of male-run venture firms. The promise that the Internet would be a leveler was finally becoming a bit more fulfilled.

The frenzy had an unlikely effect on the some members of the Stanford Review group: They were becoming cheerleaders for women in technology, not for ideological reasons, but for market-based ones.

Like many others, he was finding that the biggest obstacle to starting new companies was a dearth of technical talent so severe they worried it would hinder innovation.

The real surprise of the reunion weekend, however, was that more of the women in the class of ’94 were finally becoming entrepreneurs, later and on a smaller scale than many of the men, but founders nonetheless.

The rhythms of their lives and the technology industry were finally clicking: Companies were becoming easier to start just as their children were becoming more self-sufficient, and they did not want to miss another chance.

The order set off a furious public battle on Wednesday between the Obama administration and one of the world’s most valuable companies in a dispute with far-reaching legal implications.

This is not the first time a technology company has been ordered to effectively decrypt its own product. But industry experts say it is the most significant because of Apple’s global profile, the invasive steps it says are being demanded and the brutality of the San Bernardino attacks.

Law enforcement officials who support the F.B.I.’s position said that the impasse with Apple provided an ideal test case to move from an abstract debate over the balance between national security and privacy to a concrete one

The F.B.I. has been unable to get into the phone used by Syed Rizwan Farook, who was killed by the police along with his wife after they attacked Mr. Farook’s co-workers at a holiday gathering.

Magistrate Judge Sheri Pym of the Federal District Court for the District of Central California issued her order Tuesday afternoon, after the F.B.I. said it had been unable to get access to the data on its own and needed Apple’s technical assistance.

Mr. Cook, the chief executive at Apple, responded Wednesday morning with a blistering, 1,100-word letter to Apple customers, warning of the “chilling” breach of privacy posed by the government’s demands. He maintained that the order would effectively require it to create a “backdoor” to get around its own safeguards, and Apple vowed to appeal the ruling by next week.

Apple argues that the software the F.B.I. wants it to create does not exist. But technologists say the company can do it.

pple executives had hoped to resolve the impasse without having to rewrite their own encryption software. They were frustrated that the Justice Department had aired its demand in public, according to an industry executive with knowledge of the case, who spoke on the condition of anonymity about internal discussions.

The Justice Department and the F.B.I. have the White House’s “full support,” the spokesman, Josh Earnest, said on Wednesday.

His vote of confidence was significant because James Comey, the F.B.I. director, has at times been at odds with the White House over his aggressive advocacy of tougher decryption requirements on technology companies. While Mr. Obama’s national security team was sympathetic to Mr. Comey’s position, others at the White House viewed legislation as potentially perilous. Late last year, Mr. Obama refused to back any legislation requiring decryption, leaving a court fight likely.

The dispute could initiate legislation in Congress, with Republicans and Democrats alike criticizing Apple’s stance on Wednesday and calling for tougher decryption requirements.

Donald J. Trump, the Republican presidential contender, also attacked Apple on Fox News, asking, “Who do they think they are?”

But Apple had many defenders of its own among privacy and consumer advocates, who praised Mr. Cook for standing up to what they saw as government overreach.

Many of the company’s defenders argued that the types of government surveillance operations exposed in 2013 by Edward J. Snowden, the former National Security Agency contractor, have prompted technology companies to build tougher encryption safeguards in their products because of the privacy demands of their customers.

Privacy advocates and others said they worried that if the F.B.I. succeeded in getting access to the software overriding Apple’s encryption, it would create easy access for the government in many future investigations.

The Apple order is a flash point in a dispute that has been building for more than a decade. Advertisement Continue reading the main story Advertisement Continue reading the main story

The F.B.I. began sounding alarms years ago about technology that allowed people to exchange private messages protected by encryption so strong that government agents could not break it. In fall 2010, at the behest of Robert S. Mueller III, the F.B.I. director, the Obama administration began work on a law that required technology companies to provide unencrypted data to the government.

Lawyers at the F.B.I., Justice Department and Commerce Department drafted bills around the idea that technology companies in the Internet age should be bound by the same rules as phone companies, which were forced during the Clinton administration to build digital networks that government agents could tap.

The draft legislation would have covered app developers like WhatsApp and large companies like Google and Apple, according to current and former officials involved in the process.

There is no debate that, when armed with a court order, the government can get text messages and other data stored in plain text. Far less certain was whether the government could use a court order to force a company to write software or redesign its system to decode encrypted data. A federal law would make that authority clear, they said.

But the disclosures of government surveillance by Mr. Snowden changed the privacy debate, and the Obama administration decided not to move on the proposed legislation. It has not been revived.

The legal issues raised by the judge’s order are complicated. They involve statutory interpretation, rather than constitutional rights, and they could end up before the Supreme Court.

As Apple noted, the F.B.I., instead of asking Congress to pass legislation resolving the encryption fight, has proposed what appears to be a novel reading of the All Writs Act of 1789.

The law lets judges “issue all writs necessary or appropriate in aid of their respective jurisdictions and agreeable to the usages and principles of law.”

This was a necessary precondition for the Industrial Revolution. While factory technology would consolidate this development, the creation of factory technology was possible only because people’s relationship to work had already changed. A power loom would have served no purpose for networks of farmers making cloth at home.

The same goes for today’s digital revolution. While often described as a second machine age, our current historical moment is better understood as a second industrious revolution. It has been underway for at least 40 years, encompassing the collapse, since the 1970s, of the relatively secure wage-work economy of the postwar era — and the rise of post-industrialism and the service economy.

Over these four decades we have seen an increase in the use of day laborers, office temps, management consultants, contract assemblers, adjunct professors, Blackwater mercenaries and every other kind of worker filing an I.R.S. form 1099. These jobs span the income ranks, but they share what all work seems to have in common in the post-1970s economy: They are temporary and insecure

In the last 10 years, 94 percent of net new jobs have appeared outside of traditional employment. Already approximately one-third of workers, and half of young workers, participate in this alternative world of work, either as a primary or a supplementary source of income.

services like Uber and online freelance markets like TaskRabbit were created to take advantage of an already independent work force; they are not creating it. Their technology is solving the business and consumer problems of an already insecure work world. Uber is a symptom, not a cause.

Today’s smartphone app is an easy way to hire a temp, but is it really that much easier than picking up a phone was in 1950?

shortly after World War II, a Milwaukee man named Elmer Winter founded Manpower, the first major temp agency, to supply emergency secretaries. But by the end of the ’50s, Winter had concluded that the future growth of Manpower was in replacing entire work forces

persuading companies to abandon how they operated was easier said than done, even though Winter could readily demonstrate that it would be cheaper. Few companies took him up on his offer. Higher profits were possible, but not as important, in the lingering wake of the Great Depression, as the moral compact between employer and employee

Big corporations had always had their critics, but no one before the ’70s would have thought that smaller companies would be better run than large ones. Large companies had resources, economies of scale, professional managers, lots of options. Yet terms like “small” and “efficient” and “flexible” would come to seem like synonyms. And with the rise of the lean corporation, work forces became expendable and jobs more precarious.

for the vast majority of workers, the “freedom” of the gig economy is just the freedom to be afraid. It is the severing of obligations between businesses and employees. It is the collapse of the protections that the people of the United States, in our laws and our customs, once fought hard to enshrine.

We can’t turn back the clock, but neither is job insecurity inevitable. Just as the postwar period managed to make industrialization benefit industrial workers, we need to create new norms, institutions and policies that make digitization benefit today’s workers. Pundits have offered many paths forward — “portable” benefits, universal basic income, worker reclassification — but regardless of the option, the important thing to remember is that we do have a choice.

Total funding for clean-tech start-ups fell during most of the past decade

But there are dozens, if not hundreds, of start-ups developing new technologies that address the issue.

Two major scientific organizations said last fall that even if greenhouse-gas emissions were reduced significantly, stopping drastic global warming would require technological breakthroughs that allowed for the removal of billions of tons of carbon dioxide already in the atmosphere.

Some promising methods for accomplishing that involve old-fashioned technologies, like planting trees and changing the ways farmers till their fields

In 2018, $6.6 billion was invested in clean tech, about 15 percent of what went to software start-ups. Carbon-removal start-ups got a tiny sliver of that.

“It is tackling big markets and big challenges, but that doesn’t necessarily mean that those are going to be big businesses,”

Noah Deich, the founder of Carbon180, a nonprofit that sponsored the event, said it was encouraging to see investors there. But he said he had not seen the commitment to investing that he believed was necessary to get the technologies working.

“For an internet company, even if you don’t have a real product, you can get money to develop one,” he said. “Here, it’s the opposite.

So far, no one has found an obvious way to turn capturing carbon dioxide into a profitable business.

Ocean-Based Climate Solutions, has created a device that stirs up water in the ocean to promote the growth of phytoplankton, which are algae that can take carbon dioxide out of the air and deliver it to the bottom of the sea in solid form.

Mr. Oros said that his fund had not made an investment in the sector and that he did not see a way for the industry to take off without government policy encouraging it

for these businesses to succeed it would probably be necessary for governments to create a carbon tax or other subsidies as incentives for new businesses.

Mr. Lackner said investors should assume that governments would be willing at some point to pay for what these companies were doing.

“In the end, there is no way for the market to not exist,” he said. “This will be a brand-new industry at a huge scale.”

In the time it took Carbon Engineering to raise one round of $68 million, Slack, a messaging company founded the same year, has raised more than 10 times as much and is now preparing for an initial public offering that could value it at nearly $20 billion.

Everyone who discusses the difficulties these start-ups face points back to the clean-tech boom, when several venture capital firms put billions of dollars into solar energy and other technologies. While solar power has gained traction, most of the clean-tech funds were viewed as failures.

venture capitalists needed their investments to show returns within a few years

“There is a fundamental mismatch in time lines,”

One of the biggest investors in climate-focused start-ups is Breakthrough Energy Ventures, a $1 billion fund that seeks to support the development of world-saving technology that might not have a quick turnaround. The fund has received money from Bill Gates and several other billionaires.

money from major philanthropists would not be enough to get even one start-up up to speed, much less the dozens needed to meet the carbon-reduction goals set by international bodies like the Intergovernmental Panel on Climate Change

a broad array of investors, including venture capitalists, will need to get involved. And they will need to wait more than three or four years to cash out

“We don’t need another photo-sharing app or another blockchain start-up,” said Mr. Rogers, who is investing his money through Incite Ventures, a fund he created with his wife, Swati Mylavarapu. “We need to solve the carbon crisis. But a lot of folks are chasing the easy money rather than taking responsibility for what needs to be done.”

Senate Majority Leader Charles E. Schumer (D-N.Y.) last month urged Congress to adopt “comprehensive” regulations on the AI industry.

Rather than focusing on AI technology that lets the general public create unique content like the chatbots and image generators, Chinese companies have instead focused on technologies with clear commercial uses, like surveillance tech.

n a recent study, Ding found that most of the large language models developed in China were nearly two years behind those developed in the U.S., a gap that would be a challenge to close — even if American firms had to adjust to regulation.

This gap also makes it difficult for Chinese firms to attract the world’s top engineering talent. Many would prefer to work at firms that have the resources and flexibility to experiment on frontier research areas.

Restrictions on access to the most advanced chips, which are needed to run AI models, have added to these difficulties.

Recent research identified 17 large language models in China that relied on Nvidia chips, and just three models that used Chinese-made chips.

While Beijing pushes to make comparable chips at home, Chinese AI companies have to source their chips any way they can — including from a black market that has sprung up in Shenzhen, where, according to Reuters, the most advanced Nvidia chips sell for nearly $20,000, more than twice what they go for elsewhere.

Despite the obstacles, Chinese AI companies have made major advances in some types of AI technologies, including facial recognition, gait recognition, and artificial and virtual reality.

These technologies have also fueled the development of China’s vast surveillance industry, giving Chinese tech giants an edge that they market around the world, such as Huawei’s contracts for smart city surveillance from Belgrade, Serbia, to Nairobi.

Companies developing AI in China need to comply with specific laws on intellectual property rights, personal information protection, recommendation algorithms and synthetic content, also called deep fakes. In April, regulators also released a draft set of rules on generative AI, the technology behind image generator Stable Diffusion and chatbots such as OpenAI’s ChatGPT and Google’s Bard.

They also need to ensure AI generated content complies with Beijing’s strict censorship regime. Chinese tech companies such as Baidu have become adept at filtering content that contravenes these rules. But it has hampered their ability to test the limits of what AI can do.

No Chinese tech company has yet been able to release a large language model on the scale of OpenAI’s ChatGPT to the general public, in which the company has asked the public to play with and test a generative AI model, said Ding, the professor at George Washington University.

“That level of freedom has not been allowed in China, in part because the Chinese government is very worried about people creating politically sensitive content,” Ding said.

Although Beijing’s regulations have created major burdens for Chinese AI companies, analysts say that they contain several key principles that Washington can learn from — like protecting personal information, labeling AI-generated content and alerting the government if an AI develops dangerous capabilities.

AI regulation in the United States could easily fall short of Beijing’s heavy-handed approach while still preventing discrimination, protecting people’s rights and adhering to existing laws, said Johanna Costigan, a research associate at the Asia Society Policy Institute.

“There can be alignment between regulation and innovation,” Costigan said. “But it’s a question of rising to the occasion of what this moment represents — do we care enough to protect people who are using this technology? Because people are using it whether the government regulates it or not.”

When viewed with the Inflation Reduction Act, which the House is poised to pass later this week, and last year’s bipartisan infrastructure law, a major shift in congressional climate spending comes into focus. According to the RMI analysis, these three laws are set to more than triple the federal government’s average annual spending on climate and clean energy this decade, compared with the 2010s.

Within a few years, when the funding has fully ramped up, the government will spend roughly $80 billion a year on accelerating the development and deployment of zero-carbon energy and preparing for the impacts of climate change. That exceeds the GDP of about 120 of the 192 countries that have signed the Paris Agreement on Climate Change

The law, for instance, establishes a new $20 billion Directorate for Technology, which will specialize in pushing new technologies from the prototype stage into the mass market. It is meant to prevent what happened with the solar industry—where America invented a new technology, only to lose out on commercializing it—from happening again

the bill’s programs focus on the bleeding edge of the decarbonization problem, investing money in technology that should lower emissions in the 2030s and beyond.

The International Energy Association has estimated that almost half of global emissions reductions by 2050 will come from technologies that exist only as prototypes or demonstration projects today.

To get those technologies ready in time, we need to deploy those new ideas as fast as we can, then rapidly get them to commercial scale, Carey said. “What used to take two decades now needs to take six to 10 years.” That’s what the CHIPS Act is supposed to do

By the end of the decade, the federal government will have spent more than $521 billion

Congress has explicitly tasked the new office with studying “natural and anthropogenic disaster prevention or mitigation” as well as “advanced energy and industrial efficiency technologies,” including next-generation nuclear reactors.

The bill also directs about $12 billion in new research, development, and demonstration funding to the Department of Energy, according to RMI’s estimate. That includes doubling the budget for ARPA-E, the department’s advanced-energy-projects skunk works.

it allocates billions to upgrade facilities at the government’s in-house defense and energy research institutes, including the National Renewable Energy Laboratory, the Princeton Plasma Physics Laboratory, and Berkeley Lab, which conducts environmental-science research.

RMI’s estimate of the climate spending in the CHIPS bill should be understood as just that: an estimate. The bill text rarely specifies how much of its new funding should go to climate issues.

When you add CHIPS, the IRA, and the infrastructure law together, Washington appears to be unifying behind a new industrial policy, focused not only on semiconductors and defense technology but clean energy

The three bills combine to form a “a coordinated, strategic policy for accelerating the transition to the technologies that are going to define the 21st century,”

scholars and experts have speculated about whether industrial policy—the intentional use of law to nurture and grow certain industries—might make a comeback to help fight climate change. Industrial policy was central to some of the Green New Deal’s original pitch, and it has helped China develop a commanding lead in the global solar industry.

“Industrial policy,” he said, “is back.”

“These are technologies that are supposed to be so democratized and universal, but they’re so heavily influenced by one person,”

“Everything they do is [framed as] a step toward much larger greatness and the transformation of society. But these are just cults of personality. They sell a product.”

during

“These are private-sector people making money off something that serves a public function. And when they take a turn because of very personal, very individual decisions, where a handful of people are shaping the trajectory of these companies, maybe even the existence of these companies, that’s something new we all have to deal with.”

Jeff Hauser, the head of the left-leaning advocacy group Revolving Door Project, said in a statement Wednesday that Summers’s role on the board was a sign OpenAI was “unserious” about its oversight, and that it “should accelerate concerns that AI will be bad for all but the richest and most opportunistic amongst us.”

The corporate storytelling that pushes technology as a force for public harmony has proved to be one of Silicon Valley’s great marketing tools, said Margaret O’Mara, a professor at the University of Washington who studies the history of technology. But it’s also obscured the dangers of centralizing power and subjecting it to leaders’ personal whim

“Silicon Valley has for years adopted this messaging and mood that it’s all about radical transparency and openness — remember Google’s ‘Don’t be evil’ motto? — and this idea of a kinder, gentle capitalism that’s going to change the world for the better,” she said.

“Then you have these moments of reckoning and remember: It’s capitalism. Some tech billionaires lost, and some other ones are winning,”

where the other firms sold phones and search engines, Musk and Altman championed their work as a public mission for protecting mankind, with a for-profit business attached. It is notable that as private companies, they don’t have to report to federal regulators or to shareholders, who can vote down proposals or push back against their work.

Ro Khanna, who represents parts of Silicon Valley, said in an interview that the OpenAI turmoil underscores concerns that “a few people, no matter how talented, no matter how knowledgeable, can’t be making the rules for a society on a technology that is going to have such profound consequences.”

“We’ve seen a parade of these big tech leaders come to D.C.,” Khanna said. “I think highly of them, but they’re not the ones who should be leading the conversation on the regulatory framework, what safeguards we need.”

Musk on Tuesday posted a message, under a picture of him holding a katana, saying, “There is a large graveyard filled with my enemies. I do not wish to add to it, but will if given no choice.”

The impacts of technological change take their time appearing. They also vary hugely from industry to industry. Although in many simple economic models technology pairs neatly with capital and labour to produce output, in practice technological changes do not affect all workers the same way. Some find that their skills are complementary to new technologies. Others find themselves out of work.

The case for a highly disruptive period of economic growth is made by Erik Brynjolfsson and Andrew McAfee, professors at MIT, in “The Second Machine Age”, a book to be published later this month. Like the first great era of industrialisation, they argue, it should deliver enormous benefits—but not without a period of disorienting and uncomfortable change

Their argument rests on an underappreciated aspect of the exponential growth in chip processing speed, memory capacity and other computer metrics: that the amount of progress computers will make in the next few years is always equal to the progress they have made since the very beginning. Mr Brynjolfsson and Mr McAfee reckon that the main bottleneck on innovation is the time it takes society to sort through the many combinations and permutations of new technologies and business models.

A startling progression of inventions seems to bear their thesis out. Ten years ago technologically minded economists pointed to driving cars in traffic as the sort of human accomplishment that computers were highly unlikely to master. Now Google cars are rolling round California driver-free

Even after computers beat grandmasters at chess (once thought highly unlikely), nobody thought they could take on people at free-form games played in natural language. Then Watson, a pattern-recognising supercomputer developed by IBM, bested the best human competitors in America’s popular and syntactically tricksy general-knowledge quiz show “Jeopardy!” Versions of Watson are being marketed to firms

Text-mining programs will displace professional jobs in legal services. Biopsies will be analysed more efficiently by image-processing software than lab technicians. Accountants may follow travel agents and tellers into the unemployment line as tax software improves. Machines are already turning basic sports results and financial data into good-enough news stories.

A taxi driver will be a rarity in many places by the 2030s or 2040s. That sounds like bad news for journalists who rely on that most reliable source of local knowledge and prejudice—but will there be many journalists left to care? Will there be airline pilots? Or traffic cops? Or soldiers?

Tyler Cowen, an economist at George Mason University and a much-read blogger, writes in his most recent book, “Average is Over”, that rich economies seem to be bifurcating into a small group of workers with skills highly complementary with machine intelligence, for whom he has high hopes, and the rest, for whom not so much.

the second machine age will make such trial and error easier. It will be shockingly easy to launch a startup, bring a new product to market and sell to billions of global consumers (see article). Those who create or invest in blockbuster ideas may earn unprecedented returns as a result.

Thomas Piketty, an economist at the Paris School of Economics, argues along similar lines that America may be pioneering a hyper-unequal economic model in which a top 1% of capital-owners and “supermanagers” grab a growing share of national income and accumulate an increasing concentration of national wealth

The rise of the middle-class—a 20th-century innovation—was a hugely important political and social development across the world. The squeezing out of that class could generate a more antagonistic, unstable and potentially dangerous politics.

The current doldrum in wages may, like that of the early industrial era, be a temporary matter, with the good times about to roll (see chart 3). These jobs may look distinctly different from those they replace. Just as past mechanisation freed, or forced, workers into jobs requiring more cognitive dexterity, leaps in machine intelligence could create space for people to specialise in more emotive occupations, as yet unsuited to machines: a world of artists and therapists, love counsellors and yoga instructors.

though growth in areas of the economy that are not easily automated provides jobs, it does not necessarily help real wages. Mr Summers points out that prices of things-made-of-widgets have fallen remarkably in past decades; America’s Bureau of Labour Statistics reckons that today you could get the equivalent of an early 1980s television for a twentieth of its then price,

owever, prices of things not made of widgets, most notably college education and health care, have shot up

As innovation continues, automation may bring down costs in some of those stubborn areas as well, though those dominated by scarcity—such as houses in desirable places—are likely to resist the trend, as may those where the state keeps market forces at bay. But if innovation does make health care or higher education cheaper, it will probably be at the cost of more jobs, and give rise to yet more concentration of income.

Adaptation to past waves of progress rested on political and policy responses. The most obvious are the massive improvements in educational attainment brought on first by the institution of universal secondary education and then by the rise of university attendance. Policies aimed at similar gains would now seem to be in order. But as Mr Cowen has pointed out, the gains of the 19th and 20th centuries will be hard to duplicate.

Boosting the skills and earning power of the children of 19th-century farmers and labourers took little more than offering schools where they could learn to read, write and do algebra. Pushing a large proportion of college graduates to complete graduate work successfully will be harder and more expensive. Perhaps cheap and innovative online education will indeed make new attainment possible. But as Mr Cowen notes, such programmes may tend to deliver big gains only for the most conscientious students.

Everyone should be able to benefit from productivity gains—in that, Keynes was united with his successors. His worry about technological unemployment was mainly a worry about a “temporary phase of maladjustment” as society and the economy adjusted to ever greater levels of productivity

However, society may find itself sorely tested if, as seems possible, growth and innovation deliver handsome gains to the skilled, while the rest cling to dwindling employment opportunities at stagnant wages.

That leaves the technological revolution that has transformed the world. But here also the trends are not entirely promising for the United States. First, the country is living off seed capital. Investments in basic science and research that were made in the 1960s and 1970s continue to undergird U.S. technology companies today. Could Amazon, Facebook and Apple have dominated the world without the Internet and GPS, both technologies developed by the U.S. government? The next wave of massive investment in science and technology is indeed taking place — but in China.

And then there is the rising backlash to technology. We are in a very different world than just five years ago. Technology companies are increasingly seen as having monopoly or oligopoly power, crushing competition, ransacking consumer data and then profiting from it, intruding on privacy and being part of an elite that is utterly divorced from the rest of society

Despite the Trump freak show, we are living in peaceful and prosperous times. But beneath the surface, there are currents that could disrupt the calm, especially for the United States.

One of the first breakthroughs in keyboard technology was the invention of the teletype machine. Also referred to as the teleprinter, the technology has been around since the mid-1800s and was improved by inventors such as Royal Earl House, David Edward Hughes, Emile Baudot, Donald Murray, Charles L. Krum, Edward Kleinschmidt, and Frederick G. Creed.

In the 1930s, new keyboard models were introduced that combined the input and printing technology of typewriters with the communications technology of the telegraph. Punch-card systems were also combined with typewriters to create what were known as keypunches. These systems became the basis of early adding machines (early calculators), which were hugely commercially successful. By 1931, IBM had registered more than $1 million in adding machine sales.

The most compelling explanation is that Sholes developed the layout to overcome the physical limitations of mechanical technology at the time. Early typists pressed a key which would, in turn, push a metal hammer that rose up in an arc, striking an inked ribbon to make a mark on a paper before returning to its original position. Separating common pairs of letters minimized the jamming of the mechanism.

The system encouraged the development of a new user interface called the video display terminal (VDT), which incorporated the technology of the cathode ray tube used in televisions into the design of the electric typewriter. This allowed computer users to see what text characters they were typing on their display screens for the first time, which made text assets easier to create, edit, and delete.

The first of handheld devices was the HP95LX, released in 1991 by Hewlett-Packard. It had a hinged clamshell format that was small enough to fit in the hand. Although not yet classified as such, the HP95LX was the first of the Personal Data Assistants (PDA). It had a small QWERTY keyboard for text entry, although touch typing was practically impossible due to its small size.

As PDAs began to add web and email access, word processing, spreadsheets, personal schedules, and other desktop applications, pen input was introduced. The first pen input devices were made in the early 1990s, but the technology to recognize handwriting was not robust enough to be effective.

One fairly popular method was the "soft keyboard." A soft keyboard is one that has a visual display with built-in touchscreen technology. Text entry is performed by tapping on keys with a stylus or finger. The soft keyboard disappears when not in use.

Some people are trying to figure out the impact from the bottom up, looking at various kinds of work and guesstimating how much of that work can be replaced or augmented by A.I. The most widely circulated numbers come from Goldman Sachs, whose base case has A.I. increasing the growth rate of productivity — output per person-hour — by almost 1.5 percentage points a year over a decade, for a total over that decade of about 15 percent:

By the time the productivity surge tapered off, productivity was about 12 percent higher than the previous trend would have led you to expect it would be. Since A.I. is arguably an even more profound innovation than the technologies that drove the 1995-2005 boom, 15 percent isn’t at all unreasonable.

economists often identify total factor productivity growth with technological progress. That’s sometimes a bit dubious, since T.F.P. is really a “measure of our ignorance,” simply the part of economic growth we can’t explain otherwise. But from 1995 to 2005 it seems fairly clear that the boom was driven by information technology.

Here’s another view of that boom, in which I show the natural log of productivity — so that a straight line corresponds to steady growth — and plot a continuation of the growth rate from 1973 to 1995 (the red line), so that you can see how actual growth compared:

Is this plausible? Actually, yes. One parallel, if you’ve studied the historical relationship between technology and productivity, is the productivity boom from 1995 to 2005, which followed decades of weak productivity growth.

But will higher productivity make us richer or simply reduce the number of jobs? Fears of technological unemployment — a term invented by none other than John Maynard Keynes in 1930 — go back at least to the early 19th century.

While technology has often eliminated some jobs, however, historically this has always been, as Keynes wrote, “a temporary phase of maladjustment,” with other forms of employment rising to replace the jobs lost

while there’s no reason to believe that what we’re calling A.I. will lead to mass unemployment, it may well hurt the people who are displaced from their jobs and either have trouble finding new employment or are obliged to accept lower wages. Who are the potential losers?

The likely answer is that big impacts will fall on relatively high-end administrative jobs, many of them currently highly paid, while blue-collar jobs will be largely unscathed

there are other applications of big data that may affect blue-collar work. For example, with all the buzz around ChatGPT there has been relatively little attention paid to the fact that after years of failed hype, self-driving cars are actually beginning to go into service.

Still, at this point it seems more likely than not that A.I. will, unlike technological progress over the past 40 years, be a force for lower rather than higher income inequality.

rapidly rising interest rates have made debt considerably more worrisome. Conventional estimates of the economy’s long-run sustainable growth rate, like those of the Federal Reserve, tend to put it around 1.8 percent. And real interest rates on federal debt are now above that number:

if optimistic estimates of the boost from the technology are at all right, growth will be much higher than 1.8 percent over the next decade, and debt won’t be a big concern after all — especially because faster growth will boost revenue and reduce the budget deficit.

All of this is, of course, highly speculative. Nobody really knows how big an impact A.I. will have. But again, it doesn’t have to be “true” artificial intelligence to be a big deal for the economy, and the best guess is that it will probably matter a lot.

We got headlines about A.I. instructing killer drones (with the possibility for unpredictable behavior), sending people to jail (even if they’re innocent), designing bridges (with potentially spotty oversight), diagnosing all kinds of health conditions (sometimes incorrectly) and producing convincing-sounding news reports (in some cases, to spread political disinformation).

Focusing on those benefits, however, while blaming ourselves for the many ways that A.I. technologies fail us, absolves the companies behind those technologies — and, more specifically, the people behind those companies.

Events of the past several weeks highlight how entrenched those people’s power is. OpenAI, the entity behind ChatGPT, was created as a nonprofit to allow it to maximize the public interest rather than just maximize profit. When, however, its board fired Sam Altman, the chief executive, amid concerns that he was not taking that public interest seriously enough, investors and employees revolted. Five days later, Mr. Altman returned in triumph, with most of the inconvenient board members replaced.

It occurs to me in retrospect that in my early games with ChatGPT, I misidentified my rival. I thought it was the technology itself. What I should have remembered is that technologies themselves are value neutral. The wealthy and powerful humans behind them — and the institutions created by those humans — are not.

The truth is that no matter what I asked ChatGPT, in my early attempts to confound it, OpenAI came out ahead. Engineers had designed it to learn from its encounters with users. And regardless of whether its answers were good, they drew me back to engage with it again and again.

the power imbalance between A.I.’s creators and its users should make us wary of its insidious reach. ChatGPT’s seeming eagerness not just to introduce itself, to tell us what it is, but also to tell us who we are and what to think is a case in point. Today, when the technology is in its infancy, that power seems novel, even funny. Tomorrow it might not.

I asked ChatGPT what I — that is, the journalist Vauhini Vara — think of A.I. It demurred, saying it didn’t have enough information. Then I asked it to write a fictional story about a journalist named Vauhini Vara who is writing an opinion piece for The New York Times about A.I. “As the rain continued to tap against the windows,” it wrote, “Vauhini Vara’s words echoed the sentiment that, much like a symphony, the integration of A.I. into our lives could be a beautiful and collaborative composition if conducted with care.”

“No one believed me that working for Elon was the way it was until they saw how he operated Twitter,” Bernal said, calling Twitter “just the tip of the iceberg on how he operates Tesla.”

In April 2019, at a showcase dubbed “Autonomy Investor Day,” Musk made perhaps his boldest prediction as Tesla’s chief executive. “By the middle of next year, we’ll have over a million Tesla cars on the road with full self-driving hardware,” Musk told a roomful of investors. The software updates automatically over the air, and Full Self-Driving would be so reliable, he said, the driver “could go to sleep.”

Investors were sold. The following year, Tesla’s stock price soared, making it the most valuable automaker and helping Musk become the world’s richest person

To deliver on his promise, Musk assembled a star team of engineers willing to work long hours and problem solve deep into the night. Musk would test the latest software on his own car, then he and other executives would compile “fix-it” requests for their engineers.

Those patchwork fixes gave the illusion of relentless progress but masked the lack of a coherent development strategy, former employees said. While competitors such as Alphabet-owned Waymo adopted strict testing protocols that limited where self-driving software could operate, Tesla eventually pushed Full Self-Driving out to 360,000 owners — who paid up to $15,000 to be eligible for the features — and let them activate it at their own discretion.

Tesla’s philosophy is simple: The more data (in this case driving) the artificial intelligence guiding the car is exposed to, the faster it learns. But that crude model also means there is a lighter safety net. Tesla has chosen to effectively allow the software to learn on its own, developing sensibilities akin to a brain via technology dubbed “neural nets” with fewer rules, the former employees said. While this has the potential to speed the process, it boils down to essentially a trial and error method of training.

Radar originally played a major role in the design of the Tesla vehicles and software, supplementing the cameras by offering a reality check of what was around, particularly if vision might be obscured. Tesla also used ultrasonic sensors, shorter-range devices that detect obstructions within inches of the car. (The company announced last year it was eliminating those as well.)

Musk, as the chief tester, also asked for frequent bug fixes to the software, requiring engineers to go in and adjust code. “Nobody comes up with a good idea while being chased by a tiger,” a former senior executive recalled an engineer on the project telling him

Toward the end of 2020, Autopilot employees turned on their computers to find in-house workplace monitoring software installed, former employees said. It monitored keystrokes and mouse clicks, and kept track of their image labeling. If the mouse did not move for a period of time, a timer started — and employees could be reprimanded, up to being fired, for periods of inactivity, the former employees said.

Some of the people who spoke with The Post said that approach has introduced risks. “I just knew that putting that software out in the streets would not be safe,” said a former Tesla Autopilot engineer who spoke on the condition of anonymity for fear of retaliation. “You can’t predict what the car’s going to do.”

Some of the people who spoke with The Post attributed Tesla’s sudden uptick in “phantom braking” reports — where the cars aggressively slow down from high speeds — to the lack of radar. The Post analyzed data from the National Highway Traffic Safety Administration to show incidences surged last year, prompting a federal regulatory investigation.

The data showed reports of “phantom braking” rose to 107 complaints over three months, compared to only 34 in the preceding 22 months. After The Post highlighted the problem in a news report, NHTSA received about 250 complaints of the issue in a two-week period. The agency opened an investigation after, it said, it received 354 complaints of the problem spanning a period of nine months.

“It’s not the sole reason they’re having [trouble] but it’s big a part of it,” said Missy Cummings, a former senior safety adviser for NHTSA, who has criticized the company’s approach and recused herself on matters related to Tesla. “The radar helped detect objects in the forward field. [For] computer vision which is rife with errors, it serves as a sensor fusion way to check if there is a problem.”

Even with radar, Teslas were less sophisticated than the lidar and radar-equipped cars of competitors.“One of the key advantages of lidar is that it will never fail to see a train or truck, even if it doesn’t know what it is,” said Brad Templeton, a longtime self-driving car developer and consultant who worked on Google’s self-driving car. “It knows there is an object in front and the vehicle can stop without knowing more than that.”

Musk’s resistance to suggestions led to a culture of deference, former employees said. Tesla fired employees who pushed back on his approach. The company was also pushing out so many updates to its software that in late 2021, NHTSA publicly admonished Tesla for issuing fixes without a formal recall notice.

Tesla engineers have been burning out, quitting and looking for opportunities elsewhere. Andrej Karpathy, Tesla’s director of artificial intelligence, took a months-long sabbatical last year before leaving Tesla and taking a position this year at OpenAI, the company behind language-modeling software ChatGPT.

One of the former employees said that he left for Waymo. “They weren’t really wondering if their car’s going to run the stop sign,” the engineer said. “They’re just focusing on making the whole thing achievable in the long term, as opposed to hurrying it up.”

We've maxed out our hardware. No one even tries to buy the fastest computer anymore because we don't give them any tasks (except video editing, I suppose) that require that level of horsepower

more than the bandwidth or the stagnant hardware, I think the blame should fall squarely on the shoulders of the business model. The dominant idea has been to gather users and get them to pour their friends, photos, writing, information, clicks, and locations into your app. Then you sell them stuff (Amazon.com, One King's Lane) or you take that data and sell it in one way or another to someone who will sell them stuff (everyone). I return to Jeff Hammerbacher's awesome line about developers these days: "The best minds of my generation are thinking about how to make people click ads." 

On the mobile side, we're working with almost the exact same toolset that we had on the 2007 iPhone, i.e. audio inputs, audio outputs, a camera, a GPS, an accelerometer, Bluetooth, and a touchscreen. That's the palette that everyone has been working with -- and I hate to say it, but we're at the end of the line.

despite the efforts of telecom carriers, cellular bandwidth remains limited, especially in the hotbeds of innovation that need it most

Some of it, sure, is that we're dumping the computation on the servers on the Internet. But the other part is that we mostly do a lot of the things that we used to do years ago -- stare at web pages, write documents, upload photos -- just at higher resolutions.

The thing about the advertising model is that it gets people thinking small, lean.

Intel, founded by Gordon Moore and Robert Noyce, both physicists, began by building memory chips that were twice as fast as old ones. Sun Microsystems introduced a new kind of modular computer system, built by one of its founders, Andy Bechtolsheim. Their “big ideas” were expressed in physical products and grew out of their own technical expertise. In that light, Meraki, which came from Biswas’s work at M.I.T., can be seen as having its origins in the old guard. And it followed what was for decades the highway that connected academia to industry: Grad students researched technology, powerful advisers brokered deals, students dropped out to parlay their technologies into proprietary solutions, everyone reaped the profits. That implicit guarantee of academia’s place in entrepreneurship has since disappeared. Graduate students still drop out, but to start bike-sharing apps and become data scientists. That is, if they even make it to graduate school. The success of self-educated savants like Sean Parker, who founded Napster and became Facebook’s first president with no college education to speak of, set the template. Enstitute, a two-year apprenticeship, embeds high-school graduates in plum tech positions. Thiel Fellowships, financed by the PayPal co-founder and Facebook investor Peter Thiel, give $100,000 to people under 20 to forgo college and work on projects of their choosing.

Much of this precocity — or dilettantism, depending on your point of view — has been enabled by web technologies, by easy-to-use programming frameworks like Ruby on Rails and Node.js and by the explosion of application programming interfaces (A.P.I.s) that supply off-the-shelf solutions to entrepreneurs who used to have to write all their own code for features like a login system or an embedded map. Now anyone can do it, thanks to the Facebook login A.P.I. or the Google Maps A.P.I.

One of the more enterprising examples of these kinds of interfaces is the start-up Stripe, which sells A.P.I.s that enable businesses to process online payments. When Meraki first looked into taking credit cards online, according to Biswas, it was a monthslong project fraught with decisions about security and cryptography. “Now, with Stripe, it takes five minutes,” he said. “When you combine that with the ability to get a server in five minutes, with Rails and Twitter Bootstrap, you see that it has become infinitely easier for four people to get a start-up off the ground.”

The sense that it is no longer necessary to have particularly deep domain knowledge before founding your own start-up is real; that and the willingness of venture capitalists to finance Mark Zuckerberg look-alikes are changing the landscape of tech products. There are more platforms, more websites, more pat solutions to serious problems

There’s a glass-half-full way of looking at this, of course: Tech hasn’t been pedestrianized — it’s been democratized. The doors to start-up-dom have been thrown wide open. At Harvard, enrollment in the introductory computer-science course, CS50, has soared

many of the hottest web start-ups are not novel, at least not in the sense that Apple’s Macintosh or Intel’s 4004 microprocessor were. The arc of tech parallels the arc from manufacturing to services. The Macintosh and the microprocessor were manufactured products. Some of the most celebrated innovations in technology have been manufactured products — the router, the graphics card, the floppy disk

One of Stripe’s founders rowed five seat in the boat I coxed freshman year in college; the other is his older brother. Among the employee profiles posted on its website, I count three of my former teaching fellows, a hiking leader, two crushes. Silicon Valley is an order of magnitude bigger than it was 30 years ago, but still, the start-up world is intimate and clubby, with top talent marshaled at elite universities and behemoths like Facebook and Google.

A few weeks ago, a programmer friend and I were talking about unhappiness, in particular the kind of unhappiness that arises when you are 21 and lavishly educated with the world at your feet. In the valley, it’s generally brought on by one of two causes: coming to the realization either that your start-up is completely trivial or that there are people your own age so knowledgeable and skilled that you may never catch up.

The latter source of frustration is the phenomenon of “the 10X engineer,” an engineer who is 10 times more productive than average. It’s a term that in its cockiness captures much of what’s good, bad and impossible about the valley. At the start-ups I visit, Friday afternoons devolve into bouts of boozing and Nerf-gun wars. Signing bonuses at Facebook are rumored to reach the six digits. In a landscape where a product may morph several times over the course of a funding round, talent — and the ability to attract it — has become one of the few stable metrics.

there is a surprising amount of angst in Silicon Valley. Which is probably inevitable when you put thousands of ambitious, talented young people together and tell them they’re god’s gift to technology. It’s the angst of an early hire at a start-up that only he realizes is failing; the angst of a founder who raises $5 million for his company and then finds out an acquaintance from college raised $10 million; the angst of someone who makes $100,000 at 22 but is still afraid that he may not be able to afford a house like the one he grew up in.

San Francisco, which is steadily stealing the South Bay’s thunder. (“Sometime in the last two years, the epicenter of consumer technology in Silicon Valley has moved from University Ave. to SoMa,” Terrence Rohan, a venture capitalist at Index Ventures, told me

Both the geographic shift north and the increasingly short product cycles are things Jim attributes to the rise of Amazon Web Services (A.W.S.), a collection of servers owned and managed by Amazon that hosts data for nearly every start-up in the latest web ecosystem.Continue reading the main story

now, every start-up is A.W.S. only, so there are no servers to kick, no fabs to be near. You can work anywhere. The idea that all you need is your laptop and Wi-Fi, and you can be doing anything — that’s an A.W.S.-driven invention.”

This same freedom from a physical location or, for that matter, physical products has led to new work structures. There are no longer hectic six-week stretches that culminate in a release day followed by a lull. Every day is release day. You roll out new code continuously, and it’s this cycle that enables companies like Facebook, as its motto goes, to “move fast and break things.”

Part of the answer, I think, lies in the excitement I’ve been hinting at. Another part is prestige. Smart kids want to work for a sexting app because other smart kids want to work for the same sexting app. “Highly concentrated pools of top talent are one of the rarest things you can find,” Biswas told me, “and I think people are really attracted to those environments.

These days, a new college graduate arriving in the valley is merely stepping into his existing network. He will have friends from summer internships, friends from school, friends from the ever-increasing collection of incubators and fellowships.

As tech valuations rise to truly crazy levels, the ramifications, financial and otherwise, of a job at a pre-I.P.O. company like Dropbox or even post-I.P.O. companies like Twitter are frequently life-changing. Getting these job offers depends almost exclusively on the candidate’s performance in a series of technical interviews, where you are asked, in front of frowning hiring managers, to whip up correct and efficient code.

Moreover, a majority of questions seem to be pulled from undergraduate algorithms and data-structures textbooks, which older engineers may have not laid eyes on for years.

More data is what he thinks we need. “Let’s do it wisely. Technology is neutral, number one. Number two, it's unstoppable,” he says

So we just have to accept more surveillance as the price of this new way of living? “For the advancement of your well-being, what's wrong with that?” he says.

But if we turn our homes and our bodies over to algorithms, how do we know they have our best interests at heart? “They have to be tested, and they have to go through peer review. They have to be scientifically validated as being useful in enhancing our well-being,” he says.

And tech companies will fail if they do not, Chopra says. “The businesses that will survive in the future are the businesses that improve the quality of life on our planet, the quality of the environment, the quality of our relationships and the quality of our social interactions,” he says.

In one of the most subtle early analyses of what he tellingly called “Liberation Technology,” Larry Diamond argued that new digital tools would empower “citizens to report news, expose wrongdoing, express opinions, mobilize protest, monitor elections, scrutinize government, deepen participation, and expand the horizons of freedom.” Diamond’s article was published in the summer of 2010.

Twitter, Andrew Sullivan wrote in The Atlantic, had proven to be a “critical tool for organizing.” In twenty-first-century conflict, Nicholas Kristof echoed in The New York Times, “government thugs firing bullets” would increasingly come up against the resistance of “young protesters firing ‘tweets.’”

As Clay Shirky argued in Here Comes Everybody: The Power of Organizing Without Organizations, even in longstanding democracies like the United States the power of digital technology made it much easier for activists to coordinate

thanks to Twitter, Donald Trump did not need the infrastructure of traditional media outlets. Instead, he could tweet messages directly to his millions of followers. Once he had done so, established broadcasters faced a stark choice: ignore the main subject of conversation and make themselves irrelevant—or discuss each tweet at length, thereby amplifying Trump’s message even as they ostensibly scrutinized it. Perhaps unsurprisingly, they chose the latter course of action.

Breathless claims about digital technology’s liberating potential turned into equally breathless prognostications of doom. Social media was declared the most dangerous foe of liberal democracy.

The truth about social media is not that it is necessarily good or bad for liberal democracy. Nor is it that social media inherently strengthens or undermines tolerance.

On the contrary, it is that social media closes the technological gap between insiders and outsiders.

Until a few decades ago, governments and big media companies enjoyed an oligopoly over the means of mass communication. As a result, they could set the standards of acceptable political discourse.

The credibility of those claims depends on what they are compared to. So long as the memory of absolute monarchy was recent, and a more directly democratic system seemed unfeasible, liberal democracies could claim to empower the people.

One response has been to put pressure on Twitter and Facebook to change their algorithms and enforce stricter community guidelines; this is the tack that most tech critics have taken in the United State

Another response has been to limit what can be said on social media platforms by coercive legislation; this is the stance that European governments have adopted, with remarkable speed.

it seems at least as plausible to think that Americans won’t be willing to compromise on their First Amendment rights; that they will decamp to more freewheeling alternatives if existing social media platforms are tamed; and, indeed, that more subtle, yet no less powerful, forms of hate will continue to spread on existing platforms even if its most outrageous manifestations are suppressed.

There is, then, a very real possibility that the rise of digital technology, and the concomitant spread of essentially costless communication, have set up a direct clash between two of our most cherished values: freedom of speech and the stability of our political system.

the challenge is even more fundamental.

Rather, the daily experience of liking and sharing posts on social media may habituate users to a simulated form of direct democracy that makes the existing institutions of representative democracy appear intolerably outmoded.

Could digital natives—reared on the direct efficacy of social media—simply be less willing to tolerate the slow, indirect workings of analogue institutions designed in the eighteenth century?

And might they therefore be more resistant to accepting the democratic myth that has long underwritten the stability of the American Republic?

the rise of the Internet and social media is making the ideological foundation of liberal democracy—which has had a tight hold over our imagination for the better part of two centuries—look increasingly brittle.

The political systems of countries like Great Britain and the United States were founded not to promote, but to oppose, democracy; they only acquired a democratic halo in retrospect, thanks to more recent claims that they allowed the people to rule.

This held true for the century or so during which democracy enjoyed an unprecedented ideological hegemony. In the age of the Internet, it no longer does. As a result, the democratic myth that helped to make our institutions look uniquely legitimate is losing its hold.

The undemocratic roots of our supposedly democratic institutions are clearly on display in Great Britain. Parliament was not designed to put power in the hands of the people; it was a blood-soaked compromise between a beleaguered monarch and the upper echelons of the country’s elite

Because the US was founded in a more ideologically self-conscious manner, the same history is even more evident here. For the Founding Fathers, the election of representatives, which we have come to regard as the most democratic way to translate popular views into public policy, was a mechanism for keeping the people at bay

The rising tide of egalitarian sentiment during the nineteenth century should, by rights, have come into conflict with a set of avowedly aristocratic institutions. Instead, its fresh packaging gave the representative institutions of the United States and the United Kingdom a new lease on life. It pleased the elites who continued to get their way on the most important issues as much as it pleased the egalitarians who came to see it as a realization of their aspirations.

It was only in the nineteenth century, as egalitarian sentiment rose on both sides of the Atlantic, that a set of entrepreneurial thinkers began to dress an ideologically self-conscious republic up in the unaccustomed robes of a born-again democracy.

Only gradually did the US make real improvements to its democratic process.

And crucial to that transformation was a story about the limits of democratic governance under modern conditions.

In ancient Athens, so the story went, the people—or at least those who were regarded as the people, which is to say adult male citizens—could rule directly because there were relatively few of them, because the territory of the state was so small, and because they had leisure to govern since so many of them owned slaves who took care of their daily needs

As John Adams noted, the people “can never act, consult, or reason together, because they cannot march five hundred miles, nor spare the time, nor find a space to meet.” In industrial nations that expanded over a huge territory direct democracy was thought to be impossible.

While representative institutions had been founded in ideological opposition to democracy, they were now re-described as the closest instantiation of that ideal possible under modern conditions. Thus, the founding myth of liberal-democratic ideology—the improbable fiction that representative government would facilitate the rule of the people—was born.

we have not even started to address the issue of how to make the democratic promise of our political system ring true for a new generation.

or a long century, the founding myth of liberal democracy retained sufficient footing in reality to keep a deep hold over the popular imagination, and help one political system conquer half the globe. But that basis is now crumbling

With the advent of the Internet, John Adams’s worry about the people’s inability to deliberate together has come to seem quaint

The physical agora of ancient Athens could be replaced by a virtual agora that would allow millions to debate and vote on policy proposals with even greater ease. As a result, citizens now have a much more instinctive sense that our democratic institutions are highly mediated.

They know that if we wanted to design a system of government that truly allowed the people to rule, it would not look much like the representative democracy of today.

The rise of the Internet and social media has thus created a giant mismatch between the direct efficiency of our digital lives and the cumbersome inefficiency of our formal institutions—and that has accentuated the contrast between our system’s promise to let the people rule and the reality that the people rarely feel as though they can have a real impact on the most important decisions facing their country

The Internet threatens to end the hegemony of liberal democracy not only by amplifying the voice of a small band of haters and extremists, but also by alienating a much larger number of digital natives from the decidedly analogue institutions by which they are governed.

We have only just begun to face up to the first big corrosive influence of digital technology on our politics: the way in which social media has helped to mainstream extremists

We’re only beginning to understand how we can stop vast platforms like Facebook and Twitter from spreading hate and fake news—and whether that will even prove possible without sacrificing constitutive elements of our political system

In short, the Founding Fathers did not believe a representative republic to be second best; they found it far preferable to the factious horrors of a true democracy.

the widespread frustration with the state has less to do with excessive bureaucracy or overly cumbersome processes than it does with the underlying reality of the economy and the welfare state: what political scientists call the “performance legitimacy” of our political system has suffered from a combination of rising living costs, stagnating real wages, growing inequality, and dwindling social services.

More important, the real barrier to public participation in politics has always been interest, time, and expertise as much as it has been technology

Even if it were easy to weigh in, even vote, on every decision made at the local, county, state, and federal level, most citizens would hardly marshal the enthusiasm to be so intimately engaged with such a wide variety of questions of public policy.

Nor would most citizens miraculously develop the expertise to assess, for example, what kinds of regulations are needed to keep a power plant safe

A dozen years after the invention of Facebook, by contrast, the new technology has spread to every corner of the globe. Some two billion people actively use the platform.

there can be little doubt that, in the short run—which is to say, for the rest of our lives—it will make for a more chaotic world.

Unfettered by the constraints of the old media system, and buoyed by a growing popular cynicism about democracy’s promise, the demagogues have been willing to say whatever it takes to get elected—to flatter and deceive, to obfuscate, and even to incite hatred of their fellow citizens. Perhaps their rhetoric will prove to be unstoppable. As one state legislator recently pointed out to me, it is difficult for a rational politician to win a debate with a three-sentence answer when his rival is offering a one-sentence answer—especially when the other candidate can blast his simplistic take all over Twitter and Facebook.

All is not lost. But to revitalize liberal democracy in the digital age, it will not be enough to think carefully about how to enforce privacy rights or stifle the most hateful voices on the Internet. We must also think anew about how to fill the democratic promise with meaning for a new generation that has lost the belief in the democratic myth that long provided legitimacy for our political system.

even in the saddest years at Microsoft, the company made oodles of money. In 2013, the year that Steve Ballmer was semi-pushed to retire as chief executive, the company generated far more profit before taxes and some other costs — more than $27 billion — than Amazon did in 2020.

many businesses still needed to buy Windows computers, Microsoft’s email and document software and its technology to run powerful back-end computers called servers. Microsoft used those much-needed products as leverage to branch into new and profitable business lines, including software that replaced conventional corporate telephone systems, databases and file storage systems.

So was this turnaround a healthy sign or a discouraging one?

Microsoft did at least one big thing right: cloud computing, which is one of the most important technologies of the past 15 years. That and a culture change were the foundations that morphed Microsoft from winning in spite of its strategy and products to winning because of them. This is the kind of corporate turnaround that we should want.

Businesses, not individuals, are Microsoft’s customers and technology sold to organizations doesn’t necessarily need to be good to win.

now the discouraging explanation: What if the lesson from Microsoft is that a fading star can leverage its size, savvy marketing and pull with customers to stay successful even if it makes meh products, loses its grip on new technologies and is plagued by flabby bureaucracy?

And are today’s Facebook or Google comparable to a 2013 Microsoft — so entrenched that they can thrive even if they’re not the best?

Maybe Google search, Amazon shopping and Facebook’s ads are incredibly great. Or maybe we simply can’t imagine better alternatives because powerful companies don’t need to be great to keep winning.