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No Exit makes it very clear that the life of a startup founder is a miserable one, and that engineers are invariably happier when they’re working for a big company.

Financially, starting up a company in Silicon Valley makes very little sense. You have a very high chance (indeed, a certainty) of having to scrape by on a very low income in a very expensive city. At a time of your life when you should be out enjoying life and meeting friends and generally having lots of fun, you will instead be unhappily tethered to your laptop at all times. In return for sacrificing a six-figure salary elsewhere and general enjoyment of life, you’re given a lottery ticket: you get a minuscule chance of making untold millions of dollars.

So where does it come from, this intense Silicon Valley desire to buy the most expensive lottery ticket in the world?

The Silicon Valley trade is also pretty close to being zero-sum. Even on a purely financial basis, if you add up all the profits from successful investments, they barely cover the losses on all the unsuccessful ones. A few big-name angels and VCs can do OK for themselves, but in aggregate the industry of investing in startups does not make money.

Essentially the way that the startup ecosystem works is by taking the valuable labor of thousands of hopeful founders, and converting it into large amounts of capital for a tiny number of successes

On its face, the winners, here, are the people with the big successful exits. But after reading No Exit, a different conclusion presents itself. The real winners are the happy and well-paid engineers, enjoying their lives and their youth while working for great companies like Google. In the world of startups, the only winning move is not to play.

Everything in American culture would lead one to think that it is easy to launch a new restaurant, hair salon, company, or fill in the blank. I wouldn’t go so far to say that those who do it have a false sense of entitlement – but there’s seemingly no sense of contentment in being a no. 2 or lower in a company.

most of the website or mobile app start-ups that you guys in the general media (I will lazily generalize like you all do and lump you all together) lazily or ignorantly refer to as “tech” or “silicon valley” are not founded by computer engineers. They are started by coders, which are a couple notches below computer engineers on the knowledge and experience scale. They are willing to forego a big steady paycheck because they are short on knowledge and experience, and are not usually “incredibly qualified engineers – in fact, they are mostly just qualified to work on mobile apps and economically unsustainable web start-ups. Their value to established companies that need to develop products that generate revenue and profits is questionable, at best.

I don’t know if you have ever worked for a very large multi-national company that compartmentalizes your job into little tasks so that your skills can be exploited for a few years, and then discarded when they are obsolete. Many big companies are poorly managed, and while they may offer stable employment in the short term, when the errors of their executives impose their costs on the company, the employees usually pay the price. And then what do they do? People who avoid working for large companies and seek the excitement of start-ups have a different value system than you and all those who would choose the illusion of job security.

The rise of China’s venture market “signifies a shift from a single-epicenter view of the world to a duopoly,” he says.

The surge also positions Asia’s investors to win stakes in markets that Western companies covet, or that have national security implications.

. “If you think that being the locus of invention gives you a boost to your GDP and so forth, that’s a deterioration of the U.S. competitive advantage.”

Although one of the biggest Asian investors is Japan’s SoftBank Group Corp. , which has tapped Middle Eastern money to create the world’s largest tech-investment fund, it is Chinese activity that is having the greatest impact.

China is creating unicorns—startups valued at a billion dollars or more—at much the same pace as the U.S., drawing on funding from internet giants like Alibaba Group Holding Ltd. and Tencent Holdings Ltd. as well as more than a thousand domestic venture-capital firms that have raised billions of dollars a year for the past few year

Chinese-led venture funding is about 15 times its size in 2013, outpacing growth in U.S.-led financing, which roughly doubled in that time period

Most Chinese-led investment so far has gone to the country’s own firms, the Journal analysis found. Many of them, like the Yelp equivalent Meituan-Dianping, are household names with millions of customers in China, yet virtually unknown elsewhere.

Many Chinese tech companies are “at this critical size that the China market alone is not enough to support their business and valuation,

Madhur Deora, chief financial officer for Paytm, one of India’s biggest e-payments firms, says the company approached Alibaba affiliate Ant Financial instead of U.S. backers for funding in 2015 because Chinese mobile-internet innovations are “way far ahead of anything that’s happened in the U.S.

One reason China’s push into new technologies worries many in the U.S. is that, unlike the hunt for good returns that underpins most Western venture finance, a lot of Chinese investment is driven by strategic interests, some carrying the specter of state influence.

China is pushing hard into semiconductors, for which the government has provided billions of dollars in public funding, and artificial intelligence, where Beijing in July set a goal of global leadership by 2030

Mr. Lee, the venture investor, predicts that in the next five to 10 years Chinese tech companies will become pacesetters for tech-related development, vying with the likes of Alphabet Inc.’s Google and Facebook for dominance in markets outside the English-speaking world and Western Europe.

“All the rest of the world will basically be a land grab between the U.S. and China,

“The U.S. approach is: We’ll build a better product and just win over all the countries,” says Mr. Lee. The Chinese approach is “we’ll fund the local partner to beat off the American companies.”

Asia’s rise as a startup financier is even starker in the biggest venture investments—those of $100 million or more. These megadeals have become an increasingly important part of venture finance as valuations have ballooned, with their proportion of deal volume growing from around 8% in 2007 to around half of the total last year.

In Southeast Asia, a flood of Chinese money into local startups—such as the $1.1 billion Alibaba-led investment into Indonesian online marketplace PT Tokopedia last year—is drawing the region closer to China

Chinese money is also playing a big role in India, which, with a population of 1.2 billion, has been described as the next big internet market. Chinese and Japanese investors each led nearly $3 billion in venture finance in India last year, ahead of the nearly $2 billion in deals led by U.S. investors

“Think of strategic investments and M&A as playing a game of go,” said Mr. Tsai, the Alibaba executive vice chairman, at the investor conference last year. “In a game of go the strategic objective is to put your pieces on the chessboard and surround your opponent.”

AIs like ChatGPT rapidly got better in their early days, but what we’ve seen in the past 14-and-a-half months are only incremental gains, says Marcus. “The truth is, the core capabilities of these systems have either reached a plateau, or at least have slowed down in their improvement,” he adds.

the gaps between the performance of various AI models are closing. All of the best proprietary AI models are converging on about the same scores on tests of their abilities, and even free, open-source models, like those from Meta and Mistral, are catching up.

models work by digesting huge volumes of text, and it’s undeniable that up to now, simply adding more has led to better capabilities. But a major barrier to continuing down this path is that companies have already trained their AIs on more or less the entire internet, and are running out of additional data to hoover up. There aren’t 10 more internets’ worth of human-generated content for today’s AIs to inhale.

A mature technology is one where everyone knows how to build it. Absent profound breakthroughs—which become exceedingly rare—no one has an edge in performance

companies look for efficiencies, and whoever is winning shifts from who is in the lead to who can cut costs to the bone. The last major technology this happened with was electric vehicles, and now it appears to be happening to AI.

the future for AI startups—like OpenAI and Anthropic—could be dim.

Microsoft and Google will be able to entice enough users to make their AI investments worthwhile, doing so will require spending vast amounts of money over a long period of time, leaving even the best-funded AI startups—with their comparatively paltry warchests—unable to compete.

Many other AI startups, even well-funded ones, are apparently in talks to sell themselves.

the bottom line is that for a popular service that relies on generative AI, the costs of running it far exceed the already eye-watering cost of training it.

That difference is alarming, but what really matters to the long-term health of the industry is how much it costs to run AIs. 

Changing people’s mindsets and habits will be among the biggest barriers to swift adoption of AI. That is a remarkably consistent pattern across the rollout of all new technologies.

the industry spent $50 billion on chips from Nvidia to train AI in 2023, but brought in only $3 billion in revenue.

For an almost entirely ad-supported company like Google, which is now offering AI-generated summaries across billions of search results, analysts believe delivering AI answers on those searches will eat into the company’s margins

Google, Microsoft and others said their revenue from cloud services went up, which they attributed in part to those services powering other company’s AIs. But sustaining that revenue depends on other companies and startups getting enough value out of AI to justify continuing to fork over billions of dollars to train and run those systems

three in four white-collar workers now use AI at work. Another survey, from corporate expense-management and tracking company Ramp, shows about a third of companies pay for at least one AI tool, up from 21% a year ago.

OpenAI doesn’t disclose its annual revenue, but the Financial Times reported in December that it was at least $2 billion, and that the company thought it could double that amount by 2025. 

That is still a far cry from the revenue needed to justify OpenAI’s now nearly $90 billion valuation

the company excels at generating interest and attention, but it’s unclear how many of those users will stick around. 

AI isn’t nearly the productivity booster it has been touted as

While these systems can help some people do their jobs, they can’t actually replace them. This means they are unlikely to help companies save on payroll. He compares it to the way that self-driving trucks have been slow to arrive, in part because it turns out that driving a truck is just one part of a truck driver’s job.

Add in the myriad challenges of using AI at work. For example, AIs still make up fake information,

getting the most out of open-ended chatbots isn’t intuitive, and workers will need significant training and time to adjust.

That’s because AI has to think anew every single time something is asked of it, and the resources that AI uses when it generates an answer are far larger than what it takes to, say, return a conventional search result

None of this is to say that today’s AI won’t, in the long run, transform all sorts of jobs and industries. The problem is that the current level of investment—in startups and by big companies—seems to be predicated on the idea that AI is going to get so much better, so fast, and be adopted so quickly that its impact on our lives and the economy is hard to comprehend. 

Mounting evidence suggests that won’t be the case.

City leaders are scrambling to address the problem with law enforcement strategies including increased police presence and working with the FBI. The Leaders Become Legends program, by contrast, is funded through the city's economic development arm, far upstream from law enforcement.

Tax incentives and legal requirements for green energy are driving a healthy demand for this trade. For the most part, says Jackson, bosses in this industry are supportive and colleagues friendly. But not always. Part of the training, says Jackson, is managing expectations.

"Our total mission is to not let them go back to prison, or be dead," Jackson says. "As well as dealing with our trauma from just being so called Black in America."

Despite their frequency, the police stops never get easier. Between these encounters and the violence in his community, Churn acknowledges that he lives with a lot of fear and deep sense of injustice.

a “builder class”—a brains trust of youngish idealists, which includes Patrick Collison, co-founder of Stripe, a payments firm valued at $74bn, and other (mostly white and male) techies, who are posing questions that go far beyond the usual interests of Silicon Valley’s titans. They include the future of man and machine, the constraints on economic growth, and the nature of government.

They share other similarities. Business provided them with their clout, but doesn’t seem to satisfy their ambition

The number of techno-billionaires in America (Mr Collison included) has more than doubled in a decade.

ome of them, like the Medicis in medieval Florence, are keen to use their money to bankroll the intellectual ferment

The other is Paul Graham, co-founder of Y Combinator, a startup accelerator, whose essays on everything from cities to politics are considered required reading on tech campuses.

Mr Altman puts it more optimistically: “The iPhone and cloud computing enabled a Cambrian explosion of new technology. Some things went right and some went wrong. But one thing that went weirdly right is a lot of people got rich and said ‘OK, now what?’”

A belief that with money and brains they can reboot social progress is the essence of this new mindset, making it resolutely upbeat

The question is: are the rest of them further evidence of the tech industry’s hubristic decadence? Or do they reflect the start of a welcome capacity for renewal?

Two well-known entrepreneurs from that era provided the intellectual seed capital for some of today’s techno nerds.

Mr Thiel, a would-be libertarian philosopher and investor

This cohort of eggheads starts from common ground: frustration with what they see as sluggish progress in the world around them.

Yet the impact could ultimately be positive. Frustrations with a sluggish society have encouraged them to put their money and brains to work on problems from science funding and the redistribution of wealth to entirely new universities. Their exaltation of science may encourage a greater focus on hard tech

the rationalist movement has hit the mainstream. The result is a fascination with big ideas that its advocates believe goes beyond simply rose-tinted tech utopianism

A burgeoning example of this is “progress studies”, a movement that Mr Collison and Tyler Cowen, an economist and seer of the tech set, advocated for in an article in the Atlantic in 2019

Progress, they think, is a combination of economic, technological and cultural advancement—and deserves its own field of study

There are other examples of this expansive worldview. In an essay in 2021 Mr Altman set out a vision that he called “Moore’s Law for Everything”, based on similar logic to the semiconductor revolution. In it, he predicted that smart machines, building ever smarter replacements, would in the coming decades outcompete humans for work. This would create phenomenal wealth for some, obliterate wages for others, and require a vast overhaul of taxation and redistribution

His two bets, on OpenAI and nuclear fusion, have become fashionable of late—the former’s chatbot, ChatGPT, is all the rage. He has invested $375m in Helion, a company that aims to build a fusion reactor.

Mr Lonsdale, who shares a libertarian streak with Mr Thiel, has focused attention on trying to fix the shortcomings of society and government. In an essay this year called “In Defence of Us”, he argues against “historical nihilism”, or an excessive focus on the failures of the West.

With a soft spot for Roman philosophy, he has created the Cicero Institute in Austin that aims to inject free-market principles such as competition and transparency into public policy.

He is also bringing the startup culture to academia, backing a new place of learning called the University of Austin, which emphasises free speech.

All three have business ties to their mentors. As a teen, Mr Altman was part of the first cohort of founders in Mr Graham’s Y Combinator, which went on to back successes such as Airbnb and Dropbox. In 2014 he replaced him as its president, and for a while counted Mr Thiel as a partner (Mr Altman keeps an original manuscript of Mr Thiel’s book “Zero to One” in his library). Mr Thiel was also an early backer of Stripe, founded by Mr Collison and his brother, John. Mr Graham saw promise in Patrick Collison while the latter was still at school. He was soon invited to join Y Combinator. Mr Graham remains a fan: “If you dropped Patrick on a desert island, he would figure out how to reproduce the Industrial Revolution,”

While at university, Mr Lonsdale edited the Stanford Review, a contrarian publication co-founded by Mr Thiel. He went on to work for his mentor and the two men eventually helped found Palantir. He still calls Mr Thiel “a genius”—though he claims these days to be less “cynical” than his guru.

“The tech industry has always told these grand stories about itself,” says Adrian Daub of Stanford University and author of the book, “What Tech Calls Thinking”. Mr Daub sees it as a way of convincing recruits and investors to bet on their risky projects. “It’s incredibly convenient for their business models.”

In the 2000s Mr Thiel supported the emergence of a small community of online bloggers, self-named the “rationalists”, who were focused on removing cognitive biases from thinking (Mr Thiel has since distanced himself). That intellectual heritage dates even further back, to “cypherpunks”, who noodled about cryptography, as well as “extropians”, who believed in improving the human condition through life extensions

Silicon Valley has shown an uncanny ability to reinvent itself in the past.

Mensch said his new model cost less than €20 million, the equivalent of roughly $22 million, to train. By contrast OpenAI Chief Executive Sam Altman said last year after the release of GPT-4 that training his company’s biggest models cost “much more than” $50 million to $100 million.

Brave Software made a free, open-source model from Mistral the default to power its web-browser chatbot, said Brian Bondy, Brave’s co-founder and chief technology officer. He said that the company finds the quality comparable with proprietary models, and Mistral’s open-source approach also lets Brave control the model locally.

“We want to be the most capital-efficient company in the world of AI,” Mensch said. “That’s the reason we exist.” 

Mensch joined the Google AI unit then called DeepMind in late 2020, where he worked on the team building so-called large language models, the type of AI system that would later power ChatGPT. By 2022, he was one of the lead authors of a paper about a new AI model called Chinchilla, which changed the field’s understanding of the relationship among the size of an AI model, how much data is used to build it and how well it performs, known as AI scaling laws.

Mensch took a role lobbying French policymakers, including French President Emmanuel Macron, against certain elements of the European Union’s new AI Act, which Mensch warned could slow down companies and would, in his view, do nothing to make AI safer. After changes to the text in Brussels, it will be a manageable burden for Mistral, Mensch says, even if he thinks the law should have remained focused on how AI is used rather than also regulating the underlying technology.  

For Mensch and his co-founders, releasing their initial AI systems as open source that anyone could use or adapt free of charge was an important principle. It was also a way to get noticed by developers and potential clients eager for more control over the AI they use

Mistral’s most advanced models, including the one unveiled Monday, aren’t available open source. 

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.

A model-driven business is something beyond a data-driven business. A data-driven business collects and analyzes data to help humans make better business decisions. A model-driven business creates a system built around continuously improving models that define the business. In a data-driven business, the data helps the business; in a model-driven business, the models are the business.

Netflix beat Blockbuster with software; it is winning against the cable companies and content providers with its models. Its recommendation model is famous and estimated to be worth more than $1 billion a year in revenue, driving 80% of content consumption

Amazon used software to separate itself from physical competitors like Borders and Toys “R” Us, but its models helped it pull away from other e-commerce companies like Overstock.com . By 2013 an estimated 35% of revenue came from Amazon’s product recommendations. Those models have never stopped improving

Third, incumbents will be more potent competitors in this battle relative to their role in the battles of the software era. They have a meaningful advantage this time around, because they often have troves of data and startups usually don’

Looking to produce more-resilient crops, Monsanto’s models predict optimal places for farmers to plant based on historical yields, weather data, tractors equipped with GPS and other sensors, and field data collected from satellite imagery, which estimates where rainfall will pool and subtle variations in soil chemistry.

Lilt, a San Francisco-based startup, is building software that aims to make that translator five times as productive by inserting a model in the middle of the process. Instead of working from only the original text, translators using Lilt’s software are presented with a set of suggestions from the model, and they refine those as needed. The model is always learning from the changes the translator makes, simultaneously making all the other translators more productive in future projects.

First, businesses will increasingly be valued based on the completeness, not just the quantity, of data they create

Second, the goal is a flywheel, or virtuous circle. Tencent, Amazon and Netflix all demonstrate this characteristic: Models improve products, products get used more, this new data improves the product even more

inVia Robotics builds robots that can autonomously navigate a warehouse and pull totes from shelves to deliver them to a stationary human picker. The approach is model-driven; inVia uses models that consider item popularity and probability of association (putting sunglasses near sunscreen, for example) to adjust warehouse layout automatically and minimize the miles robots must travel. Every order provides feedback to a universe of prior predictions and improves productivity across the system.

Fourth, just as companies have built deep organizational capabilities to manage technology, people, and capital, the same will now happen for models

Fifth, companies will face new ethical and compliance challenges.

Until 2001, deals worth more than $15 million had to be reported to the antitrust authorities. That year, the threshold was raised and indexed to economic growth, and is now $90 million.

For transactions involving few tangible assets such as in technology and pharmaceutical startups, the threshold is $360 million.

after the 2001 changes, the number of merger notifications dropped 70% and the number of mergers that didn’t require notification jumped nearly 50%

Before the change, around a third of merger investigations involved deals worth less than $50 million. After the change, the number of such deals investigated fell to close to zero.

between 1997 and 2017 more than 4,000 acquisitions of kidney dialysis centers were proposed. About half were above the reporting threshold, and in 265 cases, the FTC required divestitures to resolve competition concerns

Among the half below the threshold, the FTC required just three divestitures. Two companies controlled about 31% of facilities in 1997. By 2016, two companies, DaVita Inc. and Fresenius Medical Care , controlled 77% of facilities

his preliminary results suggest the numbers of nurses per technician decline and patients per hemodialysis machine rise at facilities acquired in mergers below the reporting threshold. That, he said, could be evidence of reduced quality of care, though he acknowledged it could also reflect increased efficiency.

22% of markets for physicians are highly concentrated (according to federal guidelines), and they got that way mostly via acquisitions too small to be reported.

pharmaceutical companies often halt development of competing drugs at startups they acquire, especially when the acquisition is just small enough to escape antitrust reporting requirements.

For aspiring business owners, registering a business with a state is a key step. In some states, it can cost a person a few hundred dollars to file. In return, the registration protects personal assets in the event of a bankruptcy; confers tax and banking benefits; and makes hiring workers easier.

When the researchers mapped the data, they found that the ZIP codes that experienced the greatest increase in business registrations were in Black areas, particularly higher median- income Black neighborhoods.

“It feels significant that we saw this big response in neighborhoods where it doesn’t typically happen,” she said. “When you remove those gateways that have worked in some ways to limit access for certain communities, then you really do unleash potential.”

But only a handful of cancers—of the breast, lung, colon and cervix—have screening tests recommended by the U.S. Preventive Services Task Force

Many companies are developing blood tests that can detect cancer signals before symptoms occur, and Grail’s is the most advanced. A study found it can identify more than 50 types of cancer 52% of the time and the 12 deadliest cancers in Stages I through III 68% of the time.

There’s a hitch. The test costs $949 and isn’t covered by Medicare or most private insurance.

The trouble is that this cancer is almost never caught early. There’s no routine screening for it, and symptoms don’t develop until it is advanced. Mr. Royse, 64, had no idea he was sick until he took a blood test called Galleri, produced by the Menlo Park, Calif., startup Grail. He had surgery and chemotherapy and is now cancer-free.

Mr. Royse visited Grail’s website, which referred him to a telemedicine provider who ordered a test. Another telemedicine doctor walked him through his results, which showed a cancer signal likely emanating from the pancreas, gallbladder, stomach or esophagus.

An MRI revealed a suspicious mass on his pancreas, which a biopsy confirmed was cancerous. Mr. Royse had three months of chemotherapy, surgery and another three months of chemotherapy, which ended last February. Because pancreatic cancer often recurs, he gets CT and MRI scans every three months. In addition, he has signed up for startup Natera’s Signatera customized blood test, which checks DNA specific to the patient’s cancer and can signal its return before signs are visible on the scans

Grail’s test likewise looks for DNA shed by cancer cells, which is tagged by molecules called methyl groups that are specific to a cancer’s origin. Grail uses genetic sequencing and machine learning to recognize links between DNA methyl groups and particular cancers

The test “is based on how much DNA is being shed by tumor,” Grail’s president, Josh Ofman, says. “Some tumors shed a lot of DNA. Some shed almost none.

ut slow-growing tumors typically aren’t shedding a lot of DNA.” That reduces the probability that Grail’s test will identify indolent cancers that pose no immediate danger.

Grail’s test has a roughly 0.5% false-positive rate, meaning 1 in 200 patients who don’t have cancer will get a positive signal

Its positive predictive value is 43%, so that of every 100 patients with a positive signal, 43 actually have cancer

the legislation’s price tag could reduce political support. According to one private company’s estimate, the test could cost the government $39 billion to $145 billion over a decade. Mr. Goldman counters that analysts usually overestimate the costs and underestimate the benefits of medical interventions.

Because Grail uses machine learning to detect DNA-methylation cancer linkages, the Grail test’s accuracy should improve as more tests and patient data are collected

regulators may balk at approving the test, and insurers at covering it, until it becomes cheaper and more reliable.

How would the FDA weigh the risk that a false positive on a test like Grail’s could require invasive follow-up testing against the dire but hard-to-quantify risk that a deadly cancer wouldn’t be caught until it’s much harder to treat? It’s unclear.

some experts urge the FDA to require large randomized controlled trials before approving blood cancer tests. “Multicancer screening would entail tremendous costs and potentially substantial harms,” H. Gilbert Welch and Tanujit Dey of Brigham and Women’s Hospital wrote

Dr. Welch and Mr. Dey also suggested that companies should be required to prove their tests reduce overall mortality, even though the FDA doesn’t require drugmakers to prove their products reduce deaths or extend life. Clinical trials for the mRNA Covid vaccines didn’t show they reduced deaths.

One alternative is to rely on real-world studies, which Grail is already doing. One study of patients 50 and older without signs of cancer showed that the test doubled the number of cancers detected.

One recurring problem he has seen: “Epidemiologists are always getting cancer wrong,” he says. “Epidemiologists a decade ago said U.S. overtreats cancers. Well, no, the EU undertreats cancer.”

A 2012 study that he co-authored found that the higher U.S. spending on cancer care relative to Europe between 1983 and 1999 resulted in significantly higher survival rates for American patients than for those in Europe

By his study’s calculation, U.S. spending on cancer treatments during that period resulted in $556 billion in net benefits owing to reduced mortality.

He expects Galleri and other multicancer early-detection tests to reduce deaths and produce public-health and economic benefits that exceed their monetary costs

Expanding access to multicancer early-detection tests could also help solve the chicken-and-egg problem of drug development. Because few patients are diagnosed at early stages of some cancers, it’s hard to develop treatments for them

the positive predictive value for some recommended cancer screenings is far lower. Fewer than 1 in 10 women with an abnormal finding on a mammogram are diagnosed with breast cancer.

Mr. Royse makes the same point with personal force. “I would be dead right now if not for multicancer early-detection testing,” Mr. Royse told an FDA advisory committee last fall. “The longer the FDA waits, the more people are going to die. It’s that simple.”

“Over 80 technical papers published since January 2022,” LeCun responded. “What about you?”

To which Musk posted: “That’s nothing, you’re going soft. Try harder!

At stake are the hearts and minds of AI experts—academic and otherwise—needed to usher in the technology

“Join xAI,” LeCun wrote, “if you can stand a boss who:– claims that what you are working on will be solved next year (no pressure).– claims that what you are working on will kill everyone and must be stopped or paused (yay, vacation for 6 months!).– claims to want a ‘maximally rigorous pursuit of the truth’ but spews crazy-ass conspiracy theories on his own social platform.”

Some read Musk’s “science” dig as dismissing the role research has played for a generation of AI experts. For years, the Metas and Googles of the world have hired the top minds in AI from universities, indulging their desires to keep a foot in both worlds by allowing them to release their research publicly, while also trying to deploy products. 

For an academic such as LeCun, published research, whether peer-reviewed or not, allowed ideas to flourish and reputations to be built, which in turn helped build stars in the system.

LeCun has been at Meta since 2013 while serving as an NYU professor since 2003. His tweets suggest he subscribes to the philosophy that one’s work needs to be published—put through the rigors of being shown to be correct and reproducible—to really be considered science. 

“If you do research and don’t publish, it’s not Science,” he posted in a lengthy tweet Tuesday rebutting Musk. “If you never published your research but somehow developed it into a product, you might die rich,” he concluded. “But you’ll still be a bit bitter and largely forgotten.” 

After pushback, he later clarified in another post: “What I *AM* saying is that science progresses through the collision of ideas, verification, analysis, reproduction, and improvements. If you don’t publish your research *in some way* your research will likely have no impact.”

The spat inspired debate throughout the scientific community. “What is science?” Nature, a scientific journal, asked in a headline about the dust-up.

Others, such as Palmer Luckey, a former Facebook executive and founder of Anduril Industries, a defense startup, took issue with LeCun’s definition of science. “The extreme arrogance and elitism is what people have a problem with,” he tweeted.

For Musk, who prides himself on his physics-based viewpoint and likes to tout how he once aspired to work at a particle accelerator in pursuit of the universe’s big questions, LeCun’s definition of science might sound too ivory-tower. 

Musk has blamed universities for helping promote what he sees as overly liberal thinking and other symptoms of what he calls the Woke Mind Virus. 

Over the years, an appeal of working for Musk has been the impression that his companies move quickly, filled with engineers attracted to tackling hard problems and seeing their ideas put into practice.

“I’ve teamed up with Elon to see if we can actually apply these new technologies to really make a dent in our understanding of the universe,” Igor Babuschkin, an AI expert who worked at OpenAI and Google’s DeepMind, said last year as part of announcing xAI’s mission. 

The creation of xAI quickly sent ripples through the AI labor market, with one rival complaining it was hard to compete for potential candidates attracted to Musk and his reputation for creating value

that was before xAI’s latest round raised billions of dollars, putting its valuation at $24 billion, kicking off a new recruiting drive. 

It was already a seller’s market for AI talent, with estimates that there might be only a couple hundred people out there qualified to deal with certain pressing challenges in the industry and that top candidates can easily earn compensation packages worth $1 million or more

Since the launch, Musk has been quick to criticize competitors for what he perceived as liberal biases in rival AI chatbots. His pitch of xAI being the anti-woke bastion seems to have worked to attract some like-minded engineers.

As for Musk’s final response to LeCun’s defense of research, he posted a meme featuring Pepé Le Pew that read: “my honest reaction.”

"We've never really seen anything this targeted [that] impacts so many sites,"

Amazon Web Services was also experiencing connectivity issues on Friday

. On Thursday, the Financial Times reported that productivity “is set to fall in the U.S. for the first time in more than three decades.”

States with more workers in routine-intensive tasks, like administrative duties, actually saw smaller declines in labor market fluidity.

Young people are more likely to switch jobs and move around.

If young people are tumbleweeds, adults are like trees: They grow roots, and they tend to stay put. So, as a country ages, it should become less dynamic.

The fraction of workers required to hold a government-issued license has sextupled since the 1950s, from less than 5 percent to almost 30 percent today. It’s harder to switch into an industry, especially one in a new state, that’s larded with licensing.

Geographic mobility was very high in the U.S. in the 19th century. This was initially due to the settling of the western frontier. But even after the “closing" of the frontier in 1890, mobility remained high for decades, according to the economists Jason Long and Joseph Ferrie.

In every major city, there are many stores, health-care facilities, and insurance offices. By and large, less educated workers might be less willing to move between states because they assume every area has generally the same type of work.

somebody moving from a small farm to Washington, D.C., would have to visit the capital to understand its culture, job mix, pretty falls, and humid summers. But today’s potential movers are more informed and therefore more strategic:

Between 1880 and 1980, people generally moved from poor states to rich states, seeking the best jobs. “The creation of a single automobile plant—Ford’s River Rouge complex, completed in 1928—boosted Michigan’s population by creating more than 100,000 workers,” as Tim Noah reported. Migration promoted geographical equality.

Smaller counties used to lead the nation in the growth in new businesses even through the early 1990s. But this decade, small counties have lost businesses, while venture capital, the lifeblood of high-growth startups, clustered in a handful of metros.

Land-use policies prevent more middle-class families from living in productive areas, because housing becomes too expensive. Meanwhile, the rich can afford to cluster in a handful of metros where entrepreneurship is a norm, while business dynamism falls in the rest of the country.

In Seattle, New York, and the United Kingdom, the firm has set up automated “lockers” in drug stores and convenience stores. If you order something from Amazon and you work near one of these lockers, the company will offer to drop off your item there. On your way home from work, you can just stop by Rite Aid, punch in a security code, and get your stuff.

I’m a frequent Amazon shopper, and over the last few months I’ve noticed a significant improvement in its shipping times. As a subscriber to Amazon’s Prime subscription service, I’m used to getting two-day shipping on most items for free. But on about a third of my purchases, my package arrives after just one day for no extra charge. Sometimes the service is so speedy it seems almost magical. One Friday afternoon last month, I ordered three smoke alarms, and I debated paying extra for shipping so that I could install them over the weekend. The $9 per item that Amazon charges for Saturday delivery seemed too steep, though, so I went with standard two-day service. The next morning, the delivery guy arrived with my smoke detectors. I’d gotten next-day Saturday service for free

I suspect that, over the next few years, next-day service will become its default shipping method on most of its items. Meanwhile it will offer same-day service as a cheap upgrade. For $5 extra, you can have that laptop waiting for you when you get home from work. Wouldn’t you take that deal?

Order something in the morning and get it later in the day, without doing anything else. Why would you ever shop anywhere else?

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.

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.

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.

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?

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.

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.

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.

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.”

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.

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.

it wasn’t until 2010, when the founders were faced with an iPhone tripod proposal, that they had to decide if such commercial projects qualified as “creative” in the way they intended. As Strickler remembers it, the tripod’s creators responded that their particular tools happened to be injection molding and AutoCAD software, but that their enterprise was absolutely in the spirit of the platform. Kickstarter agreed to host the project, but that decision inspired a sustained effort to shape best practices for gadget campaigns. Its new rules included a requirement that there be a working prototype, and a prohibition on fancy computer renderings

Until last October, Kickstarter’s terms of use stipulated that project creators had only two options to discharge their obligations: ship or refund

Today, in response to situations like ZPM’s, creators can refund, ship or explain, and if a full audit — financial or narrative — shows that the creators have made “every reasonable effort” toward a decent outcome, the backers are encouraged to feel satisfied. Strickler told me he feels it undermines the whole concept of crowdfunding to guarantee that products will be delivered or that refund money will be held in escrow; the acceptance of some risk, after all, is an integral part of patronage

Ethan Mollick, who has published several papers on crowdfunding, has found that more than 80 percent of hardware projects ship with significant delays. Most of those do ultimately deliver something, but Mollick has found that 14 percent of the projects studied have, since 2012, shipped either nothing at all or something too shoddy to use

If backers lack an absolute right to a product, and their legal options are limited, the policy only heightens their feeling of entitlement to full disclosure. “I long ago gave up on getting a machine, but I want my $250 of information,”

A chief tenet of the Internet age is belief in the natural proliferation of democracy and decentralization, in the ability of distributed networks of everyday people to achieve what once required top-down hierarchies and a great concentration of power. When you contribute to a Kickstarter campaign that funds an album or a documentary, you’re participating in the creation of cultural value outside the risk-averse bureaucracies of mass cultural production. You’re kicking in to cut out the middlemen of music labels or Hollywood studios.

Projects like the Oculus Rift virtual-reality headset or the Pebble watch (both of which were Kickstarter successes that went on to become big independent businesses; the former was acquired last year by Facebook for $2 billion) course with a special energy that derives from the exchange of far-flung resources among the sympathetic and like-minded. The realized object takes on a kind of totemic significance through the aspirations and the values of the community that brought it into being

At the same time, like all 21st-century consumers, Kickstarter backers have been trained to expect a world custom-engineered for total frictionlessness. Everything is supposed to work easily, right away and well. O

manufacturing remains a supremely difficult process, the success of which continues to rely on marshaling a lot of resources: development money, an extensive network of trusted vendors, the dedicated personnel to sit in conference rooms in concrete campuses in Shenzhen and Dongguan and refuse to budge until the product is streaming off the assembly line in the right amounts, at the right quality and at the agreed-upon price.

the advice became overwhelming. “They’re doing it on their own schedule, nights and weekends, taking up a huge amount of Igor’s time when he’s got a ton of work to do,” she said. “People got insulted because they couldn’t be involved as much as they wanted.”

The ongoing calls for transparency put pressure on ZPM to carry on all of its business in public, but transparency and efficacy can be incommensurable ideals. ZPM couldn’t blame its vendors in updates, even for the mistakes they were consistently making, if the company wanted to keep working with them — or with anyone at all. The backers had a hard time understanding this; they continued to operate under the shared assumption that more demo­cracy, more engagement and more transparency lead inexorably to more success.

ZPM also could not publish a full financial audit, because open books make meaningful cost negotiation impossible. “I just can’t publish our financials, our cost breakdown,” Tambasco said. “If I do that then when I go to get this thing made, then everybody knows how much I have in the bank. Then what they’re going to do is just drain that money.”Which is exactly what happened.

When one of those consultants demanded, in the spring of 2014, that the project act like a “real start-up” and go into what people in Silicon Valley often call “stealth mode,” the updates came to an abrupt halt. The founders hoped that by posting their email addresses and phone numbers, and offering to field queries and complaints in person, they might assuage the backer community, but this plan, in hindsight, seemed misbegotten. It only confirmed for the backers that their one plank of accountability — the public nature of the story on offer — had been withdrawn.

the reality of ZPM’s failure was pretty banal: The founders were naïve and inexperienced, well intentioned but clumsy, and they serially trusted and paid the wrong people for ineffective help.

The backers suspected that Polyakov would try to sell the intellectual property and pocket the proceeds, but in fact Polyakov’s total asking price for the company was $35,000 to settle the founders’ legal and professional debts, equity consideration for their angel investors and, most important, a guarantee that Buckman would honor ZPM’s commitment to its backers.

The share of podcasts in Americans’ diet of audio programming grew by 18 percent from 2014 to 2015, according to Edison. People who listen to podcasts daily spend about two hours a day, on average, with podcasts, a larger share than for any other form of audio, Edison reported

For Gimlet, listeners’ willingness to try new podcasts has translated into instant audiences for its newest shows. “Startup,” the company’s first show, took 30 days to reach 100,000 listeners a week. Its weekly audience is now more than 500,000. “Mystery Show” took four days to reach 100,000 listeners, mainly because it was being promoted by Gimlet’s two other shows. By its fourth episode, its audience was about 250,000 weekly listens.

Several advertisers told me that podcast ads had proved to be tremendously effective. They can’t be easily skipped, and because they are often read by hosts, audiences are often convinced of their authenticity. “We feel it creates a deep personal connection to our brand,”

Podcasting is destined to be huge, both as a medium and a business. “It’s the future of radio,” Mr. Turck of Panoply said. Just don’t expect that future to come tomorrow.