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Intel/OpenAI logos
(Bronson Stamp for Sherwood Media)

OpenAI is Intel

Created a breakthrough product but missed major market trends.

Jeff Wise

In 1959, physicist Robert Noyce and his colleagues at the Fairchild Semiconductor Corporation created a nifty little piece of technology: a wafer of silicon with an entire electronic circuit printed on it. As a technical breakthrough, it was an impressively clever feat, but what made Noyce’s achievement historically important was the cascade of innovations that followed. By making it possible to progressively shrink computing power into smaller and cheaper packages, the integrated circuit gave rise to wave after wave of transformative products, from the pocket calculator to the personal computer, the internet, and mobile computing. It’s no exaggeration to say that Noyce’s invention engendered the entire digital world as we know it today.

Noyce and some of his collaborators left Fairchild to form their own company, Intel, in 1968. Like OpenAI today, Intel had a head start on a new field of almost unlimited promise, and it used its advantage to great effect. In 1971 the company invented the microprocessor chip, a development that would later enable personal computers to bring the information age into homes of retail consumers. In 1997, Time magazine named Intel CEO Andrew Grove its Man of the Year. By 2000, the company had a market cap of $250 billion and ranked as the sixth-most-valuable business in the world.

That, however, proved to be the company’s high-water mark. In the years that followed, the company learned that being present at the birth of a technology and riding it to great heights doesn’t automatically mean that you’ll be able to sniff out market trends and outcompete newcomers indefinitely. In 1999, Nvidia, a much smaller rival, began shipping its first graphics processing unit, or GPU. In the decade to come, these chips would prove essential to the growth of PC gaming and multimedia applications, and then proved equally vital to both cryptocurrency mining and the large language models that power OpenAI. This time it was Nvidia, not Intel, whose chips were powering the revolution, and the changing of the guard was reflected in the company’s share prices. Ten years ago, Intel’s market cap was 15x bigger than Nvidia’s; today Nvidia’s is 30x bigger.

Investor Warren Buffett famously once said that he likes to invest in companies whose business has an “economic moat” around it — some factor, whether brand strength, network effects, patents, or economies of scale, that prevents rivals from swooping in and poaching its business. A head start in a technology can function as a moat for a while. But success inevitably draws rivals, and sooner or later some of them will catch up, or the demands of the market will change and favor a different technology. For now, OpenAI has a first-mover advantage in machine learning, and it has every reason to be optimistic about its fortunes in the years ahead. But if history is any guide, its halcyon days will eventually go the way of all flesh.

Read the other arguments for OpenAI's future here.


Jeff Wise is a New York-based journalist specializing in aviation, technology, and psychology.

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Meta is planning on spending between $125 billion and $145 billion on AI capital expenditure this year alone.

Shares dropped more than 5% on the news.

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Anthropic’s Mythos AI model was deemed too dangerous to release to the public, with the company citing its ability to orchestrate novel cyberattacks.

And that’s just what the National Security Agency is doing, with the help of Anthropic staff embedded at the agency, according to a report from the Financial Times.

Only a small number of companies and US allies have been given access to the advanced model, which means America’s adversaries have not had the chance to shore up their defenses against the AI model’s new offensive capabilities.

The arrangement is especially unusual as the Pentagon has deemed Anthropic’s AI a national security supply chain risk — effectively blacklisting it for defense work — in response to the company’s refusal to allow its technology to be used for any legal application, which could include autonomous killing or mass surveillance. Anthropic is currently suing the US government to fight the determination.

Only a small number of companies and US allies have been given access to the advanced model, which means America’s adversaries have not had the chance to shore up their defenses against the AI model’s new offensive capabilities.

The arrangement is especially unusual as the Pentagon has deemed Anthropic’s AI a national security supply chain risk — effectively blacklisting it for defense work — in response to the company’s refusal to allow its technology to be used for any legal application, which could include autonomous killing or mass surveillance. Anthropic is currently suing the US government to fight the determination.

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Longtime Tesla bear JPMorgan upgraded Tesla and raised its price target to $475 from $145

For more than a decade, JPMorgan was Wall Streets most stubborn Tesla skeptic, anchored by auto analyst Ryan Brinkman’s strict focus on traditional car fundamentals and near-term delivery numbers.

But JPM recently handed coverage of the stock to a new analyst, Rajat Gupta, who is throwing that playbook out the window. In a note Friday, the firm upgraded Tesla to neutral from underweight and raised its price target 228% to $475 from $145. (The analyst consensus on FactSet is $403.) Instead of focusing on the company’s struggling vehicle business, the new analyst is orienting himself more toward Tesla’s idea of the future, now modeling Tesla’s physical AI and robotaxi fleets all the way out to the year 2040.

Here are the main reasons for the capitulation:

  • Looking past the car lot: Gupta argues that Tesla is at the forefront of physical AI, entering uncharted TAMs” and therefore deserves the benefit of the doubt to be valued on LT earnings potential rather than near-term speed bumps.

  • Unmatched vertical integration: Teslas control over everything from battery cells to custom silicon gives it a massive moat. JPM notes this starting point advantage is unmatched at an industrial level scale” and “still somewhat under-appreciated and misunderstood.

  • The AWS flywheel effect: Deploying Optimus robots inside its own factories should not only lower COGS for the base automotive business, but more importantly, help validate the product at an industrial scale.” Gupta called it “a classic flywheel effect, somewhat analogous to AWS and Kiva at AMZN.

For Tesla bulls who have argued for years that this is an AI company and not a carmaker, JPM’s sudden $3.9 trillion valuation model is the ultimate validation.

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