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As Big Tech chases enterprise AI, Meta and Apple double down on small businesses

Apple and Meta are playing to their strengths in small business.

Rani Molla

While much of Silicon Valley is racing to build the best AI “coworker” for big corporations, Meta and Apple are betting the bigger opportunity lies elsewhere: small businesses.

Meta is launching a new companywide initiative aimed at helping entrepreneurs start and grow businesses using AI tools across Facebook, Instagram, and WhatsApp, Axios reported Wednesday. A day earlier, Apple unveiled Apple Business, a platform that bundles together tools for managing devices, employees, and customer interactions.

These moves seem different from the AI copilots coming out of Microsoft, Anthropic, and others. Those companies are focused on selling AI into enterprises, with tools that help employees write code, draft documents, and potentially replace some of their coworkers.

Meanwhile, Meta and Apple are leveraging their already strong base of small-business customers — Meta for communication tools and advertising revenue, and Apple through its devices, payments, and app ecosystem — to less flashy ends.

Meta is leaning into its strength as a distribution and marketing platform for small businesses, aiming to offer them AI tools to attract customers, create content, and manage interactions.

“In the AI era, it should be easier than ever for people to build new businesses,” CEO Mark Zuckerberg wrote in an internal memo. “We want to build the services that enable this.”

Unlike rivals pouring hundreds of billions into enterprise AI infrastructure, Meta doesn’t have a major enterprise software business to offset those costs.

Apple’s move is less explicitly about AI — and comes as it has struggled to match competitors’ momentum in the space — but similarly plays to its strengths. By consolidating business tools into a single platform, it’s positioning itself as the infrastructure layer for smaller companies operating within its ecosystem.

The AI race may be focused on the enterprise, but Meta and Apple are going after everyone else.

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FT: Meta considering “tens of billions” in new capital to fund AI

Just days after Google announced a monster $85 billion upsized equity raise, the extremely profitable Meta is seeking to sell “tens of billions of dollars” in stock, according to a new report from the Financial Times.

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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FT: Anthropic staff helping the NSA use Mythos for offensive cyberattacks

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.

skynet terminator

Anthropic ponders self-improving AI

Anthropic says Claude already writes 80% of its code. A new post asks what happens when the models can improve themselves — and whether anyone could stop them.

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