MIT Technology Review AI (4 hours ago)
Consider what passes for success in technology policy. An AI company promises that its chatbot will not encourage suicide. A social media platform makes it more difficult for children to buy fentanyl. These are important protections, and they should have existed all along.
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The company may be valued as high as $2 trillion.
Anthropic has filed its S-1 prospectus with the Securities and Exchange Commission, preparing the company behind Claude to beat OpenAI to the punch with an IPO. In a filing with the SEC, Anthropic said it plans to list on the Nasdaq exchange under the ticker symbol “ANT.”
The company will be the first true artificial intelligence company to go public as tech firms continue to invest heavily in AI.
Cadence unveiled Maestro, domain-specialized AI models for its chip design agents, with early access for select customers in Q4 2026.
What do the IRS, banks, airlines, and government infrastructure around the world have in common? They’re all running on systems propped up by antique computer code that nobody dares touch, lest everything come crashing down. OpenAI thinks it may have a solution.
American Express is cutting about 5% of its global workforce and taking a $336M restructuring charge. Chief executive Stephen Squeri told analysts about the cuts on Friday, CNBC’s Hugh Son reported. The company had almost 77,000 employees at the end of 2025, according to CNBC.
The next competitive advantage in enterprise AI has less to do with which model you choose than with what you build around it. That’s an important shift.
A scalable AI system requires a strong foundation. Data, engineering, platforms, operating models, and governance must converge to work as one.
Enterprise AI can generate recommendations, automate tasks, and help organizations make decisions faster. But without an understanding of how a business actually operates, even powerful models can miss the constraints and relationships that determine whether a decision is the right one.
Ask most executives to picture the cost of AI and they’ll likely see GPUs—racks of accelerators with gigawatts of power—and a waitlist for silicon. But that picture leaves something out: Data storage costs. What happens to data after a model responds?
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