Casey Newton
Casey Newton is a technology journalist, founder and editor of Platformer, and co-host of Hard Fork, a New York Times technology podcast. His public work focuses on technology platforms, democracy, social networks, and AI.
Figma’s CEO Says AI Makes Average Work Easier to Ignore
Figma co-founder and chief executive Dylan Field argues in a Hard Fork interview that AI is not killing design so much as making average work cheaper and more abundant. Field’s case is that writers, designers and software makers will be judged less on their ability to produce a first draft or prototype than on whether they can give it a distinctive voice, point of view and level of craft. He expects design work to broaden rather than disappear, even as AI labs push further into application software.
AI’s Economic Test Is Broad Diffusion, Not Frontier Capability
Microsoft chief executive Satya Nadella told a New York Times Hard Fork live audience that AI’s economic test is not whether a few companies build stronger frontier models, but whether the technology spreads widely enough to raise productivity, justify its token costs and create visible benefits for workers and communities. He argued that Microsoft’s role is to build platforms for that diffusion, while warning that job displacement, data center burdens and concentrated gains will make the backlash rational unless humans remain stakeholders through new “glue work” and local upside.
SpaceX, Anthropic, and OpenAI Listings Could Reshape AI Governance
Kevin Roose and Casey Newton argue that the expected IPOs of SpaceX, Anthropic and OpenAI would turn the AI boom into a public-markets event with consequences far beyond Silicon Valley insiders. On Hard Fork, they say the listings could mint vast private fortunes, reshape San Francisco housing and philanthropy, and force ordinary index-fund investors into companies whose governance and safety choices remain unsettled. The episode then turns to Kevin Hartnett, who says recent AI advances in mathematics have moved from benchmark wins to publishable research, leaving mathematicians divided over whether the technology is a tool, a threat, or both.
Google Says It Is at the AI Frontier, Except in Coding
Google chief executive Sundar Pichai told Hard Fork’s Kevin Roose and Casey Newton that Google is at the frontier in some areas of AI and behind in others, particularly long-horizon coding tasks. He argued that the race is moving fast enough for public judgments of leadership to change within months, while defending Google’s broader platform strategy in search, agents, cloud infrastructure and chips. Pichai also treated public anxiety about AI as rational, saying the technology is advancing toward AGI quickly enough that companies and governments need to prepare without either dismissing disruption or slowing progress excessively.
Google’s AI Strategy Emphasizes Scale Over Frontier Model Leadership
Kevin Roose and Casey Newton read Google’s I/O announcements as evidence of a company that has regained operational confidence in AI without yet proving frontier leadership. Roose argues Google is leaning on speed, cost, distribution and infrastructure — putting capable models across search, coding, video and cloud tools at enormous scale. Newton is more skeptical: fast and cheap, he says, is not the same as best, and many of Google’s most important product claims remain untested until users can rely on them in real workflows.
AI Cyber Models Push Trump Administration Toward Pre-Release Safety Reviews
Kevin Roose and Casey Newton argue that the Trump administration’s shift toward AI safety is being driven by frontier models that can find and chain software vulnerabilities, not by a broad ideological conversion. Drawing on New York Times reporting about a possible executive order for pre-release model review, they describe a policy scramble over Anthropic’s Mythos, chip access to China and which federal agency should judge dangerous models. Nikesh Arora, Palo Alto Networks’ chief executive, says the cyber problem is already operational: attacks that once unfolded over days may soon move in minutes.
OpenAI Trial Records Show Founders Anticipated an AGI Governance Fight
Kevin Roose and Casey Newton argue that the Musk v. OpenAI trial is notable less for its personal theatrics than for the written record it has exposed from OpenAI’s early years. In their reading, the evidence shows founders and executives anticipating fights over the governance, financing and control of artificial general intelligence before the technology appeared capable of justifying those stakes. The trial’s stranger artifacts — journals, trophies, succession questions and private channels — matter because they illuminate how closely OpenAI’s mission was tied from the start to power.
Prediction-Market Scandals Spur Calls for Insider-Trading Rules
Hard Fork’s Kevin Roose and Casey Newton argue that prediction markets have entered a more dangerous phase, with recent scandals showing how liquid event-betting platforms can reward insider knowledge, manipulation and even national-security breaches before regulators have caught up. The episode broadens that concern into a larger question about technologies whose incentives are outrunning public rules, through Joanna Stern’s year-long test of AI in daily life and Rachel Cohn’s reporting from a Brooklyn school trying to resist the commodification of attention.