AI News Archive: June 17, 2026 — Part 15
Sourced from 500+ daily AI sources, scored by relevance.
- ChatGPT’s market share dips below 50%: Key AI trends to know
Hit by Google Gemini's continued expansion and Claude's recent growth, ChatGPT's share of the AI assistant market fell below 50% for the first time in March 2026, as per a report published by market intelligence firm Sensor Tower.
- ChatGPT falls below 50% market share despite its lead
ChatGPT falls below 50% market share despite its lead YourStory.com
- ChatGPT market share slips below 50% as Gemini, Claude gain ground: Report
ChatGPT reached one billion users faster than any platform in history, but rivals are growing faster and reshaping the AI assistant market, according to Sensor Tower's Start of AI 2026 report
- ChatGPT market share falls below 50% for first time, but remains top AI assistant: Report
ChatGPT market share falls below 50% for first time, but remains top AI assistant: Report
- ChatGPT’s AI market share slips to a historic new low
For the first time since its explosive debut, ChatGPT has slipped below a crucial market share threshhold.
- ChatGPT Market Share Dips Below 50% for First Time: Here's Why
ChatGPT Market Share Dips Below 50% for First Time: Here's Why PCMag UK
- ChatGPT Market Share Dips Below 50% for First Time: Here's Why
ChatGPT Market Share Dips Below 50% for First Time: Here's Why PCMag
- Wipro Opens Anthropic Claude AI Centre in Bengaluru
Wipro Opens Anthropic Claude AI Centre in Bengaluru YourStory.com
- Anthropic's design assistant now works better with its coding agent
Anthropic's tools are getting chummy with each other.
- Anthropic Is Bringing Together AI Design and Coding in Claude
New updates mean you should be able to go back and forth between coding and designing without interruptions.
- Samsung’s pet tech only needs a picture to detect health issues hurting your furry friends
Samsung's next Galaxy AI feature takes a photo of your dog or cat and uses AI to flag potential health issues before they become a vet emergency.
- Samsung phones will soon let you check your pet’s health with a photo
Galaxy owners can soon take a photo of their pet and use AI analysis to spot any health issues.
- Copilot Cowork becomes generally available
Copilot Cowork is now generally available
- Estonia intends to recognize AI agents with digital IDs
I am not a number! I am a free agent (that just happens to have a number)
- Apple investors are tired of AI promises, want tangible progress
Apple investors are tired of AI promises, want tangible progress The Mercury News
- SpaceX Acquires Cursor, the AI Coding Startup Competing With Claude Code and OpenAI Codex
SpaceX has acquired AI coding startup Cursor in a $60 billion (roughly Rs. 5,66,500 crore) all-stock deal. The companies confirmed the acquisition on X and said they have been jointly training an AI model that will be released through Cursor and Grok Build. The deal follows a partnership announced in April that gave SpaceX the option to buy Cursor. Founded in 2022 as ...
- Why SpaceX is spending $60 billion to acquire AI coding startup Cursor
Why SpaceX is spending $60 billion to acquire AI coding startup Cursor
- Cursor officially joins the SpaceX AI machine
PLUS: Conduct better stock research with Perplexity Finance
- First Take: SpaceX’s Cursor Acquisition Gives Its Enterprise AI Ambitions a Launch Vehicle
First Take: SpaceX’s Cursor Acquisition Gives Its Enterprise AI Ambitions a Launch Vehicle Gartner
- SpaceX, Cursor, and the Race to Build the Best Coding LLM in the World
SpaceX’s $60 billion acquisition of Cursor is the largest deal in the history of AI software and one that reshapes the competitive landscape for agentic coding. The all-stock transaction, filed with the SEC and expected to close in Q3 2026, follows an option SpaceX secured in April and exercised just two trading days after its […] The post SpaceX, Cursor, and the Race to Build the Best Coding LLM in the World appeared first on IDC .
- SpaceX to take over Cursor in $60bn play for AI coding market
A new coding model will come to Cursor and xAI's Beta-stage Grok Build platform.
- SpaceX strikes $60bn deal for AI coding agent Cursor
SpaceX has signed an agreement to acquire Anysphere, the company behind the AI coding agent Cursor, in an all-stock deal valued at $60bn.
- SpaceX’s First Big Move After Its Record IPO: Buying a $60 Billion AI Coding Startup
SpaceX’s First Big Move After Its Record IPO: Buying a $60 Billion AI Coding Startup entrepreneur.com
- Everpure announces Data Stream to expand AI-ready data offerings
Everpure Data Stream, based on NVIDIA AI Data Platform reference design, brings advanced AI capabilities directly to enterprise data
- Everpure unveils data-primacy architecture for AI era
New capabilities shift enterprises from app-centric to data-centric model with enhanced governance and AI-ready intelligence
- Data primacy puts Everpure at the center of enterprise AI: theCUBE’s Pure Accelerate 2026 keynote analysis
As artificial intelligence transforms the enterprise, the old model of managing data in application-controlled silos is breaking down. The companies that will win the AI era are those that embrace data primacy by treating data itself, rather than the applications sitting on top of it, as the primary asset. That shift was on full display […] The post Data primacy puts Everpure at the center of enterprise AI: theCUBE’s Pure Accelerate 2026 keynote analysis appeared first on SiliconANGLE .
- Companies question cost of AI as tokenmaxxing spending adds up
Companies question cost of AI as tokenmaxxing spending adds up CBC
- Z.ai pitches GLM-5.2 for long-running software engineering tasks
Z.ai pitches GLM-5.2 for long-running software engineering tasks InfoWorld
- Z.ai pitches GLM-5.2 for long-running software engineering tasks
Z.ai has released GLM-5.2, an MIT-licensed open-source AI model designed for long-running software engineering tasks, as the Chinese company seeks to challenge proprietary coding models on cost and performance. The company said GLM-5.2 ranked just behind Anthropic’s Claude Opus 4.8 on FrontierSWE, a long-horizon coding benchmark, trailing it by 1%. Z.ai said the model also edged out OpenAI’s GPT-5.5 by 1%. Z.ai said GLM-5.2 supports a one-million-token context window with up to 131,072 output tokens, positioning it for agentic coding workflows that require reasoning across large codebases. The company is also making an efficiency argument. It said GLM-5.2 uses a technique called IndexShare, which reduces per-token compute by 2.9 times at a one-million-token context length. It also said changes to the model’s multi-token prediction layer increased the acceptance length for speculative decoding by up to 20%. The changes are aimed at a practical problem for developers: long-context coding agents can be expensive to run when they are asked to work across large repositories. Enterprise appeal GLM-5.2’s clearest appeal is that it pairs stronger coding capabilities with the cost advantages of an open-source model. But capability alone will not be enough to make it a credible alternative. “Western enterprises will want independent benchmark validation, successful deployments at global enterprises, strong security and governance controls, and long-term support commitments,” said Pareekh Jain , CEO of Pareekh Consulting. Jain said the fastest route to enterprise credibility would be hosting by a major cloud provider like AWS. That would allow customers to use the model under standard enterprise terms, with service-level commitments and compliance certifications. Tulika Sheel , senior VP at Kadence International, said GLM-5.2 would also need to prove it can operate as a stable enterprise product. “Demonstrated success in real-world deployments and transparent governance will be just as important as benchmark scores,” Sheel said. The performance and cost claims will also need to hold up against established models. “Enterprise leaders generally consider two major factors when evaluating new models,” said Lian Jye Su , chief analyst at Omdia. “First, they look at overall performance against competitors, where GLM-5.2 performs well in long-horizon agentic coding and software engineering. Second, they look at the cost of adoption. As an open-source model, GLM-5.2 has clear cost advantages.” Su said the model could appeal to engineering teams under pressure to control AI costs. It may also attract open-source advocates and companies with significant operations in the Asia-Pacific. But the claims still need wider validation, particularly around hallucination control and coherence during extended tasks. These are critical issues for enterprises considering AI coding agents, which may need to work across large codebases and multi-step software engineering workflows. Jain said the one-million-token context window could be useful for large codebase analysis. It could also help with legacy modernization projects and complex engineering documentation. He said long-context capability may also help with audit logs or legal contracts, where splitting material into smaller chunks can create errors across document boundaries. But for everyday coding tasks, effective retrieval systems may matter more than very large context windows, making some of the benefits more limited in practice. Governance risks The governance question depends largely on where the model runs. Sheel said enterprises should evaluate GLM-5.2 as they would any strategic technology partner, rather than as a standalone model. That means looking at where data is stored and whether the model can be used in environments that customers control. That deployment choice is central to the risk calculation, according to Jain. Because GLM-5.2 is available under an MIT license, companies can download the weights and run them on their own infrastructure, reducing the need to send sensitive data to Z.ai. “The risk flips completely if you use Z.ai’s hosted API instead,” Jain said. He said Chinese national security rules could require domestic companies to cooperate with government requests, making hosted use difficult for regulated industries or workloads involving sensitive data. Su said the issue is not limited to Chinese vendors. Recent restrictions affecting access to some Anthropic models have also highlighted the risk that enterprises may have limited control over the availability of AI services from foreign providers. “Selecting solutions from American and Chinese AI vendors does expose non-US Western enterprises to additional risk of having zero control over the availability and uptime of these models,” Su said. The article originally appeared on InfoWorld .
- Vercel Releases Eve: An Open-Source AI Agent Framework Where Each Agent is a Directory of Files Mapped to Capabilities
Vercel Releases Eve: An Open-Source AI Agent Framework Where Each Agent is a Directory of Files Mapped to Capabilities MarkTechPost
- Introducing eve
Today, we are proud to introduce eve , an open-source agent framework for building, running, and scaling agents. eve is designed around the idea that building an agent should mean defining what it does without assembling all of the pieces that it needs to run in production. Instead, eve comes with production already built in: Durable execution Sandboxed compute Human-in-the-loop approvals Subagents Evals And more eve is the framework that we build and run our own agents on. Agents today are where the web was before frameworks, with everyone hand-rolling the same plumbing and nothing carrying over to the next one. Next.js ended this for the web, and eve is doing the same for agents. An agent is a directory This is an eve agent. Each file describes one component of the agent, so at a glance, the tree tells you what an agent is, what it does, where it lives, and when it acts on its own. Create an eve agent in minutes Every agent starts with its definition. The agent.ts file is where you configure the agent itself. You can define the model with one line, with provider fallbacks supported through AI Gateway , and compaction, model options, and other optional fields are there when you need them. Giving your agent a job and personality is as simple as creating an instructions.md file, which serves as the system prompt that eve puts in front of every model call. You create files for what your agent does, like post_chart.ts and revenue-definitions.md for tools and skills, and eve wires them into a working agent without any boilerplate or plumbing to manage. You can just focus on what your agent does instead of how it does it. Why we built eve We had built agents for years at Vercel, v0 among them. But once coding agents made building one something anyone could do, everyone did. We shipped hundreds of agents and internal apps, and it looked like a productivity revolution. But underneath it, every team was building and rebuilding the same plumbing before their agent could do anything, and none of it carried over from one use case to the next. Each agent was designed for a different task, but they all had the same needs, and the same structure kept emerging to meet them. Agents have a shape. eve is that shape made into a framework. Every generation of software earns its abstractions once enough people have built the same thing the hard way, and agents are there now. Batteries included Everything an agent needs in production ships with the framework. A durable session for every conversation Agents wait on people, call slow systems, and run for hours, days, or weeks. In eve, every conversation is a durable workflow with each step checkpointed, so a session can pause, survive a crash or a deploy, and resume exactly where it stopped. This durability is built on the open-source Workflow SDK . A sandbox for every agent The code your agents write should be treated as untrusted, so eve keeps agent-generated code out of your application runtime entirely. Every agent gets its own sandbox, an isolated environment for shell commands, scripts, and file reads and writes, running in a separate security context from the harness that controls the agent. The backend behind this sandbox is an adapter. When deployed, it runs on Vercel Sandbox . Locally, it runs on Docker, microsandbox, or just-bash , and you can write an adapter for any other provider. Human-in-the-loop approvals Agents act on real systems, and some of those actions should require a person to approve them. Any action in eve can be configured to require approval, and the agent will pause there and wait, indefinitely if it has to, without consuming any compute. Once approved, eve continues the task right from where it left off. Secure connections to tools, data, and services Agents need to connect to your backends, data, and other third-party services. In eve, a connection is a file that points at an MCP server or any API with a compatible OpenAPI document. eve discovers the remote tools, hands them to the model, and brokers the auth, and the model never sees the connection's URL or credentials. Vercel Connect handles interactive OAuth with consent and token refresh built in. At launch, eve agents can connect to Slack, GitHub, Snowflake, Salesforce, Notion, and Linear, plus anything else you can reach over OAuth, an API key, or an MCP server. The same agent on every channel Most agents live in exactly one place because every new surface is its own integration to build. In eve, the same agent serves every surface, and each channel is just a small adapter file. The HTTP API is on by default, with Slack, Discord, Teams, Telegram, Twilio, GitHub, and Linear included, and defineChannel covers custom channels. One channel can also hand off to another, so an incident webhook can open an investigation thread in Slack. Tracing and evals built in When an agent gets something wrong, the first question is what the agent actually did. In eve, every run produces a trace. Each model call and tool call appears in order with its inputs and outputs, down to the commands the agent ran in its sandbox, so you can replay the run instead of piecing it together from logs. The spans are standard OpenTelemetry and export to any tracing service you already run, whether that is Braintrust, Honeycomb, Datadog, or Jaeger. On Vercel, they surface in an Agent Runs tab under Observability, giving you one place to watch every session and drill into any run. Evals let you go further, with scored test suites you can run locally or wire into CI. That leaves the part no framework can write for you: what your agent actually does. Extend an agent one file at a time The most common way to give an agent capabilities is to give it tools, and to teach it how to do things with skills. Today that means building the tool, writing the skill, and then wiring both into whatever runs your agent loop. With eve, a tool is one TypeScript file and a skill is one markdown file. Notice what is missing. Instead of writing all of the boilerplate to wire these up and register them with your agent, eve handles it for you. A file's name and place in the tree are its definition. eve picks up the tool and skill at build time, hands the model their descriptions, and the model takes it from there. Just as Next.js turns a folder into a route by owning the routing, eve turns a file into an ability by owning the agent loop. Add human-in-the-loop approval Requiring approval for an action is one field on the tool. Now you can guard the expensive query, the destructive write, or anything else you would not want running unsupervised. Let the agent write its own code The tools you define aren't the ceiling. eve gives your agent a real computer with a shell, so it can run bash, grep, and anything else you'd run in a terminal. When a job calls for code that doesn't exist yet, the agent writes and runs it. Your agent can solve problems on its own in a secure sandbox, reshaping a dataset, running a one-off analysis, or writing whatever code a job needs that no tool covers. Delegate work to a subagent An eve agent can also delegate. A subagent is the same shape one level down, a directory inside subagents/ with its own instructions, tools, and sandbox. The parent calls it just like it calls a tool. The child starts with a clean context window and only the tools you gave it, does the work, and hands the result back to the parent. Start and interact with your agent Now comes the part every developer looks forward to, testing their agent. That used to mean starting the process, asking a question, and reading logs, with no simple view of which tools were used, what the model loaded, or why it answered the way it did. You wanted to talk to your agent and watch it work, and what you got was stdout . With eve, the dev loop is one command. Run the agent locally To start an eve agent, you run its dev server. Everything the agent did is visible in the TUI. The agent loaded the skill, ran the query, answered by the team's rules, and each of those lines is a checkpointed step in the durable session. The terminal UI is just a client, and the agent serves the same structured events over HTTP, so curl , a test script, or CI can drive it and check exactly what it did. Test the agent with evals Talking to the agent proves one run at a time. Evals test your agent the way you test the rest of your software, with scored checks written in files like everything else in the project. You can run eve eval locally or point it at a deployed app, so a prompt change or a model swap shows you what it broke before your users do. Ship it The agent has lived on your laptop long enough. Shipping it is normally the step where the agent work stops and the infrastructure work begins. With eve there is nothing to provision, because the agent is an ordinary Vercel project, and it deploys the way any other frontend or backend does. Nothing about your agent changes when you deploy, because eve was designed from the ground up with adapters in mind. At launch eve deploys to Vercel, with support for other platforms on the way. The same directory runs in production exactly as it ran on your laptop. The sandbox swaps to Vercel Sandbox without a code change, and the agent you were talking to in dev is now reachable at a public URL. Deploying does not even interrupt the agent; a session that is mid-task when you push finishes on the version it started on. There is no dashboard step required in any of this. The same coding agent that built your agent can ship it and verify its work. But deployed is not the same as done. In production, an agent has users to meet and work to do on its own schedule. Introduce the agent to your team Getting an agent into Slack used to mean building a Slack app first, including the app config, bot token, event subscriptions, webhook endpoint, and signing secret, all before the agent said a word. With eve, a channel is one command. The command writes channels/slack.ts , a single file that ships like any other code change, and the agent you just deployed now answers in Slack. The platform affordances come with the channel, so approvals render as Slack buttons, questions as select menus, and the agent posts typing indicators while it works. Route the credentials through Vercel Connect and there is no bot token to copy into a .env file. Run the command again with discord or teams , and the same agent is there too, one file per channel. Channels are the user interface of your agents, and sessions move between them. A question asked in Slack can continue on the web, and an incident webhook arriving over HTTP can open an investigation thread in Slack and finish the work where the team already is. Put the agent on a schedule The Monday revenue report should not wait for someone to ask. A schedule is one more file, a cron expression and a handler that starts the agent on its own clock. On Vercel, each schedule deploys as a Vercel Cron Job , so the report posts every Monday with nobody on the hook to remember it. Run the agent like the rest of your software An agent your team depends on is production software, and a change to its instructions can break it as surely as a change to its code. Because an eve agent is files in a directory, it lives in Git like the rest of your code, and a new prompt, tool, or skill is a commit with a diff, a review, and a history. Wire eve eval into CI and the suites you wrote become the deploy gate, scoring every commit so a regression stops in CI rather than in production. Every commit also gets its own preview deployment, and it carries the agent's channels with it. The team can talk to the next version of your Slack bot before it replaces the one they use every day. And when a change goes bad in a way no eval caught, you can roll production back to the previous version instantly. How we run Vercel on eve We run more than a hundred agents in production at Vercel, and they are part of how the company operates every day, each one taking on a role in the business. Here are a few of them. The data analyst The most-used internal tool at Vercel is an agent, handling more than 30,000 questions a month. Anyone can ask d0 anything in Slack and get an answer from the warehouse. Every query is scoped to the asker's own permissions, so d0 can never show you a table you could not already see. The autonomous SDR Lead Agent runs the playbook of our best rep around the clock. It works every new lead the moment it comes in and follows up on its own, so none go cold overnight. It costs about $5,000 a year to run, returns 32 times that, and one engineer maintains it part-time. The sales cockpit RevOps built Athena in six weeks without engineers. It answers pipeline and forecast questions from Snowflake and Salesforce in plain language, and pipeline coverage nearly doubled after it went live. The support engineer Vertex is our support agent that handles tickets across the help center, docs, and Slack around the clock, ensuring people get a fast response no matter when they ask. It reads the ticket, finds the right answer, and responds, solving 92% of tickets on its own and escalating the rest to the support team so they can focus on the problems that most need their attention. The content agent Anyone at Vercel can write, not just the content team. draft0 runs a full review pipeline, catching the most glaring issues and building up an analysis of what the piece is actually about before it ever reaches us. By the time it does, the obvious work is done and we have a much clearer picture of what it needs. That means smaller pieces move fast, and we can give our full attention to the ones that demand it, like this one. Routing agent We rely on hundreds of agents every day, but keeping track of which one handles what workloads is not efficient. So instead of routing tasks ourselves, everything goes to V in Slack first. V figures out which agent can actually answer the task and routes it there, which means the whole fleet works like one agent instead of a hundred different options. These agents all began as separate projects on separate stacks, each with its own way of holding state, brokering credentials, and emitting logs, which is where most teams find themselves after their second or third agent. Today they live in one monorepo, and are built, observed, and upgraded the same way, no matter which team owns them. Because they all share the same shape, a hundred agents run with the same tools and the same conventions as one. Get started A year ago, agents triggered less than 3% of the deployments on Vercel. Now, they trigger around 29%, and we expect half of all deployments to come from agents soon. You have probably built an agent already, and the next one does not have to start from scratch. The public preview is open today, and the CLI wizard walks you through your first agent, from picking a model to a running dev server, in under a minute. Coding agents just need a prompt: Everything eve can do is at eve.dev/docs and development happens in the open at github.com/vercel/eve , where issues, discussions, and contributions are welcome. Hundreds of agents already run on eve at Vercel. What will you build? Read more
- Vercel launches a new framework and enterprise controls for agentic AI infrastructure
Front-end software development startup Vercel Inc. introduced a set of new products today at Ship, its annual conference, to deepen its agentic artificial intelligence infrastructure platform to align how enterprises deploy, run and scale with AI agents. “Each new generation of software needs a new generation of infrastructure. For the agent era, that’s Vercel,” said […] The post Vercel launches a new framework and enterprise controls for agentic AI infrastructure appeared first on SiliconANGLE .
- GLM-5.2 🤖, DeepSeek raises $7.4B 💰, Android MCP 📱
GLM-5.2 🤖, DeepSeek raises $7.4B 💰, Android MCP 📱
- MolmoMotion: Language-guided 3D motion forecasting
MolmoMotion: Language-guided 3D motion forecasting
- Inquiry about MCP setting (w/ Claude)
Inquiry about MCP setting (w/ Claude) Atlassian Community
- Apple investors are tired of AI promises, want tangible progress
Apple investors are tired of AI promises, want tangible progress East Bay Times
- Anduril Wins Production Contract for U.S. Air Force CCA Program
Anduril Wins Production Contract for U.S. Air Force CCA Program
- G7 leaders vow closer ties on AI as they hash out 'trusted partners' scheme
G7 leaders vow closer ties on AI as they hash out 'trusted partners' scheme The Straits Times
- G7 pitches trusted partnership to AI chiefs in a bid for frontier model sharing
G7 pitches trusted partnership to AI chiefs in a bid for frontier model sharing The National
- G7 leaders vow closer ties on AI
Hash out 'trusted partners' scheme.
- Singapore's May exports rise 38%, boosted by AI demand
Singapore's May exports rise 38%, boosted by AI demand Nikkei Asia
- Elon Musk's AI tool Grok was used in strikes against Iran: US govt
Elon Musk's AI tool Grok was used in strikes against Iran: US govt
- French President Macron to wind up G-7 summit with focus on AI, Trump dinner
French President Macron to wind up G-7 summit with focus on AI, Trump dinner The Straits Times
- AP Exclusive: Nvidia's Jensen Huang says society needs 'new social norms' in the age of AI
AP Exclusive: Nvidia's Jensen Huang says society needs 'new social norms' in the age of AI San Francisco Chronicle
- AP Exclusive: Nvidia's Jensen Huang says society needs 'new social norms' in the age of AI
AP Exclusive: Nvidia's Jensen Huang says society needs 'new social norms' in the age of AI Houston Chronicle
- Nvidia’s Jensen Huang shares 3 key points about the future of AI
Nvidia CEO Jensen Huang — whose work helped propel artificial intelligence — stressed in an Associated Press interview Tuesday that society needs to change with the advent of AI , arguing that a fuller embrace of the technology would improve people’s lives. Huang has been optimistic about AI’s potential to rapidly transform society, creating faster economic growth and more scientific breakthroughs. But as the head of a computer chip company now developing AI systems, he and others are confronting a public increasingly concerned about the potential harm the technology might bring. Huang has felt obligated to respond to critics who warn of job losses and threats to humanity itself. “We need to create new social norms,” Huang said in an interview. “I would advocate that everybody use AI. Just go engage it.” Huang made his case as AI has emerged as a political flashpoint, with objections to plans to build more data centers and fears that the speed with which it’s being adopted could spur the layoffs of workers who might not have a safety net. Such questions have threatened public support of the technology at a time when a race has kicked off with China , a contest Huang believes can best be won by a U.S. that is open to competing globally in AI. His close relationship with President Donald Trump also has been a source of criticism among Democrats, even as he emphasized that the computing power created by AI is vital to adding the factory jobs that have been promised for decades without much enduring success. It was an argument delivered by a 63-year-old man who has watched the technology develop and described himself as “boring” because his own life revolves mainly around work and his family. Huang disclosed during the interview some personal details, saying his favorite movie is “Kingdom of Heaven,” the 2005 epic about the 12th century Crusader Kingdom of Jerusalem. He said he had watched the movie “Project Hail Mary” three or four times and “I think we might watch it again this weekend.” Huang said the ability of AI to design a website, analyze complex documents, guide advanced research or even plan a kitchen remodeling has helped to close the technological divide in America. People can now do advanced work on computers without having to know how to program or write software, he added. Huang contended that there is a need for some government regulation and safety standards for AI, emphasizing that national security also needed to be a priority for the technology that has been powering stock market gains and U.S. economic growth in recent years. Huang said society will adapt to AI just as it did to automobiles. He said cars were once portrayed as killing children, but the world changed its norms by having sidewalks and crosswalks and stopping kids from playing in the streets. Huang skeptical of what government ownership of AI companies would achieve With a market capitalization of roughly $5 trillion, Nvidia has soared in valuation in recent years to become the world’s most valuable company. AI modeling companies OpenAI and Anthropic are potentially set to also clear the $1 trillion mark once their stocks are publicly traded. That explosive surge in wealth concentrated in AI companies has prompted renewed worries about economic inequality. Trump has tried to defuse those concerns, recently musing about the prospect that the U.S. government could own some shares in AI firms, so any windfalls would be more broadly shared with the public. That idea has also been advanced by Sen. Bernie Sanders, I-Vt., and even OpenAI CEO Sam Altman. Huang expressed skepticism about the idea, saying he expects the country will already benefit broadly from AI advancements. “I’m not exactly sure what they’re trying to achieve,” he said regarding government ownership. “I haven’t had a dialogue with them about that. But just remember that these are American companies. Their success benefits the stock price, of which many Americans are investors in. It generates taxes, which helps many Americans. It creates a lot of jobs.” He noted that AI companies could also lead to higher profits for energy, construction and hardware technology firms. “Americans have a stake in American companies already, naturally, in a whole lot of different ways,” Huang said. Huang says national security needs to be a priority on AI The Trump administration has recently reversed course from using a light touch on regulating AI to taking a heavier hand. It placed export controls on the AI company Anthropic’s latest models, leading the company on Friday to shutter all public access to those models over security concerns. Trump, a Republican, also signed an order to have new AI models voluntarily screened by the government before their release. Huang said the government was properly focused on national security issues, but it was important to provide clear guidance. “National security should always be the top concern of all technologies,” Huang said. “But having said that, you know, you have to be very specific about the risk that you’re concerned about, before setting up policies for export controls.” During the Biden administration, Nvidia pushed back against export controls that were designed to restrict its ability to sell chips to China, rejecting the administration’s premise that a ban would preserve an American edge on AI. Huang had warned that the export controls might limit America’s ability to develop the world’s AI ecosystem, as China would respond with its own advanced chips. Huang says energy is key problem for America’s AI development Huang stressed that the U.S. is vulnerable because of its deficient energy supply. The data centers performing the computations used in AI are creating a huge demand for electricity, which could be a strain on the power grid. Some data centers will be constructed with their own electricity sources, but Huang said the U.S. is starting from a disadvantage on energy. And without more energy, it can be harder to play to American strengths in its AI infrastructure, models and computer chip development. “The United States is woefully behind in energy production,” Huang said. “We just suffocated energy production for too long.” Huang complimented Trump on his approach to generating more energy in the U.S.. The president has aggressively supported the use of oil, coal and natural gas, but he has scorned the use of solar and wind power. The Nvidia CEO was not commenting on Trump’s opposition to climate-friendlier energy sources. But the gap he identified goes to some of the fears that U.S. households have about AI increasing their utility bills. Huang was speaking Tuesday in Sherman, Texas, at an expansion of the Coherent factory to develop a laser for transmitting data among chips, which could cut power use by AI systems by up to 50%. Trump’s fondness for Huang started at a Mar-a-Lago dinner Trump, not known for technological expertise, quickly developed a friendship with Huang. The president has called him “smart” and “amazing,” insisting that Huang accompany him on foreign trips. Most recently, Trump had Air Force One pick up the leather-jacketed CEO in Alaska while en route to his state visit to China. Their relationship started last year with an invitation to dinner at Mar-a-Lago, Trump’s home and private club in Florida. Huang was in the area to receive the Edison Achievement Award for his AI work. “He says drop by for dinner, and so I did,” Huang said. He went with his wife, Lori. “He was incredibly engaging, incredibly charismatic, conversational, asked a lot of questions,” Huang recalled. “From the moment that I met him, the only thing that he’s ever talked to me about is creating more jobs, reindustrializing the United States, protecting national security, winning.” He added that Trump “calls me in the middle of the night and wants to talk about one of these topics.” But his proximity to Trump has also led to criticism from Democratic lawmakers. Sen. Elizabeth Warren, D-Mass., objected to Huang not testifying before a Senate committee even as “he has time to attend a $1 million-a-head dinner at Mar-a-Lago.” Huang said he wants the U.S. president and other officials — regardless of party — to succeed. “We could differ with politics, but we should want him to succeed,” he said. “Because when President Trump succeeds, our country succeeds.” —Josh Boak, Associated Press
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