President Trump Participates in a Meeting and Luncheon on Super Intelligence

Summary of the videoThe YouTube video (posted by the official White House channel on September 29, 2026) is a roughly 24-minute recording of President Donald Trump participating in a White House meeting and luncheon focused on “Super Intelligence” (Trump’s preferred term for artificial intelligence / advanced AI).Key context and contentThe event gathers top U.S. tech and AI industry leaders for discussions on the technology’s rapid progress, U.S. leadership (especially versus China), and related policy.

Trump has publicly pushed to rename “artificial intelligence” as “super intelligence,” arguing the word “artificial” makes it sound fake. Seating placed Trump between Nvidia CEO Jensen Huang and Elon Musk. Other notable attendees included Meta’s Mark Zuckerberg, Google’s Sundar Pichai, Amazon’s Jeff Bezos, Anthropic’s Dario Amodei, OpenAI’s Greg Brockman (Sam Altman was absent for OpenAI’s DevDay), Palantir’s Alex Karp, House Speaker Mike Johnson, Vice President JD Vance, and various Cabinet/administration officials. Apple was not represented.

The luncheon occurred the same day Trump unveiled America.gov, a new AI-powered federal website/chatbot intended to help Americans navigate government services. Trump’s broader messaging around the event (and consistent with recent statements) emphasizes maintaining America’s lead in the technology, rejecting efforts to “stifle” growth, and opposing close cooperation with China on AI governance/safety (on the grounds that it could help China catch up). This comes amid growing public warnings from some AI lab leaders about risks from increasingly powerful systems.

The video itself is official event coverage rather than a narrated documentary or interview; it shows Trump’s participation in the meeting and lunch. Full detailed transcripts of the private discussions are not publicly detailed in available coverage.

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40 years ago I was considered competent at office computer management...disk drives, defraging, Simms sticks, shell scripts, server mgmt and the like. As I said, some 40 years ago.

My current desktop PC ran out of HD space when it should have had some 200gb. I could find no reason for it. Using Copilot and a couple days work, ‘we’ finally IDed a rogue Windows program logging endless and mindless entries into a hidden log file. Another part of the problem was having no free space limited the ability to run utility tools to determine the problems and attempt a fix.

The AI would type a command, I’d paste it into an admin window to check this and that and narrow down the issue, repeated over & over. A secondary problem was the drive manufacturer taking a shortcut when configuring drives and partitions, yielding inaccurate drive info via queries. This issue was also corrected.

My out-of-date expertise was of little help on today’s PC environment, so the AI’s help was invaluable. Though - and here’s where the AI limitation comes in - I swear we did the same things over and over but from different directions. A better reasoning AI entity I suspect could have located the root causes much faster.

Anyway, my box works fine now and without an expensive service call or home tech visit.

40 years ago, I had returned to college after 8 years working in a family lumber business. I was an early computer enthusiast, so I was hired as a computer lab assistant at a community college. This was pre-Windows, so I am very familiar with the early PC techniques as well.

Artificial Intelligence Apps have been very helpful to me setting up computers as servers for various uses including running LLMs locally and on remote VMs using various iterations of Linux with Ollama and ComfyUI. This is something that I initially had little experience with previously. But using AI apps I have been able to get these to work often after they first led me down numerous dead ends. In most cases watching how-to-videos on YouTube or following a how-to-book would have been quicker, but the AI apps did work eventually.

I have never had anyone work on any of my computers, stereos, or TVs over the last 50 years, or my cars, trucks, motorcycles, tractors, roofing, plumbing, or wiring for that matter. I have always been a very committed do-it-yourselfer. AI can be an aid in all of these areas but books and videos typically are less time consuming and don’t lead you down as many dead ends.

I would never argue that AI tools are not useful and I enjoy the conversations that I end up with them, but the sooner people realize that they are not actually conversing with an entity with actual reasoning the better. What we have is a tool that has access to an incredible amount of information available, but not necessarily sharing information to get things done in the best or most efficient manner and often it will present a complete hallucination as fact.

I would add that I enjoy experimenting by running LLMs locally and using remote Virtual Machines. But at this point I would have to admit however that at this point this is not a truly worthwhile endeavor. This is because the “frontier models” used by major AI providers have been perfected and “fine tuning” by using almost unimaginable amounts of processing power to train them.

But it is also because the major AI providers are still massively subsidizing their products to businesses, governments, and people. I have used these products to the point that it would not be possible to calculate what percentages the paltry sums that I have paid for subscriptions and billing through APIs covered the actual expense that the large providers incurred because of my activities.

I initially assumed that it was the large companies and governments that use AI to keep track of the activities of massive numbers of people that took up the vast majority of compute being used by AI providers. But when you look at the numbers as closely as possible as to how much computing power is being used locally and remotely by virtual machines to do similar work, it is staggering. The experience that I am referring to is generating images and video.

I paid a couple hundred dollars to a company called LTX Studio, https://app.ltx.studio/ to restore and animate old family pictures. The results were quite amazing, but I thought that there must be a cheaper way to do this. So, this motivated me to spend $Thousands on hardware, remote VMs and subscriptions and APIs from the major AI providers trying to duplicate what LTX studios did.

I did have some success, but by and large the only viable alternative for this type of activity were subscriptions to the major AI providers. You can get results that would and did cost hundreds of dollars... from a heavily discounted subscription to one of the major AI providers. Even though I spent a substantial amount on local hardware that I intended to use for this purpose, it just wasn’t enough.

Currently, the hardware available that comes close to being viable for this purpose is from Apple and costs around $20,000. There are specific purposes and reasons why this type of expenditure is worth it for some businesses, but for a hobbyist such as me it would be impossible to justify at this point in time.

I don’t do much with AI excepting the PC issues I mentioned and a laptop using battery overnight when it is supposed to be hibernating. Not some battery, all of it. I suggested to turn off wifi overnight; the AI took that farther with a script that shuts down most processes before I close the lid and my battery now maintains. Windows was the culprit again, with complex crap waking the sleeping device every few minutes or seconds for no good reason.

I discuss & compare w/ the AI such as SpaceX, Blue Origin, NASA programs, goals and timelines and it gives me lots of particulars that I trust, for the most part, if it covers reported facts. Or any random thing about a movie, current events and so forth. It makes stupid mistakes rather frequently; it is a reference source but...trust but verify.

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