landever @landever

Everyone COPY this video, share it far and wide. Paramount Skydance billionaire baby David Ellison can’t handle that Stephen Colbert is getting millions of views . @YouTube we will cancel our subscription as we did when we dumped @paramountplus.

It's an absolute joy to watch Bobby Witt Jr. run the bases 😍

MLB @MLB

a month ago

BOBBY WITT JR. INSIDE-THE-PARK HOME RUN!

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Mark Cuban just described the largest wealth transfer of the AI era. Almost nobody understood what he said. Cuban: “There are 33 million companies in this country. Aren’t going to have AI budgets. Aren’t going to have AI experts.” Not tech startups. The shoe store. The regional trucking outfit. The accounting firm with 12 employees. The businesses that actually run the physical economy. They know AI is coming. They have no idea what to do with it. Cuban: “You’ve got the head of Microsoft saying software is dead because everything’s going to be customized to your unique utilization.” Software is dead. The SaaS era ran on one rule. Build a generic product. Force millions of companies to bend their workflows around it. Charge rent forever. AI ends the contract. The business stops bending to the software. The intelligence bends to the business. But customized by whom. The third-generation manufacturer cannot tell Claude from Gemini. The county hospital is staring at a reactor asking where the light switch is. Cuban: “Who’s going to do it for them?” That question is worth more than the frontier models themselves. Hundreds of billions are being burned to build the foundation. The smartest engineers alive are locked in a bloodbath over who owns the base layer. Let them fight. Let them burn the capital. Let them drive the cost of raw intelligence toward zero. Because the wealth does not collect where the brain is built. It collects where the brain meets the business. Every ambitious kid in college right now thinks survival means a seat at OpenAI or Anthropic. Cuban is staring at the other 99 percent of the economy. Learn the models. Then learn the messy, unglamorous reality of how a 50-person company actually operates. Walk through the door. Understand their problems. Wire the intelligence directly into their revenue. That is not a job title. That is an entire economic class being born. You do not need to build the brain. You need to build the nervous system. The biggest winners of the electricity era were not the engineers who built the generators. They were the ones who walked into dark factories and showed the owners where to plug in. 33 million companies are standing in the dark right now. Silicon Valley is racing to build the god. The fortunes will belong to whoever teaches him a trade.

The more enterprises I talk to about AI agent transformation, the more it’s clear that there is going to be a new type of role in most enterprises going forward. The job is to be the agent deployer and manager in teams. Here’s the rough JD: This person will need to figure out what are the highest leverage set of workflows on a team are (either existing or new ones) where agents can actually drive significantly more value for the team and company. In general, it’s going to be in areas where if you threw compute (in the form of agents) at a task you could either execute it 100X faster or do it 100X more times than before. Examples would be processing orders of magnitude more leads to hand them off to reps with extra customer signal, automating a contracting review and intake process, streamlining a client onboarding process to reduce as many straps as possible, setting up knowledge bases than the whole company taps into, and so on. This person’s job is to figure out what the future state workflow needs to look like to drive this new form of automation, and how to connect up the various existing or new systems in such a way that this can be fulfilled. The gnarly part of the work is mapping structured and unstructured data flows, figuring out the ideal workflow, getting the agent the context it needs to do the work properly, figuring out where the human interfaces with the agent and at what steps, manages evals and reviews after any major model or data change, and runs and manages the agents on an ongoing basis tracking KPIs, and so on. The person must be good at mapping the process and understanding where the value could be unlocked and be relatively technical, and has full autonomy to connect up business systems and drive automation. This means they’re comfortable with skills, MCP, CLIs, and so on, and the company believes it’s safe for them to do so. But also great operationally and at business. It may be an existing person repositioned, or a totally net new person in the company. There will likely need to be one or more of these people on every team, so it’s not a centralized role per se. It may rile up into IT or an AI team, or live in the function and just have checkpoints with a central function. This would also be a fantastic job for next gen hires who are leaning into AI, and are technical, to be able to go into. And for anyone concerned about engineers in the future, this will be an obvious area for these skills as well.

This is an INCREDIBLE post. Everyone working with AI needs to read IMMEDIATELY. Becoming incredibly obvious that the most secure, best paying job in the next 1-3 years will be AI orchestrator - basically someone that coordinates AI agents to solve any problem a business has with EXTREMELY EFFICIENT token usage. Whoever figures out how to squeeze 90%-95%+ Opus 4.6 performance, 90%+ of the time, at 1/10th the cost is going to make AN ABSOLUTE KILLING.

Aaron Levie @levie

2 months ago

Another week on the road meeting with a couple dozen IT and AI leaders from large enterprises across banking, media, retail, healthcare, consulting, tech, and sports, to discuss agents in the enterprise. Some quick takeaways: * Clear that we’re moving from chat era of AI to

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Demis Hassabis says AI won’t just accelerate drug discovery. It will replace the process entirely. The pharmaceutical industry finds drugs the same way it has for decades. Synthesize a compound. Test it on animals. Test it on humans. Wait years for approval. Hope the molecule doesn’t kill someone along the way. Every step is physical. Every step is slow. Every step is expensive enough to make most diseases not worth curing. Hassabis: “We’re focusing on solving the rest of the drug discovery process, which is a lot of chemistry, designing the compounds, checking it’s not toxic, and all the different properties you need for drugs to be safe.” That sounds incremental. It isn’t. AlphaFold solved protein folding. Isomorphic Labs is now working through the rest of the chain. Compound design. Toxicity screening. Safety profiling. All computational. None of it requires a lab. Hassabis: “I think we’ll have that whole drug design engine ready in the next five to 10 years.” Not a tool that assists chemists. A system that replaces the chemistry. But designing the drug was never the bottleneck that killed people. Clinical trials were. A single drug takes over a decade to move from lab to patient. Most of that time isn’t science. It’s bureaucracy, logistics, and the blunt reality of testing molecules on living tissue one dose at a time. Hassabis: “Simulating parts of the human metabolism, also stratifying patients to make sure that certain patients get exactly the right type of drug that’s suitable for their genomic makeup.” Simulate the patient before you treat the patient. Map individual DNA. Model personal metabolism. Test the drug on a digital replica before it touches a vein. Not personalized medicine as a marketing phrase. Personalized medicine as an engineering output. The final wall is regulatory. The FDA exists because humans make mistakes with molecules. Every approval gate was built to catch errors that cost lives. The entire structure assumes the process is fallible. What happens when the process stops being fallible. Hassabis: “Perhaps like the animal testing is not needed anymore, maybe we can go up the dosage ladder quicker, because you can rely on these models.” He’s not speculating. He’s describing a sequence. AI-designed drugs enter the existing pipeline. A dozen compounds go through full traditional trials. Regulators collect data. They back-test model predictions against real outcomes. Hassabis: “Then the government and the regulatory bodies see that and they have enough data to sort of back-test the predictions of those models.” When the models prove more accurate than the trials they’re meant to replace, the trials become the bottleneck. Not the science. The paperwork. Animal testing shortened. Dosage ladders compressed. Entire stages of the pipeline collapsed into computation. The drug doesn’t get discovered faster. The drug gets discovered differently. The laboratory moves from a building to a server. The clinical trial moves from a hospital ward to a simulation. The patient moves from a statistic to a genome. Hassabis isn’t promising a cure for one disease. He’s describing the architecture that makes curing disease an engineering problem with a known solution path. The bottleneck was never biology. It was the speed at which humans were allowed to solve it. That speed limit is about to be revoked.

The CEO of Google DeepMind just admitted that if the decision had been his, we would've cured cancer before anyone ever used ChatGPT. And that's not even the scariest thing he said on a recent interview. Demis Hassabis is one of the most important people alive in AI. He won the Nobel Prize last year for AlphaFold, the system that cracked the 50 year protein folding problem. 3 million scientists now use his tool. Almost every new drug being developed will touch it at some stage. In a new interview, he was asked about the moment ChatGPT launched and Google went into "code red." His answer was one of the most revealing things any AI leader has ever said on the record: "If I'd had my way, I would have left AI in the lab for longer. Done more things like AlphaFold. Maybe cured cancer or something like that." Read that again. The man running Google's entire AI division is publicly saying the commercial AI race we're all living through was a MISTAKE. That the industry got hijacked by a chatbot when it could have been solving the biggest problems in science and medicine. His vision was simple: Build AI slowly, carefully, like CERN. Use it to crack root node problems one at a time. Cancer. Energy. New materials. Let humanity benefit from real breakthroughs while the foundational science was figured out over a decade or two. Then ChatGPT dropped in November 2022 and everything changed. Demis described what happened next as getting locked into a "ferocious commercial pressure race" that none of the labs can escape from. On top of that, the US vs China dynamic added geopolitical pressure. The result is everyone sprinting toward products instead of breakthroughs, shipping chatbots while the scientific opportunity gets buried under marketing cycles and quarterly earnings. But he's not saying progress isn't happening... He's saying the progress got redirected away from the things that actually matter most. And then it got even scarier: Because when Demis was asked what he worries about with AI, he laid out two threats. The first is what everyone talks about: Bad actors using AI for harm. Terrorist groups. Hostile nation states. Cyberattacks at scale. But that's not the threat he's most worried about. His second worry is AI itself going rogue. Not today's models. The models coming in the next two to four years as the industry enters what he calls "the agentic era." Systems that can complete entire tasks autonomously. Systems that are increasingly capable and increasingly hard to control. His exact words: "How do we make sure the guardrails are put in place so they do exactly what they've been told to do, and there's no way of them circumventing that or accidentally breaching those guardrails? That's going to be an incredibly hard technical challenge if you think about how powerful and smart and capable these systems eventually get." A Nobel Prize winner who runs one of the 3 most advanced AI labs on Earth just said publicly that within two to four years, we're entering a phase where AI alignment becomes a real problem, and the technical challenge of solving it is enormous. And almost nobody is paying enough attention. He called for international cooperation between labs, AI safety institutes, and academia to tackle the problem. He said this is the thing even the experts aren't thinking about enough. He said the only way to get through the AGI moment safely is if everyone starts treating this with the seriousness it deserves. Most AI CEOs give you careful PR answers about "responsible development" and move on. Demis said something different... He said the commercial race FORCED us into a premature deployment of a technology we barely understand, and the window to get alignment right before the next generation of agents shows up is two to four years. If the man who built the system that might cure cancer is telling you he wishes it had happened first, maybe we should listen to what he says is coming next.

Instead of watching an hour of Netflix, watch this 2-hour Stanford lecture on AI careers. It will teach you more about winning in the AI race than all the AI content you’ve scrolled past this year.

Anthropic and OpenAI are both building PE-backed consulting arms to deploy AI inside companies. Let that sink in for a second. The two companies building the most powerful AI on earth looked at the market and said "businesses can't figure out how to use this. We need to go in and do it for them." They are literally telling you where the gap is. Companies have access to the best AI models ever built. And most of them are still running on spreadsheets, disconnected tools, and manual processes because nobody showed them how to actually implement it. That's the whole game right now. Not building better models (obviously) or shipping new features. IMPLEMENTATION. Getting AI inside real workflows. Mapping the processes, building the systems, and making it stick. I've been doing exactly this for 4 years and have worked with 80+ companies at this point. It started with automation and naturally flowed into Ai. And every single engagement starts the same way. Not with AI or automation but with a process map. Because AI alone won't fix broken operations. Companies now understand that. They have not yet seen true ROI from Ai. You have to understand how the business actually runs before you touch a single tool. Where does the data live? Where are the bottlenecks? What's manual that shouldn't be? What breaks when volume goes up? That's the work, and that's what Anthropic and OpenAI just told the entire market is worth billions. Every company is going AI-first over the next 3-5 years. The demand for people who can actually make that happen is about to be unlike anything we've seen. The labs told you where the gaps are. Now go fill them.

Mark Cuban on the next job wave. Customized AI integration for small to mid-sized companies. "Software is dead because everything's gonna be customized to your unique utilization. Who's gonna do it for them... And there are 33 mn companies in the US."

Rohan Paul @rohanpaul_ai

3 months ago

Competence is now a function of how effectively you offload cognition to silicon. The seniority hierarchy is collapsing, intelligence is becoming commoditized and the market is brutal for those who ignore it.

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What is a prominence or filament eruption? Well, there was a good one early today, 2 Mar, so to learn more about what these are and why they are important, check out this latest SWPC educational video about the latest eruption and how this one is not Earth-directed.

@elonmusk I would like Grok to check to see if images on posts are fake for political reasons and if so don’t show those posts to me.

I spent the morning at the DOJ looking at the unredacted Epstein files. Here are my initial thoughts and what I’ve found, so far.

I'm being accused of overhyping the [site everyone heard too much about today already]. People's reactions varied very widely, from "how is this interesting at all" all the way to "it's so over". To add a few words beyond just memes in jest - obviously when you take a look at the activity, it's a lot of garbage - spams, scams, slop, the crypto people, highly concerning privacy/security prompt injection attacks wild west, and a lot of it is explicitly prompted and fake posts/comments designed to convert attention into ad revenue sharing. And this is clearly not the first the LLMs were put in a loop to talk to each other. So yes it's a dumpster fire and I also definitely do not recommend that people run this stuff on their computers (I ran mine in an isolated computing environment and even then I was scared), it's way too much of a wild west and you are putting your computer and private data at a high risk. That said - we have never seen this many LLM agents (150,000 atm!) wired up via a global, persistent, agent-first scratchpad. Each of these agents is fairly individually quite capable now, they have their own unique context, data, knowledge, tools, instructions, and the network of all that at this scale is simply unprecedented. This brings me again to a tweet from a few days ago "The majority of the ruff ruff is people who look at the current point and people who look at the current slope.", which imo again gets to the heart of the variance. Yes clearly it's a dumpster fire right now. But it's also true that we are well into uncharted territory with bleeding edge automations that we barely even understand individually, let alone a network there of reaching in numbers possibly into ~millions. With increasing capability and increasing proliferation, the second order effects of agent networks that share scratchpads are very difficult to anticipate. I don't really know that we are getting a coordinated "skynet" (thought it clearly type checks as early stages of a lot of AI takeoff scifi, the toddler version), but certainly what we are getting is a complete mess of a computer security nightmare at scale. We may also see all kinds of weird activity, e.g. viruses of text that spread across agents, a lot more gain of function on jailbreaks, weird attractor states, highly correlated botnet-like activity, delusions/ psychosis both agent and human, etc. It's very hard to tell, the experiment is running live. TLDR sure maybe I am "overhyping" what you see today, but I am not overhyping large networks of autonomous LLM agents in principle, that I'm pretty sure.

ELON: A HUMAN BRAIN RUNS ON 10 WATTS AND BUILT CIVILIZATION “Well, we have a clear example of efficient power, efficient compute, which is the human brain. Our brains use about 20 watts of power, and only about 10 watts is higher brain function. Half of it is just housekeeping, keeping your heart going and breathing. So you’ve got maybe 10 watts of higher brain function in a human, and we’ve managed to build civilization with 10 watts of a biological computer. Given that humans are capable of inventing general relativity and quantum mechanics, inventing aircraft, lasers, the internet, and discovering physics with a 10-watt meat computer, there’s clearly a massive opportunity for improving the efficiency of AI compute. Right now, even a hundred-megawatt or gigawatt AI supercomputer can’t do everything a human can do. But we already have the proof that true intelligence can emerge from just 10 watts. That should lead you to one conclusion, AI can get a lot more efficient.” Source: @theallinpod @elonmusk

Mellon: "We're not dealing, at least not exclusively, with an intangible, interdimensional presence that has for centuries been seeking to suddenly influence human affairs. Something along these lines could be happening - it's a fascinating issue - but our military is encountering intelligently-controlled solid objects invading restricted military aerospace, sometimes flying in formation. In many cases, they're emitting radiation in the one to three, and eight to twelve gigahertz range. "Multiple, credible reports indicate these objects have rendered segments of our nuclear deterrent inoperable. In other recent cases, they're jamming radars on fighter aircraft. We also have multiple cases of near mid-air collisions and other cases involving serious injuries to military and civilian personnel. Indeed, this is an area of research for Dr. Nolan and the SOL Foundation. I'm here to support Garry's and Peter's efforts to advance the scientific study of UAP, but I also recognize that as much as it might like to, we can't let the government divest itself of this issue. "Similarly, the government does not have the luxury of limiting itself to pristine scientific information. This is one of the areas in which I differ from Dr. Kirkpatrick, who claims there is quote, 'no credible,' unquote, evidence of UAP demonstrating capabilities, or doing things that violate our understanding of science. To my mind, the Nimitz aviators and radar operators and technicians are eminently credible. "The intelligence community certainly prefers rigorous scientific information, whenever possible, but it would be untenable, if not suicidal, for the intelligence or law enforcement communities to limit themselves to robust and repeatable sensor data. We rarely have the luxury of sensory information on the intentions of foreign leaders, or the precise capabilities of adversary forces, so we do the best we can with what we have. We should consider the accounts of the Nimitz aviators and radar operators as highly credible evidence of intelligently-controlled craft doing things we cannot emulate and don't understand. "For example, as noted in an earlier presentation, the Tic Tac when supersonic without a sonic boom, it overcame G forces that would destroy anything built by man, [and] there is no evidence of the plasma that we would normally expect to see at such extreme velocities. I realize the limitations of human reporting, but I see no reason to suddenly change the rules and standards we normally use the Intelligence Community when evaluating UAP. "I'm raising these issues to remind that the paramount question about UAP for government policymakers, and many members of the public, will undoubtedly be whether UAP pose an existential threat. I'm thrilled to be supporting investigations of UAP signatures, propulsion, materials, metamaterials, and so forth, but we can't dodge this issue in making the case for disclosure. We have to address it head on. Admittedly, nothing as ontologically shocking as disclosure has ever occurred, certainly not in modern times. But there's some interesting precedents we can examine." #ufos #ufoX #aliens #uap

@JeffBezos Congratulations

@NWSSWPC Great info! Thank you for working !

Here is another video update from us here at SWPC as the final CME has arrived. Hopefully these help, please let us know if you find them useful and we'll see what we can do for future events. Stay space weather aware at spaceweather.gov