热门科技推文 - 2026年3月24日

2026年3月24日科技每日简报

Today's top tech conversations are led by @kimmonismus, whose post about 'OpenAI is offering private equ...' garnered the highest engagement. Key themes trending across the top stories include https, people, model, chips, personal. The community is actively discussing recent developments in AI, engineering practices, and startup strategies.

1. kimmonismus (Group Score: 218.6 | Individual: 35.0)

Cluster: 9 tweets | Engagement: 126 (Avg: 304) | Type: Tech

OpenAI is offering private equity firms a standout 17.5% guaranteed return plus early access to new AI models to win enterprise partnerships.

I digged a bit into this (deleted last post).

This kind of incentive is unusual but strategic in a fast-moving, high-stakes market. OpenAI is essentially using strong financial guarantees (17.5% return) and perks like early model access to lock in enterprise customers quickly, where long-term contracts and integration create huge switching costs.

It does signal pressure, though: the race with Anthropic and others is intense, and securing distribution via private equity portfolios could decide who dominates enterprise AI. It's less "panic" and more high-confidence land grab with expensive incentives.\n\nQT @AndrewCurran_: OpenAl is offering private-equity firms a guaranteed minimum return of 17.5%, as well as early access to models not yet in public release. https://t.co/pms8AOR4GK

See 8 related tweets

@aakashgupta: OpenAI is paying 17.5% per year to create lock-in across entire industries before Anthropic can get ...
@cryptopunk7213: holy sh*t lol

OpenAI is guaranteeing a "minimum return of 17.5%" to private equity firms that roll...

@chatgpt21: OpenAI is bypassing traditional sales and going straight to Private Equity buyout giants to secure b...
@Cointelegraph: ⚡️ JUST IN: OpenAI is reportedly offering private equity firms a 17.5% guaranteed return and early a...
@minchoi: 🚨OpenAI is reportedly offering private-equity firms a guaranteed 17.5% return to raise fresh capital...

2. rohanpaul_ai (Group Score: 163.6 | Individual: 38.2)

Cluster: 6 tweets | Engagement: 292 (Avg: 93) | Type: Tech

RT @rohanpaul_ai: Elon Musk finally announced the most ambitious manufacturing project since the Manhattan Project.

A $20B Austin chip fab meant to supply the AI hardware for Tesla, SpaceX, and xAI at enormous scale.

80% of chips go to space for giant solar-powered AI data centers (launched by Starships). 20% stay on Earth for Optimus robots, robotaxis, and self-driving. Production starts 2027.

Musk estimated the Terafab would aim to initially produce 100,000 silicon wafers a month and could eventually grow to 1 million.
Target output: over 1 terawatt (1TW) of compute per year
Combines logic chips + memory + advanced packaging in one fab
Vertically integrated with recursive self-improvement
Chips for FSD, Optimus, Grok, Dojo and Starlink
80% powers solar-powered orbital AI data centers
AI5: edge/inference chips for FSD and robotaxis
AI6: next-gen chips powering Optimus robots
D3: space-optimized chip variant
D3 designed to run hotter to minimize radiator mass in orbit
Showed 100kW AI Starlink Mini satellite prototype
Future AI Starlink satellites scale to megawatt range
Optimus target scale: 1-10 billion units per year
Optimus projected compute need: 100-200 GW
Targeting 2nm process technology
Elon Musk quote: "the next step towards becoming a galactic civilization"

The Terafab idea is vertical integration at the chip level, with an Austin site meant to design, test, package, and eventually manufacture chips fast enough that product plans are not stuck behind supplier timelines.

See 5 related tweets

@cryptopunk7213: elon musk's terafab is so ambitious it should fucking scare you, these numbers blow my mind:
tera...
@WesRoth: Elon Musk has officially announced "Terafab," a profoundly ambitious $20 billion to$ 25 billion semi...
@MarioNawfal: 🇺🇸 Elon says he’s done waiting on the chip industry

Tesla and SpaceX are planning to build their ow...

@Reuters: SpaceX and Tesla will build two advanced chip factories at a sprawling facility in Austin, Texas, on...
@ReutersBiz: WATCH: SpaceX and Tesla will build two advanced chip factories at a sprawling facility in Austin, Te...

3. awnihannun (Group Score: 136.7 | Individual: 33.3)

Cluster: 7 tweets | Engagement: 3496 (Avg: 597) | Type: Tech

RT @claudeai: You can now enable Claude to use your computer to complete tasks.

It opens your apps, navigates your browser, fills in spreadsheets—anything you'd do sitting at your desk.

Research preview in Claude Cowork and Claude Code, macOS only. https://t.co/sVymgmtEMI

See 6 related tweets

@ns123abc: Anthropic just dropped Claude Remote Worker

ITS HAPPENING https://t.co/zBYqGTWCvA\n\nQT @claudeai: ...

@adocomplete: Now this is next level!\n\nQT @claudeai: You can now enable Claude to use your computer to complete ...
@minchoi: Claude just got INSANE upgrade...

Claude can now use your computer to do work for you.

Apps, brows...

@Austen: OpenClaw one feature at a time\n\nQT @claudeai: You can now enable Claude to use your computer to co...
@chrisalbon: Claudebot lives!\n\nQT @claudeai: You can now enable Claude to use your computer to complete tasks. ...

4. nvidia (Group Score: 118.2 | Individual: 33.3)

Cluster: 5 tweets | Engagement: 589 (Avg: 189) | Type: Tech

"Every carpenter, every accountant, every student, will use AI to build.”

Jensen Huang on how AI expands human potential, reshapes work, and defines the next era of computing.

New episode with @lexfridman out now.\n\nQT @lexfridman: Here's my conversation with Jensen Huang, CEO of NVIDIA, the most valuable & one of the most influential companies in the history of human civilization. It is the engine powering the AI revolution.

This was a fascinating & inspiring conversation, in parts super-technical on engineering of every part of the AI stack, memory, power, supply chain (TSMC, ASML, etc), in parts about leadership & psychology, and in parts personal & philosophical about life, consciousness, mortality, and human nature.

It's here on X in full and is up everywhere else (see comment).

Timestamps: 0:00 - Introduction 0:33 - Extreme co-design and rack-scale engineering 3:18 - How Jensen runs NVIDIA 22:40 - AI scaling laws 37:40 - Biggest blockers to AI scaling laws 39:23 - Supply chain 41:18 - Memory 47:24 - Power 52:43 - Elon and Colossus 56:11 - Jensen's approach to engineering and leadership 1:01:37 - China 1:09:50 - TSMC and Taiwan 1:15:04 - NVIDIA's moat 1:20:41 - AI data centers in space 1:24:30 - Will NVIDIA be worth $10 trillion? 1:34:39 - Leadership under pressure 1:48:25 - Video games 1:55:16 - AGI timeline 1:57:29 - Future of programming 2:11:01 - Consciousness 2:17:22 - Mortality

See 4 related tweets

@nvidianewsroom: Just dropped: NVIDIA CEO Jensen Huang joins @lexfridman.

On the next era of AI, from extreme co-des...

@BrianRoemmele: This is an absolute must listen delivered by the amazing @lexfridman!

One of the best and most insp...

@NVIDIAAIDev: "We want AI to diffuse into every industry and every country, every researcher, every student. And i...
@theinformation: Nvidia CEO Jensen Huang envisions AI agents as integral to both digital and physical AI, extending f...

5. dps (Group Score: 102.3 | Individual: 40.1)

Cluster: 3 tweets | Engagement: 578 (Avg: 209) | Type: Tech

Excited to announce that @hbarra , @alcor and I are joining Meta Superintelligence Labs with the entire @Dreamer team today.

The last few months have been extraordinary: we built Dreamer, put the beta in the world just a month ago, and saw magic come to life for real people. Since then, thousands of people have used Dreamer to build personal, intelligent software with our Sidekick in the world’s newest and most popular programming language: English!

They're building and sharing agents to manage email, calendar, and to-do’s, create learning tools for their kids, learn new languages, plan trips with friends, become better cooks, help them with work, achieve their health goals, or simply to creatively express themselves—all sorts of surprising and uniquely personal needs. These are agents as unique as the people building them, because they're built exactly the way each person wants them to be. We’ve captured some of our favorites at https://t.co/G6nrHTsN5i.

What matters most here isn’t the early momentum; it’s what Dreamer has enabled people to do. People are building things they’ve wanted for years. They’re solving real, important problems no traditional software company would ever prioritize, because they’re too niche, too bespoke, too personal. What company would ever build for an “n of 1”?

Our bet from the beginning has been that software should be personal, malleable, and shaped by the person using it. The constraint was never people’s imagination. It was the fact that building software is out of reach for most people. This early chapter gives us conviction that the idea resonates, the need is real, and the moment is now.

@alexandr_wang was helpful to us from the very beginning, and when we showed Dreamer to Mark Zuckerberg and @natfriedman earlier this year, it was clear right away that we share the same vision of the future: one where billions of people have the power to create software that makes their lives better. We’re thrilled to accelerate this mission by joining Meta Superintelligence Labs and licensing our technology to Meta. Read more at https://t.co/KvmdoPJVbU.

Deeply grateful to our investors @jillchase124 and @ninaachadjian for supporting our vision for a more personal, creative, and intelligent future for software. Thank you for the trust, the thought partnership, and for being in our corner at every step.

To everyone in our community who built with us: thank you. You've taught us what's possible, and you're the proof this works. We're so grateful, and we're just getting started!

See 2 related tweets

@testingcatalog: BREAKING 🚨: Dreamer team is joining Meta MSL and licensing Dreamer’s technology to Meta.

Dreamer 👀...

@swyx: the pattern is clear:

come on @latentspacepod /@aiDotEngineer , get billions thrown at you by @alex...

6. OpenAI (Group Score: 94.3 | Individual: 33.9)

Cluster: 3 tweets | Engagement: 3110 (Avg: 2309) | Type: Tech

It’s now easier to find, reuse, and build on the files you upload and create in ChatGPT.

You can quickly reference files in a chat using recent files in the toolbar, ask ChatGPT about something you’ve uploaded, or browse your files in the new Library tab in the web sidebar.

Rolling out globally for Plus, Pro, and Business users, and coming soon to users in the EEA, Switzerland, and the UK.

See 2 related tweets

@testingcatalog: OpenAI rolled out Library feature for ChatGPT, allowing users to easily browse and reuse earlier upl...
@ns123abc: GROK FILES https://t.co/iR43S20m36\n\nQT @OpenAI: It’s now easier to find, reuse, and build on the f...

7. MiniMax_AI (Group Score: 92.3 | Individual: 32.0)

Cluster: 3 tweets | Engagement: 343 (Avg: 294) | Type: Tech

your favorite model's favorite model\n\nQT @openclaw: OpenClaw 2026.3.22 🦞

🏪 ClawHub plugin marketplace 🤖 MiniMax M2.7, GPT-5.4-mini/nano + per-agent reasoning 💬 /btw side questions 🏖️ OpenShell + SSH sandboxes 🌐 Exa, Tavily, Firecrawl search

This release is so big it needs its own table of contents. https://t.co/XvRbXEduGC

See 2 related tweets

@OpenClaw: OpenClaw 2026.3.22 🦞

🏪 ClawHub plugin marketplace 🤖 MiniMax M2.7, GPT-5.4-mini/nano + per-agent rea...

@heyshrutimishra: OpenClaw JUST dropped a new release after 9 days

This feels like forever 👇\n\nQT @openclaw: OpenCla...

8. TriatomicCap (Group Score: 76.9 | Individual: 30.8)

Cluster: 3 tweets | Engagement: 6 (Avg: 31) | Type: Tech

Inference is the decade's defining infrastructure challenge. @GimletLabs is building the heterogeneous orchestration layer the industry has been waiting for, delivering 10x efficiency gains for agentic workloads without application changes.

Huge congrats to @zainasgar, @nserrino & the Gimlet Team on their Series A!\n\nQT @gimletlabs: We're thrilled to announce Gimlet Labs' Series A funding, led by @MenloVentures, and joined by @EclipseVentures, Factory, Prosperity7, and @TriatomicCap.

9. jerryjliu0 (Group Score: 76.3 | Individual: 34.8)

Cluster: 3 tweets | Engagement: 10 (Avg: 86) | Type: Tech

We're excited to collaborate with @googledevs on building an agentic workflow over complex financial documents - using LlamaParse and Gemini 3.1 Pro

Brokerage statements have complex layouts, dense tables, and oftentimes visual elements like charts. Our multi-step agentic workflow does the following:

Ingest PDF into LlamaParse
Extract text and tables
Generate human-readable summary using Gemini

Shoutout to @Vish_ow and @itsclelia 🙌

Check it out: https://t.co/6dd7mKNkyk\n\nQT @googledevs: Improve document parsing accuracy by 15% for financial PDFs.

Use LlamaParse and Gemini 3.1 Pro to extract high-quality data from unstructured brokerage statements and complex tables. 📈 Precise reasoning 📂 Structured PDF data ⚡️ Event-driven scaling

Dive into the code on GitHub → https://t.co/yi7KxVzNPY

See 2 related tweets

@jerryjliu0: RT @llama_index: We’ve published a new blog with @googledevs on how to build a smart financial assis...
@itsclelia: RT @jerryjliu0: We're excited to collaborate with @googledevs on building an agentic workflow over c...

10. chris_j_paxton (Group Score: 75.8 | Individual: 33.0)

Cluster: 3 tweets | Engagement: 138 (Avg: 82) | Type: Tech

Cool work but I feel like such an old man when people call an action-conditioned world model a jepa. Or even a world model. Its a learned dynamics model, isn't it??\n\nQT @lucasmaes_: JEPA are finally easy to train end-to-end without any tricks!

Excited to introduce LeWorldModel: a stable, end-to-end JEPA that learns world models directly from pixels, no heuristics.

15M params, 1 GPU, and full planning <1 second.

📑: https://t.co/cpTzgvbTS0 https://t.co/Z2De9ASzcW

See 2 related tweets

@chris_j_paxton: RT @lucasmaes_: JEPA are finally easy to train end-to-end without any tricks!

Excited to introduce ...

@RoundtableSpace: LeWorldModel learns world models directly from pixels - no tricks, no heuristics.
15M params, 1 G...

11. kimmonismus (Group Score: 75.0 | Individual: 35.4)

Cluster: 3 tweets | Engagement: 141 (Avg: 304) | Type: Tech

OpenAI has brought in former Meta executive Dave Dugan to lead global ad solutions, a big signal that the company is getting serious about building an advertising business around ChatGPT and other products.

Has any big AI company other than OpenAI hired ex-Meta-ad managers? Not a good sign tbh.\n\nQT @WSJ: Exclusive: OpenAI has hired Dave Dugan, a former top advertising executive at Meta Platforms, to lead ad sales as the AI startup works to strengthen its ties with major advertisers https://t.co/ruyRr5aQxu

See 2 related tweets

@WSJ: Exclusive: OpenAI has hired Dave Dugan, a former top advertising executive at Meta Platforms, to lea...
@FirstSquawk: OPENAI HAS HIRED DAVE DUGAN, A FORMER META PLATFORMS AD EXECUTIVE, TO LEAD AD SALES - WSJ...

12. CarinaLHong (Group Score: 71.3 | Individual: 36.8)

Cluster: 2 tweets | Engagement: 29 (Avg: 58) | Type: Tech

Love the taste\n\nQT @yusan_lin: Today @mirrormirror_ai is launching the marketplace where fashion models license their likeness and brands get stunning AI-generated imagery featuring real people. Commercially licensed, model-approved.

Try our platform: https://t.co/7u72P5xvmq

As a fashion model I used to spend hours on fashion photoshoot sets. I later did my PhD in CS and became a Research Scientist on AI for fashion. I can see clearly that AI image generation is replacing a large portion of my old job. But brands that use AI recklessly have already paid the price. It damages reputations and hurts the bottom line. Putting real people at the core of AI-generated imagery isn't just about avoiding backlash. It's better business. That's what Mirror Mirror AI is built for.

Right now, Mirror Mirror AI houses agency-signed models who have graced the covers of Vogue and Harper's Bazaar. You can digitally book them using our fashion-centric AI software, get your campaign done in hours instead of weeks, and never have to fly anyone in. You purchase a license for commercial use upon approval, and the models get paid.

Mirror Mirror AI is also opening a global call for independent models from anywhere in the world to apply to be featured on the platform. Work with fashion brands internationally, choose the projects you take on, and earn from your own likeness on your own terms. Selected models will be announced at an exclusive event in New York during @Techweek_ this June.

Apply for the open call: https://t.co/NutihoE9qO

A huge thank you to our incredible team for pouring their hearts into this launch, and to a16z @speedrun for believing in our vision from the start. We're just getting started.

See 1 related tweets

@brianzhan1: AI imagery without real people at the center is a liability. This is the smarter approach for brands...

13. svpino (Group Score: 71.2 | Individual: 36.4)

Cluster: 2 tweets | Engagement: 123 (Avg: 360) | Type: Tech

We've 10x'd the speed of writing code, but we are still in the Stone Age with everything that happens after the code is written.

This is a great idea:

Agents that simulate how your code will run in production and learn from every incident so it doesn’t happen again.\n\nQT @akoratana: Introducing: PlayerZero

The world's first Engineering World Model that puts debugging, fixing, and testing your code on autopilot.

We've raised $20M from Foundation Capital, @matei_zaharia (Databricks), @pbailis (Workday), @rauchg (Vercel), @zoink (Figma), @drewhouston (Dropbox), and more

PlayerZero frees up 30% of your engineering bandwidth by: 1.⁠ ⁠Finding the root cause for bugs & incidents in minutes that engineering teams take days to identify. 2.⁠ ⁠Predicting in minutes, edge case issues that a 300-person QA team would take weeks to find.

Here's why this matters:

No one in your org has a complete picture of how your production software actually behaves.

Support sees tickets. SRE sees infra. Dev sees code. Each team builds their own fragmented view - and none of these systems talk to each other. When something breaks, everyone scrambles to stitch the picture together by hand.

PlayerZero connects all of it into a single context graph -

→ The Slack thread where your lead said "we went with X because Y fell apart in prod last time" → The PR review where an engineer explained the tradeoff → The lifetime history of your CI/CD pipeline, observability stack, incidents, and support tickets

So you can trace any problem to its root cause across every silo.

And it compounds. Every incident diagnosed teaches the model something new. The longer it runs, the deeper it understands - which code paths are high-risk, which configurations are fragile, which changes tend to break which customer flows.

So when you sit down to debug a live issue, you have your entire org's collective reasoning and production memory behind you - instantly.

Zuora, Georgia-Pacific, and Nylas have reduced resolution time by 90% and caught 95% of breaking changes and freeing an average of $30M in engineering bandwidth.

Our guarantee:

If we can't increase your engineering bandwidth by at least 20% within one week, we'll donate $10,000 to an open-source project of your choice.

Book a demo - https://t.co/dH1dulIwSS

See 1 related tweets

@alex_prompter: 90% reduction in resolution time. 95% of breaking changes caught. $30M in engineering bandwidth free...

14. MiniMax_AI (Group Score: 65.1 | Individual: 33.5)

Cluster: 2 tweets | Engagement: 187 (Avg: 294) | Type: Tech

Recap from #MiniMax AI Founder Day this weekend (3/21). 💫

Full house in SF with founders, engineers, and AI leaders packed the room. Conversations stayed high-signal all afternoon.

Cofounder keynote → M2.7 demos → a stacked founder panel.

Real builders in the room. Real conversations. This is the ecosystem we're building.

More to come from MiniMax in 2026! 🦾✨ #IntelligenceWithEveryone

+++

Thanks to everyone who joined us, and to our partners and speakers. @alexocheema (@exolabs) @RobRizk1 (@blackboxai) @tydsh (ex-@Meta FAIR) @grmcameron (@ArtificialAnlys) @yaboilyrical (@NousResearch) @steveshou (@duolingo)

See 1 related tweets

@yaboilyrical: thanks so much for having me here!!! was a fantastic time and we're really loving M2.7 <3\n\nQT @...

15. steph_palazzolo (Group Score: 63.5 | Individual: 32.1)

Cluster: 2 tweets | Engagement: 105 (Avg: 78) | Type: Tech

Engineers are notoriously fickle customers and don't have much loyalty to any one coding tool if a better one comes out.

In the latest example, hundreds of engineers at Notion are switching from Cursor to Anthropic's Claude Code and OpenAI's Codex.

https://t.co/vJpKqvVhDz

See 1 related tweets

@tunguz: And the worst thing that your company can do to tank your engineers morale is force all of them to u...

16. WesRoth (Group Score: 62.9 | Individual: 40.9)

Cluster: 2 tweets | Engagement: 271 (Avg: 65) | Type: Tech

Anthropic rolled out Scheduled Cloud Tasks for Claude Code, allowing developers to automate recurring agentic workflows that run entirely in the background.

You no longer need to keep your terminal, browser tab, or local machine running. Once a task is scheduled, Claude executes it autonomously via cloud infrastructure at the designated time.\n\nQT @noahzweben: You can now schedule recurring cloud-based tasks on Claude Code.

Set a repo (or repos), a schedule, and a prompt. Claude runs it via cloud infra on your schedule, so you don’t need to keep Claude Code running on your local machine. https://t.co/Vse4WfVnKC

See 1 related tweets

@lydiahallie: RT @noahzweben: Use /schedule to create recurring cloud-based jobs for Claude, directly from the ter...

17. victormustar (Group Score: 62.6 | Individual: 33.5)

Cluster: 2 tweets | Engagement: 83 (Avg: 135) | Type: Tech

Hugging Face V2 is going to be wild 👀\n\nQT @_lewtun: You can now pretrain LLMs entirely on the HF Hub 💥

Last week, @OpenAI launched a competition to see who can pretrain the best LLM in under 10 minutes. So over the weekend, I made a little demo to automate this end-to-end using the Hub as the infra layer:

Jobs to scale compute
Buckets to store all experiments
Trackio to log all the metrics

The cool thing here is that everything is launched locally: no ssh shenanigans into a cluster or fighting with colleagues over storage and GPUs ⚔️

All that's left is coming up with new ideas, but luckily Codex can automate that part too 😁

Can I have a job now please @reach_vb 🙏?

See 1 related tweets

@_akhaliq: RT @_lewtun: You can now pretrain LLMs entirely on the HF Hub 💥

Last week, @OpenAI launched a compe...

18. aakashgupta (Group Score: 62.5 | Individual: 35.9)

Cluster: 2 tweets | Engagement: 641 (Avg: 663) | Type: Tech

Every CEO will be running an OpenClaw within 12 months. Zuck is just building his in-house.

Here’s what to expect.

Six days ago Jensen Huang stood in front of 30,000 people at GTC and said every company needs an OpenClaw strategy. OpenClaw hit 250,000 GitHub stars in 60 days, faster than Linux, React, or any open-source project in history. Peter Steinberger built the first version in an hour. Nvidia shipped NemoClaw, an enterprise security layer, on top of it. Anthropic shipped Cowork Dispatch before OpenAI shipped anything. The infrastructure for autonomous AI agents went from side project to enterprise standard in under two months.

Zuckerberg building a personal CEO agent is what this looks like when a Fortune 5 company does it internally.

A CEO at Meta’s scale sees maybe 1% of the information that determines whether a decision is right or wrong. The other 99% gets filtered through VPs, chiefs of staff, dashboards, and whatever made it into the pre-read. The agent replaces the filter.

Think about what a CEO agent actually does. It ingests every product metric, every internal thread, every customer escalation, every competitive intelligence report across every team simultaneously. Then it surfaces the three things that actually matter this morning. Before every 1:1, it pulls that person’s team metrics, open headcount, recent launches, and the two things they said they’d deliver last quarter. When the CEO asks “what happens to our glasses timeline if we move 200 engineers to AI infra,” the agent gives a first-pass answer in minutes instead of a two-week strategy team exercise. And it never forgets. The person who remembered why the company killed that project in 2019 left two years ago. The agent didn’t.

Meta employees are already running their own versions. Tools called “My Claw” and “Second Brain.” Engineering output up 30%, power users up 80% year over year. Zuckerberg is doing what his employees are doing. Applying it to the highest-leverage seat in the company.

Now think about what that means for the people currently doing this work.

Chief of staff. Executive assistant. BizOps. Strategy and planning. These roles exist to perform one loop: gather information from across the org, filter it, synthesize it, route it to a decision-maker, track the follow-through. Every step is a text-in, text-out task. Summarize this doc. Pull these metrics. Draft this brief. Follow up on action items. Cross-reference what engineering said with what finance approved.

A typical Fortune 500 CEO has 8 to 12 people whose primary job is making them effective. Multiply that by every SVP with a chief of staff, every VP with a BizOps partner, every director with an EA. Thousands of roles per large company built around the information-routing function.

The agent reads 400 pages of internal docs in seconds. It never misses context from a meeting three months ago. It doesn’t need to Slack four people for the latest numbers because it’s already connected to the source systems. The human in BizOps spends 70% of their week on information gathering and synthesis. The agent does that in minutes.

That’s a 90% headcount reduction across chief of staff, EA, BizOps, and strategy roles over the next five years. The surviving 10% will be the ones doing work agents can’t: reading a room, managing a difficult exec relationship, knowing that the CFO’s “sure, let’s revisit” actually means no. Political judgment and human navigation. Everything else dissolves into software.

The question every board should be asking: if your CEO isn’t running one of these by 2027, what are they making decisions on?\n\nQT @unusual_whales: Meta CEO Mark Zuckerberg is creating a CEO agent to assist him in his job, per WSJ

See 1 related tweets

@garrytan: RT @shiri_shh: The working style of OpenClaw founder @steipete is insane.

bro runs 4–10 AI coding a...

19. nvidia (Group Score: 62.5 | Individual: 24.9)

Cluster: 3 tweets | Engagement: 346 (Avg: 189) | Type: Tech

NVIDIA and @EmeraldAi_ are announcing a new class of flexible AI factories at #CERAWeek 🔋

Working with partners @TheAESCorp, @Conste11ation, @InvenergyLLC, @NextEraEnergyR, and Vistra, this collaboration aims to speed up time to power and support grid reliability. These next-generation facilities will use grid intelligence to help the United States win the AI race.

Read the full announcement: https://t.co/3zRFvQJdnE

See 2 related tweets

@Dagnum_PI: LFG $DAG 🇺🇸\n\nQT @nvidia: NVIDIA and @EmeraldAi_ are announcing a new class of flexible AI factorie...
@nvidia: NVIDIA and @EmeraldAi_ are announcing a new class of flexible AI factories at #CERAWeek 🔋

Working w...

20. rohanpaul_ai (Group Score: 59.5 | Individual: 37.5)

Cluster: 2 tweets | Engagement: 141 (Avg: 93) | Type: Tech

The US military used Palantir’s Maven with Anthropic’s Claude to help turn huge streams of classified data into about 1,000 targets in 24 hours.

~ per moneycontrol reports.

Modern war produces more satellite feeds, sensor logs, maps, and text reports than human teams can sort fast enough.

Maven appears to act like a giant military search-and-sorting layer that pulls many data sources together, while Claude helps summarize, rank, and suggest which locations look most operationally important.

That does not mean the AI is “deciding” by itself, but it does mean the slow part of planning shifts from humans building target packages by hand to humans checking machine-generated options.

The real shift is speed, because a workflow that once took days or weeks can move close to real time when software fuses data, writes summaries, proposes coordinates, and orders priorities.

The sharpest concern is not science fiction autonomy but ordinary error, because a system that is very fast can also scale bad guesses very fast if review is weak.

moneycontrol .com/world/how-palantir-and-anthropic-ai-helped-the-us-hit-1-000-iran-targets-in-24-hours-article-13853331.html\n\nQT @rohanpaul_ai: 🚨 BREAKING: Anthropic just put out a new hiring post.

They’re really hiring chemical weapons experts now. https://t.co/lGlFVWHvZO

See 1 related tweets

@WesRoth: U.S. Department of Defense is officially adopting Palantir’s Maven artificial intelligence system as...