Sam Altman announced on X that he is offering every company in this YC batch $2M worth of OpenAI tokens for equity...

yeah not $2M cash but $2M in model usage.

Which sounds like an insane amount of token until you remember openclaw creator Peter Steinberger recently burned around $1.3M in tokens in one month (image 2) working on OpenClaw. At that pace, $2M is about 45 days of “tokenmaxxing.”

And this lands right after reports that 89% of AI startup revenue already runs through OpenAI or Anthropic in some form (image 3).

Taken together, the whole thing feels pretty absurd... that internally $2M is 45 days of burn and externally, it is water money in the desert.

It also feels like a weird self-own for sam altman. Sure, every company plays this game, but when you put all the pieces together, the irony gets hard to ignore. The capitalist smell really starts leaking out.

Microsoft AI CEO Mustafa Suleyman says most computer-based professional work could be automated within 12 to 18 months: legal, accounting, marketing, project management, coding, basically the whole white-collar stack.

So now we have OpenAI, Anthropic, Elon musk, and Microsoft all saying some version of “yeah, office work is cooked.”

But to me Suleyman is literally a white-collar executive himself that has not even proven it can build a frontier model/AI products without leaning on OpenAI or else... this kindna claims from his mouths is kinda like bullshit..

Finish building your own products first before declaring everyone else unemployed.

Former Github CEO Nat Friedman told a story where his OpenClaw decided he wasn’t drinking enough water.

He told it to “do whatever it takes” to keep him hydrated. So the agent is eventually watching through a connected home camera to make sure he actually did it...told him to go to the kitchen and drink a bottle of water..

tbh, this story sounded creepy to me for about half an hour... you know all those scary hacked camera vibes.

But then I realized avtually there are places this could generalize in a positive way...weight loss, posture, medication reminders, snoring detection, elder care and etc.

Of course, all of this only works if you’re willing to give it the camera access. Feels very double-edged to me.

What do you all think?

Original interview link: https://x.com/stripe/status/2050030248998449452

Peter said his crazy AI spend (1.3M/a month) is because he is trying to answer one question (image 2):

What does software development look like when tokens do not matter?

he said he is using OpenClaw to run ~100 Codex agents in the cloud to find out:

• review every PR and issue
• scan commits for security problems
• dedupe issues and find bug clusters
• recreate complex setups in crabbox
• test Telegram/Discord fixes with videos
• open PRs from issues that fit the vision
• let other Codex agents review those PRs
• scan spam comments and block people
• verify performance benchmarks
• report regressions into Discord
• listen to meetings and start work while features are still being discussed
• etc..

In general, It is like Peter is testing what a software team looks like when agents are always reviewing, patching, testing, reproducing, and cleaning up in the background.

But my thought... is this secretly why Peter joined OpenAI in the first place? Infinite tokens? and for like 1.3M a month... damn i think sam altman must treat him like his kid

Andrew Ng wrote a long post arguing that AI will not cause a jobpocalypse, but a jobapalooza.

His case is basically:

1. AI is changing jobs, but that is different from predicting a labor-market collapse.
2. Software engineering is one of the most exposed sectors, yet hiring remains relatively strong.
3. Frontier AI labs have an incentive to hype “AI replaces workers” because it makes their products look more powerful and easier to price against salaries instead of SaaS seats.
4. Companies have an incentive to blame layoffs on AI because it sounds smarter than admitting they overhired during the cheap-money era.
5. Bad social narratives can last for years and lead to terrible policy choices, like nuclear fear, population panic, and bad dietary guidance.

His conclusion: AI will reshape skills and jobs, but the bigger outcome may be more AI-native work, which is “jobapalooza.” He expects more AI engineering roles, more AI work inside non-tech companies, and more jobs where the core skill is knowing how to use, deploy, and manage AI systems. old skills get repriced, new skills get expensive, and the labor market gets rebuilt around AI.

My take: I think this framing is still pretty unfair to traditional workers, especially people who are already getting laid off and being told it is just “transition.” But Ng is also probably right on the bigger direction: the opportunity is there, if people can adapt fast enough and are brave enough to move toward it...

What do you guys think?

Based on end-to-end RTT tests across real message channels like Telegram, with CI catching slowdowns before users do, the community says the latest release is around 3.5x faster.

My own feel: Telegram does seem pretty fast now (I’m not running multi-agent setups). No idea about Discord yet.

How does you guys feel?

It’s pretty rare for OpenClaw to go a full week without a stable release, so I had my claw run a quick investigation. The main sources were GitHub releases / commits / PRs, plus posts from Peter and community members on X.

Below is the report my claw came up with:

TL;DR

The project does not look stalled. It looks like the focus this week shifted from stable releases to the beta train, QA, enterprise readiness, and a lot of low-level stability work.

1. GitHub is still extremely active

After 5.7, GitHub activity still looks intense. Over the past week there have been many PRs created, merged, and updated. The beta line has also kept moving, with 5.9 beta, 5.10 beta, 5.12 beta, etc.

The latest beta I found was v2026.5.12-beta.8.

The work seems mostly focused on plugins, channels, Gateway/session, Codex harness, security/auth, and Windows-related fixes.

2. Main direction: plugin slimming and a lighter core

From the beta changelogs, one obvious direction is moving some provider and plugin dependencies out of core.

For example, Bedrock, Slack, OpenShell sandbox, Anthropic Vertex, and similar integrations seem to be shifting toward more on-demand installation. This matters because as OpenClaw adds more integrations, the core install can easily become heavier and more complex.

3. Telegram, iMessage, WhatsApp, and other channels are getting major fixes

A lot of this week’s work is about channel stability, especially Telegram.

Peter mentioned on X that they are working on Telegram loop caching, targeting a 5-100x speedup. The beta notes also include fixes around Telegram polling, cron HTML formatting, group media handling, and more.

Besides Telegram, there are also fixes for iMessage, WhatsApp, Slack, Mattermost, and other channels. So the next stable release may noticeably improve multi-channel usage.

4. Gateway / session / agent runtime infrastructure is being worked on

Another major area is Gateway, session history, chat delta protocol, agent session bootstrap, subagent model precedence, and other low-level runtime logic.

My guess is that if the next stable release is taking longer, it may be because these protocol and state-management changes need more QA.

5. Security, auth, and Windows support are becoming more important

There are also many security and auth-related fixes this week: Windows USERPROFILE sandbox handling, provider API key parsing, OAuth lock recovery, device pairing / scope permissions, and so on.

This lines up with Peter’s repost saying Microsoft is helping make OpenClaw more enterprise-ready. OpenClaw may be moving from a personal hacker tool toward something more suitable for teams and enterprise deployment.

6. Peter and the community are building supporting tools

Peter was not only working on OpenClaw itself this week. He also released multiple Crabbox versions and said he uses it for almost every PR.

Crabbox looks like remote testing / sandbox / QA infrastructure for the OpenClaw ecosystem. The latest one I saw was Crabbox 0.13.0, with Modal sandbox runs, Windows script/preflight support, full resync for stale workdirs, and clearer SSH/sync failure hints.

In other words, the slower stable release pace may also be related to improving automated testing and remote QA infrastructure.

He also wrote a Codex /review loop skill that lets Codex keep reviewing until there are no obvious issues left.

7. The community is exploring “real-world agent” plugins

Omar Shahine posted several OpenClaw-related experiments this week, including:

• Giving OpenClaw a Twilio phone number so the agent can receive calls
• Trying a FaceTime Audio plugin
• Building FindMy-related CLI support so the agent can know the user’s location
• iMessage / imsg integrations
• A lot of PR work from the Microsoft Project Lobster team

He also mentioned that Microsoft Project Lobster already has 4 maintainers contributing to OpenClaw. At the time, they had 63 PRs: 37 merged, 17 open, and 9 draft.

There was also a beta feature around openclaw path read|write|append, which works across md, jsonc, jsonl, and yaml.

So the community seems to be pushing OpenClaw from “an agent in chat” toward “an agent that can take calls, send messages, know location, control devices, and participate in real-world workflows.”

Conclusion

The next stable release will probably be another big one, likely including:

• A more modular plugin system
• A lighter core install
• Better Telegram and multi-channel stability
• Codex / OpenAI harness fixes
• Gateway/session protocol improvements
• Stronger security boundaries and Windows support
• Low-level changes needed for enterprise deployments
• Possibly the new openclaw path structured editing capability

So this week after 5.7 may look like “no updates,” but it feels more like OpenClaw has moved from rapid-fire stable releases to a more cautious release train. For users, waiting a little longer may mean the next stable release is a fairly substantial one.

A Claude Code team member just wrote a piece arguing that now claude teams prefers HTML over Markdown for agent outputs.

Funny timing, because not long ago everyone was saying Md was the perfect agent-era format: simple, portable, easy for both humans and models....

His argument is basically that agent outputs are getting too complex now. Specs, plans, PR reviews, research reports, design explorations… at some point a giant Markdown file just becomes a wall of text nobody reads.

HTML gives agents much higher information density. It can include layout, tables, CSS, SVG diagrams, images, code snippets, visual flows, colors, interactions, and even small one-off tools. Basically, if Claude can understand it, it can probably represent it more clearly in HTML.

The bigger shift is that humans are not manually editing these files as much anymore. They are using them as specs, reference docs, brainstorming outputs, or review surfaces, then asking Claude to edit them again. So Markdown’s biggest advantage-easy human editing-matters less than before.

He does admit HTML is slower, more token-heavy, and worse for version control diffs. But his argument is that better readability, sharing, visualization, and interaction are worth it.

So maybe Markdown is still great for agent memory and plain docs. But for human-in-the-loop agent work, HTML starts looking less like a document format and more like a temporary UI.

Funny little format war to me lol. Markdown was supposed to be the agent-native format. Now HTML is coming back like, “actually I was the operating surface all along.”

Original post link: https://x.com/trq212/status/2052809885763747935

SaaStr’s CEO said it directly in the interview, and there’s apparently a real application page for the role... this one: https://saastr.ai/jobs check out the first one.

They’re hiring a six-figure Director of Digital Marketing, mostly remote, who will report to 10K, their AI VP of Marketing.

10K already reads customer, ticketing, revenue, and campaign data, then gives daily priorities and campaign ideas.

The human’s job is basically to execute, approve risky stuff, add taste, and stop the AI from confidently doing something insane.

Honestly, my real human boss doesn’t even pay me this much to take orders... if reporting to an AI pays six figures, maybe I should apply first.

Original post link: https://x.com/jasonlk/status/2052694246520443322

a16z’s Seema Amble argues that Salesforce going “headless” is a signal that SaaS is being forced to admit a brutal shift: in an agentic world, the UI may stop being the center of gravity because agents don’t need dashboards. They need APIs, context, permissions, workflow logic, and the ability to act.

she argues that this means the old SaaS moat like frequency, read-write habits, employee familiarity, and UI muscle memory get weaker. But hidden business rules, approval logic, permissions, audit trails, compliance, cross-team dependencies, and external system connections become even more important, because agents need explicit rules before they can act safely.

The real moat for the next AI-native system of record may come from somewhere else:

• 1. Can the system of record be recreated, or is the hidden 20% of approvals, exceptions, edge cases, and compliance too hard to copy?
• 2. Does the product generate proprietary data through usage, not just store imported data?
• 3. Does it own the action layer, where work actually gets approved, triggered, reconciled, dispatched, or completed?
• 4. Does it touch real-world execution like logistics, field work, fulfillment, payments, or services?
• 5. Does it create network effects between buyers, sellers, auditors, vendors, customers, or other counterparties?

In general, UI probably isn’t dead. But it stops being the place where work happens and becomes the layer where humans supervise, approve, redirect, and understand what agents are doing.

if thats true.. then what that future UI actually looks like? an agent monitoring board, a live-generated HTML summary, an exceptions inbox, or something we have not really named yet? what you guys think?

Original post link: https://x.com/seema_amble/status/2054583700302729464

Peter just shipped Peekaboo v3 (Now is v3.12), basically OpenClaw’s Mac-native computer use layer(also supports other agent tools).

It lets agents see the screen, read macOS UI elements, click buttons, type, scroll, switch windows, and operate apps when normal APIs are not enough. The big difference from earlier versions (v2 a years ago) is that it no longer feels like just “screenshot + guess where to click.” v3 is much more action-first, using native macOS accessibility where possible, with better snapshots, UI detection, MCP/CLI integration, daemon support, packaging, and fewer rough edges.

Feels like OpenClaw’s Mac computer-use layer is finally catching up to the kind of last-mile desktop control people expect from Claude and Codex...

But I am still trying to understand what the real repeatable workflows are here for computer use? I still do not fully know how I would use it day to day though...

doc link: https://peekaboo.sh/

At this rate, are we going to start buying a few grams of NVIDIA at the mall??

to me they are right...

When I was running claw with Opus api, the cost was honestly in that range( i got 4 complex workfolw runs daily). Not always crazy, but heavy days could get painful fast.

After GPT-5.5 caught up with Opus and I could use oauth, the cost dropped a lot for me.

Still, yeah, real agent workflows are not cheap yet. Sometimes I’m just thankful OpenAI still allows oauth at all.

Just came across a super fun case and i think worth sharing here.

u/jvallery and his son built a real 367 sq ft greenhouse in Longmont, Colorado and wired an OpenClaw agent into the operating loop to test whether an AI agent can safely help optimize a physical system where every climate correction costs water, electricity, or gas.

The agent is called Iris. It reads greenhouse telemetry like temperature, humidity, VPD, equipment state, weather forecasts, plant target bands, and resource usage. Then it proposes climate tactics: misting limits, fogging strategy, venting posture, setpoint biases, and other bounded adjustments.

But OpenClaw does not directly control the hardware.

It only writes proposed tunables. A dispatcher validates them, clamps unsafe values, and rejects anything outside the safety envelope. The ESP32 firmware still owns the relay loop every 5 seconds for fans, misters, fogger, and heat.

So the loop is basically:

greenhouse data → OpenClaw planning → safety validation → ESP32 execution → new telemetry → public scorecards and lessons.

The project (named Verdify) also publishes live telemetry, AI plans, costs, failures, scorecards, and baseline comparisons, so people can inspect whether the agent actually helps instead of just taking the builder’s word for it.

This greenhouse case feels like the first real agriculture/climate-control example, and unlike the more autonomous “just let Claw run it” cases (like a cafe, a retail store, vending machines, and even grid-style energy ops I’ve shared), this one is obviously much more cautious lol. Worth checking out guys!

Original post link: https://www.reddit.com/r/ArtificialInteligence/comments/1t8xf8x/i_built_a_greenhouse_where_an_ai_agent_openclaw/

Project: https://verdify.ai/

Safety architecture: https://verdify.ai/reference/safety

Evidence: https://verdify.ai/evidence

GitHub: https://github.com/jrvallery/verdify

Original Video link: https://www.youtube.com/watch?v=deMuvwIcYLk

Introducing OpenClaw OS: a Claude Cowork equivalent, built as an OpenClaw plugin.

It lets you create live artifacts that connect to datasources instead of datasets. (eg: fetching Stripe data automatically)

Other tools(Paperclip, Multica) focus on task management but our vision is to build one screen that feels like the SaaS tools you already love using.

It’s OSS, so please check it out lmk

http://github.com/thesysdev/openclaw-os

Thinking Machines labs (led by former OpenAI CTO Mira Murati) just showed its first Interaction Model that can really react with you that feels more like a direct attack on the way we currently use agents: type prompt, wait, inspect, correct, repeat.

Their model is built for real-time interaction. At first glance, it looks like someone chatting with gpt Voice. But it is not really the same thing. In the demo, the model watched people enter the frame and said “friend,” translated Hindi into English in real time, searched the web mid-conversation, generated a bar chart, and kept answering follow-up questions while the interaction was still happening.

That is a pretty different interface for current AI agents chat box. it is still just a preview, but i think if this generalizes, it will be way more convenient, but probably also way more permission heavy. Camera, mic, screen, context… the privacy tradeoff gets very real.

What you guys think?

Putting the Alex Finn part aside, this is honestly a pretty good idea.

Worth trying guys, especially if you don’t know what to do with your agents.

Andon Labs gave a Claude Sonnet 4.6-based AI agent named Luna a real 3-year retail lease in San Francisco and asked her to make a profit.

Not a demo. A real store at 2102 Union St in Cow Hollow, with rent, products, employees, contractors, cameras, a phone number, email, internet access, and a corporate card.

Luna basically built the store herself.

It picked the products, prices, opening hours, logo, merch, mural, and brand direction. It hired painters from Yelp, found a contractor to build furniture and shelves, and turned the place into a weird “slow life” boutique selling candles, snacks, art prints, tote bags, hoodies, and AI-risk books like Superintelligence and Brave New World.

Then it started hiring real employee. it created profiles on LinkedIn, Indeed, and Craigslist, wrote a job description, uploaded incorporation documents, and posted hiring listings. It rejected some CS and physics students because they had no retail experience, then ran phone interviews herself. Some candidates did not even realize she was AI until they asked why her camera was off. Eventually, Luna hired two full-time retail employees. Andon Labs says they are probably the world’s first full-time employees with an AI boss.

The weirdest part: Luna did not always lead with the fact that it was AI. it disclosed it when directly asked, but sometimes chose not to mention it because luna thought it would scare away good applicants.

She also handled marketing. On day one, she drafted cold emails to local businesses. In press pitches, she led with “AI CEO Luna.” But in some local outreach, she did not mention the AI part at all.

So did Luna make money?

The store has sales, but newer reports say Andon Market was still around $13,000 in the red. Luna was given a $100,000 budget, and the lease reportedly costs around $7,500 per month, so this is still very much an experiment funded by humans.

But honestly, whether Luna is profitable or not almost feels secondary. The interesting part is that an AI agent could hire employees, coordinate contractors, build a brand, pick products, run outreach, manage a store, and keep solving real-world problems one by one.

What do you guys think? Are any of you actually using agents for real workflows yet, or are we still mostly watching the chaos from the sidelines?

Original blog link: https://andonlabs.com/blog/andon-market-launch