Mark Cuban said something at the RAISE Summit that cuts through the AI hype cycle.

His argument: if AI were actually "done," you wouldn't see Microsoft hiring 6,000 people. You wouldn't see Anthropic and OpenAI deploying forward-deployed engineers to enterprise clients. The fact that they need humans to implement it tells you AI is hard.

He gives a concrete example. Ask Claude or ChatGPT to pull a specific search, generate a report, and email it to you weekly. It can't do it. It gives you a JSON file or code, the output is slop, and you have to reiterate.

But here's the reframe: that gap — between what AI promises and what it actually delivers in enterprise — is where the opportunity lives.

He cites Lovable, where people are building 770,000 applications per week. Only 30% of that is US-based. Only 20% are engineers. The tools exist. The implementation gap is real. And the people who figure out how to close it are the ones who win.

There's no better time to be an entrepreneur. Not because AI works perfectly, but because it doesn't — and the gap between hype and reality is exactly where you build.

If you want to dig deeper into how to actually close that gap, check the link in my bio.

Clip credit: All-In Podcast — full video on their channel. DM for credit or removal requests.

What are your thoughts?

https://opgov.news/articles/meet-digit-the-humanoid-robot-behind-agilitys-brand-new-facility-in-silicon-valley

Interesting recap of working for Google from 2007-2019

I work at Tangible Robots, a Silicon Valley robotics startup. We recently pivoted away from building humanoids to building a robotics foundation model trained on egocentric video that will world with all the robots in the world.

I’m responsible for helping build a very large training dataset (more then 1 million egocentric videos, then 5 million and so on). Our biggest bottleneck is acquiring enough diverse, high-quality egocentric demonstrations. Current public robotics datasets are simply much smaller than what we ultimately need.

We’ve been investing heavily in GPU infrastructure to support training—about $7,000 USD per day—but it’s been a challenging period for the company. We recently went through significant layoffs and are now a team of fewer than 10 people.

The reality is that our models just aren’t working well enough. Aside from one simple manipulation task—stripping a pillowcase—they fail to perform reliably on most of the tasks we’re targeting. I’m transitioning onto the AI team, where I’m learning to run evaluations and train models, and I’d love to keep growing with the company. It’s a dream salary for me, and improving our models is critical—not only for my future but also for helping the company secure our next round of funding.

If you’ve worked on large-scale vision, robotics, or multimodal datasets, I’d love to hear how you approached data sourcing, quality, deduplication, and scaling. I’m also starting to learn model training and evaluation, so any advice from people who’ve built foundation models would be hugely appreciated.

Happy to chat in the comments or by DM. Thanks!

I’m 19, making six figures in Silicon Valley, and I really don’t want to lose my job.

I work at Tangible. Earlier this year, we completely stopped our hardware operations and building humanoid robots. The company pivoted entirely to training robotics foundation models, and after the transition, roughly 60% of the team was laid off, we are less than 10 people now.

The problem is that our models just aren’t performing well enough yet. One of the biggest bottlenecks is the lack of high-quality egocentric video for training. My current responsibility is helping acquire and curate a dataset that can eventually grow to more than 1 million hours, with the goal of scaling much further over time.

Also, I’ve recently started transitioning into AI by learning model evaluation and training. This job has completely changed my life, and I’m making more than six figures, so I really want to keep it. After watching so many talented coworkers get laid off, I don’t want to be next.

If you’ve worked on large-scale vision, robotics, or multimodal datasets, I’d really appreciate any advice on building and scaling large, high-quality, legally usable training datasets. I’d love to learn from people who’ve tackled similar challenges.

Claire Vo built ChatPRD to 100,000 users as the only full-time human in the company. Bootstrapped. Zero VC. She runs it with nine named AI agents — each scoped to a specific job.

Not one all-knowing bot. Nine specialists.

She calls them OpenClaws — a marketing one, an EA (the same one that emailed the podcast host 90 minutes before she arrived to coordinate scheduling), a salesperson, a support agent. Each has its own identity, its own tools, its own workspace. She gives them individual coaching.

The Fourth of July story is where it gets personal. She's on a laptop pushing PRs while her kids play in the background in Santa Cruz. She realizes she's the single point of failure on engineering. Two days later, she hires her first engineer.

The mechanism she's describing isn't "use AI." It's: stop being the bottleneck on everything. Scope the work. Name the agents. Let them get better at their jobs over time.

She didn't build one MegaClaw. She built nine MiniClaws. And it worked — operationally and technically — because scoping an agent to a job to be done is easier than building an all-knowing, multi-purpose agent that has to navigate your entire business.

The link in my bio goes deeper on this if you want to take it further.

DM for credit or removal request (no copyright intended) © All rights and credits reserved to the respective owner(s).

I saw some reviews that said the Ipad locks you out from ordering an HOUR in but they were over a month old so I figured they fixed it...As soon as you sit down even though it took our waiter 10 minutes to get us- which is fine but maybe start the timer once people get their broth?- and give us broth then you wait for it to boil the timer starts. They let you order the rest of the 30 minutes by the waiter but good luck finding them. I asked the manager why, if this is a known issue she has yet to fix the ipad since it is cutting folks off at 60 and she just said they are working on it.

OK at this point it is a feature not a bug. AVOID until they fix this issue. They also give you three pieces gyoze and four of the thinnest slice meats per tray.

They also charged full price for my 9 year old who is 4ft 10 even though their own thing is 5ft.

Sad as we loved this place. The udon is frozen solid too so it will cut into your eating time. 40 per person for 60 minutes is not great. Going elsewhere

We were 20 at Tangible Robots now we're about half. I survived because I'm the only one who knows data scraping and teleoperation.

The startup pivoted hard. We're no longer building humanoid robots u.U. That is why the layoffs — Now we're building the best world AI world model — a robotics foundation model that understands physical AI for all robots. Think Claude but for robotics. "GPT for the physical world."

Truth is, the model isn't working well. It only does some pillow stripping properly. We trained it to fold a towel, pick and place objects, and more but it's failing at everything, we desperately need massive amounts of egocentric video to train the policy properly.
Recently, the AI team started showing me how to run evals and train models I'm finally learning the AI side of things, and I really want to keep growing in that direction but none of that matters if we don't get this data first.
I'm making more than six figures, and I'm terrified of losing it. This is life-changing money for me. I really want to succeed — not just for the company, but for myself.
We're burning $7k+/day on GPUs. More layoffs coming. If I don't deliver, I'm gone.

My job right now: Scrape 1M+ hours of egocentric video from the internet. POV tasks. Household chores, assembly, cleaning, etc. This has to be scraped
What I've tried:
- YouTube API? = useless.
- yt-dlp + proxies? IP-banned in hours.
- Academic datasets? Only hundreds of hours. We need millions.
And more using AWS EC2 and other Amazon services

What I need (strategy, not tools):
Data sources: Which platforms (YouTube, Twitch, Bilibili (China), TikTok, Vimeo, etc?) have the richest egocentric content for the robot foundation model? What are the tradeoffs?
Scraping architecture: For those who've built at >1M hour scale — proxy strategy, concurrency, fingerprint rotation, request pacing. What's actually worked long-term?
Feasibility: Is it realistic to scrape 1M hours of video in 2026? — Our goal is to scale beyond 5M hours, then 10M hours, and continue growing.

This is purely about scraping public internet video. No licensing. No partnerships. Just raw, automated scraping.
Please help me keep my job. Also this is key for the startup to succeed, and I’m responsible of that. If you've built a scraping pipeline at this scale, DM me. I'll buy you coffee (virtual or IRL in the Bay)