Joseph Yeh 💛🖤🤎

Executive & Tech Recruiter, Executive Career Coach, Community Builder, Startup Advisor always searching for 🦪🧋🛸🧘 adventures

[Joseph Yeh 💛🖤🤎 @JosephSYeh]

Apple Knew Its AI Was Failing. It Took One Secret Meeting to Finally Fix Siri. | iPhone in Canada https://t.co/9UZMKZW7ik

[a16z @a16z]

.@tylercowen on why AI creates more jobs than it destroys:

"One of the neatest properties of current AI models is they allow a small number of individuals working with AI to really do a lot more work than was possible previously."

"This will mean more companies, more projects, more nonprofits, just more ventures."

"One area is generally energy, electricity, the grid... It's completely screwed up. It will take twenty years, thirty years, forty years to fix... The AIs cannot do that on their own."

"The biomedical sector and medical trials, there will be many, many, many more ideas to test. AIs will help with the testing, but I don't think pure testing by simulation will be possible anytime soon."

"Simply care for the elderly. There will be robots, personal companions. We have this already. But the elderly also will want human care. It wouldn't surprise me if in the future, fifteen, twenty percent of all jobs were elderly care."

"Luis Garicano had an excellent online essay. He referred to what he called 'messy jobs': jobs where it's hard to explain exactly what the job is, but on a given day you're doing eleven different things, and it requires coordination and figuring out what you ought to do next and getting other people to help you... There's a real future in messy jobs."

Tyler Cowen with @dataWyatt

[Joseph Yeh 💛🖤🤎 @JosephSYeh]

At $1.78 trillion, why bother with price discovery? - PitchBook https://t.co/iQa6rIHhkg

[Aaron Levie @levie]

Good thought provoking post from Anthropic. I think this paragraph points to the key element of the optimistic scenario of AI:

“There has been an explosion of new ideas, initiatives, tools, and simulations, as a result of Anthropic employees working with highly capable models—far more than we have the capacity to pursue. The rate at which organizations can spot and fix these bottlenecks may be a skill that improves over time, and it may become the most important skill for any organization.”

AI lowers the barrier dramatically to allowing us to do more. As a result of that, we have far more ideas than we can pursue, and for the ones that we want to pursue we’re ultimately limited by our ability to go take on the surrounding work to execute those ideas. There’s almost no amount of AI progress that can happen where that goes away.

AI is going to let us build much more software, launch more marketing campaigns, research more drugs, and so on. All of this work, even when augmented by agents, still ultimately requires people to manage.

[Eric Glyman @eglyman]

As I wrote this, I saw X go into meltdown over tokens.

You've seen the headlines: “Uber blows yearly AI budget in just one quarter.” “Meta employee burns 281 billion tokens in April.”

But, the problem isn't spending. Spending works. Since 2023, the top quartile of our AI spenders doubled their revenue. The bottom quartile? Flat.

It's blind spending. We don’t know which spend worked.

A sales team has qualified leads. A support team has resolved conversations. These are units you can measure against. All a token tells you is the meter ran, not whether the work was worth it or not.

Finance says, “half the budget,” engineering says, “double it” and you don’t know who’s right because there is no shared language of value. There’s no attribution, and no attribution means no allocation.

For example, right now, all work, no matter the size or shape, defaults to frontier models. But meeting summaries and calendar updates don’t require GPT-5.5 Pro.

In isolation this seems trivial, but re-route just 10% of a $10M AI bill from frontier to GPT-4 level intelligence you’ve saved nearly one million dollars. This sounds like a made-up stat — it’s not. It truly is that much cheaper.

This is the future of finance: not blindly rubber-stamping or rejecting AI spend, but allocating it with the same rigor companies apply to headcount.

[Joseph Yeh 💛🖤🤎 @JosephSYeh]

https://t.co/dlv9m70Z3X

[Joseph Yeh 💛🖤🤎 @JosephSYeh]

Lightricks to split into two companies as it cuts another 75 jobs | Ctech https://t.co/FLQmXUS9Fm

[Joseph Yeh 💛🖤🤎 @JosephSYeh]

Bernie Su’s ‘Whispers’ Showcases A New Kind Of AI-Informed Cinema https://t.co/PjQolAK5Bu

[Robert Scoble @Scobleizer]

Hanging out with so many creatives today at @magnific’s @upscaleconf. This morning they announced a new platform for people making AI films. @jerrod_lew got the demo.

I got @martinleblanc Chief Experience Officer to tell me what it all means.

[McKinsey Global Institute @McKinsey_MGI]

Eight of the world’s ten largest companies now compete in “arenas”—up from just one in 2005.

That’s not a sector shift. It’s structural. Value is concentrating in a small set of fast-growing, high-dynamism spaces.

The companies pulling ahead build capabilities that travel across arenas, and compound over time. https://t.co/IJ0CSb9blo

[Robert Scoble @Scobleizer]

The second AI glasses launch of the day.

And Apple is waiting for 2027?

[a16z @a16z]

World Labs CEO Dr. Fei-Fei Li: "The world is not made of words."

"Language models have given machines an extraordinary command of concepts, vocabulary, and reasoning, but the physical world, virtual or real, runs on a different substrate."

"Where language models learn the statistical structure of text, world models learn the statistical structure of space and time: how light falls on a surface, how a garden looks from an angle no camera has captured, how objects respond to force and follow the laws of physics."

"Language gave machines a way to talk about that world. World models are how machines will finally come to understand, imagine, reason and interact with it."

Full piece: https://t.co/C9qOJg5wuc

[Ed Sim @edsim]

reiterating:

"We're using the more expensive models to explore. Once we scale some of these experiences, we'll look to bring in more efficient models that are more efficient on a token basis or are open source."

wrote more about this here last week: https://t.co/QAJOsYkBiK

[steijnpelle @steijnpelle]

Today, we're introducing Lassie and $47M in funding led by a16z.

We're building AI that runs small businesses, starting with doctors' offices.

Lassie is already trusted by 700+ practices across the country, working autonomously to provide them with 30 hours of labor per month.

To get here, we first had to leave Robinhood and Superhuman to work in offices ourselves.

Here's how that went.

[Simon Taylor @sytaylor]

Ramp has launched Stack a "Harvey for Accountants"

An AI operating system that codes transactions, posts journal entries and runs the close, sitting inside the firm's data instead of bolted on top.

To me it looks like Claude Cowork, fine-tuned for accountants.

Watch the demo: building client plans, categorizing spend by tax code, working through complex amortization.

All of that still posts to QuickBooks. The ledger stays the system of record. What moves to @tryramp is the work: the judgment, the categorization, the close.

The race to put AI inside accounting firms is already crowded. Basis raised $100M at a $1.15B valuation last month. Black Ore is rolling Tax Autopilot out to thousands of firms. Both spent years building for this exact buyer.

Ramp shows up late on product and years ahead on distribution because those accountants had been channel partners in the past. It puts the market TAM at $150bn.

92 of the top 100 CPA firms already have clients running on Ramp. Basis, for contrast, says it works with about 30% of the top 25. The standalone players are still knocking on doors Ramp walked through a while ago.

I never bet against the innovators. But when the incumbent is Ramp, that's a much harder call.

And the way in is almost too clean. The accounting partner program was a referral engine: recommend Ramp to your clients, collect the payout. Now Ramp sells to the firm itself. The channel becomes the customer, and firms staring down a 20-year low in accounting graduates are in no position to say no.

QuickBooks keeps the books.

Ramp takes the work.

[Joseph Yeh 💛🖤🤎 @JosephSYeh]

Uber Layoffs 2026: Is AI The Reason? Which Teams Are Impacted And What Employees Will Receive https://t.co/D9mi2127XM

[a16z @a16z]

The most interesting visual AI tools today are generating the underlying source code behind the final output.

This change is unlocking editability, iteration, and a feedback loop that pixel-native models can't match.

And the market for visual code generation is organizing around the runtime where the artifact is rendered or executed.

a16z's Yoko Li on why the next frontier of visual AI is code: https://t.co/tIA8luD4OG

[Joseph Yeh 💛🖤🤎 @JosephSYeh]

GitLab to Lay Off 350 Employees in AI Pivot - WSJ https://t.co/JGQouKweRD

[Joseph Yeh 💛🖤🤎 @JosephSYeh]

https://t.co/zjkeiKhBhO

[Alex Lieberman @businessbarista]

In 2023, everyone was hype about ChatGPT.
In 2024, it was GenAI.
2025 was the year of Agents.
And 2026 started with OpenClaw, but now attention has turned to The Software Factory.

Unless you're an engineer or take residence in the depths of X, you may not know what a Software Factory is or why you should care.

But when some companies are attributing 90% of their production software to AI (read: Anthropic) and best-in-class ICs are matching the output of a 20-person pre-AI engineering org, you need to care.

So let me break the whole thing down...

What a software factory actually is, why it's suddenly everywhere, and a simple way to figure out exactly how close your org is. Even if you've never written a line of code in your life.