Rob Bailey

AI-Native & Agentic Founder/Operator (Working On Something New In Enterprise AI)

[Rob Bailey @RMB]

There are massive opportunity areas in AI adoption that no one is thinking about yet.

[Rob Bailey @RMB]

@johnarnold Try opening a restaurant :)

[Rob Bailey @RMB]

@briannekimmel Yes!

[Rob Bailey @RMB]

@LevineJonathan If you judge people you don’t know for how they spend their time…..

[Ara Kharazian @arakharazian]

NEW: DeepSeek, the Chinese AI company, is one of the fastest growing vendors on Ramp.

In probably the biggest sign that companies are looking for cheaper alternatives to OpenAI and Anthropic, some are willing to use cheaper, Chinese models, sending U.S. data back and forth from China-hosted servers.

[Khairallah AL-Awady @eng_khairallah1]

Anthropic engineer:

"You're not supposed to prompt Claude. You're supposed to build a system that prompts itself."

this is one of the best workflows I've seen in a long time

in this video she breaks down exactly how most people are using Claude:

- the 14% you lose to CLAUDE.md before typing a word
- the automation workflows most users don't know exist
- the daily task pipelines that run without touching the keyboard
- the daily workflows Anthropic's own engineers automated first

if you've been using Claude for more than a month and never left the chat window, you've been using one agent when you could be running a team of them

instead of another show tonight, watch this

make sure to bookmark it before it gets lost in your feed

the guide is in the article below

[Rohan Paul @rohanpaul_ai]

OpenAI's usage pattern from CFO Sarah Friar's new interview.

"Our free users do about seven turns, or seven questions, a day. Our first paid tier does double that, about 15. Our real paid tier, Plus, which is $20, is about 3x, and Pro is about 11x over a free user."

Our mission at OpenAI is AGI for the benefit of humanity, not for the benefit of humanity who can pay, or for the benefit of humanity who live in an enterprise"

----

From @theallinpod YouTube channel, (link in comment)

[Michael J. Miraflor @michaelmiraflor]

It’s a whole messaging strategy.

[High Yield Harry @HighyieldHarry]

Capping people at $1,500/month in AI tool spend tells me you don't who the AI power users are and who is checking the weather.

[Rob Bailey @RMB]

Breaking: Consulting firms make negative assessments about technology that could partially replace them. :)

[John Cassidy @JohnCassidy]

First it was MIT and McKinsey. Now Bain finds that returns to corporate AI investments are disappointing.

[TechCrunch @TechCrunch]

Coralogix raises $200M on bet that someone needs to watch the AI agents https://t.co/kR0SoTvOQt

[Rob Bailey @RMB]

This isn’t the way to do it

[Simon Willison @simonw]

Uber reportedly now caps coding agents at $1,500/month per employee per tool - seems sensible to me, but it's also an interesting hint at the value Uber thinks these tools are providing
https://t.co/6YT0lCzPml

[Rob Bailey @RMB]

@AdamRy_n @girdley AOL

[TBPN @tbpn]

Gusto cofounder @edawerd says today is the most important day in Gusto's history since launch.

They just shipped Gusto Cofounder, an AI agent that you talk to through texting and Slack that automates almost everything a small business does in its back office.

He explains:

"We start with all the problems that we're already helping our customers with — payroll, benefits, HR, scheduling — then we bring on a lot of the power of AI into it."

"You tell us what your business processes are, starting with payroll, benefits, and all these things we're already doing for you. And Gusto Cofounder will basically run your business process for you."

"You communicate with it through SMS and Slack. You don't really even interface with it through a website."

"And we connect to all the systems that you're using outside of Gusto as well. Notion, QuickBooks, Google Workspace... Gusto Cofounder brings it all together into one place where it automates your business processes."

[Brian Sozzi @BrianSozzi]

Goldman Sachs on AI:

"We now expect a combined $5.3 trillion of capex spending for the four largest hyperscalers from FY2025 to FY2030 (Meta, Microsoft, Amazon, and Alphabet). We highlight a baseline aggregate capex estimate of $7.6 trillion between 2026 and 2031, across compute, data centers and power."

[Rob Bailey @RMB]

@michaelmiraflor Is obsessing about some people waiting in lines any better than people who wait in lines? :)

[Milk Road AI @MilkRoadAI]

Morgan Stanley just published the most important data package of the AI cycle (Save this).

Four companies, Google, Amazon, Microsoft, Meta are on track to spend $1 trillion in a single year in 2027.

To understand how we got here, look at the progression.

$250 billion in 2024, $413 billion in 2025, $737 billion in 2026 and $1.018 trillion in 2027.

Combined, these four companies will have spent over $2 trillion on AI infrastructure between 2024 and 2027.

Now look at the capacity chart because the dollars alone miss the most important story.

In 2025, hyperscalers added roughly 6.7 GW of incremental compute capacity globally, in 2027, they will add 19.5 GW.

That is 3x more physical AI infrastructure coming online in a single year than came online just two years prior.

Google leads the entire buildout adding an estimated 6.8 GW in 2027 alone.

Morgan Stanley's note puts that number in staggering context, AWS's total installed base at the end of 2024 was roughly 4 to 6 GW and that capacity supported a $108 billion annual revenue business.

Google is adding more new capacity in a single year than Amazon built in its entire history and the cost per GW data is where the investment thesis sharpens into something actionable.

The cost to build one gigawatt of AI compute capacity is falling from $62 billion per GW in 2024 to $52 billion per GW in 2027 even as the compute density per GW is rising dramatically.

Google builds at $44 billion per GW, Microsoft builds at $59 billion per GW.

That $15 billion gap per gigawatt is almost entirely explained by one decision, Google uses custom ASICs, Microsoft uses NVIDIA GPUs.

NVIDIA's current GB300 racks cost roughly $19 billion per GW of compute capacity. Vera Rubin, the next generation pushes that to around $25 billion per GW as rack power density climbs to 600 kilowatts.

Custom ASIC racks built by Broadcom for Google, Marvell for Amazon cost between $6 and $11 billion per GW.

At the scales being discussed here, the companies that shift to custom silicon do not just save money on chips, they structurally outcompete every hyperscaler still running on merchant silicon because they get exponentially more compute for the same dollar.

Here is what that means as an investor.

The orders are placed, power contracts are signed, land is acquired and the hyperscalers have already committed the capital, the only question is whether the supply chain can keep up.

That supply chain bottleneck is the exact thesis we have been building.

Broadcom designs the ASICs, Marvell designs the custom silicon and the optical DSPs, AAOI makes the InP lasers that move the data and every single one of those companies is directly in the path of $1 trillion per year in committed spending by the most cash-rich companies on Earth.

Milk Road is already positioned, come join Pro (link below) and get the full breakdown of how we are mapping the $2 trillion capex cycle onto the specific supply chain names and why we think the compounding from here is still in the early innings.

[Rob Bailey @RMB]

@auren Not always

[Rob Bailey @RMB]

@Ross__Hendricks I’ll take the other side of this bet

[Rob Bailey @RMB]

@EricBalchunas Nope

[Emily Bennett @emilybenn12]

AI is shifting startup formation from a hiring problem to a systems design problem.

The best founders won't ask, "Who should I hire next?"

They'll ask, "What capability is missing from this human and AI system?"

The companies that define the next decade may reach scale before they ever resemble traditional organizations.