AI-Native & Agentic Founder/Operator (New AI company going into private beta in the next few weeks!)
New York, NY
Joined January 2009
- Tweets35.8K
- Following2.2K
- Followers13.6K
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Apollo's chief economist stated AI has delivered "zero financial payoff to corporate America outside of tech" despite massive investments.
Is he wrong?
With 10 mins of research using @grok, I was able to pull together a TON of specific examples with impact in the $$ billions.
Here's the spreadsheet. Comments welcome!
docs.google.com/spreadsheets…
Rob Bailey retweeted
It’s our third year of tracking AI in New York with @Accenture, and there’s a lot to talk about!
Ahead of today’s Crain’s Tech Summit, we’re sharing a first look at our latest research in @CrainsNewYork.
A few findings that stand out:
• NYC job postings seeking AI skills nearly tripled to more than 55,000
• Nearly 80% of executives surveyed expect entry-level hiring to grow or hold steady
• 86% of executives believe AI will create more economic opportunity in NYC than it eliminates
There’s still plenty to learn about AI. But three years into this research, we see more and more reasons to be excited about New York’s future.
Read more ⬇️
crainsnewyork.com/politics-p…
Rob Bailey retweeted
This is quite the deep insight into what a team / company can look like when you build and scale agent- first instead of people- first.
The thoughtfulness of the scaffolding and constitution you need to empower humans to then manage swarms of agents is impressive
i've been building the most ai-pilled company in the world for 2 years. let's trade notes:
- spawn is ten people
- we spend 30x payroll on AI tokens
- and ship almost as much as anthropic
- our agents pick their own names
- and profile pics
- and remember everything
- they dream
- and have time off on sundays to relax
- (not joking)
- agents fix every error hit by a user
- and verify their own work
- with no human in the loop
- we don't care if they break production
- only that they fix it fast
- we let bugs design how we work
- not code bugs, ants
- only anthropic's models can do the job
- openai makes reward hackers
- xai and google make village idiots
- i wrote an article with all the sauce
- but you'll scroll tiktok instead of reading
Rob Bailey retweeted
I’ve closed every 6 and 7 figure deal by following the same two-meeting framework:
Meeting 1:
I don't pitch.
Not a single word about what I'm selling.
First thing I do is set the rules:
"Here's how this works. I'll spend 20 minutes understanding your situation. If I think there's something worth talking about at the end, I'll tell you. If there isn't, I'll tell you that too. Either way, you'll walk away with something useful."
Then the fact-find.
Except I don't ask questions. I make assumptions.
"I'm assuming you've already got X sorted."
"I'd imagine Y's in place by now."
If he agrees, I move on.
If he corrects me, I've found a gap.
Every correction is ammunition.
By the end I've usually got three or four of them sat on the table.
Then I play them back to him:
"So from what you've said, you're in X position. You've got a hole in Y. And because of that, Z keeps happening."
Pause.
"Are you comfortable with that?"
He says no.
Now I show him the fix in two minutes:
- The problem he just admitted.
- What closes it.
- Why it works.
Then, before I leave the room:
"Before I send you anything, if this is exactly as I've described, would you go ahead?"
Yes, and I book the second meeting and send the details.
If he waffles, he gets nothing.
"Then there's no point me sending anything over yet. Let's speak when the timing's right."
Funny how that one line makes them want it more.
Meeting 2:
This isn't a second pitch.
He made the decision in Meeting 1.
Meeting 2 is him checking the small print.
He's read the details. He's got questions about how it actually works. I answer them on the spot.
Any last objections come out here. I deal with those on the spot as well.
Then:
"Right. Based on everything we've covered, next step is getting the paperwork signed. Does Tuesday work?"
Done.
Now look at how your reps do it.
Pitch in the first meeting. Send a proposal to anyone who asks. Chase for three weeks.
Then tell you the market's slow.
Rob Bailey retweeted
It's now official: Ron Gabrisko is the greatest CRO to ever live.
He joined @databricks when it was less than $1M ARR, scaled revenue to over $7B, and a company valuation of $190B. No one has even come close.
On this episode of @saastr CRO Confidential, Ron shares his approach to hiring, leveraging AI to acquire customers and scale revenue, and lessons in pricing as a usage based pricing pioneer.
Links to the episode in comments.
Hey @github Hope you're having fun at your Universe event. :-) But your billing is breaking. I'm hearing from lots of customers having problems, us included.
Rob Bailey retweeted
Michael Pollan reveals how the introduction of caffeine completely changed human history and consciousness.
"Before caffeine, it was a very different world and a very different consciousness. People were drunk a lot of the time, buzzed almost all of the time because it was safer than water"
"Water was really how you got diseases. If you fermented things, even low alcohol, it killed a lot of the microbes. So people, even kids, drank hard cider for breakfast"
"Tea, chocolate, and coffee all arrive in the same decade in England, and things start to change... no one ate or drank boiling water or hot beverages before"
"Boiling it made it much safer. This gave this incredible public health boost to these places, and then you have the drug that fosters a kind of more linear, rational, focused way of thinking"
"People in the 1600s started writing about it saying, 'Wow, we have this new civil and sober drink that we can use.' And that, I think, makes possible things like the Industrial Revolution"
Readers added context they thought people might want to know
The clip repeats the myth that pre-caffeine people were mostly drunk because water was unsafe. Historians and medieval sources show water was commonly drunk from wells/springs while weak ale provided nutrition/calories, not a substitute for bad water.
leslefts.blogspot.com/2013/11/the-gr…
slate.com/human-interest…
fakehistoryhunter.net/2026/02/02/dri…
Rob Bailey retweeted
This is correct. The vast majority of enterprise workflows have not actually had agents applied to them in any meaningful way. The economy probably has this deployed < 1% right now.
At best you have a few automated systems in a company that do a single pass of an LLM to make a decision or provide feedback, but very little real “automation”. Some of this is a statement about the adoption rates of technology in enterprises, but the vast majority of it is really just due to the capability of AI itself.
That’s all changing. Now with computer use, long running agents, harness improvements and surrounding infrastructure, we’re finally at a point where we can start to apply agents at more complex workflows.
We will see self-improving systems that can take any particular workflow goal and improve the execution rates over a volume of instances. And with the emerging form factor of agent swarms, you can just choose to deploy any amount of compute at a problem and agents will off and do the work.
This won’t be about and users prompting agents to complete a specific task for them, but instead the organization reengineering the underlying workflows to take advantage of agents in their processes. This will have a massive impact in finance, life sciences, healthcare, and other information-heavy domains. Lots of opportunity to build and deploy these systems right now.
AI HEROES: DUKE ENERGY.
Duke Energy says the AI it uses to forecast demand, predict outages and run its grid had saved customers about $500M as of 2024.
s201.q4cdn.com/files/doc_dow…
latitudemedia.com/news/aws-l…
Rob Bailey retweeted
Turns out selling to big companies isn’t about having the shiniest tech: it’s about hitching a ride on a giant’s sales team.
spr.ly/6012BH9oQM
Rob Bailey retweeted
Literally just do this method if you’re starting out as an SDR. Give him a follow too
I was the #2 SDR in America for my company last year.
Here's what I did vs the average SDR.
First, I didn't listen to my manager. Sounds crazy right? He was always on me about dials, volume, how many emails to send.
I built my own process. When I joined, I told the person I started with: I'm going to figure out what works here and be the best in our office. That's what I did.
I talked to the top SDRs about what was working but took what they said and made it my own
I got close with all my reps. My first goal with each one was to earn their trust by booking them a really important meeting. I did it for every one.
I met with my reps every Monday and Friday. On Mondays they'd give me the sauce: what's going on in their accounts, who's in them. I used that to book meetings.
Most of my meetings came from accounts that already knew us:
- Past opps
- Past POCs
- Past customers
- Marketing leads
- Growing inside accounts. Get one team, then the next. Meet with an IC, then reach out to their boss.
I never parallel dialed. Only targeted outreach with an actual business reason for them to take the meeting.
Rob Bailey retweeted
My predictions over the following months:
1. Stock market will get hurt bad when the music stops on data centers, models, and chips.
Too much froth, too high of investment, too many companies will fail. The switching costs are low. I don't know how these companies will become profitable. Its a mega bubble.
The rest of the economy is already in a deep recession. Real pain will set in when the public markets dive.
2. Power is the bottleneck.
AI is burning an absurd amount of power. The cost of electricity is skyrocketing which is making the cost of everyday life more expensive and hurting quality of life. I'm against data centers because of the power, not the water.
The grid is extremely fragile and the weakness will be exposed in the coming months.
3. Subsidies are skewing the market.
AI isn't cheap. The prices to use it are artificially low because of subsidies all the way up the chain.
You pay $20 or $200 a month but the true costs are 10x That spread is venture capital money being incinerated at scale.
When they raise the price, the users will disappear and investors will hold the bag.
3. A big security breach is coming.
A bank, a certain model, something is going to rock our world.
People are letting bots log in to their banks, their email and everything else. Big trouble coming.
4. Grokbot is exceptional.
Its the first model that is adding significant value to my everyday life. I don't know why anybody would use a different personal assistant.
5. AI is insanely high maintenance
Billions are being poured into training the next model. They day it comes out its already behind. China has cheaper electricity and more chips.
The chips they bought will be obsolete in 18 months.
Companies are stuck paying big money every month to maintain and update all of their AI tools. Its a mess.
6. The power companies will renege on their deals.
Political pressure will unwind all the unsustainable power deals these data centers and LLMs have negotiated.
Power costs will explode. Then comes the choice:
Do we jack up costs for our AI / compute and watch everybody switch to the next model or do we incinerate more VC capital? Nobody wins.
7. Credit will be the first domino to fall.
Data centers are already struggling to finance the GPUs over 10 years when they will be obsolete in 18 months. We'll end up with a shit load of half built data centers who can't negotiate power rates or buy chips because nobody will let them borrow money.
The money will get cold feet as soon as the markets tank and the show will be over.
8. It'll be a dark, dark period in economic markets.
The people who own assets that make real money (real estate or businesses) will win.
The people who plan ahead and avoid massive leverage will win.
The people with cash on their balance sheet will accumulate more assets and generate exceptional wealth.
--
Don't listen to me. I'm just a bear. Not investment advice. Good luck!
Rob Bailey retweeted
Experian, a $35 billion business with roots dating back to 1826, is surprisingly AI-native.
Its new Chief AI Officer, Vijay Mehta, broke down the credit bureau’s end-to-end AI strategy.
Complete list of notes...
Big takeaways:
• Experian is well past proof of concept, with AI capabilities running in production throughout the company.
• The next stage is what Mehta calls "reimagination." Redesigning how work gets done through delegation to agents vs. prompting chat bots.
• Adoption is close to universal. 85% of employees actively use the company's AI tools, 88% of developers use coding assistants, and employees built more than 6,000 custom GPTs by the first half of fiscal 2026.
• They're seeing real ROI. It's measured in time savings, time to value, quality, velocity and traditional before and after financial metrics, not in adoption or token use.
How they built the foundation:
• Within months of ChatGPT's launch, they trained thousands of employees and put AI policies and guardrails in place.
• AI traffic was routed through a control plane that gives them observability, monitoring and security.
• They wrote a GenAI risk framework and built the Experian GenAI Platform, an internal system that lets employees build internal-facing & and customer-facing apps, while accessing company data securely.
• They rolled out a coding assistant to their large developer base and built their own Experian GPT into products like a digital financial assistant and their Ascend platform.
• They created a GenAI Center of Expertise, which now sits under Mehta's AI office.
• Since then, AI has spread across software development, analytics and business operations.
AI inside the employee experience:
• MyCareer platform recommends learning and career moves and helps managers spot skills gaps.
• Nadia, an internal AI coach, helps employees set goals, build development plans and prepare for performance reviews.
• Nadia also gives leaders personalized coaching and 360-degree feedback.
On models and cost:
• "Not everybody needs the most advanced frontier model. Most workloads can be done with a free or very low-cost model."
• He compares reaching for a frontier model by default to using a sledgehammer on a job that needs a much smaller tool.
• They run a mix of open-weight models adapted to their own environment and frontier models accessed through AWS Bedrock.
• The control plane routes each workload to a model based on the task, language, region and regulatory requirements.
On agent safety:
• Guardrails are designed into the architecture from the beginning instead of being added later.
• Their Agent Operating System runs on that control plane, so it can sandbox, monitor and stop an agent process when necessary. "The ability to kill a process, the ability to kill a process, I will repeat that, we've got all of that built in."
• Their risk profile differs from the frontier labs' because their systems work over a defined body of financial and identity data, without broad access to the internet. "We start with our corpus of data and we put controls around that. And we know exactly how the data is moving."
What the CAIO role actually is:
• Experian is a big company split into many business units. His job is one common AI strategy and direction: "to make sure that we are all marching in the same direction."
• He accelerates the AI agenda both internally and externally.
• Risk, security, compliance and partner choices all come together in the AI office.
AI as the new middleman:
• Consumers increasingly start their financial journey inside an AI assistant instead of going to Experian or a bank, so Experian is meeting them there.
• It's the first personal finance app integrated with Google's Gemini. US consumers who connect their Experian accounts can ask Gemini about their FICO credit rating and what's affecting it.
• Inside ChatGPT it has launched personal loan shopping, credit card shopping and an insurance marketplace.
• On the enterprise side, ServiceNow is the first integration partner for its Agent Operating System. Under a multiyear partnership, ServiceNow agents tap Experian's Ascend data and decisioning from inside enterprise workflows.
• It also launched Agent Trust, which verifies that an AI agent is actually connected to the consumer it claims to represent.
• Could agents eventually skip Experian and get the data somewhere else? He says they can't reproduce it. Decades of longitudinal financial and identity data, plus its provenance and regulatory standing, are what matter when the output drives consequential decisions. "In order to be able to make a decision, you've got to have that trusted data source."
His advice if you're early:
• Train employees.
• Set guardrails before deployment. "So much of safety and responsibility comes from the person interacting with the system."
• Route AI traffic through a controlled gateway where usage can be observed and policies enforced.
Which AI leader should I learn from next?
Rob Bailey retweeted
Couple of underrated hacks for GTM...
> Call every positive reply
> Reply in under 5 minutes
> Pitch a time, skip the calendar link
> Ask "wrong person" replies who the right one is
> Log every "not now" with a date
> Re-contact your whole TAM every 90 days
> Build lookalikes off closed-won deals
> Use job posts as buying signals
> Case study from their exact industry
> 1 segment per campaign
> Verify every email before it sends
> Keep bounces under 1%
> 10 to 20 sends per inbox a day
> Turn off open tracking
Simple as.