Cofounder @ 4MINDS | ex-AWS, ex-Google
Los Angeles, CA
Joined April 2015
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Jason Nocco retweeted
LA needed another big demand driver.
And its here:
Physical AI.
San Francisco is the capital of AI. They build the models.
LA builds what the models run on: rockets, satellites, drones, defense, and autonomy.
11,000 aerospace jobs added in LA County in two years
NASA JPL, SpaceX, Northrop Grumman, Rocket Lab, Relativity, Boeing, Raytheon all w/n 30 miles of each other.
$4B+ in LA-area defense tech venture funding, more than double the year before.
$1B Anduril campus coming to Long Beach, with 5,500 jobs
$4.4B up-front price for Culver City’s Silvus Technologies, which Motorola is acquiring
And it’s moving up the coast.
Playa Vista sits on the old Hughes Aircraft campus. Santa Monica and Culver City are producing hard tech and defense startups.
Even LA’s version of software AI is about the physical world.
Culver City is becoming the capital of AI filmmaking, with Promise next to the Sony lot, TrueShort moving in from Santa Monica, and the city council backing AI on the Lot.
Luma AI’s COO said it plainly: “Culver City is the future of AI + filmmaking.”
The Bay reigns supreme in AI.
LA is putting it into the physical world.
Jason Nocco retweeted
Cisco is going through one of the most remarkable AI transformations, and not many know about it. Two years ago, Cisco quietly started a new business - AI infrastructure for hyperscalers - and it went from 0 to a $9B business in two years.
@jpatel41, Cisco's President, joined Cisco 36 years in and runs it like a late stage co-founder. This is the wild story of how he's going Founder Mode on an 80,000 person company.
Jeetu Patel is Cisco’s President and Chief Product Officer, helping turn one of the world’s largest technology companies into what he calls “the world’s largest startup.”
Cisco is now positioning itself as the picks-and-shovels company for the AI era, spanning networking, security, silicon, and infrastructure. Its AI infrastructure business went from zero to $9.3B in orders in just two years.
In this episode of Main Function, Jeetu joins YC's Jared Friedman to talk about bringing a founder mindset to the company, and what the massive data center buildout looks like from the inside. He also shares the unlikely path that took him from waiting tables at Sizzler to leading product at Cisco, and the lessons he’s learned about ambition, compounding, and making the most of the opportunities in front of you.
00:00 — Cisco’s Bet on AI
03:38 — Running Cisco Like a Startup
06:45 — The Infrastructure AI Agents Need
10:00 — Getting 32,000 Engineers to Embrace AI
14:07 — Bringing AI to Hardware and Silicon
17:15 — Inside the AI Data Center Buildout
21:27 — Are We Overbuilding AI Infrastructure?
25:10 — Building Trust in AI Agents
28:18 — From India to Waiting Tables at Sizzler
32:12 — Choosing Learning Over Ego
35:31 — Ambition, Teamwork, and Compounding
39:31 — Don’t Squander Your Opportunity
Jason Nocco retweeted
SPONSORED: When Every Company Has AI, What Creates Advantage? From, 4MINDS s.hbr.org/4gDWCXR
Jason Nocco retweeted
🎯 88% of companies use AI, but 95% of GenAI pilots fail. The problem? Incomplete context.
In our latest feature, Ryan Trimberger of 4Minds shares a 7-factor test to vet real contextual AI tools before you buy. ⬇️
HBR + AWS just published "When Every Company Has AI, What Creates Advantage?"
We sponsored this one because it's the question we actually think about every day. Frontier models are table stakes. The gap is what you build around them.
What's the one thing your company does that your competitors can't copy?
“The White House Wants Anthropic to Block All Jailbreaks.” If only there was a polymarket @elder_plinius
Ghost weights mechanics:
Shadow model trains on new data alongside production.
Eval gate runs regression vs. production baseline.
If shadow wins → atomic swap.
If it regresses → discarded.
Every version retained. Rollback is one command.
No sprint. No window. No manual QA.
The enterprise AI failure mode that gets no coverage:
A perfectly functioning system that gives the right answer to the wrong version of the question.
Confident. Wrong. Invisible until it matters.
The silent enterprise AI failure mode:
Model passes eval. ✓
Passes smoke tests. ✓
Returns confident answers. ✓
Answers reflect your business as it was 8 months ago. ✗
Ghost weights don't announce themselves.
Model drift ≠ ghost weights.
Drift is statistical, your input distribution shifted.
Ghost weights are semantic, the ground truth the model learned is no longer true.
Your drift monitor won't catch ghost weights. These are different problems.
Poll: when was your production LLM last retrained?
Most teams answer this question slower than expected.
Because nobody owns it.
Q2 enterprise AI reality check:
The first generation of deployments solved the deployment problem.
The second generation is solving the maintenance problem.
Models degrade. Business environments change. Compliance requirements evolve.
Ghost weights + continuous eval is the architecture for the second generation.
A question for every FSI AI deployment:
When the next regulatory guidance drops, how long before your model reflects it?
If the answer is "next quarter" or "when we schedule a retrain" that's a compliance gap.
Ghost weights close that gap.
Ghost weights in financial services:
- New AML typology → model behavior updated in 48 hours, not next quarter
- KYC requirement change → eval suite flags drift, weight update proposed
- Counterparty risk threshold → reflected in model immediately
No retraining cycle. No compliance gap window.
This is what continuous learning looks like in regulated industries.
The AI failure mode regulators don't talk about yet:
Model temporal drift in regulated workflows.
Your model was trained and validated against last quarter's regulatory environment. That environment changed.
The model doesn't know. The answers are confident, coherent, and wrong.
This is a 2026 compliance risk.
Continuous learning isn't a product feature. It's an infrastructure decision.
What it actually requires:
- Eval harness to detect regressions before they reach production
- Rollback mechanism when a swap degrades performance
- Audit log of every weight update for compliance review
Most enterprises have the eval layer.
Almost none have the rollback or the audit trail.
That's what ghost weights exploit.
Quick check for ML teams starting their week:
When was your production model last updated?
Not your RAG index.
Not your prompt templates.
The weights.
If that answer takes more than 30 seconds to find, you have a process problem.
The AI evaluation checklist most enterprises skip:
- Does the vendor improve the model without accessing your data?
- What happens when a ghost weight is swapped mid-deployment?
- Can you audit which model version answered which query?
- Is your eval suite test-time or production-time?
These aren't advanced questions. They're table stakes in 2026.