Welcome, humans.

If you've ever felt that gut-punch moment when AI looks right but is catastrophically wrong, you're not alone. And if you work in data science, you're probably wondering: is AI going to replace me, or is it just going to make my job harder?

We're going LIVE RIGHT NOW with Dennis Salguero (aka Data Science With Dennis,) to answer exactly that question.

Here's what we're covering:

• The brutal truth about AI's impact on data science jobs (Dennis has the data)

• Which AI model actually works best for data science tasks

• Live comparison: Claude Code vs. Claude Cowork vs. OpenAI's Codex on real data

• How to tell if you're ready for AI adoption (or if you're setting yourself up to fail)

Why watch this? Because Dennis is bringing the receipts on what's actually happening to data science jobs right now, plus we're doing a LIVE tool showdown between Claude Code, Claude Cowork, and OpenAI's Codex to see which one handles real data tasks without hallucinating.

Join the stream now 

P.S. Dennis just told us he has a controversial take on whether you should even be using AI for data science work. You probably won't agree with him. (We didn't.)

IN CASE YOU MISSED IT…

Check out our other recent favorite episodes below!

"We Watched a Brain Emerge..." The AI That Might Kill Transformers

Zuzanna Stamirowska, CEO of Pathway, breaks down their post-Transformer architecture "Baby Dragon Hatchling" (BDH). Unlike static models like GPT-4 that freeze after training, BDH continuously learns like a biological brain—with real-time memory updates and temporal awareness. Backed by Lukasz Kaiser, co-inventor of the Transformer itself.

Key insight: Transformers suffer from "amnesia"—they can't easily learn new things once training is done. BDH is designed to physically change its structure as it learns, opening the door to AI that never stops improving. (YouTube | Spotify | Apple)

This 24-Year-Old Raised $64M to Build an AI Smarter Than the World's Best Mathematicians

Carina Hong dropped out of Stanford's PhD program to build "mathematical superintelligence." Her startup, Axiom Math, just raised $64M and has already solved a 130-year-old problem and disproved a 30-year-old conjecture.

Key insight: Unlike ChatGPT (which hallucinates on math), Axiom uses formal verification—meaning it provides mathematical proofs for its answers, ensuring 100% correctness. Hong argues that superhuman math AI will unlock breakthroughs in chip design, physics, and software verification. (YouTube | Spotify | Apple)

Everyone's Building AI Agents. Most Companies Are About to Break Their Systems.

Companies are rushing to deploy AI agents, but here's the dirty secret: most organizations lack the data infrastructure to support them. Without a centralized "Source of Truth," AI agents will fail because they can't tell the difference between outdated, duplicate, or incorrect information.

Key insight: Deploying autonomous agents without fixing your data structure first is a recipe for operational disaster. You need a verified knowledge layer between the AI and your raw data, or you're just building a very confident hallucination machine.

(YouTube | Spotify | Apple Podcasts)

Stay curious,

The Neuron Team

And if you haven’t subscribed yet, please do! Click the image below to go to our channel and hit “subscribe” to get notified right when new videos go live.

We have a goal to hit 50K subscribers by the end of the year (if not 100K), and we’re only 36K away! If you like learning about AI, and already watch some of our videos, do us a favor and click here to subscribe today. We won’t bite… but sometimes, we do scratch…

Stay curious,

The Neuron Team

P.P.S: Love the newsletter, but don’t want to receive these podcast announcement emails? Don’t unsubscribe — adjust your preferences to opt out of them here instead.