Thinking On Paper

What if the biggest bottleneck in AI isn’t data or algorithms—but the lack of talent? In a world where demand for AI solutions is skyrocketing, businesses are facing a critical shortage of skilled engineers.

In this episode of we're back at the busy intersection of artificial intelligence and human potential. Our special guests are Saurabh and Gaurav Vij, co-founders of Neo.

The biggest challenge in AI and machine learning today isn't data sets or training data, its a human resources problem. With only 300,000 skilled engineers globally - and only around 600 truly brilliant engineers - demand far outstrips supply.

How can businesses meet the soaring demand for AI-powered solutions? With salaries for the best developers as much as €2 million per year, how can the average company keep up?

Neo’s solution is the worlds first autonomous machine learning engineer. And it promises to bridge this gap by automating tedious processes and enabling engineers to focus on transformative innovations.

You'll learn how Neo’s multi-agent system outperforms traditional workflows, why Kaggle grandmasters are the gold standard for AI expertise, and what this means for the future of work.

The Vij brothers also share insights from their journeys, including the transformation of GPUs for AI and lessons from working at CERN.

And because this is Thinking on Paper, you'll also get the ethical implications of hyper-intelligent systems, how automation might redefine creativity and human ingenuity and what it all means for business, culture and your day-to-day existence.

Please enjoy the show. And click subscribe.

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Timestamps
0:00 Why Are There So Few Machine Learning Engineers?
1:54 Meet Gaurav Vij and Saurabh Vij
4:08 Lessons Learned from Working at CERN
4:32 The Role of Distributed Computing in AI
6:51 How to Explain AI to Your Parents
10:06 Misinformation: How Critical is AI Education?
12:16 The World’s First Autonomous Machine Learning Engineer
16:17 AI Competitions and Kaggle Grandmasters
18:17 How Many AI/ML Engineers Do We Need?
20:38 An Easy Analogy for an Autonomous ML Engineer
23:48 The OpenAI Benchmark
28:04 Hot Buttons: 5 Questions in 30 Seconds
29:09 Why AI Agents Won’t Use Dogecoin
30:22 Hollywood: Doomed by AI or Reborn?
32:49 AI News: Nvidia Digits Explained
35:16 What Will Replace Moore’s Law?
40:41 Could AI Models Be Motivated by Rewards?
43:44 Jensen’s Law
44:28 The Thinking on Paper Carry-Over Question
49:16 Questions for the Next Guest
52:17 After Hours: Backstage Extra

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