
Jeetu Patel
3.6K posts

Jeetu Patel
@jpatel41
Technology Executive & Board Member. President & CPO, Cisco. Proud dad. Love design. Views are mostly my own, but sometimes not entirely my own ;-)



This is happening in plain sight. The leading AI companies themselves are embroiled in the fiercest battle to hire the most highly paid software programmers in the history of the world. And so it goes.

But who will those jobs go to?

If your goal is to get smarter about AI, follow @jpatel41. Hoping this means he’ll be posting here more often


The Alpha on X has grown so exponentially in the past two years. It is now the go-to platform for high-density learning. Unlike other platforms where you are drained after spending an hour and have nothing to show for, X tends to be energizing because each time you walk away smarter.








Great post on some of the dynamics to think through for the future competitive advantage in world when AI models are shared amongst firms and packing so much for the intelligence of that industry. This is going to become a core question for companies and the economy broadly over the next decade and beyond. If AI is trained on the best datasets in every single industry - like law, finance, healthcare, or life sciences - then how do you compete and differentiate in the future? This is a great open question that I don’t think is perfectly knowable right now because of how fast AI progress is happening. But ultimately it stands to reason that if intelligence is abundant and broadly available to anyone in a field, then the companies that effectively use it the best and against a set of data and knowledge that grows in value over time, will be in a strong position. There’s a huge reinforcing loop between the intelligence from models, a company’s own data, the connection of that data and AI in their workflows, and how employees ultimately interact with that system to create value. There’s no obvious point where this will become uniform across all companies in a vertical because each company will approach this in a different way, just as they already do with their talent and workflows. If anything, there will be compounding returns to those that do this best that accelerate their advantage over time. Overall, super interesting question to see how this plays out over time.


We audited SWE-Bench Pro, one of the most widely used AI coding benchmarks, and found it no longer reliably measures frontier coding capability. We find 30% of SWE-Bench Pro tasks to be broken, and are retracting our previous recommendation that the research community use it as a leading coding eval. openai.com/index/separati…



A few patterns we frequently use with Fable 5: Use Fable 5 as an "advisor." An executor (Sonnet 5) calls Fable 5 for guidance. Most tokens are billed at the lower executor rate.

Sol is rising. It’s a good model.

The customers are now benchmarking the labs A real reversal: official benchmarks don’t mean much anymore. Every lab trains for them & every launch tops them. So DoorDash built its own test and ran the models through it No model won on everything. The best setup was a mix… and it changed when a new model shipped A lot of companies are going to copy this. If you can test the models yourself, you don’t have to take the labs word for anything





Whether you think you can, or think you can’t... Either way, you’re right.

One of the most underappreciated ways to play the AI semiconductor buildout may be through materials rather than chips themselves. As the industry races to produce more advanced semiconductors, demand isn’t just rising for GPUs and wafer fab equipment, it’s rising for the critical materials that make modern chips possible. (1/6)🧵
