Ron Lerner

[Ron Lerner @LernerRon]

private evals are the new proprietary asset

[Cuy Sheffield @cuysheffield]

Visa is for vibe coders.

Anyone can spin up an app and any API can become a merchant.

Starting today we’re onboarding x402 and MPP merchant endpoints into Visa CLI so APIs, LLMs, data products, and dev tools can be discovered and purchased by verified agents with cards on file.

If you’re building an x402 or MPP endpoint, or want Visa to help you stand one up, sign up below.

We’re also opening Visa CLI to initial users in the US. DM me for an invite.

[Ron Lerner @LernerRon]

Great insights on the growing need for AI talent

[Aaron Levie @levie]

Take whatever number of people you thought might be in jobs related to AI deployment in the enterprise and multiply it by 10. Then probably 10 again.

A major topic that keeps coming up in talking to CIOs across enterprises of all sizes and industries is the implementation gap for getting agents to work at scale and organizations on mission critical work.

As the task goes from implementing a chat system that’s basically an LLM plus search, to connecting to real production systems that both can deliver meaningfully better productivity gains but also introduces meaningfully more risk, a whole new set of work has to be done.

You have to ensure the right level of protection of data, updates to access control controls, migration of legacy systems to common modern platforms, create observability across what agents are doing, implement new workflows, figure out the human in the loop moments, drive the change management of the new workflows, and more.

Then, all of a sudden the model capabilities get updated and you have to do a set of the above steps over again. Half of what you’ve done is obsolete, and the other half needs to be upgraded to take advantage of new capabilities. Or, token budgets run hot and you have to peel off some of the workloads to lower cost models that will be more cost effective. But then you have to go through those same steps.

Enterprise are trying to figure out what is the right set of roles to go and implement the systems in their organization to ensure that the workflows are actually being executed properly, ensure it’s not just slop being produced, and to make sure their organization remains safe and secure.

Many companies are starting by repositioning existing IT talent in these functions, but there’s also a growing need for the equivalent of internal FDEs to go take on these tasks in an enterprise. The looks incrementally closer to software engineering than it does traditional IT implementation.

Next, almost all AI vendors (labs and the software players) will have some form of next-gen FDE or Applied AI architecture functions to help support these use-cases. The benefit here will be these companies have an incentive to make their capabilities work well so they can bring best practices from a range of customers they’re seeing and directly from the product innovation.

And finally, we’re seeing the rise of all new AI services firms or major parts of existing services firms move into AI implementation. Companies will often want to bring in ostensibly neutral players that can work across their tech stack but also have seen best practices across their vertical. There are going to be tons of new service providers that get launched to do this, and many will eventually go and disrupt (or get acquired) by the larger player.

Either way, all told, we’re in for years of AI diffusion, and along with it tons of new roles and areas of work to be done to deploy AI at scale.

[Aaron Levie @levie]

Take whatever number of people you thought might be in jobs related to AI deployment in the enterprise and multiply it by 10. Then probably 10 again.

A major topic that keeps coming up in talking to CIOs across enterprises of all sizes and industries is the implementation gap for getting agents to work at scale and organizations on mission critical work.

As the task goes from implementing a chat system that’s basically an LLM plus search, to connecting to real production systems that both can deliver meaningfully better productivity gains but also introduces meaningfully more risk, a whole new set of work has to be done.

You have to ensure the right level of protection of data, updates to access control controls, migration of legacy systems to common modern platforms, create observability across what agents are doing, implement new workflows, figure out the human in the loop moments, drive the change management of the new workflows, and more.

Then, all of a sudden the model capabilities get updated and you have to do a set of the above steps over again. Half of what you’ve done is obsolete, and the other half needs to be upgraded to take advantage of new capabilities. Or, token budgets run hot and you have to peel off some of the workloads to lower cost models that will be more cost effective. But then you have to go through those same steps.

Enterprise are trying to figure out what is the right set of roles to go and implement the systems in their organization to ensure that the workflows are actually being executed properly, ensure it’s not just slop being produced, and to make sure their organization remains safe and secure.

Many companies are starting by repositioning existing IT talent in these functions, but there’s also a growing need for the equivalent of internal FDEs to go take on these tasks in an enterprise. The looks incrementally closer to software engineering than it does traditional IT implementation.

Next, almost all AI vendors (labs and the software players) will have some form of next-gen FDE or Applied AI architecture functions to help support these use-cases. The benefit here will be these companies have an incentive to make their capabilities work well so they can bring best practices from a range of customers they’re seeing and directly from the product innovation.

And finally, we’re seeing the rise of all new AI services firms or major parts of existing services firms move into AI implementation. Companies will often want to bring in ostensibly neutral players that can work across their tech stack but also have seen best practices across their vertical. There are going to be tons of new service providers that get launched to do this, and many will eventually go and disrupt (or get acquired) by the larger player.

Either way, all told, we’re in for years of AI diffusion, and along with it tons of new roles and areas of work to be done to deploy AI at scale.

[Garry Tan @garrytan]

It's working guys. This is exactly what I built for myself and what you can have for you now.

It's also free and open source and you can fork it and make it your own. I don't want to hear the hate. This is a gift!

[Aaron Levie @levie]

It’s pretty clear that the emerging paradigm of agents will be like if you had a human expert in any domain, and they had all the capabilities of a top engineer who could use any tool (or the write their own on the fly) to complete any task, along with unlimited compute and a file system to work with.

That combination of skills and technology primitives provides you with somewhat limitless capability in AI. You’re no longer limited by only what the model was trained on, or the inherent context window limitations. The agent will simply spin up subagents to work on component parts of the workflow, and get expertise as needed throughout the process.

For all known types of tasks that are frequently repeated, they have quick access to existing skills and tools to complete their work. We’re already seeing this in a range of fields where skills are being written for agents to follow either domain-wide or company-specific processes. Doing legal analysis in a specific way, running financial models, processing spreadsheets for complex data work, generating PowerPoints, and so on.

And for areas they’ve never seen before, they can simply write code on the fly to do the work one-off. Imagine pairing an industry expert with an engineer that can code up any custom script whenever it wants. Compute is your only limiter.

This approach seems to cover a fairly wide range of knowledge work. Obviously the first space to benefit the most from this has been in coding itself, but it’s clear that this go across all other areas of work and even personal agents. Kind of wild.

[Ron Lerner @LernerRon]

If you were always comfortable, you wouldn’t know what comfortable is.

#YinYang

[Chamath Palihapitiya @chamath]

Tell me you’re working for our adversaries without telling me you’re working for our adversaries.

[Ron Lerner @LernerRon]

The only way to make sense out of change is to plunge into it, move with it, and join the dance.
- Alan Watts

[Ron Lerner @LernerRon]

I love you very much, now I’ll change you

[Ron Lerner @LernerRon]

One definition of #woke is that it's hubris in the service of virtue — pride in one’s moral or political role — which can seduce reason just as much as lust or greed.

[Steve McGuire @sfmcguire79]

What on Earth is the New York Times thinking with this framing?

[The White House @WhiteHouse]

https://t.co/dx9Fn0qnHm

[Lex Fridman @lexfridman]

"The problem with the world is that fools and fanatics are always so certain of themselves, and wiser people so full of doubts." - Bertrand Russell

[Luana @LuanaGoriss]

Many don’t know that Shiri Bibas’ parents were also murdered on October 7, 2023.

Three generations.

[Shaun Maguire @shaunmmaguire]

People that don’t want DOGE to succeed hate America

[Secretary Brooke Rollins @SecRollins]

Live update from our @DOGE_USDA team’s meeting tonight 👇

Look what we just found (and cancelled!): $324,671 grant for “Increasing DEIA Programming for Integrated Pest Management” … you can’t make this up🤦🏻‍♀️

cc: @DOGE @elonmusk

[Ron Lerner @LernerRon]

Great essay on our state.

[Niall Ferguson @nfergus]

"Deep State (They /them) vs. the Wonder Boys." I find myself in violent agreement with this brilliant essay by @micsolana. Everyone should read it.

[Ritchie Torres @RitchieTorres]

I am deeply grateful for the latest release of four hostages: Liri Albag, Karina Ariev, Daniella Gilboa, and Naama Levy, who, on October 7th, was dragged from the back of a Jeep into Gaza.

The world must never forget the sheer savagery and sadism of Hamas in not only abducting 251 people on 10/7 but also holding many of them hostage for 476 days and counting. #NeverForget

[mark pincus @markpinc]

Its offensive to see the MSM and left refer to tech leaders as *oligarchs*. As if theyre corrupt Russian robber barons. When the truth is every one of them got there by generating massive value for our country and the world.

And this isnt coming from a single person who has delivered 1/trillionth of that value.

Its fucking cool that we’re talking about putting an American on Mars.