Chris Keith

orchestrating humans + agents → ∞ productivity

[Chris Keith @itschriskeith]

@JayKapoorNYC 😭

[Chris Keith @itschriskeith]

@FreezingFinTake 😭

[Chris Keith @itschriskeith]

@NYCSanitation Incredible

[NEW YORK KNICKS @nyknicks]

YOUR NEW YORK KNICKS PLAY IN THE FINALS THIS WEEK.

[Chris Keith @itschriskeith]

@TickPick @darrenrovell @KnicksMuse @LegionHoops @TheDunkCentral Come on!

[Chris Keith @itschriskeith]

Check that out

[Shyam Sankar @ssankar]

Jevons paradox playing out in real time.

Cheaper technology is creating more demand and more jobs.

https://t.co/aTSZe409yr

[Chris Keith @itschriskeith]

@Vivek4real_ Preach

[Chris Keith @itschriskeith]

@AlexHormozi Would also add

*MORE dumb things…. Faster

[Chris Keith @itschriskeith]

Well well well

[Polymarket @Polymarket]

JUST IN: Sam Altman says AI probably won’t trigger the “jobs apocalypse” he once predicted.

[Chris Keith @itschriskeith]

@levie - Yep

We’re seeing this HEAVY in the MSP space

Both internally and externally

1 - Managed Service Providers are already positioned as the Technology experts for their clients

2 - They are naturally being looked at at the ‘AI implementation’ experts

But

They often don’t have those experts in house, as its not a traditional IT/support role

This means, (and we’re seeing this too) some MSPs will build out AI native deployment services - its a new revenue stream for them

The other KEY is highlighted at the bottom (neutral players that have SEEN best practices across their vertical)

So they know exactly HOW and WHAT to implement first

[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.

[Chris Keith @itschriskeith]

@harleyf Top advice:

Hire people who give a shit

[Chris Keith @itschriskeith]

@VicLombardi Thoughts and prayers, Vic!!

[Chris Keith @itschriskeith]

@danshipper @every Fantastic read

[Chris Keith @itschriskeith]

@MurrayHillGuy1 This is a great take

[Chris Keith @itschriskeith]

@morganpolotan @elonmusk The type I / type II / type III / visual is incredible

[Chris Keith @itschriskeith]

@benln We love to see it

[Chris Keith @itschriskeith]

@naval And then to repeat that exercise

Indefinitely

[Chris Keith @itschriskeith]

@Gramercy_Goblin Solid

[Chris Keith @itschriskeith]

@MorganBarrettX Not all heros wear capes

[Chris Keith @itschriskeith]

Only thing better than an FDE (or extremely complimentary)

A fully embedded employee in the day to day workflow

That doesn’t have to learn what good looks like

Because they’ve already been doing it :)

[vas @vasuman]

https://t.co/PetMluobip