Beau Lyddon

AI @ Workday

[Beau Lyddon @LyddonB]

Amen!

My long-winded way of saying the same:
https://t.co/ZMTH0CF2ce https://t.co/qWhxB9nJWg

[Charity Majors @mipsytipsy]

(Sorry, got distracted.)

In summary, I’d love to see senior engineers get promoted for managing excellent vendor integrations. For *not* writing unnecessary code. For getting rid of special snowflakes.

Code is a liability.

[Beau Lyddon @LyddonB]

@Scoots_McGots @TomSlic08693047 @xanbarksdale @bipolarguy86 Omg. Yes. This. Why are these people so obtuse?

[Beau Lyddon @LyddonB]

@SergioGarc20223 Post your prompts and agent traces?

[Patrick Skinner @SkinnerPm]

We are the dumbest country in the universe. This is a twice-elected president speaking here.

[Gene Trevino @GenoVeno73]

😂🤣😂🤣😂🤣😂🤣

[Beau Lyddon @LyddonB]

Why is Meta destroying its engineering organization?, by @GergelyOrosz https://t.co/WVKwv208ib

[Gergely Orosz @GergelyOrosz]

So apparently after Meta leadership:

- Force reassigned some of the best devs on teams to AI data labelling fulltime

- Laid off another 10%

- Started to record every dev’s screen in the US 24/7

They now realized that it has, indeed started to destroy their eng culture. And are now trying to walk back.

All of the above was unprompted, not forced by anything external or even business reasons (Meta recorded record revenue, record profits)

The biggest self-inflicted eng culture destruction I’ve seen in a matter of weeks

[Aaron Levie @levie]

It’s very easy to say “we need an FDA for AI” or some equivalent government agency. Well this is what that would look like.

The capabilities of AI models have near infinite permutations. It’s going to be very hard have a purely objective set of metrics that can be universally applied before every model release, without extended back and forth, research, and debate between model labs, academics, and the government.

Now, imagine this same process with every country that you’re doing business with, globally, for every single model release. Add in a backlog of dozens of AI model releases, and you can quickly see how in the limit this will dramatically slow down all AI progress.

This is why we need to primarily focus on regulating the applied uses of AI, where the risk actually shows up.

[Beau Lyddon @LyddonB]

@TalkinYanks They need to send him down. Boone can’t be trusted to do the right thing.

[Aaron Levie @levie]

There’s no amount of intelligence that can get packed into AI models that replaces the need for context. For any sufficiently general purpose AI, you will always have to guide it in the direction you want as it has an infinite range of directions it can go in.

As long as the same model is used by a lawyer, an engineer, a financial analyst, or a healthcare professional, and as long as you’re trying to do anything uniquely differentiated or specific, then instructions, domain context, and proprietary data will always need to get into the context window for the model to be useful.

This is partly why AI automation doesn’t come for free, and why there’s still a wide spectrum of who’s getting the largest gains from AI and who’s not. You have to put in real work, and you get real value on the other end.

This is one of the advantages that applied AI will also have in the market. Any layer of abstraction above just the raw intelligence that can meaningfully get you off to the races faster will likely continue to be valuable.

[Ben Springwater @benspringwater]

I think about this Seinfeld interview all the time.

[Beau Lyddon @LyddonB]

TBM 425: AI and Agency, by @johncutlefish https://t.co/ML767R19WP

[Dave @davebftv]

The best call Stacey King ever gave.

Derrick Rose's poster on Goran Dragić

"I want to go higher. Do you not know who this kid is. He is from Chicago."

[Gergely Orosz @GergelyOrosz]

On the whole “just use loops”

Outside of the increasingly few people who

1) have unlimited AI token budgets

2) feel like prompting agents are holding them back (usually thanks to no #1)

I don’t think many have a use case for them. I’m more than content prompting (esp w #1!)

[Beau Lyddon @LyddonB]

Damn. RIP

[Chicago Bulls @chicagobulls]

We are devastated by the passing of 3x NBA Champion and beloved broadcaster Stacey King.

[samir kaji @Samirkaji]

Stories like this are what founders talk to each other about all the time. Every single round, we've had some variation of a bad experience with an investor.

1) Seed round: One investor said, "We don't think ex-bankers can be entrepreneurs".

2) Series A: Large firm meeting; they send their most junior person, who spends 80% of the time talking about themselves and other companies, and at the end, with 5 minutes left, says, So, what do you do again?

3) Series B: Asked for 4 meetings, 6 different asks for additional models (which we had to custom build), and then just went silent. No pass note, no acknowledgement.

Founders don't forget those experiences.

[Elizabeth Yin 💛 @dunkhippo33]

I once pitched an investor and at the end of the mtg, I asked him what he thought of my startup. He said I don’t want to say the wrong thing and call you a meek Asian woman, but I question how you’ll lead 100+ people…

[Gergely Orosz @GergelyOrosz]

“My company did layoffs a few weeks ago.

The main metric for productivity is AI usage (% of PRs with AI assistance) and PR count per week.

People are burnt out after layoffs, but they don’t want to be next so they are doing what’s requested: use AI, inflate PR count….”

[Matthew Prince 🌥 @eastdakota]

Two of our worst VC stories:

1. A Sequoia partner passed on Cloudflare because he didn’t think a woman could lead a security infrastructure company. Seriously. 🙄

2. I got introduced to @pmarca. Meeting got scheduled for a Monday, which should have been a clue. I thought it was just a casual meeting. He thought it was a pitch and brought the whole @a16z partnership team. Hilarity ensued. 🤪 At one point one of them said: “You don’t seem very prepared.” Which was true because I wasn’t. I framed the rejection letter they sent.

[Beau Lyddon @LyddonB]

Has AI Made VCs Forget Basic Competitive Strategy? https://t.co/2ZEJcIyhLx

[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.