Darrell Ross

I help brands and emerging SW platforms with Agentic Commerce and Commercial Intelligence product strategy.

[Darrell Ross @darrellross]

@gregisenberg We saw this movie 25 yrs ago when OTA bots started to hammer air, car and hotel booking platforms. Look-to-book ratios go down and infra costs go up.

[Darrell Ross @darrellross]

@AsFoundX @StephenFleming And what has HE done?

[Darrell Ross @darrellross]

@zachcoelius @benthompson Yep just finished it as well. What was the strongest takeaway for you?

[Darrell Ross @darrellross]

Love how everyone is falling back to old CAS research and applying its relevancy to AI.

Bringing this into the physical realm —> Remember Y2K? Points #1-#5 are why the possible failures in utilities, dams and other physical infra were overhyped.

Many complex systems can downgrade and still partially function (albeit poorly).

(On the other hand, the Crowdstrike incident from two years ago illustrates how a single root cause can disrupt a highly complex system (global air travel). )

[Josh Rosen @JoshARosen]

https://t.co/nsNwfHr4oP

[Darrell Ross @darrellross]

Honestly, why does @SlackHQ suck so badly in its core function?

[Darrell Ross @darrellross]

“What allows an organization to safely delegate action?” That’s the primitive underneath all of this.

When you read the Claude Tag description, it’s actually describing four new organizational capabilities:
1.Shared memory
2.Context accumulation
3.Initiative
4.Asynchronous execution

Those aren’t software features. They’re organizational design behaviors.

The moment Claude can:
•remember prior work,
•observe multiple conversations,
•take initiative,
•continue working without being explicitly instructed,

you have effectively created a new organizational actor.

Which means the enterprise suddenly has to answer questions it previously avoided:
•What decisions can this actor make?
•What authority does it have?
•What information can it access?
•How is it evaluated?
•Who is accountable for its actions?

That’s why I think @emollick is correct that AI adoption is becoming an organizational design problem.

[Ethan Mollick @emollick]

Decisions about how to use AI in your organization are increasingly organizational design and strategy decisions, not IT choices: How do you integrate agents into your firm? What intelligence will you outsource? What are the boundaries of the firm? What is the role of people?

[Darrell Ross @darrellross]

The deep dive into this topic: Air-conditioning America: Engineers and the Controlled Environment, 1900-1960 (Johns Hopkins Studies in the History of Technology) https://t.co/fbRM0x9aQG

Fascinating history of the engineers behind early HVAC. You realize that HVAC has been more impactful than most innovations (and yes, more impactful than the Internet)

Commercial air travel, sky scrapers, enclosed shopping malls, industrialized textile factories, even the motion picture industry would not exist without A/C.

[Darrell Ross @darrellross]

To dive deep into this topic, check out this fascinating, obscure book: https://t.co/jMm67aeuKt

Essentially, HVAC enabled the motion picture industry (theatres), textile factories in humid environments, air travel, modern office environments, enclosed shopping malls…a much more impactful innovation than the Internet.

[Darrell Ross @darrellross]

Interesting paper. This reminds me of Ritz-Carlton’s old MR. BIV initiative (Mistakes, Rework, Breakdowns, Inefficiencies, and Variation). We operated from the belief that guest loyalty was downstream of organizational reliability, and organizational reliability was largely a function of how effectively we reduced entropy in the system. What’s interesting about Chronos is not the conclusion that reliability matters, but its attempt to quantify the long-term economic impact of reliability improvements without waiting years of guest stays to observe the outcomes.

[Darrell Ross @darrellross]

YES!!!

This is a chapter for your next book.

To expand on a few of your points:

- consultancies will invest in partner level hires to place asymmetric options for growth. This new practice lead for “X” is a hope that their decades of experience will be the “5 mins” in the room to keep things in play (prospect engagement, internal resource alignment, etc). Most of their initial weeks and months of “work” will appear non-value add but it’s really about applying their mental model to a new organizational game board

- there are MANY non value-added contributors inside the enterprise which drives up Coordination Tax. As a leader, you need to constantly monitor your meetings and comms to understand which of this corporate theatre is cosplay versus helping drive alignment, decisions, etc

- young competent leaders can/will get promoted quickly based on their ability to navigate the internal dynamics and apply their own “5 minutes” of genius which reflects their mental model of how the org works

[Darrell Ross @darrellross]

To clarify, I was looking at this from a different angle…not from a “let’s build a Large-E enterprise context graph” lens.

Rather….if one is building a NewCo SW platform that is focused on “context graphing” an enterprise function (say, marketing campaign orchestration) then having this E2E observability is key to getting the context capture nailed.

To make the example more visceral, a campaign is defined in Adobe Workfront and executed across a Martech and Adtech stack with 10+ channels, multiple ad agencies, multi reporting platforms, etc. The Workfront record is a snapshot / byproduct of hundreds of Slack/Teams convos, Otter/Zoom transcripts, and other contextual alignment which is “observed” but usually not synthesized in any coherent manner.

Having a NewCo that has observes, catalogs, tag/classify these behind the scenes interactions to curated metadata and then applies to the campaign record is the business oppty. Large consumer brands are drowning in N million variants of campaigns and treatments. Until these N million campaigns and variants are able to be “context graphed”, then applying agentic scaling will be limited.

Campaigns is just one example - hundreds more exist in key workflows.

[Darrell Ross @darrellross]

Interesting thought experiment which ties to the current chatter re: context graphs.

This observability and file changes is the heart of @JayaGup10’s decision traces

[John Suh @john_ssuh]

Increasingly, I believe companies may need to be rebuilt from the ground up, where you have a single timeline of all observability + product metrics + file changes laid out in a retrievable system, like Datadog + Posthog + Google Drive + Slack (really unified filesystem of Claude Code chats + Codex chats). This might be the new data foundation for any and all companies to maximize AI. Needs to be rebuilt because keeping track of diffs on existing system basically impossible to produce longitudinal information on decisions and rollbacks, something coding agent storage companies are actively trying to figure out, but this should extend to businesses as a whole.

Highly skeptical existing businesses will adopt this though because it means overhauling everything about their instrumentation and business data, but I think businesses built on this foundation probably can execute 100x better and faster

[Darrell Ross @darrellross]

“As a leader you don’t want AI to microwave your thinking….you want to slow cook things”

Great tips for how to use AI in a more nuanced way for deck/presentation content creation.

[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.

[Darrell Ross @darrellross]

A reminder that transformative infrastructure rarely sells itself…from Barry Diller’s autobiography:

“I was soon to learn that ABC was the best possible place for anyone to carve out a TV career in the mid-1960s. In those days, the three networks commanded more than 90 percent of all television viewing—but as the youngest of the three, ABC was the also-ran network.

We were scrappy and adventurous, with a kind of nothing-to-lose sensibility that allowed us to take program risks that CBS and NBC would never do. CBS had been number one for many years and was referred to as the Tiffany Network. NBC, which had been founded on Toscanini concerts during the radio era, considered itself the quality network. It had been created by RCA primarily to sell radio consoles and then televisions, both of which it manufactured. By the time I arrived at ABC, NBC had just spent millions to adapt its programming to color; as a result, it was about to sell even more television sets.”

Many parallels to today.

[Darrell Ross @darrellross]

Guardrails, guardrails, guardrails…

[Aaron Levie @levie]

This is true of all agents, not just coding agents. Probably the biggest challenge that most companies run into in their agent strategy is getting agents the right constrained context to work with for a task.

Too much information or conflicting sources, and the agent can easily draw from the data and produce the wrong result. Conflicting sources of truth for documents, data sources that haven’t been kept up to date, knowledge management systems that rely on tribal knowledge to navigate, and so on.

On the other end, of course, too little information and the upside is highly limited of agents in the first place. Thus, a lot of challenges with AI strategies are actually data strategy challenges in disguise.

This is why there’s such a significant premium on getting structured and unstructured data environments setup properly so agents can work with information effectively. Critical for any large enterprise adopting agents, and also a clear benefit in some cases to startups that can be designed this way from scratch.

[Darrell Ross @darrellross]

Glad to see we’re past the ninja and rockstar phase of job titles

[deepfates.delve.town @deepfates]

The new thing in San Francisco is no longer chief of staff or MTS. It's wizards. Everybody's got to have a wizard. If your company doesn't have a wizard and a 10,000 year cosmic plan you're ngmi. At some top startups each C-Suite exec has a wizard of their own

[Darrell Ross @darrellross]

@davidsenra @tobi Norman doors is the classic example of afffordances discussed by DNN in the Psychology of Everything Things. Classic - once you read it and see it, you can’t unsee it.

[Darrell Ross @darrellross]

But hey, this will all work out okay, right? /4

[Darrell Ross @darrellross]

@OpenAI launched a Deployment Company today. $4B, FDEs embedded in enterprise, promise of "durable systems that don't need consultants coming back."
The partner list is where the story gets complicated. 🧵

[OpenAI @OpenAI]

Today we’re launching the OpenAI Deployment Company to help businesses build and deploy AI.

It's majority-owned and controlled by OpenAI. It brings together 19 leading investment firms, consultancies, and system integrators to help organizations deploy frontier AI to production for business impact. https://t.co/GnyjGFaLLA

[Darrell Ross @darrellross]

So why are they in this?

Not because they've made peace with disruption. They're there for DEAL FLOW into their portfolios and client bases. The equity stake is the hedge against becoming irrelevant while appearing to embrace the thing that makes them irrelevant. OpenAI's goal is to shrink the consulting TAM which their partners built careers expanding it. /3