Fear and Loathing in Silicon Valley

Analyzing the system of record and software debate

“The possibility of physical and mental collapse is now very real. No sympathy for the Devil, keep that in mind. Buy the ticket, take the ride.”

Anthropic in particular seemingly drops a release every other day that makes writing, debugging, or securing code easier. So of course, the rational response is to say “code moats are dead” and freak out.

But this is obviously a reductive narrative. Software with a true agent play still isn’t being repriced. There are constant whispers from the labs around their software-layer ambitions to compete with Workday, Salesforce, and others—a blatant hint that there is at least some value in the software layer. Even agent applications like Claude Cowork seem to be prioritizing integration surfaces into other apps.

I will concede that software as a category has fewer moats, more distributional questions, and even existential questions about its future.

But let’s make no mistake: there is no conceivable world where systems of record die. Systems of record are going to be here until the sun goes out or the earth becomes one mega datacenter.

After all, what is a system of record? It’s a shared data plane and workflow architecture for firms to coordinate their decisions. Right now that coordination is done by humans. In the future it will increasingly be done by agents.

Agents are not the Borg. There is no collective memory latent in their architecture. And agents, while faster than humans, are still bound by time. Businesses have historically solved the Borg problem and the time problem with systems of record.

The current systems of record debate is obfuscating several themes and drawing conclusions that are unwarranted. I agree with two points:

• Current systems of record are quite obviously limited.

• They were not made for agents. They were not even really made for people. They were made for managers.

That’s going to limit your upside when a sudden mass of agents wants to use your system to actually get things done.

Current systems of record impose a tax on agent productivity rather than being additive to it. No existing system is immune to this. And users are increasingly not going to tolerate your high gross margin business if it has dropped the ball on helping them get more work done productively.

The fact that current systems of record are bad for agents does not logically mean that software productive for agents is going to be thrown out or shouldn’t be built. You need another premise.

And the only premises I can imagine here involve who takes over the system of record. This is ultimately the only question that matters for the terminal value of systems of record and how much of the incoming agent workload they capture.

From my perspective, the system of record question matters more, not less.

It’s so important that it informs everything from agent training to inference costs and all the way to how work gets done by agents in the future.

There are roughly four scenarios.

The labs own every single system of record

In this scenario, your buyer chooses between OpenAI Sales and Claude Sales. Maybe, if they’re a software hipster, they roll their own Kimi open-source Salesforce.

I think the labs will try. I think they will even succeed in a lot of cases. Salesforce isn’t cool. You know what’s cool? OpenAI Sales.

But I also think the labs run into foundational questions with this approach. First, how does the game theory shake out? Do companies have an incentive to fully lock in their AI provider as their system of record?

The existential challenge for labs pre-AGI is that their ambitions in the application layer in every vertical are held in check by the competitive landscape in model training. They are fighting thousand-front wars over inference where customers expect growing improvements across video, images, text, computer use, shopping, and more.

Two insights:

• The labs will attack the system-of-record layer to the degree they need to in order to tackle the true TAM of human labor.

• The labs are well aware of how ill-suited current systems of record are for agents. If this remains a fundamental constraint, they will certainly compete in the app layer or even rebuild for individual customers.

On the flipside, the coding wars have proved that switching costs on the inference layer are near zero. Customers by and large want to commoditize the model layer and will choose the best-performing model with zero loyalty.

This somewhat prohibits any concerted pre-AGI effort to build enterprise-grade integrated applications. The minute you commit to building a CRM and integrated sales agent is the minute you commit to having the best sales evals and the best outcomes for your customers.

The alternative is to let vertical companies fight proxy wars on your behalf. Let them build the neo-systems of record, develop the right evals and datasets, and then buy them outright.

This is in fact what’s playing out with Anthropic and OpenAI competing for the finance, health, law, and coding verticals with a mix of model wars and proxy wars through software partners.

As a result, I’d posit the following: the rumors about the labs entering the application layer are more of a condemnation of current systems of record than a stance that systems of record are going away.

If you move into the system-of-record layer, it’s obviously because you think your agent is capable of performing work at human parity but is not able to because the core system of record doesn’t allow it to.

In the same way, I think the emphasis on computer use by labs is interesting. It’s a concession that if we could only improve agents’ ability to use UIs built for humans, they’d be able to accomplish way more. Maybe the problem isn’t just the UI. Maybe it’s the underlying system of record itself.

A quick sidenote on MCP: MCP was meant to be a Band-Aid over current systems of record and apps. They work, but they still don’t work that well for actual work. Some of this is a training problem, and some of this is that current software products are simply incapable of exposing anything other than database rows and not decisions. It’s not actually that useful to get an API connector into a current system of record. The agents are smart, but if you dropped an outsider into a random company’s CRM and asked them what any of it means, they would look at you like you’re crazy.

When OpenAI hints that it wants to build a CRM, what it’s really saying is: Benioff can’t give our agents what they need.

So how should we view the labs as system-of-record entrants? They’re interested, but content to fight proxy wars. They want partners across verticals that structure themselves as both research partners and product companies.

Custom software dominates to such a large degree that the long tail proliferates without large software megacaps

The second scenario involves coding models essentially solving software as a category. The plausible version, in my view, involves a long tail of developers using coding models to create custom solutions for businesses at scale.

In this case, there’s no CRM category to speak of. There’s simply a sea of custom developers creating massive productivity for individual companies, and they all sort of roll their own software.

You could even imagine some of these custom developers deciding to specialize in a unique customer segment across a vertical. Let’s call them vertical software company-specific software builders. The game sounds familiar, doesn’t it?

If someone develops expertise in deploying custom software inside a vertical, but it’s made even more custom to specific companies, isn’t this just the next evolution of vertical software? Yeah, maybe it actually is.

Now maybe you surmise that Claude itself is going to be just that damn good. You don’t need the team, you just need Claude. And as long as Claude can get the right context for your specific business, how it works, what your priorities are, what would bring you new revenue, what would solve your operations, and what your customers would think about... wait. What if we built a system of record for Claude to build the right system of record and software for your business?

It really is just turtles all the way down, isn’t it? Aren’t we all merely competing to see who gets the true system of record to render agents extraordinarily productive? The layer of abstraction may just start to look a bit different.

In some raw way, the thing that is going to matter for all these approaches is a context graph spanning the decisions of an organization. Everything comes back to intent, even in building software to crystallize business intent.

I ultimately think this direction is highly plausible, but it will normalize to different systems of record that unleash custom software viability for businesses, likely in a vertical. To a large degree, the ontology of businesses in a vertical is similar. In other words, maybe the Palantir approach comes to fruition for the mid-market in a significant way.

Current systems of record prevail in their current state. Long live Salesforce. Long live Veeva. Long live every other incumbent.

I’ll go on record: I think this is actually the most implausible direction for the vast majority of verticals. As I’ve said, I think most current systems of record are simply not up to snuff for agent work.

I’m of course biased. Others like Brendan Keeler have made strong cases for why the systems of record will prevail. Keeler makes the point that Epic has entered the medical scribe wars and simply priced scribing for free to prevent competitors from entering.

This, in my opinion, gets at the heart of the current system-of-record conundrum. Systems of record are entirely capable of developing point solutions for a specific pain point in the industry. But their architecture is not conducive to what I’m coining: decisioning at token speed.

Transcribing a medical visit is a largely different task than coordinating all of care delivery, follow-ups, insurance operations, pharmaceutical operations, and a litany of other matters. Hospitals currently rely upon dozens of specialists coordinating this care across an individual patient. They’re specialists in one area of the business, have dedicated modules for their tasks, and frankly, do a damn good job in this current construct.

This architecture made tons of sense when our limitation was human inference. But agents and humans reason differently, with different ramifications.

It’s not clear that replicating human specializations to agent specializations 1:1 is the right approach. For instance, agents are context hungry and increasingly capable of long-horizon tasks. As long-horizon tasks approach years, will it make sense to have dedicated agent teams that handle one aspect of care delivery for all patients, or one agent per patient that handles all aspects of care delivery?

You can follow this same trendline across every human team in every industry. The answers will differ, but the throughline is clear: if agents are capable of longer-horizon tasks at higher speeds than humans, the supply chains of intelligence may be radically different than our current ones.

But again, this isn’t how current systems are built to operate. Structuring systems for inference requires structuring for decisions, context, and long-horizon tasks. How many current systems of record fit the bill?

Systems of record can build a point solution. That’s no longer the game. The game is which system of record can support 10x the workforce, maybe even 100x the workforce, inside a system making 1000x the decisions per day. Most are not built for this, either from an agent lens or from a human operator who has to monitor these decisions.

Neo-systems of record providers proliferate:

Let me draw a scenario out.

Imagine a world where the agent layer is mostly detached from the system-of-record layer. What would that world look like?

It would probably look like a world where users primarily interface with agents via their phone, in a dedicated app like Claude Cowork, or through a business messaging platform. Companies would spin up dedicated servers for agents where they want to fully automate work. They could seamlessly switch from chatting with their AI boyfriend to performing work with their AI intern.

That world looks highly plausible to me. The lesson from OpenClaw and Claude Code and others is that the more agents can replicate the local compute environment of their user, the more productive they are. Long-running agents likewise imply long-running compute with a wide-reaching span of tools across email, docs, Excel, software systems, and more.

How does a business coordinate all these different agent systems to accomplish real work? I think they’re going to need an excellent system of record.

And if the rate of agents, and the value of their decisions, continue to grow, buyers are going to want someone on the other end of the line to help them manage 10,000 agents working concurrently in their actual business, with corresponding proof that the work product being produced is up to par.

What if the game in front of vertical AI is less about building a voice agent and more about building the neo-systems of record that can ultimately support hundreds, if not tens of thousands, of agents performing valuable work for a business?

Will it make sense to internalize that? Maybe. Doubt it for everyone in the mid-market and down. Can the agents do it themselves if they get smart enough? Yeah, they’d build their own system of record. Turtles all the way down.

I realize this is not the trend. Vertical AI is still mainly shipping product form factors around model capabilities.

But now, I think it’s not ambitious enough. And it’s not even necessarily what buyers need. Vertical AI has spent so much time trying to be slightly better than Claude at vertical work product. That may be the wrong front.

If the MO is flipped and vertical value is decided on concurrent operations managed across local and cloud agents, I think vertical startups will end up more resilient to the model layer.

First, value capture seems likely better in the long run. Price according to the number of agents working concurrently and productively. This may scale to far greater dollar amounts than seat-based pricing ever could.

Engineers now commonly spin up 4–5 Codex or Claude instances to work concurrently on their codebase. I know OpenClaw users spinning up 5–7 sub-agents to work on parallel tasks. What if the number of agents working inside a neo-system of record dwarfs the number of employees at the firm by 4–5x? Account for cloud agents, and this easily could eclipse 10x.

Second, rather than trying to create a harness around current model capabilities, you’re instead spending time on harder research questions in the vertical: namely, how to empower increasingly long-horizon tasks and complex decisioning. These are valuable data and training questions that companies built around this realization will likely encounter and solve far faster than we otherwise would.

And this brings me to my final point. So much of the agent architecture game right now is about building the right harness for agents. Give them the right context, the right tools, and let them recursively work through problems.

I agree. But my contention is that the best harnesses will simply be the best systems of record.

In my view, SaaS and systems of record are not dead. In fact, we need far superior systems of record than what we currently have if we want the agent productivity boom we need across verticals.

Buy the ticket, take the ride