This week I’m going solo to talk about what I think is one of the biggest mistakes companies will make with AI: starting with the economics. When you look at what AI agents can do and start asking whether 20 people could become 10 or 5, you end up calculating the savings before you’ve redesigned the business to actually capture them.

Using a customer success team of ten, I walk through what goes wrong when you cut to three, from lost context and stuck handoffs to one overwhelmed person holding everything together. Then I show a better path: start with the work, responsibility, execution, and infrastructure, and let the economics emerge from the design. I close with an exercise you can run on any AI calculation you’ve already done. If you’re an investor or operator weighing AI labor in your business, this one is for you.

Episode Timeline & Highlights

[0:00] – The biggest mistake companies will make with AI: starting with the economics

[0:23] – Could 20 people become 10 or 5, or handle three times the volume, and why that math is tempting

[1:28] – A ten person customer success team and what AI agents can actually do around the clock

[2:24] – Before you cut to three, did you understand the work, the judgment, and the exceptions those people carry

[4:02] – The spreadsheet looks great, then handoffs break, context disappears, and exceptions pile up

[4:31] – The one remaining person who becomes the overwhelmed hub holding everything together

[5:28] – The hidden assumption: that your organization is just a collection of work done by people

[6:40] – Economics first on the growth side: can the business absorb ten times more opportunities

[7:38] – Why economic architecture sits in the AI labor architecture framework as an outcome, not a pillar

[8:50] – Start with the work, then responsibility, execution, and the infrastructure that connects them

[10:57] – Redesigning the ten person team with the economics taken off the table

[12:30] – Maybe three people can do it, or maybe ten people with AI agents can serve five times the customers

[14:17] – Beyond payroll: speed, consistency, capacity, availability, and where people create the most value

[15:21] – Breaking the stubborn link between growth and headcount, and what happens if the architecture is wrong

[19:06] – The exercise: set your AI math aside and go underneath it to the work, the people, and the infrastructure

[20:32] – Why economic architecture comes at the end of the framework and has to be earned

Key Takeaways

1. Don’t Start With the Spreadsheet: Calculating AI savings before you redesign the business means your math rests on assumptions you haven’t tested.
2. People Carry Invisible Work: The people on your team hold context, make decisions, and move work between systems, so cutting them without understanding that work breaks the infrastructure.
3. Economics Is an Outcome: Economic architecture sits at the end of the AI labor architecture framework because better margins and capacity come from redesigning the work, not from declaring a number.
4. Redesign Before You Resize: When you understand the work first, you may find three people can do what ten did, or that ten people with AI agents can support five times the customers.
5. Break the Link Between Growth and Headcount: With the right architecture, growth no longer has to mean more people, more handoffs, and another layer of managers.

Links & Resources

• smrtPhone: https://www.smrtphone.io
• That Real Estate Tech Guy: https://thatrealestatetechguy.com

Pick one place in your business where you’ve already done the AI math, set the number aside, and go underneath it to the work, the people, and the infrastructure. You may find the real opportunity is bigger than the savings you first imagined. More high-signal conversations coming next.

This week I’m going solo to make a case that some of the most important infrastructure in your business isn’t software, it’s your people. Your systems can move data, but they can’t understand the work, so people like a customer success rep named Mark have become the intelligent integration layer between your CRM, billing platform, Slack, and email.

I walk through Mark’s customer request step by step, show where traditional APIs and automations stop, and explain how AI agents can move work with the right context when the movement isn’t deterministic. The goal isn’t to remove people from the business but to get them out of the infrastructure layer, and I close with an exercise to find where that’s happening in your own company. If you’re an investor or operator designing a workforce of people and AI agents, this one is for you.

Episode Timeline & Highlights

[0:00] – Why some of the most important infrastructure in your business is your people, not your software

[1:00] – Moving data is not the same as understanding the work, and the customer request that shows the gap

[1:31] – Mark opens the CRM, billing platform, Slack, product platform, and email to assemble the full picture

[2:56] – What Mark really did: connected systems, interpreted information, and acted as the integration layer

[5:09] – The limits of traditional integration, and a failed payment that an API can move but not understand

[6:45] – Where deterministic movement is perfect: if X happens, do Y

[7:50] – Systems could never answer who should receive the work, so people provided the intelligence

[8:28] – How an AI agent handles the failed payment using customer context, policies, and its authority

[10:21] – Moving the work with the relevant context, not a task that just says please handle

[12:20] – Not every movement needs an agent: if X reliably means Y, use the API, automation, or rule

[13:19] – Scale: a person monitors 50 situations while an agent can stay aware across thousands

[14:52] – The goal is to get people out of the infrastructure layer, not out of the business

[16:32] – A person who is the right worker for the job versus a person required to connect the work

[17:36] – Receive, execute, hand off, and who provides the intelligence between those stages

[19:59] – The exercise: find the people who seem to know how everything connects and watch what they do

[21:29] – If the movement needs intelligence, could an AI agent provide it instead of a person?

Key Takeaways

1. People Became the Infrastructure: Your systems could move data but couldn’t understand the work, so people filled that gap.
2. Moving Data Is Not Understanding Work: An API can tell your CRM a payment failed, but it can’t tell you what that failure means.
3. Keep Deterministic Work Deterministic: If X reliably means Y every time, use an API, automation, or business rule instead of an agent.
4. Agents Provide Intelligent Continuity: An agent can decide where work should go and send it along with the context the next worker needs.
5. Get People Out of the Layer, Not the Business: People should be involved because they’re the right worker for the responsibility, not because the infrastructure can’t understand the work.

Links & Resources

• smrtPhone: https://www.smrtphone.io
• That Real Estate Tech Guy: https://thatrealestatetechguy.com

If this made you think of the Mark in your own business, spend a week watching what he actually does between your systems. You may find work that was never his job to begin with. More high-signal conversations coming next.

This week I’m going solo to talk about the pattern underneath almost everything your business does: receive, execute, hand off. Handing work to the next person or AI agent isn’t enough, because they also need the context to continue it, and I show why with an AI customer success agent named Maya and a customer success leader named Sarah.

We get into why access to your CRM, Slack, and email is not the same as context, how Sarah’s call with an at risk customer creates new context that has to travel back to Maya, and why a handoff and true continuity are two different standards. I also connect it to infrastructure architecture, the third pillar of the AI labor architecture framework, and close with an exercise you can run on one handoff this week. If you’re an operator or investor designing a workforce of people and AI agents, this one is for you.

Episode Timeline & Highlights

[0:00] – Almost all work follows one cycle: receive, execute, hand off

[1:31] – Meet Maya, an AI customer success agent who spots a customer at serious risk of leaving

[2:35] – Maya hands the at risk customer to Sarah, a customer success leader, with no background

[3:35] – Sarah has to rebuild the story from the CRM, support system, billing, email, and Slack

[4:54] – Why context is not more information, a bigger context window, or access to everything

[6:20] – Access to the CRM is not the same as context, only the raw materials to build it

[7:32] – The better question: what does this worker need to understand to execute this responsibility?

[8:06] – Sarah calls the customer and creates new context: a business change and a promise by Friday

[9:31] – When the work returns to Maya, a CRM status change is not the same as context

[11:45] – This isn’t an AI problem, it’s a work problem: meaning gets left behind every time work moves

[12:13] – Why so much context lives between your systems or inside your people’s heads

[14:14] – Good infrastructure gives each worker the right context for their bounded responsibility

[16:37] – Infrastructure architecture as the third pillar of the AI labor architecture framework

[18:37] – The difference between a handoff and continuity

[19:39] – The exercise: pick one handoff and stand in the shoes of the worker receiving it

[21:30] – People inside your business have become part of the infrastructure, compensating for the friction

5 Key Takeaways

1. Handoff Is Not Continuity: Moving work to the next person or AI agent means little if they have to rebuild the story before they can act.
2. Access Is Not Context: Giving a worker access to the CRM, Slack, and email only hands them raw materials to build understanding themselves.
3. Execution Creates New Context: Every decision, conversation, and commitment made along the way has to travel forward with the work.
4. Bounded Context Beats Total Access: Each worker needs the context that fits their responsibility, not the entire history of the organization.
5. Follow One Handoff: Pick a single handoff in your business and look at what the receiving worker has to search for, ask, and reconstruct.

Links & Resources

• smrtPhone: https://www.smrtphone.io
• That Real Estate Tech Guy: https://thatrealestatetechguy.com

If this made you think about how work changes hands in your own business, pick one handoff this week and stand in the shoes of the person receiving it. You’ll probably find someone quietly carrying context around in their head. More high-signal conversations coming next.