Scaletics | AI Revenue Engineering

Most MSP AI offers don’t generate predictable revenue.
We engineer the revenue system that makes them sell and scale.

[Scaletics | AI Revenue Engineering @scaletics]

AI creates opportunity.

But most companies can't turn it into revenue.

Why?

• No packaged offer
• No GTM system
• No pipeline

So AI stays capability not revenue.

#Scaletics fixes that with:

→ Offer architecture
→ Pricing systems
→ Pipeline engines
→ #GTM activation

[GREG ISENBERG @gregisenberg]

My 30+ observations on the greatest opportunities in AI agents right now:

And some ideas that are keeping me up at night.

1. The new buyer on the internet is an AI agent. Imagine billions of new customers showing up with money to spend but they only shop via MCP. That's what's happening. No MCP server means you're invisible to the fastest growing buyer on the internet.

2. Every franchise system in America (30,000+) needs an agent layer and none of them have one. One founder per franchise vertical. That's 30,000 businesses waiting.

3. Everyone said "distribution is the only moat" a year ago. Now I'd add that the only moat is distribution plus memory. The company that has your audience AND your agent's accumulated context is impossible to leave.

4. Consumer mobile is more interesting than it's been since 2012. Apps can finally DO things for you instead of showing you things. The next wave of $100M apps are being built right now.

5. The most interesting startup nobody has built is an agent marketplace where you rent access to someone else's trained agent. A recruiter spent 6 months training a sourcing agent on healthcare hiring. That agent is worth renting to every other healthcare recruiter on earth. The agent itself becomes the product.

6. A sorta strange phenomenon that's happening right now is agents are developing preferences. Give the same agent the same task 100 times and it starts developing patterns in how it approaches it. Nobody is studying this yet. But the agents that develop good patterns are worth more than the ones that don't. That's a new kind of asset.

7. Dead internet theory is about to become dead SaaS theory. Half the apps you use will quietly replace their support team, their onboarding team, and their content team with agents. You won't notice for months. Then you'll realize you haven't talked to a human at that company in a year.

8. The most valuable data in the world right now is sitting in the support tickets of small or mid tier SaaS companies. Every ticket is a customer telling you exactly what to build next. Mine this.

9. The most interesting pricing problem nobody has solved is how do you price a product when your costs change every time OpenAI or Anthropic updates their model pricing? Your margins can swing 40% overnight based on a decision made in San Francisco. The company that builds dynamic pricing infrastructure for agent-based businesses solves a problem every AI company has.

10. The best AI products feel like they're reading your mind. The worst ones feel like filling out a form with extra steps.

11. An interesting arbitrage I've noticed lately is hiring a human VA for $20/hour to supervise an AI agent that does $200/hour work. The human just checks the output.

12. The managed AI agent business is becoming the new agency model. $5k/month per client. You build it, run it, maintain it. The client gets a digital employee they never have to think about. This will be a $50 B+ category.

13. The first "shadow agent" scandals are about to drop. Employees running personal agents on company infrastructure without telling anyone. Using company API keys. Agents accessing internal docs. IT departments have little visibility into this right now. Lots of opportunity to build companies here. Definitely a painkiller not a vitamin type of business.

14. Right now there are probably millions of agents running on autopilot that their creators forgot about. Still burning tokens. Still sending emails. Still scraping websites. Still costing money. The "find and kill your zombie agents" tool is a product that writes itself.

15. Companies are starting to hire based on someone's agent portfolio instead of their resume. "Show me 3 agents you built that are running right now." It's REALLY early but it's starting.

16. Your Slack archive is a product. Every company's internal Slack has thousands of messages explaining how they actually do things. The company that lets you point an agent at your Slack history and auto-generate SOPs and agents from it will be enormous.

17. We're watching the cost of intelligence fall faster than the cost of distribution. Which means distribution is now the expensive thing.

18. The most underrated asset a human can have in 2026: the ability to sit in a room with another human, make eye contact, and have a real conversation. As AI handles more of the transactional stuff, the humans who can do the relational stuff become disproportionately valuable. The soft skills people used to dismiss as fluffy are becoming the hard skills. The hard skills people spent decades acquiring are becoming the soft ones.

19. There are MANY huge companies to be built around the fact that most people's agents are running on their personal laptops which they also use to browse the internet, check email, and download random files. The attack surface is enormous. One compromised Chrome extension and your agent's API keys, customer data, and workflows are exposed.

20. There's a new type of burnout forming that doesn't have a name. It's not from working too hard. It's from context switching between human work and agent work 50 times a day. Reviewing agent output, correcting it, approving it, reviewing again. The mental load of supervising agents is different from the mental load of doing the work yourself. Some founders are telling me they were less tired when they did everything manually because at least the cognitive pattern was consistent.

21. The cheapest form of market research: search "[your industry] spreadsheet template" on Google. Whatever people are tracking manually is your product.

22. Half the YC companies pivoted within 8 weeks of demo day. Not because they failed. Because agents let them test 5 ideas in the time it used to take to test one. The concept of "committing to an idea" is dissolving. Serial pivoting is becoming the default because 1) AI lets you move fast 2) the world is moving fast.

23. The loneliest job in tech right now is being the only person at your company who understands what the agents are doing. You can't explain it to your boss. You can't hand it off to a colleague. If you leave, everything breaks. You've become a single point of failure for an entire automated system. That person needs a title, a team, and a backup plan. Most companies haven't figured this out yet.

24. Your browser history is the most valuable training data you own and you're giving it away for free. Every site you visit, every product you research, every competitor you study, every pricing page you screenshot. That behavioral data, structured and fed to an agent, would make it understand your business better than any onboarding call. The company that lets you turn your browser history into agent context builds something nobody can replicate.

25. Everyone is building AI wrappers. Nobody is building AI unwrappers. The tool that takes an AI-generated document and tells you which parts a human wrote and which parts were generated.

26. Stripe just became the most important company in the agent economy and they barely had to do anything. Every agent that sells something needs Stripe. Every agent that buys something needs Stripe. They're the payment rail for the entire agentic internet by default.

27. The most undervalued API in the world right now is the US Postal Service address verification API. It's practically free. Every local business lead gen agent needs it. Every real estate agent needs it. Every direct mail agent needs it. Boring government infrastructure is quietly becoming the backbone of agent-native businesses.

28. The concept of "business hours" is for humans. Your agent closed a deal in Tokyo at 3am, processed the payment, sent the onboarding email, and updated the CRM before your alarm went off.

29. What happens when agents start recommending other agents? Your research agent finds that a competitor's sales agent is better and suggests you switch. Agent referral networks are forming organically. The first agent affiliate program is probably 6 months away.

30. Cal dotcom closed their source code. That's the canary. When open source companies start closing up, it means agents were cloning their product too easily. Every open source company is quietly asking the same question right now.

31. "AI for pet groomers" sounds like a joke and that's exactly why it will work. 150,000 of them in America. Zero tech. All scheduling by phone or IG DMs. The joke ideas always win.

32. The thing that will seem most obvious in hindsight: we spent 2025-2026 arguing about which model is best while the entire value was in the orchestration layer. The model is the CPU. Nobody buys a computer based on the CPU anymore. They buy it based on what they can do with it. Makes so much sense in hindsight. What else will be obvious in hindsight?

I'll share more notes soon.

I can't sleep with all that's going on. Maybe you too.

What an incredible time to be building.

[Scaletics | AI Revenue Engineering @scaletics]

CPUs take turns.
GPUs bring the whole squad to work. ⚡️

#AI #GPUComputing #ParallelProcessing

[Scaletics | AI Revenue Engineering @scaletics]

AI agents aren’t just replacing tasks.

They’re reducing coordination friction across the business.

That’s the real economic shift most companies are underestimating.

#AI #AIAgents #FutureOfWork

[Scaletics | AI Revenue Engineering @scaletics]

@AdluminCEO Shadow AI is not just a security problem.

It’s simultaneously:

an operational risk problem
a data governance problem
a procurement problem
a workflow management problem
& eventually a regulatory problem

Im confident we'll see firms rapidly adopt layered orchestration models

[Scaletics | AI Revenue Engineering @scaletics]

AI doesn’t create governance problems.

It exposes the ones you ignored at adoption.

#AIGovernance #MSP #Copilot #Cybersecurity
#AIReadiness

[Scaletics | AI Revenue Engineering @scaletics]

@AdluminCEO 💯@AdluminCEO Then, you don’t just have “shadow AI.”

You have:
- shadow IT
- ungoverned data exposure
- invisible automation dependencies
- undocumented decision systems
- and zero centralized visibility into what’s actually running where

#Cybersecurity #Cloudsecurity

[Scaletics | AI Revenue Engineering @scaletics]

A key challenge for businesses adopting AI-powered tools and cloud collaboration?

How to protect data while enabling innovation

#AIinBusiness #DataProtect #AI #Cybersecurity

[Scaletics | AI Revenue Engineering @scaletics]

@gregisenberg Hey @gregisenberg - is 2 Agentic AI Orchestration or something else?

[Scaletics | AI Revenue Engineering @scaletics]

@luagaroz EBITDA

[Scaletics | AI Revenue Engineering @scaletics]

The real #MSP opportunity is usually NOT:
“What can I automate?”

The better one is:

“Which operational constraints are suppressing profitability, scalability, & expansion revenue - and which automations meaningfully change unit economics?”

#ManagedServices
#RevOps
#MSPGrowth

[Bessemer @BessemerVP]

"Israeli cybersecurity M&A slows as venture capital flows" by BVPs @AdamRFisher & @TomerDiari.... https://t.co/HZ5To2VqCI #israel #cybersec

[Scaletics | AI Revenue Engineering @scaletics]

@adefilaadeyinka Great point, @adefilaadeyinka!

This is the exact issue many organizations are about to face at scale.

Most AI governance conversations start at policy…

…but the real starting point is across-the-board visibility.

Who/What/Where/How/When access control.

[Scaletics | AI Revenue Engineering @scaletics]

RT Detection ≠ Control

Seeing risk is not the same as being able to stop it.

AI safety needs more than alerts,audits,or warnings. It needs enough signal,time,authority,and policy to intervene before harm becomes irreversible.
#CloudSecurity #AIGovernance @htetkokonaing_

[Scaletics | AI Revenue Engineering @scaletics]

Most MSPs are adding AI.

Very few are operationalizing it into recurring revenue.

That gap will separate market leaders from commodity providers.

#MSP #AI #RevOps #AIGovernance

[Scaletics | AI Revenue Engineering @scaletics]

OpenAI just validated it:

AI is shifting from software → operational infrastructure.

Deployment wins.
Not more demos.
Not more tools.

#AI #MSP #DevOps #AIInfrastructure

[Scaletics | AI Revenue Engineering @scaletics]

OpenAI just positioned themselves as a deployment company.

That's a wake-up call to every #MSP delivering #AI like an add-on.

The next category winners run AI infrastructure & operationalize intelligence.

Deployment is the new battleground.

#AIGovernance #AIInfrastructure

[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

[Scaletics | AI Revenue Engineering @scaletics]

@elgermerlo lets connect @elgermerlo! Building with Claude and Codex.

[Scaletics | AI Revenue Engineering @scaletics]

@AlexFinn Very underrated & not respected.

It gets really fun when you start with the desired end goal/outcome/state, and ask to identify:

→ gaps
→ bottlenecks
→ blind spots
→ repetitive workflows
→ foreseen inefficiencies

An Ultimate Theory of Constraints (TOC) laid out.

[Himanshu Kumar @codewithimanshu]

Anthropic's applied AI team just dropped a 24-minute workshop on how to actually prompt Claude properly.

Free. From the people who built it.

You've been prompting Claude for months without the 6 elements they teach in this.

I built a skill that applies them automatically. Full guide below.

Save This. Watch This Today. Start Building This Weekend.

Follow @codewithimanshu for more high-signal content that actually moves your skills forward.