Linear #181: Everyone Needs To Have A Quota, AI Roll Ups In Home Health, Industry Expertise In The AI Era
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Alright, let’s get to it…
Everyone Needs To Have A Quota
I’ve been digging deep into all sorts of vertical businesses lately with their management teams.
I thought it was pretty standard for sales people to be have quotas, and for non-sales people to have bonuses based on their performance / the companies performance.
It’s interesting to see the amount of early stage companies, I’m talking anyone sub $10M that don’t have this or a similar framework.
If that’s you, it is literally the easiest / most important lever you can quickly plug into your business.
If you already have some resemblance of it, probably still worth reading as I’m going to go into what I did after 10 years of trials and tribulations that worked.
IT’S SO SIMPLE.
Highly recommend NOT making it complicated.
#1. Everyone has a quantitative number they are gunning for EVERY QUARTER
That natural push by employees is to have qualitative goals, and it’s also to have a bunch of them. This just allows for them to hide behind something. Don’t let this happen. REGARDLESS of their department, make it quantitative. Make it black and white. Even if they are in HR, you can give them something quantitative IE an eNPS score. 30 days into working with me everyone knew I hated/didn’t believe in qualitative goals.
#2. 50% of everyones bonus is tied to the attainment of this number
You can’t just give people a goal. You have to incentivize them. You have to reward them. Even if every non sales person is comp’d 90% base, 10% bonus, it’s better than nothing. Humans are funny, they will fight hard for that extra 10%. Do SOMETHING.
#3. Your company needs to have a quantitiatve number they are gunning for EVERY QUARTER
For most companies, this number is usually ARR. But whatever you choose, it’s important that you show it EVERYWHERE. That folks can always see how they are doing.
#4. The remaining 50% of everyones bonus is tied to the team achieving said goal.
If the company doesn’t attain AT LEAST 70% of their quarterly plan, no bonus for anyone. It makes no sense to pay people out when the company isn’t performing. This ties in the team dynamic you don’t want to lose in these types of plans.
#5. Sales people are comp’d differently, ideally 50% base, 50% commission. Commission plan should be scaling IE if their quota is $1M of ARR per year, they get 6% for the first $250K of ARR signed, 7% for the next $250K of ARR signed, 8% for the next $250K of ARR signed, 9% for the next $250K of ARR signed, 12% for everything signed thereafter.
Overtime, you can adjust this. For example if your laser focused on growth and profitability, you can bonus people 50% their own goal, 25% company ARR goal, and 25% company EBITDA goal. We definetly did this based on the stage of the business.
So to wrap it up
People will give you a bunch of reasons why it won’t work, why it doesn’t make sense for X department or Y person. It’s all bullshit. In the rare events that actually happens just correct it. Get the baseline data and work from there.
NO MATTER WHAT do something.
Just make sure it’s quantitative.
Make sure there is a cash component tied to it.
And make sure there is a clear company goal for every quarter.
This alone will work wonders for you company.
Quick PSA —
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Adaptive Innovations:
A New AI Roll Up For Home HealthCare
If you want to understand where Vertical AI gets really interesting, don’t look at another copiloting layer slapped onto legacy software.
Look at a company that said: we are not going to sell software to the incumbents — we are going to become the operator.
That’s what Adaptive Innovations is doing in home health.
And I think it’s one of the clearest examples yet of what an AI-native roll-up can look like in the real world. Adaptive isn’t pitching itself as a better dashboard, a clever note-taking assistant, or a workflow add-on. It is pitching itself as the AI-native healthcare provider rebuilding the way care is delivered in America, starting with home health. That distinction matters a lot.
The big idea
Most software companies try to improve a broken workflow.
Adaptive is trying to own the workflow, automate the bureaucracy around it, and then use those savings to serve more patients, pay clinicians better, and compound operational advantage.
That is a very different ambition.
In home health, the market is massive, the demand is growing, and the administrative drag is absurd. Adaptive says home health is a $140B industry, that 40% of home health patients never receive care, and that around 40% of industry spend goes to admin. Felicis frames the problem even more sharply: over $100B is spent annually on home health, 40%–50% goes to coordination overhead, and roughly $40B in referrals get rejected every year because the administrative math doesn’t work.
That is why this story matters.
This is not “AI for paperwork.”
This is AI as the basis for a new operating model in a market where the incumbents were built for a different era.
Why home health is such a compelling market
A lot of founders look at home health and see a messy, regulated category.
That’s true.
But the best markets often look messy from the outside.
Home-based care sits at the center of multiple long-term tailwinds:
the aging of the U.S. population,
staffing shortages,
rising costs in institutional care,
payer pressure for better outcomes,
and strong patient preference to stay at home.
The demographic case alone is enormous. Americans age 65+ are projected to grow from 58 million in 2022 to 82 million by 2050, and that cohort’s share of the population is expected to rise from 17% to 23%. On top of that, Alzheimer’s prevalence could more than double to 13 million by 2050. This is exactly the kind of demand curve that makes “last-mile care delivery” a generational business opportunity.
And unlike many sexy AI categories, this isn’t a market that depends on creating new demand.
The demand is already here. The system just can’t serve it efficiently.
The real bottleneck is not care. It’s coordination.
This is the most important insight in the entire story.
Home health doesn’t fundamentally break because clinicians don’t exist or because patients don’t need care.
It breaks because the administrative stack surrounding care is so heavy that providers reject patients who are clinically eligible.
Felicis lays it out plainly: two out of every five patients referred to home health never get treated not because they shouldn’t be treated, but because intake, scheduling, prior auth, documentation, and billing make the economics unattractive. In other words, the problem is not clinical demand. The problem is delivery infrastructure.
That is a huge unlock.
Because once you identify that the bottleneck is administrative, not medical, the solution changes.
You stop asking, “How do I sell software to providers?”
And you start asking, “How do I build a provider that is structurally better because AI is embedded into every non-clinical workflow?”
Adaptive’s core strategic move: become the provider
This is the move.
Adaptive’s founders reportedly first looked at home health through an AI lens and initially tried to improve the back office from the outside. But they kept hitting the same wall: the agencies were too calcified to absorb the redesign. So they changed the thesis.
Instead of selling tools into the system, they decided to become the system.
That is why I think “AI-native roll-up” is the right framing here.
Adaptive is not just software. Adaptive is not just services. Adaptive is not just an AI assistant.
It is an AI-native operator in a fragmented, high-friction, regulated market.
Home Health Care News says Adaptive uses AI across intake, eligibility, scheduling, charting, coding, QA, and other back-office functions. D Magazine says the company acts as the provider while using proprietary AI to run intake, scheduling, documentation, and billing. That gives Adaptive end-to-end control over both the care-delivery layer and the tech layer.
That is the key strategic difference between vendor AI and operator AI.
Vendor AI sells efficiency.
Operator AI captures the economics of efficiency.
This is what the best AI-native companies are starting to realize
If you only sell software into a broken system, you often inherit the adoption bottlenecks of that system.
The provider is busy. The workflows are entrenched. The incentives are messy. The procurement cycle is slow. And even if your tool works, the operator may not change behavior enough to unlock the full value.
Adaptive’s founders seem to have recognized that the highest leverage point was not selling intelligence to providers but embedding intelligence inside a provider they controlled.
That’s a much harder company to build.
But it’s also a much more ambitious one.
Because if you control the workflow, you get:
the data,
the margin,
the speed of iteration,
the customer experience,
and the operational flywheel.
That is how you go from “AI feature” to “category-defining business.”
The founding team is what this model requires
This is not a business you can fake your way into.
Felicis says the founding team includes:
Alex Wendland and Ryan Tolsma, who came at the market from an AI lens,
Logan Stinson, who spent years scaling home health operations across Texas,
and Hunter Stinson, a former nurse and U.S. Army Ranger officer who had worked directly within these broken workflows.
That blend matters.
Because this is not a “two cracked engineers and a prompt” company.
This is a business that requires:
clinical credibility,
operational density,
reimbursement fluency,
local-market execution,
and technical talent strong enough to automate ugly real-world workflows.
Adaptive also says it has assembled talent from places like Citadel, Jane Street, Scale AI, and Palantir, while Home Health Care News reports a 30+ person engineering team building the tooling in-house.
That is the right recipe for an AI-native operator: domain operators + clinical talent + serious engineers.
The traction is what makes this story go from interesting to important
A lot of AI-native care companies sound compelling in theory.
Adaptive is more interesting because there are already real operating metrics behind the pitch.
Across its own site, Felicis, Home Health Care News, and D Magazine, the company is reported to have:
delivered 100,000+ patient visits,
built a network of 500+ referring healthcare organizations,
employed 200+ clinicians,
operated with 24/7 clinical coverage across five Texas regions,
reduced clinician documentation time by around 80%,
achieved a 4.9% rehospitalization rate,
and admitted 99% of patients within 48 hours of referral.
Felicis also cites:
4.9% rehospitalization vs 12.9% Texas average and 10.2% national average,
95%+ improvement in ambulation vs 89.5% national average,
and a 4.5-star CMS quality rating against an industry average of roughly 3.
Those are not “we think AI could help” metrics.
Those are operating metrics.
And that is the whole point.
The economic flywheel is the real product
The best part of this business is not the AI layer itself.
It is the flywheel the AI layer makes possible.
Home Health Care News quotes Alex Wendland saying the company’s goal is to drive admin costs to zero, then recycle those savings into higher clinician pay and a compounding flywheel that lets it serve the full market. Adaptive also describes itself as payer-agnostic, saying its lower admin overhead allows it to accept patients regardless of insurer and work across Medicare, Medicare Advantage, commercial insurance, fee-for-service, episodic, and value-based models.
This is why the model is so attractive.
If you reduce admin friction enough, you can:
accept patients others reject,
improve response time,
improve clinician economics,
attract better labor,
generate better outcomes,
deepen payer and referral relationships,
and compound scale.
That is a serious operating flywheel.
And unlike many AI businesses, it is anchored in something very concrete: the removal of bureaucratic labor from a system drowning in it.
This market is structurally built for roll-ups
Another reason I like this story: the market structure fits the strategy.
The home health sector is fragmented enough that both PE and strategic buyers have been consolidating it for years. The PMC study identified 749 unique home health agencies involved in PE transactions from 2006 to 2024, with acquisitions clustering in waves and heavily concentrated in the South. Capstone also says private company buyers represented 67.2% of strategic volume in 2025 because of the sector’s fragmented composition, while overall home care M&A activity rose 40.5% YoY to 104 transactions.
That matters because it tells you two things:
1. The market is fragmented enough to consolidate.
2. Buyers already understand the density game.
What Adaptive appears to be doing is not the old PE playbook of buying local operators and layering in modest cost discipline.
It is a new version:
build the AI-native operating system,
use it inside owned care delivery,
improve economics and outcomes,
then scale across regions and potentially service lines.
That is a much more interesting form of roll-up than “buy 20 branches and centralize accounting.”
Funding: the capital markets are starting to recognize the difference
Adaptive emerged from stealth with $60 million total funding, including a $50 million Series A led by Felicis and a previously undisclosed $10 million seed. Other investors include Bain Capital Ventures, Optum Ventures, Sunflower Capital, Conviction, BoxGroup, Dorm Room Fund, Constellation, and angel investors from OpenAI.
That syndicate tells you what the market sees here.
This is not just a software play. This is not just a healthcare services play. And it is not just an AI infra play.
It sits in the middle of all three.
That’s exactly why it can become important.
The broader lesson: in AI, the biggest opportunities may belong to operators, not vendors
This is the lesson I keep coming back to.
A lot of AI founders still think the best move is to sit one layer above the incumbent, sell productivity, and hope adoption follows.
But in markets like home health, the real prize may go to the company that says:
“We are going to own the workflow, own the economics, and own the customer experience.”
That is much harder.
But the reward can also be much bigger.
Now let’s talk about Special
Because Adaptive is not the only company seeing the opportunity.
On June 2, former DOGE operatives Nate Cavanaugh and Justin Fox announced Special, an AI company they describe as building an operating system to transform critical American industries through AI. Their financing was led by Andreessen Horowitz. The round size was not disclosed in the sources I reviewed.
Their thesis sounds very familiar:
massive inefficiency in essential service businesses,
AI as the engine for productivity,
and vertical integration through acquisition as the way to capture value.
Their first vertical is Figure Health, focused on the aging population. Special says it is already under contract for its first acquisition in Texas, serving 1,400+ patients and employing hundreds of nurses. It plans to use SpecialOS to improve efficiency and increase nurse pay.
So yes — there are now multiple serious teams converging on roughly the same insight:
Critical service industries with labor constraints + regulatory complexity + old workflows are perfect terrain for AI-native operators.
But here’s the key difference from traditional vertical software
Adaptive is starting with home health. Special is starting with elder care via Figure Health. Those are adjacent, not identical, bets.
And even within home-based care, the sector is large enough to support multiple scaled businesses. Capstone highlights the entire home care continuum — personal home care, home health, hospice — as a major and growing part of U.S. healthcare infrastructure, while Mordor estimates the home healthcare software market alone at $5.08B in 2026, growing to $9.27B by 2031. The care-delivery market itself is obviously much larger than the software layer.
That means this is not a “winner-take-all app category” like we typically see in vertical software. When you actually own the service business themselves, theres a lot more room…
My take on Adaptive vs. Special
I think these companies are reading the same macro correctly.
The next wave of great AI businesses will not just be better SaaS tools.
They will be AI-native operators in sectors where:
labor is expensive,
admin is suffocating,
outcomes matter,
compliance matters,
and local execution matters.
Adaptive seems more clinically and operationally specific today. It already has meaningful reported traction in Texas, clear patient-outcome metrics, and a tightly defined wedge in home health.
Special feels broader and earlier — more of a platform thesis around “Main Street services” with elder care as its first proving ground. It is more explicitly ideological and acquisition-first in how it frames the opportunity.
But I don’t think the right takeaway is “pick one.”
I think the takeaway is the category is real.
And once multiple smart, well-capitalized teams start attacking the same ugly operating problem from different angles, that is usually a sign the market is bigger than people think.
The real opportunity
The best AI companies in healthcare may not look like software companies at all.
They may look like:
providers,
care networks,
staffing systems,
revenue engines,
and compliance machines
That just happen to be built on top of deeply integrated AI.
That’s why Adaptive is so interesting.
It is not selling “AI for home health.”
It is trying to build the AI-native home health provider.
And if that works, the upside is not a slightly better software multiple.
The upside is building a new kind of healthcare company altogether.
Final thought
I think a lot of people still underestimate how many categories will be rebuilt not by software vendors, but by AI-native operators who own the workflow end-to-end.
Adaptive is one of the best examples I’ve seen so far.
And Special’s raise is further proof that serious capital is now chasing the same idea.
Home health, elder care, hospice, and adjacent categories are not too small. They are not too messy. They are not too regulated.
They are exactly the kind of markets where deep operational pain + demographic demand + AI automation can create very large companies.
That’s what makes this time so exciting.
Industry expertise is becoming MORE important in the AI era, not less
A lot of people think the AI era means the winning founders will just be the best engineers.
I think the opposite.
The software layer is getting easier to build every quarter. Models are better. coding is faster. infrastructure is more accessible. You can compress months of product work into weeks now.
But the industry layer?
That is still brutally hard.
You can learn Cursor.
You can learn Claude.
You can learn how to ship product faster.
It is much harder to learn:
how claims actually move through a healthcare workflow
how reimbursements break economics
which compliance steps are “must have” vs “nice to have”
who really makes the buying decision
where the workflow actually breaks at 4:47 PM on a Tuesday
That stuff is usually in the founder’s DNA, or it isn’t. Bessemer makes this point really clearly: vertical AI rewards insider expertise more than horizontal SaaS did because you are reimagining nuanced, regulated workflows, not just digitizing generic tasks. Greylock says something similar: deep domain-specific focus is one of the best ways to build a durable moat at the AI application layer.
So I think the advantage in this era shifts toward teams with industry-native pattern recognition.
Why?
Because when software gets cheaper, the scarce asset becomes:
trust
workflow understanding
proprietary data
edge-case intuition
speed of customer empathy
That’s why pure technologists are often at a disadvantage in regulated verticals. The bottleneck is no longer “can you build the tool?” The bottleneck is “do you understand the work deeply enough to know what should be built?” Greylock explicitly argues that founding teams with both domain experience and technology background are advantaged, especially in regulated industries.
My simple takeaway:
In 2018, the hard part was building software.
In 2026, the hard part is building the right software for a very specific workflow.
And the founders who move fastest will usually be the ones who already know:
the language of the customer
the pain that actually matters
the ugly workaround everyone pretends is fine
the metric that gets the buyer promoted
That’s why I’d rather back a great operator from the industry who learns AI than a great AI builder who has to spend 24 months learning the industry from scratch.
Because in vertical AI, context compounds faster than code.
If you made it thus far, go check out the 2026 Vertical Software Summit. We’ll have 400+ vertical founders/operators/investors in Miami in November. Two days, 6+ billion dollar vertical founders. The Vertical AI event of the year.
Do me a solid and forward to a friend :-)