I liked that you picked a service that has a relatively low barrier to entry. The real asset are local
operators and referrals. Making them more efficient without being controlled by a big company would be a boon for everyone involved.
Consider being a platform coop with regional operators as members. See https://platform.coop/
Yes, the barrier here is the desire to study and pass the exam. If willing, you are up and running relatively quickly - but only as a technician under someone else's operating license. To get the operator license (eg to be a full on pest control company) requires 2+ year documented experience and another set of exams.
The operating license holder is also on the hook for legal action if (when) things go wrong.
"Control" is interesting and I have found in all trades that people value their freedom. The good companies don't monitor employees too tightly, and are rewarded with loyalty and longer tenures generally. Of course you have to run a good recruitment and referral process to find the good people!
I’ve never heard of platform Co-ops. Cool! Lots of people predicted that a beloved local coffee shop was doomed to fail when the workers got a loan and bought it to run as a completely flat cooperative. It’s been a few years and they are absolutely killing it. I’d love to see the tech version of that.
Not long ago I left a reasonably cool AI startup to join an ops heavy (like people physically doing work, running warehouses etc) company. There was some adjustment but the ability to deliver real, concrete, monetary value to people working in the field is incredibly rewarding (and oddly the pay is on par with most bay area startups).
I recently talked to a few companies in the AI space, from (smaller) frontier model labs to companies still looking to build "AI products" and my take away was that, if you're not working for one of the big players, the market hasn't really figured out if there is an "AI engineer" job yet.
I'm increasingly starting to believe that the future of work for people that have technical skills (more than just 'software') is likely going to be working in places that are less about "shipping software" and more about supporting teams doing something physical in the real world.
These companies are also the most ripe to truly leverage AI: they have tons of messy problems that need to be solved and iterated on extremely fast. Operations people tend to be "EoD" deadline people, not quarterly planners. Getting solutions solved in an actionable way on time often means really understanding the core business, the technical space surrounding it, and how to leverage AI to pull of some miracles. It can be stressful, but when you pull it off your stakeholder have sincere and real gratitude and you're actually moving the needle for the company.
I don't think the Bay area, even those sniffing the AI vapors the hardest, is really willing to accept what AI is going to do to software and software companies.
I love working for those companies also, where they are used to waiting months for a small software update and I can do it in hours and they think I'm a wizard.
The best outcome is bespoke software for every company and small "ops heavy" (in startup context) startups have a window to grow like weeds. Imagine the culture shock and legal / procurement process for an established player to bring a vendor in to build this for them. It won't work, it needs to be an internal team, but even then, the internal politics, and short term affects to people's bonuses and incentives will make it almost impossible.
I give this example as I previously worked at a big European REIT. My job was to implement renewable energy across the portfolio which on paper was a no-brainer due to legislation and grants / feed in tariffs etc.
We got huge pushback from every angle with the local teams, people paying lip service to drag it out and delay. Eventually I got to the root cause... The capex had to come out of the business unit, and the payback would negatively affect their KPIs and bonus. Next time I came across this kind of issue, I asked to see the incentive structure before approaching anyone.
Doing something similar. Bought a business in the petroleum equipment service space. Building internal tools for ourselves. Pen and paper still dominates the industry.
> We have an acquisition of a small residential operator lined up, which we'll build the tooling for and grow a platform around once we’ve proven the model works and can scale.
This is the exact process private equity tries to do at scale, right?
How long was the employment at the pest company? At any point, did anyone treat you like you were stealing their business? I thought about this approach, but I chickened out many times because of the possible confrontation.
There’s no way to build domain knowledge like working in the field you want to target. This could be a reusable model for people looking to serve a well-targeted vertical with one’s own software company for that vertical.
I think taking the technician job is brilliant and exactly how you find the 'better way' for vertical SaaS, similar to how EquipmentShare understood the deep inefficiencies in heavy equipment rental. It's
I love this, the perfect antidote to all the stupid startup-bro grind bullshit posts.
You put in real work to understand the business landscape and typical pain points. With AI, implementing solutions has become much easier but knowing what the problems are and how to solve them hasn't.
182 comments
Consider being a platform coop with regional operators as members. See https://platform.coop/
The operating license holder is also on the hook for legal action if (when) things go wrong.
"Control" is interesting and I have found in all trades that people value their freedom. The good companies don't monitor employees too tightly, and are rewarded with loyalty and longer tenures generally. Of course you have to run a good recruitment and referral process to find the good people!
I recently talked to a few companies in the AI space, from (smaller) frontier model labs to companies still looking to build "AI products" and my take away was that, if you're not working for one of the big players, the market hasn't really figured out if there is an "AI engineer" job yet.
I'm increasingly starting to believe that the future of work for people that have technical skills (more than just 'software') is likely going to be working in places that are less about "shipping software" and more about supporting teams doing something physical in the real world.
These companies are also the most ripe to truly leverage AI: they have tons of messy problems that need to be solved and iterated on extremely fast. Operations people tend to be "EoD" deadline people, not quarterly planners. Getting solutions solved in an actionable way on time often means really understanding the core business, the technical space surrounding it, and how to leverage AI to pull of some miracles. It can be stressful, but when you pull it off your stakeholder have sincere and real gratitude and you're actually moving the needle for the company.
I don't think the Bay area, even those sniffing the AI vapors the hardest, is really willing to accept what AI is going to do to software and software companies.
We got huge pushback from every angle with the local teams, people paying lip service to drag it out and delay. Eventually I got to the root cause... The capex had to come out of the business unit, and the payback would negatively affect their KPIs and bonus. Next time I came across this kind of issue, I asked to see the incentive structure before approaching anyone.
> We have an acquisition of a small residential operator lined up, which we'll build the tooling for and grow a platform around once we’ve proven the model works and can scale.
This is the exact process private equity tries to do at scale, right?
There are lots of antiquated operators not having newer technology for pest control, which makes this area lucrative for even $50K MRR.
Go for it!
You put in real work to understand the business landscape and typical pain points. With AI, implementing solutions has become much easier but knowing what the problems are and how to solve them hasn't.