- software is eating the world (& APIs are eating software)
- there are great product opp's for those who know where to look
- theres no real competition
- SaaS/subscription is the best delivery model out there (build once, sell twice+)
There is real opportunity to:
- scale a HoldCo
- focused on a portfolio of software & digital-forward assets
- serving niche and/or traditionally sleepy verticals
A playbook to follow...
- software is eating the world (& APIs are eating software)
- there are great product opp's for those who know where to look
- theres no real competition
- SaaS/subscription is the best delivery model out there (build once, sell twice+)
We’re entering what I call the ‘Deployment Era’ ... where more traditional (sleepy) businesses will increasingly leverage software/tech to improve their model
And the best part is... we're on the front-end of riding this longer-term wave
Software (& other digital-first products) will eat more of the ‘traditional SMB’ stack
But most aren’t focused here bc it isn’t sexy or cool…
Which is where the opp is for those who have the right deal nose & know what to build & for who
Where you can build/acquire assets that have the best econ delivery model (SaaS/sub), w/ low competition, where you have an inside edge/know everyone in industry, & can do it when no one is looking
1. A true operator who knows the industry cold
2. A rockstar dev who can ship quickly, effectively (not over-engineer), & efficiently (on budget)
3. A capital allocator with good deal nose for buying/building + scaling assets
- saw an emerg trend
- picked industries w/ long runway w/ less competition
- & applied best in class execution w/patiently impatient capital allocation
Lets look at petcare services
And more specifically – veterinary clinics, daycare/boarding operators, aftercare (crematories), etc.
Note: we own/operate a handful of different operators within this vertical
- large (50K+ operators doing billions in annual profit)
- fragmented
- & most are run by baby boomers who hung a shingle 20+ years ago... and haven’t changed much (if at all)
But you can’t get ahead of yourself… the near-term opportunity is to keep it simple & effective by offering to take them to 2.0 (not 10.0)
You should look at different line items of an operator P&L… a few examples:
- revenue --> pricing optimization saas
- clinical compensation --> payroll automation saas for complicated production based-comp
More from Software
Kubernetes vs Serverless offerings
Why would you need Kubernetes when there are offerings like Vercel, Netlify, or AWS Lambda/Amplify that basically manage everything for you and offer even more?
Well, let's try to look at both approaches and draw our own conclusions!
🧵⏬
1️⃣ A quick look at Kubernetes
Kubernetes is a container orchestrator and thus needs containers to begin with. It's a paradigm shift to more traditional software development, where components are developed, and then deployed to bare metal machines or VMs.
There are additional steps now: Making sure your application is suited to be containerized (12-factor apps, I look at you: https://t.co/nuH4dmpUmf), containerizing the application, following some pretty well-proven standards, and then pushing the image to a registry.
After all that, you need to write specs which instruct Kubernetes what the desired state of your application is, and finally let Kubernetes do its work. It's certainly not a NoOps platform, as you'll still need people knowing what they do and how to handle Kubernetes.
⏬
2️⃣ A quick look at (some!) serverless offerings
The offer is pretty simple: You write the code, the platform handles everything else for you. It's basically leaning far to the NoOps side. There is not much to manage anymore.
Take your Next.js / Nuxt.js app, point the ...
Why would you need Kubernetes when there are offerings like Vercel, Netlify, or AWS Lambda/Amplify that basically manage everything for you and offer even more?
Well, let's try to look at both approaches and draw our own conclusions!
🧵⏬
1️⃣ A quick look at Kubernetes
Kubernetes is a container orchestrator and thus needs containers to begin with. It's a paradigm shift to more traditional software development, where components are developed, and then deployed to bare metal machines or VMs.
There are additional steps now: Making sure your application is suited to be containerized (12-factor apps, I look at you: https://t.co/nuH4dmpUmf), containerizing the application, following some pretty well-proven standards, and then pushing the image to a registry.
After all that, you need to write specs which instruct Kubernetes what the desired state of your application is, and finally let Kubernetes do its work. It's certainly not a NoOps platform, as you'll still need people knowing what they do and how to handle Kubernetes.
⏬
2️⃣ A quick look at (some!) serverless offerings
The offer is pretty simple: You write the code, the platform handles everything else for you. It's basically leaning far to the NoOps side. There is not much to manage anymore.
Take your Next.js / Nuxt.js app, point the ...
The Great Software Stagnation is real, but we have to understand it to fight it. The CAUSE of the TGSS is not "teh interwebs". The cause is the "direct manipulation" paradigm : the "worst idea in computer science" \1
Progress in CS comes from discovering ever more abstract and expressive languages to tell the computer to do something. But replacing "tell the computer to do something in language" with "do it yourself using these gestures" halts that progress. \2
Stagnation started in the 1970s after the first GUIs were invented. Every genre of software that gives users a "friendly" GUI interface, effectively freezes progress at that level of abstraction / expressivity. Because we can never abandon old direct manipulation metaphors \3
The 1990s were simply the point when most people in the world finally got access to a personal computer with a GUI. So that's where we see most of the ideas frozen. \4
It's no surprise that the improvements @jonathoda cites, that are still taking place are improvements in textual representation : \5
The Great Software\xa0Stagnation https://t.co/A6peSPERaU
— Jonathan Edwards (@jonathoda) January 1, 2021
Progress in CS comes from discovering ever more abstract and expressive languages to tell the computer to do something. But replacing "tell the computer to do something in language" with "do it yourself using these gestures" halts that progress. \2
Stagnation started in the 1970s after the first GUIs were invented. Every genre of software that gives users a "friendly" GUI interface, effectively freezes progress at that level of abstraction / expressivity. Because we can never abandon old direct manipulation metaphors \3
The 1990s were simply the point when most people in the world finally got access to a personal computer with a GUI. So that's where we see most of the ideas frozen. \4
It's no surprise that the improvements @jonathoda cites, that are still taking place are improvements in textual representation : \5
@JuliaLMarcus @Iplaywithgerms This paper gives documentation on software (with causal reasoning, assumptions reviewed in appendix) for a parametric approach to estimating either "total effects" or "controlled direct effects" with competing events and time-varying
@Iplaywithgerms Total effects capture paths by which treatment affects competing event (e.g. protective total effect of lifesaving treatment on dementia may be wholly/partially due to effect on survival). Controlled direct effects do not capture these paths
@Iplaywithgerms More detailed reasoning on the difference and tradeoffs between total and controlled direct effects and causal reasoning in the point treatment context provided here along with description of some estimators and
@Iplaywithgerms If you are familiar with more robust approaches like IPW or even better TMLE for time-varying treatment, these are trivially adapted to go after the controlled direct effect by simply treating competing events like loss to follow-up (censoring). e.g.
@Iplaywithgerms Examples of IPW estimation of the total effect of a time-varying treatment described in Appendix D of this paper:
https://t.co/RNhcgTBMkb
And here
https://t.co/rMWmwFBWwV
Others in reference lists of above papers.
@Iplaywithgerms Total effects capture paths by which treatment affects competing event (e.g. protective total effect of lifesaving treatment on dementia may be wholly/partially due to effect on survival). Controlled direct effects do not capture these paths
@Iplaywithgerms More detailed reasoning on the difference and tradeoffs between total and controlled direct effects and causal reasoning in the point treatment context provided here along with description of some estimators and
@Iplaywithgerms If you are familiar with more robust approaches like IPW or even better TMLE for time-varying treatment, these are trivially adapted to go after the controlled direct effect by simply treating competing events like loss to follow-up (censoring). e.g.
@Iplaywithgerms Examples of IPW estimation of the total effect of a time-varying treatment described in Appendix D of this paper:
https://t.co/RNhcgTBMkb
And here
https://t.co/rMWmwFBWwV
Others in reference lists of above papers.
