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AI Just Repriced Every Pet Software Company
Dashboards and workflows are now commodities. Here's what isn't.
Collin Armstrong
January 21, 2026
Issue #294
January 21, 2026
Quick Hits:
The Value Shift Nobody Saw Coming
A profound shift is underway in the software industry.
As highlighted in the "Death of Software 2.0" thesis by Fabricated Knowledge, advances in AI are commoditizing user interfaces and predefined workflows. In this new paradigm, the traditional hallmarks of software value - slick dashboards, complex forms, and streamlined clicks matter less.
Instead, enduring value shifts to underlying data, APIs, and system-of-record state that AI agents rely on.
Put simply, AI can dynamically generate or navigate a UI on the fly, making a polished front-end less of a moat.
What remains crucial is the persistent information and the interfaces to access it.
Recent investor reactions underscore this shift.
When Anthropic announced new industry-specific AI tools, software stocks tumbled.
Salesforce, Workday, Intuit, and Snowflake all fell 6%–13% in a single week.
Analysts noted that investors are "reassessing whether software companies can defend their pricing power as AI capabilities expand."
AI's reach is now extending into vertical SaaS domains once thought "AI-proof." Anthropic's Claude for Healthcare integrated directly with medical databases and demonstrated specialized skills for clinical workflows, encroaching on territory traditionally occupied by niche software vendors.
The assumption that domain-specific workflow software can remain insulated from AI?
Shattered.
Pet tech software is particularly exposed.
The pet care industry's software landscape is highly fragmented, with single-purpose SaaS tools for grooming salons, vet clinics, pet sitters, and trainers each handling narrow workflows.
Pet businesses often juggle separate apps for scheduling, client communication, pet records, forms, and billing.
This fragmentation means no single platform holds a unique network effect, and most of these tools deliver similar functional workflows that an AI could replicate or stitch together.
Industry observers note that today's pet health and service data exists in isolated silos across vet offices, grooming software, daycare systems, and consumer apps—a patchwork ripe for AI-driven unification.
In an AI-first world, the very fragmentation of pet software becomes a vulnerability.
The value of pet software will shift away from proprietary dashboards and rigid workflows, and toward serving as the persistent system of record and API layer that smart agents build upon.
The Death of Software 2.0
"Software 2.0 is dying," as Fabricated Knowledge provocatively frames it, referring to the end of an era where software's value lay in feature-rich interfaces and predefined processes.
Large AI models can now handle tasks that traditionally required specialized software logic and UI—scheduling an appointment, generating a report, filling out a form—through natural language commands or autonomous agents.
Many user-facing workflows are no longer proprietary assets; they're simply prompts an AI can execute.
In his analysis, Doug O'Laughlin draws an analogy to a computer's memory hierarchy.
He likens AI agents to fast but ephemeral memory (RAM) that can rapidly process tasks and present results.
These agents dynamically generate UIs or decisions in real time but don't hold long-term state.
The persistent memory in this analogy is the underlying database and source-of-truth where information lives durably.
Traditional software workflows are being re-imagined as a handshake between a fast, intelligent "cache" (the AI doing on-demand computation) and a reliable datastore (the system of record software).
The key insight: AI will handle the "information processing, GUI, and workflow" on the fly, all ephemeral, while the software system provides accurate persistence and an API to read/write that state.
In practical terms, AI commoditizes the UI.
Why click through menus and forms when a cognitive agent can do it for you?
O'Laughlin notes that "faster workflows, better UIs, and smoother integrations will all become worthless, while persistent information (exposed via an API) will become extremely valuable."
The memory hierarchy analogy captures how value is migrating downward in the stack.
The top layer (UI/experience) becomes transient, auto-generated as needed by AI, and the bottom layer (data storage and core functionality) becomes critical long-term infrastructure.
Software doesn't disappear, but its role changes.
It must serve as the reliable data store and transaction engine that AI interfaces plug into, rather than as a self-contained user-facing product.
This is a structural shift.
Just as cloud commoditized computing power, AI is commoditizing "software workflows as code."
Many horizontal software products that exist primarily as UI veneers over data face an "extinction-level event" under this model.
Why pay for a SaaS that is essentially a fancy to-do list or form generator when an AI agent can present a custom interface or automation on demand?
Microsoft CEO Satya Nadella captured this sentiment in late 2024 by bluntly saying, "SaaS is dead."
Around the same time, IDC predicted that by 2028, the standard per-user, per-month SaaS pricing model will be obsolete, with 70% of software providers forced to change how they charge.
This doesn't mean software vanishes, but it means software can't charge a premium just for packaging routine features behind a pretty interface.
The defensible value lies beneath the interface.
Next-generation software companies must refocus on being the "single source of truth" and the best possible "NAND in the stack" for AI agents to build upon.
AI is turning software into infrastructure—and the winners will be those who embrace that role.
Vertical SaaS Isn't Safe Either
If this sounds theoretical, financial markets are already pricing it in.
In January 2026, Anthropic launched Claude for Healthcare & Life Sciences and Claude CoWork, triggering a broad selloff in software stocks.
Recent headline from Wired about Claude Cowork
Companies from enterprise SaaS giants to niche vertical players saw their shares dive within days.
The reaction wasn't about immediate revenue loss—it was about expectations.
RBC Capital Markets analysts noted that investors are questioning whether even big SaaS firms can defend their pricing and moats as advanced AI becomes ubiquitous.
Crucially, vertical SaaS players were no longer seen as safe havens.
Historically, software serving specific industries was thought to have built-in protection due to domain-specific requirements, compliance hurdles, and deep workflow know-how.
That logic was shaken when Anthropic's healthcare AI not only demonstrated medical expertise but also plugged directly into specialist data sources to perform tasks an expert user or vertical app would normally handle.
As the RBC analysts observed, AI providers are "encroaching on niches historically served by specialized vendors."
Tools once considered "AI-proof" because of their industry nuance may prove to be less defensive than originally thought.
If a general AI can quickly be trained or prompted to handle a regulated workflow, the unique advantage of that vertical software diminishes.
The stock market's message aligns with the Software 2.0 thesis: the durable advantage is shifting away from the application layer.
One by one, the pillars of SaaS value—automation of a workflow, nice analytics dashboards, domain-specific UIs—are being undercut.
An AI can deliver "on-demand capabilities accessible through a single interface," flattening what used to be a whole product category.
For pet tech, the implication is sobering but also galvanizing. Pet care software startups can no longer assume that having a convenient scheduling app or a pretty client portal is enough.
If healthcare and HR software aren't safe from AI, neither is pet grooming software.
Investors will increasingly ask - What stops an AI from doing this?
Pet tech founders must be ready with a good answer—one that likely involves proprietary data or network effects, not interface niceties.
Features vs. Durable Assets
Walk through the offerings of a typical pet care SaaS product.
Software for a dog grooming salon or a veterinary practice management system, and you'll see online booking calendars, customer intake forms, pet profile databases, automated reminders, and reporting dashboards.
These are valuable tools, but at their core, they orchestrate information that could be described in an API.
AI is making it possible to replicate or replace these workflows with generic intelligence.
Consider scheduling.
An AI agent with access to a groomer's calendar could schedule appointments via natural language.
An owner says, "Book Fluffy for a grooming early next week," and the AI finds an opening, confirms with the salon's system via API, and sets it up.
No dedicated "Book Now" user interface needed.
The same goes for forms and data entry, or reporting dashboards. An AI can generate a custom summary on demand without the user clicking through predefined charts.
What, then, is left of the pet SaaS product's value if these surface features can be synthesized by AI? The enduring value lies in the data and persistent state that the AI cannot conjure from thin air.
A scheduling AI still needs a calendar database to write to and read from.
A form-filling AI still needs a structured repository of pet profiles and medical records.
A reporting AI is only as good as the historical data it can query.
Pet software that maintains a rich, accurate "single source of truth" about pets, clients, and operations doesn't lose relevance, it becomes the indispensable back-end.
What becomes commoditized is the delivery mechanism.
Consider the distinction between a scheduling UI and a pet appointment ledger.
The calendar interface can be AI-generated or voice-driven, but the authoritative list of appointments with pet IDs, owner info, times, and services is a persistent asset.
Dashboard analytics can be created by an AI on the fly, but only if it has access to years of service records, revenue transactions, and health outcomes.
Those raw data archives are the valuable asset, not the visualization tool.
Digital forms may eventually be completed via chatbot, but the unified pet identity and health record that multiple parties contribute to and reference is what endures.
In all these cases, the "brain" (AI) can be swapped in or out, but the "memory" (data) needs to be there and well-structured.
Pet SaaS companies historically focused on making nice scheduling tools or portals; now they must ensure they are the system of record for pet care data.
Another angle is trust and network data.
Many pet service platforms accumulate trust networks and reputation data—reviews of pet sitters, verification of trainers, referral relationships among vets. These are assets that can't be easily reinvented by a new AI app.
A large language model can answer a pet question generally, but it can't magically produce a vetted list of your most trusted local dog walkers with availability next Tuesday.
That comes from a platform that has built that network and data over time.
Platforms vs. SaaS: Who Holds the High Ground?
Not all software businesses are created equal in the face of this AI upheaval.
Platforms and marketplaces have structural strengths that standalone SaaS products may lack—if they evolve correctly.
Why are platforms better positioned?
First, they accumulate unique data and network effects. A pet services marketplace records every booking, each review, pet preferences, provider availability, and perhaps communications and payments.
This yields a rich operational dataset and an active network of users. An AI can assist users on such a platform, but it has to work through the platform's data and relationships.
The platform, if designed as an open system of record, becomes the API layer for the real world of pet care.
Second, platforms excel at orchestrating real-world actions and trust, which is hard for AI alone to replicate.
If an AI agent is booking a grooming appointment, the actual value delivery requires confirming a slot with a specific groomer, handling payment, sending reminders, and possibly enforcing cancellation policies.
A well-run platform provides the infrastructure for those transactions, trust guarantees like verified reviews or insurance, and conflict resolution.
AI can interface with that, but the platform's role as a coordinator and guarantor gives it leverage and defensibility.
In essence, platforms manage the doing (services fulfillment, multi-party coordination), not just the planning.
SaaS tools historically helped human operators "run the business," whereas platforms can directly "run the transactions" or even do parts of the work.
This aligns with the concept that a platform can evolve into a "System of Action", not merely a system of record.
However, platforms cannot be complacent. To capture the high ground, they must embrace AI and perhaps integrate agents into their ecosystems.
If a pet services platform remains a walled garden with a clunky UI, a third-party AI could attempt to bypass it.
On the other hand, if the platform offers robust APIs, MCPs, webhooks, and even its own AI-assisted concierge features, it cements itself as the go-to hub.
There's a cautionary tale in other industries. Think of how some travel agencies got displaced when data became aggregated in open platforms, versus how others transformed into APIs that power Kayak, Expedia, or Google's travel agents behind the scenes.
Pet platforms have a similar choice.
Those that become more interoperable and agent-friendly could harness AI to enhance their services.
Those that resist and try to lock down users in proprietary workflows might find AI working around them.
Strategic Guidance for Pet SaaS Founders
For founders and product leaders building software for pet businesses, the writing is on the wall…don't mistake AI-powered features for a durable moat.
A fancy chatbot in your app might delight users in the short term, but it can and will be replicated widely.
The real strategic moat is owning critical data and being the integration hub.
Invest in schemas and single source of truth
Ensure your application is the authoritative system of record for something valuable—pet medical histories, a directory of pet owner preferences, business operations data.
This may require expanding the breadth or depth of data you handle.
A grooming software could evolve to maintain a full "pet identity profile" including vaccinations, behavior notes, and grooming history across locations.
That kind of data asset is hard to recreate. As Fabricated Knowledge advises, "data's safekeeping and longer-term storage" should be a primary role—your product should look much more like infrastructure software meant to be consumed by AI agents.
Develop and expose APIs
If your software currently doesn't play well with others, change that. Interoperability is key in an AI-driven ecosystem.
Offer comprehensive APIs so that external tools and AI agents (with proper authorization) can read/write data or trigger actions.
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This turns your product from a closed box into a platform component.
Focus on persistent value, not just AI gimmicks
It's tempting to bolt on AI features like a chatbot that answers pet health FAQs.
These can be useful, but ask - does this feature improve our core data asset or network effect?
Don't rely on being the only one with a particular AI feature; rely on having the best data and integration such that even if many have similar AI capabilities, yours performs better due to richer context.
Embrace AI as a user of your system
Rather than seeing AI purely as a feature within your product, also see it as a user persona.
Design your system assuming an AI agent will read your documentation and attempt to use your software like a power user.
If your pet SaaS becomes known as "the system that any pet-care AI can plug into easily," you gain relevance even if your human-facing UI becomes secondary.
Guidance for Pet Service Operators
If you operate a pet care business and rely on software vendors, you might wonder how these shifts affect you.
The good news - AI will not replace pet professionals or the core services.
An AI isn't going to groom a dog or perform surgery on a cat.
However, the tools you use to run your business will evolve.
Prioritize data portability
Ensure that the data you input into any software can be exported or integrated elsewhere easily.
Whether it's client contact lists, pet medical records, appointment history, or billing info—you don't want it locked in a proprietary silo.
Ask your software providers if they offer data export or APIs to retrieve your information.
A lot of valuable pet data today is trapped in isolated silos with minimal cross-platform integration due to legacy systems lacking good export capabilities.
Don't let your business be stuck in one of those silos as the landscape shifts.
Demand vendor transparency and AI compatibility
When evaluating software for your pet business, inquire about how it's adapting to AI.
Does it integrate with voice assistants or scheduling bots?
Is the vendor planning features that allow AI to automate tasks in their system?
Be wary of vendors who dismiss AI or keep everything manual; they could become outdated, leaving you scrambling later.
Choose partners who embrace interoperability.
Foster direct digital relationships with clients
One potential shift is that clients might start using AI assistants on their end.
A pet owner might say to their home voice assistant, "Schedule a vet checkup for Fluffy next month," and an AI will interact with your booking system.
Encourage clients to use online booking or digital communication channels you support.
If you simplify and digitize interactions now, you're effectively becoming "AI-ready" for when those intermediaries become common.
Durable Value Patterns in Pet Tech
To make these abstract ideas concrete, here are pet-native patterns where value will likely endure:
Pet ID Graphs
A persistent digital profile for every pet that links their microchip ID, medical history, vaccination records, insurance, and preferences.
Such an identity graph might span multiple services. In a fragmented landscape, few if any companies own this outright today.
But whoever helps consolidate a trusted pet identity across providers will hold a valuable position.
It becomes the reference point that AI agents will use to make decisions.
Operational State Aggregators
Context is everything in pet care.
What appointments are coming up?
Which pets are currently in daycare?
Has payment been received?
An "operational state" layer is basically a real-time snapshot of the business. We might see unified operational data hubs that span businesses or can be queried by authorized agents.
The durability here is in the completeness and timeliness of that state data.
Trust and Reputation Networks
Pet owners are discerning about who cares for their animals.
A trust network graph in pet tech could include verified reviews, repeated interaction links, and credentials for professionals.
This is a durable asset. A new AI scheduling app would likely prefer to query an existing trust network rather than start a review system from scratch.
Each pattern shares a theme - the value is in being the source of truth or the engine behind the scenes, not the front-end presentation.
The Shift Ahead
Is pet software "dying"?
Not at all—but it is demoting itself from the star to the stage crew.
In an AI-first world, the glitzy SaaS apps and portals will matter less to end-users, while the behind-the-scenes systems that actually hold and move pet data will matter more.
Pet software is evolving into infrastructure. The reliable databases, integration hubs, and transactional platforms that intelligent agents and diverse front-ends plug into.
The value is in being the source of truth and coordination layer in the pet ecosystem. Founders, operators, and investors in pet tech should internalize this core thesis - the new moats are data and integration, not UI and workflow.
This means the competitive landscape might shift.
A small company with the richest pet health dataset could become more important than a larger company with a slick UI but little unique data.
A marketplace that owns the trust network of thousands of pet sitters could outlast a software firm that just sells scheduling to a subset of kennels.
Those who adapt will find opportunity. Essentially becoming the "AWS of pet data" or the interoperable layer every pet AI uses.
The search volume for "smart collar" tells a clear story. Consumer interest in pet wearables has hit an inflection point.
We're looking at 55K searches last month with 93% year-over-year growth and that trajectory line isn't slowing down.
The real momentum kicked off in May 2024, peaking at the end of summer before settling into a new, elevated baseline.
That timing tracks with a wave of new entrants and meaningful advances in both hardware capabilities and the data insights these devices can actually deliver.
This isn't a fad cycle—it's category maturation driven by products that are finally starting to deliver on the promise.
For operators watching this space, Q1 will be telling.
We're past the holiday spike and into the period where we'll see if this elevated demand has staying power or if it cools back toward historical norms.
The $1.00 CPC is still relatively cheap for a growing category, but that won't last if volume holds.
Worth noting "smart collar" as a search term suggests consumers across the globe are starting to understand the category exists—they're not just searching brand names or "GPS dog tracker" anymore.
Category awareness is the prerequisite for category growth, and the search data says we're there.
Where the trajectory ultimately settles will tell us whether the new players have cracked the code on retention and word-of-mouth, or if we're still in the "curious consideration" phase.
See you Friday!
