The Rise of “AI Disruption Anxiety”

We live in a moment when generative AI (large language models, image synthesis, multimodal agents) is perceived as a tectonic shift in how software gets built, sold, and used. Many observers wonder: could AI render whole classes of software obsolete the next “Uber moment” for productivity tools?

This anxiety is not entirely irrational. Some horizontal tooling e.g. simple text editors, low-code scripting, customer service chatbots may face erosion as AI becomes more capable and more integrated. The concern is that conventional software companies, particularly those whose value is in recurring tasks or predictable logic, might lose ground if AI systems can internalize or automate what the software used to do.

Yet many analysts push back against hyperbole. RBC Capital Markets argues that the narrative of “software’s death by AI” is overstated.

They see a more nuanced story: that AI will reconfigure how software is delivered and consumed, not eliminate it wholesale.

In short: the threat is real for many software layers, but it is not uniform. Some parts of the software landscape are more exposed than others, and conversely, some may become stronger in an AI‑augmented world.

It is against that backdrop that analysts have pitched the idea of “AI‑proof” software a class of businesses with greater resilience, at least over a relevant time horizon.

What Does “AI‑Proof” Really Mean in Software?

Before accepting any claim about “AI‑proof” companies, it’s useful to clarify what that label implies, and what caveats it carries.

Degrees of Resilience rather than Absolute Immunity

No software is truly invulnerable forever. The term “AI‑proof” is more a shorthand for “less vulnerable in the medium term” or “harder to displace by AI.” It does not mean unassailable. A better framing is a spectrum:

High resilience: Extremely unlikely to be disrupted by AI in the next 5–10 years without radical shifts.
Medium resilience: Some risk, but with meaningful structural defenses (e.g. regulatory, integration, data moats).
Low resilience: Likely to face substitution or major transformation pressure from AI or platform shifts.

When analysts call a company “AI‑proof (for now),” they generally mean it falls in the “high resilience” bucket relative to peers. For example, RBC describes vertical software as “one pocket of software that is likely to be viewed as ‘AI‑proof’ (for now).”

Criteria & Signals

What concrete criteria or signals lead analysts to tag a software company as more AI‑proof? Some recurring themes:

Deep domain expertise / specialization
When software captures domain-specific logic, terminologies, rules, and workflows, it's harder to replace with a generic AI.
Regulatory, compliance, or governance constraints
In industries like healthcare, insurance, finance, or life sciences, compliance demands often require software that is trusted, auditable, and validated making wholesale AI substitution riskier.
Data and contextual moats
Firms that accumulate rich, proprietary, industry-specific datasets or models have a defensible data advantage. They can use that data to fine-tune AI or build vertical AI offerings that are more effective than “generic” AI.
High switching costs & retention (customer stickiness)
When customers are deeply integrated into workflows, migration is expensive in time, risk, and cost. High gross revenue retention (GRR) is a key metric here.
Mission‑critical / “need‑to‑have” status
If the software performs core functions (not optional or nice-to-have), disruption is harder to execute.
Long implementation and entrenchment cycles
If deployment, integration, validation, training, and change management are lengthy, new entrants face big barriers.
Ability to evolve rather than stay static
The best “AI‑proof” companies won’t resist AI they’ll absorb and integrate it, turning AI into a differentiator rather than a threat.

When a software company exhibits many of these traits, analysts are more willing to regard it as durable in an AI-augmented world.

With that framework, let’s see what vertical (industry‑specific) software brings to the table.

Why Vertical (Industry‑Specific) Software Is Viewed as More Resilient

The analysts highlighted in the local newspapers article point to vertical software (also called vertical SaaS) i.e. software built for specific industries like healthcare, insurance, infrastructure, life sciences, and others as among the most AI-resilient players.

What makes vertical software stand out? Here’s a deeper breakdown of the structural advantages:

Domain and Context Complexity

Vertical software is not generic it encodes detailed domain knowledge: industry rules, regulatory structures, data semantics, specialized workflows, and idiosyncrasies. AI models often struggle with domain nuances, or make errors when dealing with corner cases. Those deep contextual intricacies serve as a kind of defense.

Regulatory & Compliance Moats

Industries like life sciences, insurance, healthcare, and financial services are heavily regulated. Software must support audit trails, compliance reporting, security, risk modeling, validation, and legal accountability. These requirements raise the barrier for any substitute even a powerful AI to displace existing systems.

High Switching Costs & Use‑Case Entrenchment

In vertical applications, software is woven into daily operations, staffing, training, reporting, and integration with other systems. Migrating off it is risky and expensive. Customers tend to stay, leading to high gross revenue retention. The article cites Clearwater Analytics as an example boasting GRR rates of 98% to 99%, which underscores strong customer lock‑in.

Underpenetrated Technology Adoption

Many vertical industries are still in earlier phases of digitization; some rely heavily on legacy or manual systems. Their customers are less inclined to jump into untested AI solutions, especially when their operations are mission critical. The slower pace gives incumbents breathing room.

Opportunity to Become AI Enablers rather than Victims

A vertical software company doesn't necessarily have to be displaced by AI it can become part of the AI stack. In fact, analysts foresee vertical players supplying context, domain-specific features, and curated data to power specialized AI models. These firms can evolve into “vertical AI” platforms.

In other words, their defensibility not only protects them, but gives them a chance to lead the next wave of AI adoption.

Empirical Evidence & Market Momentum

Investors and analysts are noticing market trends consistent with this thesis:

Vertical SaaS companies are commanding higher valuations as “safe harbor” bets in turbulent markets.
According to Activant Capital, vertical software is outpacing traditional enterprise software in growth and traction.
Analysts writing about generative AI note that vertical AI (i.e. context‑infused, domain‑aware AI) is where much of the future value will lie.

Altogether, the case for vertical software’s resilience is strong though far from unassailable. Next, we’ll examine specific companies that analysts frequently point to as more AI‑resistant.

Spotlight: Companies That Analysts Rate as More AI‑Resistant

In the local newspapers article, RBC Capital Markets highlights a slate of vertical software firms they believe combine AI‑resistance with growth potential.

Let’s walk through them, explore what makes each a candidate, and note caveats.

The RBC “AI Survivors” List

Analysts at RBC placed an “Outperform” rating on these companies, citing defensibility and innovation potential:

Autodesk — widely known for design, engineering, and architecture tools (AutoCAD, Revit, etc.).
Bentley Systems — infrastructure engineering software (roads, bridges, utilities).
Clearwater Analytics — investment accounting, reporting for insurers, asset managers, corporations.
Guidewire — software for property & casualty insurers (underwriting, claims, billing).
Hinge Health — digital health company focused on musculoskeletal care, virtual therapy, pain management.
Samsara — IoT & operational software for fleet, logistics, industrial monitoring.
PTC — industrial software, product lifecycle, IoT, augmented reality for manufacturing.
Veeva Systems — cloud software for life sciences, regulatory compliance, clinical trials, commercial operations.

Let’s look at the attributes and risks of several of these:

In-Depth Highlights & Risks

Clearwater Analytics

Strengths: Extremely high gross revenue retention (98–99%) suggests deep customer loyalty and entrenched usage.
Challenges: The finance industry is increasingly data-driven, so new AI tools or fintech players could erode parts of its value if they can supplant accounting/reporting workflows. Also, being narrower in scope, growth ceilings could arise.

Guidewire

Strengths: Insurance is a heavily regulated vertical with complex actuarial, claims, risk, and regulatory constraints. Any AI displacement would need to match those demands precisely difficult to do quickly.
Challenges: If a generic AI becomes good at claims modeling, fraud detection, or risk scoring, parts of Guidewire’s modules may be pressured. Their success depends on adaptability and embedding AI into their platform.

Veeva Systems

Strengths: Operating in life sciences and pharma, Veeva faces intense regulation (FDA, GxP, clinical trials, drug safety). It has domain depth, data, customer stickiness, and trust.
Challenges: AI models for drug discovery, regulatory prediction, and real-world evidence analysis are active areas; there is pressure from adjacent AI-powered tools. Veeva will need to partner, integrate, or acquire carefully.

Autodesk & Bentley Systems

Strengths: These belong in “design / engineering / infrastructure” verticals. They handle complex CAD, simulation, modeling, structural analysis tasks areas where AI tools (e.g. generative CAD, design AI) are still nascent. Their user base expects precision, correctness, and compatibility with industry standards.
Challenges: Generative design and AI-based modeling are intensifying. If underlying AI models become strong at drafting, optimizing, or simulating designs, these incumbents must absorb those capabilities or risk disintermediation.

Samsara

Strengths: In industrial operations, fleet/logistics, hardware integration, edge computing, sensors, and real-time constraints. AI alone can't replace the physical components.
Challenges: AI software focused on predictive maintenance, supply chain optimization, or autonomous operations could compete. Samsara needs to integrate advanced AI analytics and ensure its hardware + software stack remains indispensable.

PTC

Strengths: In manufacturing, product lifecycle, IoT, AR/VR this is highly domain-specific and mission-critical. AI alone won’t displace systems that control industrial hardware.
Challenges: AI-driven simulation and generative engineering may eat into parts of their value chain. PTC must be an AI enabler in that domain.

Hinge Health

Strengths: Health and wellness vertical, with regulated clinical aspects, patient safety, evidence, outcomes. Building trust is nontrivial, and switching care providers is hard.
Challenges: Clinical AI, telehealth diagnostic tools, personalized medicine are evolving fast. If AI systems can offer comparable outcomes or cost advantages, Hinge Health must stay ahead via data, outcomes, and partnerships.

Complementary Views from Other Analysts

Beyond RBC, other analysts have echoed or nuanced the vertical software + AI resilience thesis:

Goldman Sachs analyst Kash Rangan pointed out ServiceNow and Intuit as more resilient, thanks to their leadership and incremental AI integration. But Rangan was more cautious about Salesforce, noting its new AgentForce AI product must prove traction and monetization.
Some vertical SaaS commentators argue that vertical AI (i.e. AI built for specific industries, not horizontal) will become the dominant architecture, reinforcing the value of domain-specific platforms.

Thus, while RBC’s list is a useful starting point, the broader industry is actively debating who will survive and thrive in the AI era.

Risks, Blind Spots, and Scenarios That Could Break the “AI‑Proof” Assumption

It would be naïve to assume vertical software is forever immune to AI-driven disruption. Here are critical risks and potential breaking points:

AI Improvements & Generalization

As AI models improve especially in context sensitivity, few-shot adaptation, self‑supervised learning, and domain transfer they may erode parts of domain logic previously thought too niche. As “foundation models” evolve, they may nibble away at vertical software’s edges.

New Entrant Playbooks

A well-funded AI-native competitor could build domain-specific models from scratch and aggressively target vertical workflows. Because AI-based newcomers often start with zero legacy burden, they may have flexibility incumbents don’t.

Data Shifts & Platform Leverage

If large AI platforms or cloud providers (e.g. Google, Microsoft, Amazon) bundle vertical AI features into their core offerings (e.g. generative modules tailored to insurance, healthcare, engineering), vertical software vendors could see downward pressure. The platform providers may enjoy scale advantages in compute, data, and infrastructure.

Regulatory or Liability Shocks

In regulated sectors, if new legislation or liability norms change (e.g. stricter rules on AI explainability, audits, insured liability), vertical software vendors might be forced to re-engineer or risk obsolescence. Conversely, regulatory backlash or bans on certain AI forms could also disrupt the competitive landscape unexpectedly.

Integration Disruption

If AI agents can plug directly into underlying data sources or APIs and bypass “middle‑layer” software, vertical platforms might see disintermediation. In other words: what if AI becomes the interface, with domain logic embedded, making standard vertical UIs or workflows obsolete?

Complacency & Failure to Adopt AI

One of the biggest risks is strategic misexecution. If a vertical software provider denies AI’s importance, fails to invest, or misintegrates AI in clumsy ways, it may lose ground to more nimble competitors.

In short: “AI‑proof” is not permanent. The question is whether vertical software can maintain a transitional head start, adapt fast, and evolve into vertical AI leaders.

What to Watch: How AI Might Enhance Rather Than Replace Vertical Software

The bullish thesis is that vertical software doesn’t merely survive it upgrades in the AI era. Here are key dynamics to watch:

Vertical AI: Domain‑Aware Intelligence

Rather than generic AI, the future is likely vertical AI i.e. models trained on domain-specific data, rules, semantics, and usage patterns. Vertical software companies are well placed to own that stack, combining their data, domain knowledge, and workflows.

The shift is from “system of record” to “system of intelligence” where software not only records and organizes data, but proposes actions, predictions, and decisions.

AI as Embedded Feature, Not Replacement

Rather than trying to replace the vertical system, AI features (suggestions, automation, anomaly detection, augmentation) can be embedded into existing workflows. This enhances the value proposition rather than undermining the incumbent.

AI + Human in Loop

In many regulated domains, humans retain oversight. AI may assist, but humans validate, correct, and supervise. Vertical software can become the platform where human-AI collaboration happens. This hybrid model reinforces defensibility.

Partnerships & Ecosystem Integration

Vertical vendors may partner with AI firms, cloud platforms, or specialists integrating AI modules or co‑building applications. This way, they remain central nodes in clients’ tech stacks.

Monetization of AI Add‑Ons

Once AI is integrated, vertical software firms can monetize add-ons (premium models, optimization modules, predictive analytics, coaching layers). These become new upsell streams.

Data Network Effects

As more clients feed domain data, vertical software’s internal models can improve becoming more accurate, more tailored, and harder to replicate. That feedback loop is a powerful moat.

Thus, the future is less about resisting AI, more about co-evolving with it and vertical software may be uniquely positioned to do so.

Conclusion & Outlook

The narrative that generative AI will sweep aside traditional software companies is powerful but largely oversimplified. What we’re more likely to see is selective disruption: some software layers will be replaced, others will be transformed, and some may even emerge stronger.

In that context, vertical (industry-specific) software companies stand out as among the more “AI‑proof” players, at least in the medium term. Their deep domain expertise, regulatory moats, stickiness, data advantages, and mission-critical functions give them structural defenses. Analysts such as those at RBC have flagged names like Autodesk, Bentley Systems, Clearwater Analytics, Guidewire, Hinge Health, Samsara, PTC, and Veeva Systems as particularly well positioned.

Still, “AI‑proof” is a relative, not absolute, label. Advances in AI, aggressive entrants, shifts in platforms, or product missteps can erode defenses. The critical question is: can vertical software companies move from merely resisting AI to becoming AI platforms themselves?

My prediction is that over the next 5–10 years:

Many vertical software firms that survive will evolve into vertical AI platforms, embedding intelligent agents tuned to their industries.
Firms that cling to legacy models and resist AI will struggle.
Partnerships, data-sharing, and modular AI stacks will win out over all-or-nothing battles.