The Second-Order Effects of Adding AI to Every Feature

Why the race to AI-enable everything is creating hidden costs, complexity, and strategic risks that most companies aren't prepared for.

The AI gold rush is in full swing. From customer support chatbots to intelligent document summarizers, companies are racing to add AI features to every product and workflow. Big Tech alone is projected to spend over $320 billion on AI infrastructure in 2025—a 30% jump from 2024's already massive $246 billion.

But while the first-order benefits of AI are obvious—automation, efficiency, capability—the second-order effects are proving far more complex and costly than most organizations anticipated.

This isn't an anti-AI argument. It's a reality check. The companies that win won't be those with the most AI features. They'll be the ones who understood the full cost structure before they shipped.

1. The Death of Predictable Margins

Traditional SaaS companies enjoyed one of the most elegant business models in commercial history: build once, serve infinitely. The marginal cost of serving an additional customer approached zero, producing gross margins of 80-90%.

That beautiful economics is colliding with a harsh new reality.

AI features introduce variable costs that scale with usage. Every model inference, every token generated, every API call burns actual compute. This fundamentally changes the economics. As Dave Friedman's analysis put it, AI introduces two new variables to SaaS: a cost variable where two users paying the same price can have radically different costs, and a performance variable where AI can produce outputs you never programmed.

Consider a simple example: You charge $100/seat/month. To maintain 80% gross margin, total variable costs must stay under $20/seat. If your non-AI COGS is $15/seat, you have just $5 of headroom for AI costs. At $0.002 per AI interaction, that's only 2,500 interactions per month before you're destroying your margin structure.

The data confirms this pattern. AI-first companies are running at 50-65% gross margins, compared to traditional SaaS benchmarks of 75-80%. Some early-stage AI companies are averaging only 25% gross margins, trading distribution for profit. GitHub Copilot reportedly lost $20-80 per user monthly while charging just $10/month flat rate in its early days.

That's the kind of margin compression that kills companies—or forces dramatic repricing.

2. Feature Creep on Steroids

Feature creep has always been a product management challenge. AI has supercharged it.

The pressure to add AI capabilities is relentless: executives demand it, competitors announce it, sales teams insist it will close deals. The result is products bloated with AI features that add complexity without clear user value.

UX designers are increasingly vocal about this problem. According to a recent UX design survey, designers complain of "complex AI chat widgets on every screen" that "add noise, not clarity." After ChatGPT launched, countless SaaS platforms rushed to add AI-generated summaries and chat tools. Many soon realized that what sounded exciting on paper created confusion—users weren't sure why the AI was there or how to use it.

The second-order effect is particularly insidious: AI features that don't demonstrably improve core workflows still incur their full cost structure. You're paying for inference whether or not users find value. And unlike traditional features that have minimal ongoing cost once built, AI features create perpetual operational burden.

As the Nielsen Norman Group observes, organizations are confronting AI's real operational costs—not just compute, but maintenance, drift, legal exposure, security risk, and operational complexity. Users are fatigued. When everything gets an AI sparkle, it becomes noise, not novelty.

3. The Hidden Technical Debt Time Bomb

Technical debt costs the global economy over $85 billion annually in lost productivity and delayed innovation, according to McKinsey. AI is both a potential solution and a significant new source of that debt.

Organizations with high technical debt already spend up to 40% of their IT budgets on maintenance rather than innovation. Now layer on AI systems that require:

• Constant monitoring for model drift
• Retraining as data distributions change
• Updates as underlying foundation models evolve

The Harness State of Software Delivery 2025 report found that unbridled AI code generation carries a long-term maintenance burden, with the majority of developers spending more time debugging AI-generated code and resolving security vulnerabilities.

The technical debt compounds. AI systems trained on yesterday's data make decisions about tomorrow's problems. Models that worked brilliantly in testing degrade silently in production. Each AI feature becomes not just a capability to maintain, but a liability to monitor.

Legacy infrastructure presents another challenge. As MIT Sloan Management Review notes, many enterprises possess vast libraries of applications built over decades that cannot support modern AI workloads without extensive modernization. The result: technical debt prevents organizations from deploying AI solutions that could reshape how they compete. It's a vicious cycle—you need to modernize to use AI effectively, but the resources to modernize are consumed by maintaining existing AI features that were bolted onto unprepared systems.

4. The Security and Compliance Minefield

Every AI feature is also a new attack surface and compliance risk. The numbers are sobering. According to BigID's 2025 AI Risk Report:

• 69% of organizations cite AI-powered data leaks as their top security concern
• Yet nearly half (47%) have no AI-specific security controls in place
• Nearly two-thirds of organizations lack full visibility into their AI risks

The shadow AI problem makes this worse. While only 40% of companies have purchased official AI subscriptions, employees at over 90% of organizations actively use AI tools through personal accounts that IT never approved.

Research analyzing over 22 million real enterprise prompts found that 22% of files and 4.37% of prompts shared with AI tools contain sensitive information—source code, access credentials, proprietary algorithms, M&A documents, customer records, and internal financial data.

Data shared with AI apps has exploded, increasing 30x in one year. The average organization now shares more than 7.7GB of data with AI tools monthly, up from just 250MB a year ago.

Regulatory frameworks are struggling to keep pace. The EU AI Act, GDPR implications, and state-level privacy laws create compliance complexity that multiplies with each AI feature deployment.

The second-order effect: every AI feature requires its own risk assessment, data handling procedures, and compliance verification. The overhead scales non-linearly with the number of AI touchpoints in your product.

5. Vendor Lock-In and Strategic Dependency

When you add AI to every feature, you're not just adding capability—you're adding dependency. Most AI features rely on third-party models and APIs, creating strategic vulnerability that many organizations are only beginning to understand.

The risks are multi-dimensional:

Pricing volatility: The upcoming GPT-5 "high-intelligence" tier introduces pricing that can double or triple per-token costs for complex tasks. Forecasting budgets becomes difficult when price is tied to model depth rather than fixed usage metrics.

Limited control: When your costs are tied to third-party model providers, you're exposed to pricing changes, rate limiting, and API policy shifts with limited control over latency, reliability, or update cycles.

Vendor failure risk: The recent collapse of Builder.ai, once valued at $1.3 billion, exposed a harsh reality: many companies do not fully control the software and data their operations depend on. When a vendor fails, pivots, or changes terms, organizations face redevelopment costs, data migration challenges, and potential business disruption.

Organizations using multi-cloud strategies have managed to reduce vendor-related risks by 37%, but implementing true provider independence requires significant architectural investment that most companies skip in the rush to ship AI features.

6. The Seat-Based Revenue Collapse

Here's the paradox that keeps SaaS executives awake at night: AI makes workers more productive, which means companies need fewer seats. The very AI features you're adding to justify premium pricing may be undermining your entire revenue model.

A lot of software's historic unit of billing—a seat—was a proxy for the unit of work. Pay per seat because the seat does the work. AI changes the unit of work.

Customer support platforms are seeing customers consolidate seats as AI handles tier-1 queries. If AI tools make workers meaningfully more productive, the math works against traditional pricing: if seats drop by 20% but price per seat rises by 10%, revenue still falls.

The industry is scrambling to adapt. Usage-based pricing, outcome-based pricing, and hybrid models are emerging, but each introduces its own complexity. Enterprise customers need budget predictability—a CFO cannot budget for "we'll see how much we use." This tension has birthed credit systems and tiered usage bundles, adding cognitive load for buyers and sales cycles.

The stark prediction from industry analysts: if you're still seat-priced in 2027, you either have a spectacular moat or you're about to learn you don't.

7. The Build-vs-Buy Calculus Shifts

AI isn't just changing what software can do—it's changing whether you need to buy software at all.

As Martin Alderson observes, many things companies would historically find a freemium or paid service for can now be built with AI tools in minutes, exactly the way they want.

The second-order effect: if your product is essentially a SQL wrapper on a billing system or a thin layer over AI APIs, you now have thousands of competitors—engineers at your customers with a spare Friday afternoon and an AI coding assistant.

Many SaaS products contain features that many customers don't need or use. The complexity of managing that evaporates when you have only one customer (your own organization) with complete control of the roadmap.

This doesn't mean all SaaS dies, but it does mean the bar for what justifies a subscription just got much higher. Your AI features need to deliver value that customers genuinely cannot replicate themselves—which gets harder as AI tools become more capable.

What Smart Companies Are Doing Differently

The companies navigating these second-order effects successfully share common approaches:

They measure AI costs at the customer level

You can't manage what you can't measure. Before adding AI features, mature organizations implement granular cost tracking that attributes AI spend to specific customers, features, and use cases. This visibility is essential for pricing decisions, identifying unprofitable customers, and understanding true unit economics.

They apply the "core value" test ruthlessly

Every AI feature must pass a simple test: Does it make the user's core workflow meaningfully better? If the answer isn't a clear yes—if it's a "nice to have" or a competitive checkbox—it probably shouldn't ship. The features users love most often burn through margins fastest. Better to have fewer, high-impact AI capabilities than a bloated product with mediocre AI everywhere.

They build for flexibility

Smart organizations design systems with provider independence in mind. They use abstraction layers, AI gateways, and modular architectures that allow swapping underlying models without rewriting applications. They favor open standards and negotiate data export rights, code escrow clauses, and self-hosting options. The upfront investment pays dividends when providers change pricing or capabilities.

They align pricing with value delivered

The most successful AI-first companies are moving toward pricing models that capture value when AI delivers results. Usage-based components, outcome-based tiers, or hybrid approaches that blend subscription predictability with consumption-based cost recovery. They're transparent about AI costs with customers, building trust through dashboards that show value delivered versus resources consumed.

They invest in security and compliance from day one

Rather than bolting on AI governance as an afterthought, leading organizations establish AI-specific security controls, data handling policies, and compliance frameworks before deployment. The overhead is significant, but far less than remediation after a breach or regulatory action.

The Path Forward

The question isn't whether to add AI to your product—for most companies, that ship has sailed. The question is:

• Whether you understand the full cost structure before you ship
• Whether you're building capabilities that create genuine value or checking competitive boxes
• Whether your business model still works when AI becomes table stakes

The companies that win won't be the ones with the best AI. They'll be the ones whose business model still works after AI becomes table stakes.

The delayed reckoning isn't just about cost discipline. It's about whether "SaaS" is still the correct noun for describing what these businesses actually are.

The second-order effects of adding AI to every feature aren't reasons to avoid AI. They're reasons to approach AI strategically rather than reactively. In a world where everyone is rushing to add AI, competitive advantage may come from being thoughtful about where you don't.

At tknOps, we help AI-powered companies understand exactly what their AI features cost—down to the customer, feature, and use case. Because you can't manage second-order effects you can't see. If you're building AI into your product and want visibility before the margins disappear, let's talk.