What is "Enshitification" in Data Products and how to possibly prevent it

What is Enshitification?

Enshitification is not a bug. It’s an inevitable result when a tech product optimizes itself away from user value and toward extractive monetization. It’s what happens when incentives, metrics, and pressure collide — and users are the collateral damage.

It’s the slippery slope where a product that once delighted its users ends up feeding off them. Instead of delivering on its core promise, it starts delivering excessive ads, hidden fees, degraded service, or just makes the whole experience worse over time. It's a phenomenon that happens when a platform puts the maximization of short-term revenue over the long-term experience.

Enshitification describes the gradual decline in a product’s quality and user value as it increasingly prioritizes revenue and shareholder returns. In data-focused tech platforms – such as SaaS applications, data analytics tools, AI services, or developer APIs – this often follows a predictable lifecycle: the product launches with generous features or low cost to attract users, then progressively introduces fees, ads, or restrictions that degrade the experience.

In Cory Doctorow’s words,

“platforms die: first they are good to their users; then they abuse their users to make things better for their business customers; finally, they abuse those business customers to claw back all the value for themselves.” (The ‘Enshittification’ of TikTok | WIRED) (Cory Doctorow wants to wipe away enshittification of tech • The Register).

In practice, once-loyal customers suddenly find higher prices, hidden fees, bloated interfaces, gated features, or degraded service quality. For example, a free or low-tier SaaS that worked beautifully may later shrink storage space, add confusing upsell dialogs, or throttle performance to force upgrades. Similarly, data APIs may start open but then impose steep per-call charges or restrictive licensing (The Enshitification of APIs | Babbling Fish). Such shifts frustrate users and developers alike, turning a “good deal” into a costly, irritating trap.

Doctorow and others emphasize that this pattern is not an accident but a product of market incentives. Tech products often sit in two-sided markets (e.g. users vs. advertisers, or developers vs. enterprise clients). Early on, they subsidize one side (users or content producers) to build network effects; later they harvest revenue from that captive base. For instance, a SaaS startup backed by venture capital may initially run at a loss, using funding to acquire customers via free tiers or steep discounts (The ‘Enshittification’ of TikTok | WIRED). But as investors demand returns, that same company will raise prices, cut free features, and add monetization hooks (ads, premium tiers, hidden “service” fees, APIs are locked down under premium features, data export disabled and so on) – often crippling what attracted users in the first place.

Several systemic forces drive this cycle. Investor/shareholder and growth pressures are harsh realities. Venture-funded tech companies must show rapid user or revenue growth. This funding treadmill encourages spending big upfront (marketing, subsidized pricing) and postponing profit. As Basecamp CEO Jason Fried notes, “if you have a bunch of money in the bank, you’re encouraged to spend it,” fueling ever-higher expectations and eventual backlash when the money runs out (Basecamp CEO Jason Fried says venture capital funding destroys startups | Vox). The end result is a rush to monetize – raising prices, adding hidden charges or gating features – once investors balk at losses.

Another force is market concentration and lock-in. When a few big players dominate, they can degrade service without losing customers. Doctorow highlights that big tech firms have repeatedly bought or crushed competition so “people don’t have anywhere else to go”. In technical products, this can look like a dominant analytics provider changing a popular tool to push its paid cloud platform, or a leading SaaS bundling formerly-separate functions into one expensive suite. Users trapped by network effects or data lock-in grudgingly pay higher costs and endure worse UX. In these oligopolies, the “gravity” of enshitification is nearly irresistible (What is "Enshitification," and Can We Stop It? ): without strong competition or regulation, there’s little pressure to keep improving the product.

Finally, two-sided platform economics inherently create tension. On one side are end-users and developers who demand quality and fair access; on the other are advertisers, enterprise buyers, and shareholders who demand profit. Meeting one side’s needs often means squeezing the other. For example, data analytics tools may initially offer rich dashboards for free, but later paywall advanced insights for enterprise clients, leaving regular users with crippled features. Or an AI platform may lure developers with open APIs, then impose strict usage quotas when larger firms come calling.

How Enshitification manifests

In tech data products, enshitification can appear in many familiar forms:

• Inflated prices & fees: After an initial free or cheap phase, products raise prices steeply. SaaS tools add new “locked” tiers or per-seat charges, analytics services hike monthly fees, and APIs introduce per-call billing. Users feel “wallet first” – their costs spike unexpectedly. For example, developers paid for a modest API plan then saw a 10× price jump overnight.

• Shrinking free tiers: Free usage limits (requests, data, users) keep getting smaller. Companies may gut features from free plans, forcing casual or small users to upgrade. In AI services, early users often enjoy generous quotas, which later become tiny or vanish, compelling paid subscriptions.

• Feature bloat & clutter: To justify higher prices, platforms add features—often irrelevant to core users—which overcomplicate the UI. The true value (core functionality) can become harder to find or slower, frustrating users. For example, a once-simple analytics dashboard may become cluttered with marketing modules and pop-ups.

• Hidden fees & advertisements: New fees (service, transaction, “convenience” charges) appear in billing unexpectedly. Or ads creep into formerly ad-free tools. These “nickel-and-diming” tactics irritate customers. (A B2B example: platforms requiring clients to pay per invoice sent or imposing forced add-on services.)

• Quality degradation: Engineering focus shifts from product quality to monetization. Response times slow, uptime targets slip, or algorithms prioritize paid content over organic user needs. A stark case was Twitter’s throttling of follower feeds unless a blue tick subscription was bought, dramatically reducing organic reach for unpaid users.

• Access restrictions: APIs and data become closed or restricted. Early-developer community tools can be disabled. Data export/import functions are limited or removed. Reddit’s 2023 crackdown (charging $0.24 per 1000 API calls) meant third-party apps like Apollo had to shut down, since “big tech companies” wanting the data (for AI) essentially hijacked it. Users experienced this as a loss of agency – once-open platforms now extort users under the guise of protecting “valuable data”.

• Lock-in reinforcement: Companies double down on user lock-in (e.g. proprietary formats, long-term contracts). They may promise unrealistically low introductory rates that skyrocket later, making it hard to switch. The cumulative effect is a strong incentive not to leave, despite declining satisfaction – classic enshitification.

These patterns arise from the incentives described above: capital markets driving “crazy” profit expectations, network effects trapping users, and two-sided economics straining user value. Users often only recognize the problem in hindsight – by the time a platform is fully “enshittified,” they’re locked in and must endure it or move on.

Practical strategies to mitigate Enshitification

Preventing enshitification requires aligning business models, product design, and governance around long-term user value instead of short-term extraction. Below are key strategies and frameworks to help data-product companies resist this decline, along with the challenges each strategy addresses:

Many of these strategies reinforce one another. For example, usage-based pricing both shares risk (users only pay for what they use) and aligns pricing with value, avoiding sticker shock. Customer advocacy (via NPS and feedback loops) ensures business leaders hear when quality dips, prompting corrective action. Technical agility (continuous performance tuning) offsets cost pressures that might otherwise force a price jump. Industry examples abound: for instance, Cloud providers introduced tiered free credits for startups to prevent early choke points, and other productivity apps maintained a fully functional free tier even as they built paid Enterprise Grid features (preserving their small-team user base).

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Frameworks and Playbooks

In practice, companies can embed anti-enshitification safeguards throughout their processes:

• Product development: Adopt a lean feedback loop (e.g. continuous integration of user feedback) so that any negative impact of a change is caught early. Use “growth boards” that weigh revenue ideas against usability and churn risk. Periodic “health checks” (customer satisfaction surveys, usability testing) flag early signs of enshittification.

• Monetization modeling: Use pro forma scenarios before pricing changes: project user churn/loss vs. revenue gains for each pricing move. If a price hike is projected to drive more churn than revenue, revise the plan. Offer phased trials or grandfathering of old plans to minimize backlash.

• Contract strategy: For B2B SaaS, avoid auto-renewal price hikes. Instead, build in periodic contract reviews with customers (which can highlight issues). Consider giving discounts for multi-year commitments only if service levels improve, tying locked-in revenue to product health.

• Customer segmentation: Treat early adopters and “power users” as partners. For example, maintain a private beta program where new features are tested and some users are rewarded with extended free limits or input on roadmap. These engaged customers can become loyal defenders when changes come.

• Community building: Many platforms now thrive on developer ecosystems. Nurture those communities (documentation, hackathons, open-source SDKs). A supportive developer community will flag enshittification efforts (as happened with Twitter’s API changes) and can provide pressure to moderate corporate decisions.

• Transparency: Some companies publish “platform health reports” (uptime, API usage, customer success stories) to maintain trust. They might also articulate a clear mission statement reminding investors and users that “we exist to serve [customers/developers] first.” Regular updates on how revenue is reinvested into the product can create goodwill (e.g. “Your extra subscription funds helped us double our data science team”).

Tackling real-world challenges

While these strategies are ideal, real-world constraints must be managed:

• Investor demands vs. Product health: It can be hard to convince stakeholders to forgo quick wins. Mitigation: tie executive compensation partly to retention/engagement targets, so leaders personally feel the cost of alienating users.

• Competition: When rivals are aggressive, companies may fear losing market share by keeping prices low. Here, cooperation can help. For example, industry trade groups or standards bodies can create ethics guidelines (like fair-pricing codes) that all players sign onto. Shared standards (e.g. GDPR-like data portability rules) also level the field.

• User behavior: Some users exploit free tiers, so companies want to clamp down. The solution is better fraud and abuse detection, rather than blanket cuts that harm honest users. Invest in security and analytics to block bad actors individually.

• Technology limitations: If scaling costs necessitate price increases, communicate and improve efficiency. For instance, offering options like on-prem or regional hosting can share load without hitting end-users with universal hikes.

• Market shocks: In downturns, companies often scramble for revenue. Planning a reserve fund or adjusting hiring growth earlier can reduce panic monetization later.

Summary of Strategies vs. Challenges

In summary, preventing enshitification means treating the user’s experience as a first-class product deliverable. Practical steps include choosing pricing models that scale with value, holding product development accountable to user metrics, designing every change with the worst-case user in mind, and building in competition (or at least not stifling it). History shows that many companies succumb to enshitification under pressure, but those that avoid it typically do so by design – they embed customer-centric and sustainable practices throughout their playbooks. By doing so, tech data products can remain healthy, innovative, and user-friendly even while meeting market and investor demands.

Remember, a great product isn’t just about what it does for users today — it’s about what it can continue to do tomorrow, next year, and far beyond. Prioritize user value and long-term thinking, and you’ll avoid the enshitification trap.

If you have more suggestions on this topic or any insightful blogs, podcasts, etc. please do share. Happy learning, happy building! :]

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References: Industry analysis and commentary on platform decline and product management, including Cory Doctorow’s essays and speeches (The ‘Enshittification’ of TikTok | WIRED) (Cory Doctorow wants to wipe away enshittification of tech • The Register) (The Enshitification of APIs | Babbling Fish); reports on API pricing backlashes (Reddit Users Are Planning 48-Hour Blackout Over New Pricing Policy - Business Insider) (Reddit Users Are Planning 48-Hour Blackout Over New Pricing Policy - Business Insider); expert perspectives on growth versus sustainability (Basecamp CEO Jason Fried says venture capital funding destroys startups | Vox) (Cory Doctorow wants to wipe away enshittification of tech • The Register) and LLM apps of course.