SaaS Gets Repriced (and Replaced?)

On 9 February 2026, Goldman Sachs’ prime brokerage team reported a significant shift: hedge funds are offloading tech equities at a rate not seen in years.

Software stocks have faced an aggressive repricing early this year. The IGV ETF is down over 21% year-to-date and nearly 30% from its September peak. Mid-caps like Asana and Dropbox have seen 20–30% pullbacks. In previous cycles, this would simply signal macro tightening. This time, markets are pricing a deeper structural risk: that AI won’t just squeeze margins, but will replace entire software categories. For operating partners and deal teams, this repricing is already creeping into private marks, credit terms, and IC conversations.

In this edition, we move past market sentiment to examine two cases where AI fundamentally disrupted SaaS models. We’ll identify the warning signs that preceded their decline and outline the framework CFOs should use to navigate this shift.

Past SaaS drawdowns were usually defined by interest rates and growth durability. In 2026, the focus has shifted to substitution risk. High short interest in software ETFs suggests investors aren’t just worried about cash burn, they are questioning product defensibility.

Fear isn’t proof, so we look at the precedents already hitting the tape.

Chegg built a massive subscription business helping students with homework. In March 2023, the CEO noted a spike in ChatGPT interest that was stalling new customer growth. By May, the company suspended its outlook and shares dropped 47% in a day.

The market realized ChatGPT could replicate Chegg’s core value proposition at zero marginal cost. This wasn’t a valuation correction; it was a realization of obsolescence.

Jasper is an AI-powered writing assistant and was the early darling of generative AI, reaching a $1.5 billion valuation by 2022. When OpenAI launched ChatGPT, it created a free, high-quality competitor overnight.

Jasper’s growth flatlined, leading to a 20% internal valuation cut and significant layoffs. Their experience proves how quickly differentiation vanishes when the underlying technology becomes a commodity.

These are concrete operating and traffic metrics that were visible in plain sight before the blow‑ups.

Chegg: SimilarWeb data showed converted visits to chegg.com dropped 89% year-over-year in March 2023 and conversion rates collapsed from 0.36% to 0.04%. Even January 2023, typically a peak month due to start-of-semester activity, showed converted visits down 60% YoY. More recently, organic keyword volume shrank from 11.1M to 3.5M, a 68% collapse, as Google AI Overviews began intercepting student queries. Non-subscriber traffic worsened steadily: −8% YoY in Q2 2024, −19% in Q3, −37% by October, and −49% by January 2025.

Jasper: Monthly traffic fell from 8.7M visits in March 2023 to 6.1M by May, a 30% decline in just two months (SimilarWeb), coinciding exactly with ChatGPT’s viral surge.

Chegg: Q1 2023 revenue was down 7% YoY, but forward guidance was the real shock: $175–178M for Q2, 10% below the $193.6M consensus. Subscriber trajectory: 8M+ (2022) → 7.7M (2023) → 4.7M (Q1 2024) → 3.6M (Q4 2024), a 55% cumulative decline.

Jasper: Management told investors to expect $90M ARR in early 2023, with a $250M target by end of 2024. By summer, the 2023 forecast was cut by 30%. Revenue hit $120M in 2023 then fell 54% to $55M in 2024.

Chegg: Three pivots in 18 months, CheggMate (GPT-4 partnership), Scale AI proprietary models, then a “next best actions” AI assistant. CEO departed June 2024 after 14 years. Workforce cut 22% in May 2025, with further waves bringing total restructuring to ~45%.

Jasper: Both co-founders out by September 2023, replaced by a Dropbox executive. Three pivots in 12 months, GPT-3 wrapper, OpenAI partnership, proprietary model attempt. Employee equity marked down 20%.

The Chegg and Jasper cases point to a concrete monitoring framework:

If these signals start flashing for any SaaS company in your portfolio, treat them as seriously as a deteriorating burn multiple.

The market shift demands a structured evaluation rather than a reactive one. CFOs are currently auditing their models across three specific vectors:

The primary task is determining whether AI expands or erodes your addressable market. Look for where AI allows you to own new workflows versus where it turns your core modules into commodities. High-value pricing only holds if the product remains a destination, not a feature.

CFOs should score new initiatives based on expansion headroom and NRR potential in a competitive AI landscape, not just immediate ARR. If a tool is likely to be absorbed into a broader platform’s native features, its long-term valuation is at risk. Capital must follow long-term expandability.

Unit economics require a new layer of underwriting that accounts for AI-driven disruption. We recommend modeling three distinct scenarios: a base case, one where AI erodes your pricing power, and another where it reduces total usage.

Test whether your LTV/CAC and payback periods stay within healthy limits if expansion slows and churn ticks upward. Projects that rely on “perfect world” NRR or high-growth multiples are no longer safe bets. Capital discipline today means funding only the initiatives that remain profitable under stress.

Revenue is a lagging indicator. Structural decline shows up first in engagement metrics, traffic patterns, and the sudden need for aggressive discounting. CFOs need to watch for decay in cohort-level engagement and shifts in search traffic, as these almost always precede a churn spike.

Leadership teams should decide on a trigger-based response before the revenue drop hits:

The matrix summarizes the key trade‑offs between equity, venture debt and revenue‑based financing. Equity provides the most runway but dilutes more; venture debt is cheaper but carries covenants and warrants; revenue‑based financing is flexible but usually capped at 30–50% of ARR. Use this as a quick reference when mapping your capital options.

We build monitoring infrastructure for management teams to quickly react, not just static spreadsheets. Our early-warning dashboards track:

The goal is to determine whether you are being cyclically repriced or structurally displaced before the market makes that decision for you.

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