This prediction is qualitative and relative, so it can’t be cleanly labeled right or wrong.

Evidence on the 2022–2023 SaaS environment:

• Public SaaS valuation multiples and growth expectations compressed sharply after late 2021. Bessemer’s Cloud Index data shows revenue multiples and growth rates for public cloud companies falling through 2022–2023 compared to the 2020–2021 boom, reflecting a much tougher funding and demand environment for SaaS overall.
• Venture funding for SaaS and broader startup markets dropped substantially in 2022–2023 relative to 2020–2021, with many reports describing a “reset” and investors prioritizing efficient growth over growth-at-all-costs. This supports the idea that sustaining very high growth (e.g., 2x year-over-year) became notably harder and more prized versus the prior frothy period.
• However, some SaaS companies (especially AI-adjacent or infrastructure players) continued to post extremely high growth rates even in 2023–2024, sometimes exceeding 2–3x year‑over‑year from smaller bases. At the same time, other later‑stage SaaS companies struggled to grow at all. This dispersion makes it impossible to define a single, universally accepted conversion like “2x now ≈ 3x then” across the entire category.

Reasoning:

• The directional claim—that it became significantly harder and more impressive to hit 2x growth in 2022–2023 than to hit 3x in the prior boom—is broadly consistent with macro data on SaaS markets: lower valuations, tighter funding, and slower average growth.
• But the specific equivalence ("2x now is as good as 3x then") is a normative, relative judgment that depends on:
  • which segment of SaaS (early‑stage vs late‑stage, infra vs application),
  • which investor or operator you ask, and
  • what baselines they use for "impressive" in each period.
• There is no objective benchmark or consensus metric that could definitively verify or falsify this exact conversion ratio.

Because of this, even with enough time having passed, the prediction can’t be conclusively validated or refuted from available data; it’s a qualitative calibration of difficulty and impressiveness rather than a clearly testable forecast.

Therefore the appropriate classification is: "ambiguous".