Why software stocks are getting pummelled

How bad is the “pummelling”?

• Several commenters argue the headline is overblown: broader tech indices (QQQ, big AI-heavy names) are up; the damage is concentrated in enterprise SaaS (SAP, Salesforce, Workday, ServiceNow, etc.).
• Others counter that double‑digit one‑day drops for large, mature firms and ~20–30% declines over months are significant, especially when trillions in market cap are involved.
• ETFs focused on software (e.g. IGV) and specific names like ServiceNow are cited as evidence that “pure software” and enterprise apps have underperformed even as IT/hardware do fine.

AI vs SaaS moats: build vs buy

• Bearish view: AI makes software far cheaper to build; moats based on “we wrote complex code” erode; bespoke internal tools or agent‑based systems can replace expensive SaaS for many workflows.
• Bullish/cautious view: moats are in domain knowledge, integrations, regulatory compliance, support, and “being someone else’s problem,” not raw code. Large ERPs and regulated integrations (tax, healthcare, gov APIs) are seen as especially sticky.
• Some expect more “good enough,” highly specific internal tools built by tiny teams, shrinking SaaS pricing power and margins, especially where vendors already charge “not small monthly fees.”

Reality of AI coding today

• Practitioners describe big productivity gains but emphasize oversight: LLM output must be reviewed, tested, and integrated; edge cases, migrations, and undocumented business logic remain hard.
• There is concern about “slopware”: non‑engineers chaining AIs to write and “explain” code they don’t understand, creating fragile systems that will later need expensive cleanup.
• Others argue next‑gen agents could automate not just coding but testing, monitoring, and incident response, effectively becoming “SaaS in a box,” though skeptics doubt this is near‑term.

Valuations, market behavior, and macro rotation

• Many note that enterprise software P/E and revenue multiples were very high; some see the drop as a rational correction of overvaluation rather than a specific AI shock.
• Others think investors misunderstand how hard software and operations really are, over‑believing narratives like “Project Genie” and underestimating integration, governance, and politics.
• There’s discussion of capital rotating from software to hardware/AI infrastructure (GPUs, cloud), simple substitution under limited investable capital.

Long‑term outlook: commodification and labor

• One camp sees software itself becoming commoditized as tools improve, with value shifting to unique processes and data; software jobs shrink but don’t vanish.
• Another camp believes software demand and complexity will keep growing; AI is a force multiplier, not a replacement, and large “systems of record” vendors and critical infra (databases, ITSM, security) will outlast many AI darlings.