The Future of Software
Software That AI Can Replicate Has Weaker Pricing Power
April 2026 · YK Research · Software / Valuation
Core Thesis
For 15 years the software business model was attractive: sell a seat, lock in the contract, compound the ARR. The stock market rewarded it with 60–80% of equity value sitting in terminal value. The unexamined assumption that the moat would still be there in year 11 and beyond.
That assumption only holds if the product is hard to rebuild. AI just changed what “hard” means. If an LLM can replicate the core of your software in weeks, or a weekend, the competitive advantage weakens and terminal value can fall. That puts 60–80% of the equity value at risk.
The repricing began in 2025. Multiples quietly compressed. The market went long silicon, short SaaS. Software isn’t dead, but the software business model is getting rewritten in real time.
The Software Split: Durable and Vulnerable Businesses
- Distribution-moated: platforms that own the user (Google, Meta, TikTok). The LLM clone doesn’t matter if nobody finds it.
- Data-moated: proprietary data you can’t scrape (Palantir ontologies, Bloomberg terminal, private-market data).
- Infra-moated: physical silicon, cloud regions, compute capacity. The stuff the LLM runs on.
- Regulation-moated: compliance, audit, systems of record in healthcare, defense, finance.
- Workflow-moated: mission-critical systems where the cost of getting it wrong is enormous (ERP for a public co., EHR for a hospital).
- Single-feature SaaS: note-taking, grammar, transcription, basic CRM. AI can make these features much easier to replicate.
- Thin vertical tools: the “Shopify for X” wrappers where the X is commodity.
- Consumer ed-tech: Duolingo −67.5% from mid-2025 peak. AI tutors are free, personal, and infinitely patient.
- Creative-tool rentiers: Adobe −24.6% from 2025 peak. Generative models reduced demand for entry-level creative tools.
- PE-owned SaaS rollups: 1,900+ software cos bought for $440B (2015–25), now ~25% of the private-credit book. LBO debt serviced by cash flows AI is unbundling.
The one question every software company has to answer
“How will this not be unbundled by an LLM?”
If the answer is “data,” “distribution,” “regulation,” or “physics,” you might be fine. If the answer is “features” or “brand,” the terminal value is at risk.
2018 to October 2025. Big Tech doubled its share of the entire index. Every one of those percentage points is priced on the assumption that the moat is real.
Most DCFs forecast 5–10 years of explicit cash flows. Everything past year 10 is a terminal value assumption, the belief the business still earns money in year 11+. That assumption drives much of the valuation.
Put the two together: 36% of America’s public equity rests on a 60–80% terminal-value assumption. That is the exposure.
2025 Scoreboard: Long Silicon, Short SaaS
The Question Every Business Has to Answer
“How will this not be unbundled by an LLM?”
If there's no credible answer, the terminal value assumption is hard to support.
Three Stages of the Repricing
P/E compression
Market applies a larger discount factor to future cash flows. Multiples quietly compress. We're here now.
P/E → FCF multiples
Market flips from earnings-based multiples to multiples of current free cash flow. GAAP-only reporting. No more adj. EBITDA. SBC treated as the real cost it is.
Short-duration world
Growth investing breaks. Pre-revenue unicorns de-fund. Capital rotates to physical world. M&A logic inverts. Short-termism becomes rational.
Signal the flip from Stage 1 to Stage 2: watch companies stop reporting adjusted EBITDA. When SBC finally gets treated as the real cost it always was, the regime has shifted.
Four Implications If This Plays Out
Growth equity math breaks
Investing now for future returns requires financeable future cash flows. A 12x ARR multiple presumes a moat that compounds. If LLMs unbundle the moat, the 12x multiple is hard to justify.
Private Credit Is an Early Warning
PE bought 1,900+ SaaS cos for $440B (2015–25). Software is now ~25% of private credit. Cash flows repay the LBO debt. If AI replaces the product, cash flows fall and loans may default. Watch the default rate.
The AI capex paradox
$300–500B/yr of AI infra only pays back over 7–15 years. If markets reprice to short-duration multiples, that capex can't be financed. AI-driven disruption can weaken the funding for AI infrastructure.
Likely outcome: oscillation
The regime may oscillate rather than change permanently. Shorter cycles and higher volatility could make terminal values fall and recover as AI capex changes. Tail risk increases without necessarily changing average returns.
Five Forms of Leverage in an AI-Divided World
AI supply-chain access is becoming a source of geopolitical influence. Outside the US and China, countries can still negotiate from strength if they control one of five scarce inputs. Countries without them may depend on foreign AI providers and face difficult terms.
Critical Minerals
Rare earths, lithium, gallium, copper. The raw inputs to every chip and every grid upgrade.
AU, CA, CL, ID
High-Tech Manufacturing
Endemic fab capacity, lithography, advanced packaging. Hard to move, harder to replicate.
TW, KR, NL, JP
Capital
Deployable sovereign balance sheet. Can fund the next fab, the next grid, or the next deal.
SG, UAE, CH, NO
Energy
Gigawatt-scale power, cheap and firm. Data centers require reliable large-scale power.
SA, QA, US, AU
Data Center Infra
Land, cooling, fibre, permitting speed. The physical substrate AI runs on.
US, IE, SG, NL
Investment read: every one of these five levers is a physical asset class. None of them are software. This is the same point the multiples are making, just in different language.
Physical Assets
The lasting bottlenecks in the AI stack are physical: critical minerals, energy storage, and actuation. The same logic applies to capital allocation.
“If your moat requires atoms, not bits, a better language model cannot unbundle it overnight. For now.”
Before taking a long position, ask whether physical assets protect the business. For software-only exposure, keep the size small.
Large Vertically Integrated Technology Firms
The logical endpoint of Big Tech hitting its stride is the vertically integrated megacorp: a firm so embedded in physical supply chains that it becomes too big to disrupt. Elon is assembling one in the open, with Tesla, SpaceX, and xAI converging into a single industrial entity. These companies are starting to finance themselves like sovereign states.
Issued long-dated bonds that only make sense for entities assumed to still exist in 2066.
Century bond, the first tech issuer to try. Its maturity reflects expectations of very long-lived cash flows.
The market agreed. Credit markets are assigning these firms unusually long funding horizons.
The same dominance invites trust-busting. Large technology firms can concentrate talent and wealth and limit competition. Their scale creates regulatory risk as well as competitive advantage.
Portfolio read: sovereign megacorps are the closest thing to “atoms plus bits” you can own on public equity markets. Keep them long, but size around regulatory tail risk.
Positioning Takeaways
Single-feature SaaS, thin vertical software, PE-owned rollups levered against cash flows that AI competition can reduce. Especially anything still trading on 10–15x ARR post–2025.
Changing terminal values create tail risk without necessarily changing average returns. IV on hyperscalers stays bid. Fits the option seller's playbook and harvest the premium, size for the drawdown.
If Stage 2 arrives, 10-year DCFs become harder to support. Reprice your own book to what you can prove in 2–3 years. Focus on earnings evidence.
The partial move is already underway. A full repricing would be the most consequential structural shift in capital markets since the postwar era. You don't need to bet that it happens fast. You need a book that survives whether it does or doesn't: long necessary infrastructure, short businesses whose claimed competitive advantage lacks evidence.
Social Capital, “2025 Annual Letter” (April 2026). Read the original. This write-up extracts and frames the valuation-multiple thesis; interpretation and trade framing are ours.