AI Augments Software, Not Replaces It: Why SaaS Won't Be Disrupted¶

Original Text¶

Having done tons of coding, cloud engineering, as well as covering the software sector as an equity analyst over the last 15+ years, I'm really not seeing a software disruption here.

Jensen is focused on boosting the productivity of his workforce, which can easily result into an economic value of >$100 billion for his company, as opposed to doing a set of highly risky and complicated software transitions that might save him $500 million. And other enterprises are doing the same going through recent earnings calls.

All the sudden experts in software underestimate gravely how complicated it is to rip out and replace an ERP, database, CAD, supply chain management, or other core tool that runs the backbone of a business. These are complicated 2-4 year projects with mixed results of success, frequently resulting into earnings warnings and failed projects.

The real value currently is to insert as much AI into your business as possible and boost productivity. And the easiest way to do is having AI leverage your core tools. We're currently using all our software tools more than ever before, as now our AIs are also using them as well. So, we're spending more on AI and software and this continues to accelerate.

Where's the money coming from? We're spending less on human services as we can do more ourselves now. So, we'd be bearish on companies that provide services such as consulting, IT services, freelancing etc. However, for key software tools, revenues and EPS will accelerate from here in our view, as these names are typically building AI agents into their current tooling. From everything we're seeing, enterprises are very keen to lever these AI capabilities. It's not like enterprises - and especially SMBs - have a deep pool of AI talent to build this themselves.

Free open source software also has been available for decades, illustrating that price is not the key factor in selecting a tool. It's about security, reliability, and network effects around software tools.

English Summary¶

Tech Fund (15-year software analyst with coding/cloud engineering background) argues against the "AI will disrupt software" narrative, claiming enterprises are actually increasing software spend—not decreasing—because AI agents are now also using these tools alongside humans, making productivity gains via augmentation far more valuable (>$100B) than risky system replacements ($500M), with the real disruption hitting human services (consulting, IT services, freelancing) instead of software vendors.

The Jensen Proof Point: Risk-Adjusted Value¶

Jensen Huang's (Nvidia CEO) strategic choice is revealing: - Focus: Boost workforce productivity with AI - Economic value: >$100 billion for company - **Avoid:** Risky, complicated software transitions - **Potential savings:** ~$500 million

The ratio: 200:1 value creation (productivity) vs cost reduction (software replacement)

Observation from earnings calls: "Other enterprises are doing the same"

Implication: If the world's most AI-sophisticated company (Nvidia) isn't ripping out core software, why would anyone else?

Why Software Replacement is Overestimated¶

The complexity reality (often missed by "sudden experts"): - Core systems: ERP, database, CAD, supply chain management - Timeline: Typical 2-4 year projects - Success rate: Mixed at best - Frequent outcomes: - Earnings warnings - Failed projects - Operational disruption

What "sudden experts in software" gravely underestimate: - These aren't plug-and-play replacements - They run "the backbone of a business" - Migration risk >> potential cost savings

Historical precedent: Enterprises have avoided painful migrations for decades. Why would AI suddenly change this calculus?

The Actual Pattern: AI Augmentation, Not Replacement¶

The real value play:

"Insert as much AI into your business as possible and boost productivity"

The easiest implementation path:

"Having AI leverage your core tools"

Observed behavior:

"We're currently using all our software tools more than ever before, as now our AIs are also using them as well."

The spending dynamic: - Humans use software tools → baseline spend - AI agents also use software tools → incremental spend - Result: "We're spending more on AI and software and this continues to accelerate"

The counterintuitive insight: AI doesn't reduce software consumption. It increases it.

Where the Money Comes From: Human Services Disruption¶

Budget reallocation (not budget cuts):

Bearish: - ❌ Consulting firms - ❌ IT services companies - ❌ Freelancing platforms

Why: "We're spending less on human services as we can do more ourselves now"

Bullish: - ✅ Key software tools (embedding AI agents) - ✅ Enterprise software with strong network effects - ✅ SaaS with security/reliability moats

Why: - "Revenues and EPS will accelerate from here" - "These names are typically building AI agents into their current tooling" - "Enterprises are very keen to lever these AI capabilities" - "Enterprises—especially SMBs—don't have a deep pool of AI talent to build this themselves"

The structural advantage: Software vendors can integrate AI capabilities faster and more reliably than enterprises can build in-house.

The Open Source Parallel: Price Isn't the Driver¶

Historical evidence:

"Free open source software also has been available for decades"

Implication: If price were the key factor, open source would have won decades ago.

What actually drives tool selection: 1. Security 2. Reliability 3. Network effects

Extended to AI era: - Free AI models exist (open source LLMs) - But enterprises still pay for: - Managed AI services (OpenAI, Anthropic) - Integrated AI tooling (GitHub Copilot, not raw code models) - Secure, reliable infrastructure

Conclusion: AI-native startups offering "cheaper AI alternatives" face the same uphill battle as open source did.

The Capital Flow Model¶

Traditional enterprise budget:

Software tools: $X
Human services: $Y
Total: $X + $Y

AI-era enterprise budget:

Software tools: $X + ΔX (AI agents using tools)
AI infrastructure: $Z (new category)
Human services: $Y - ΔY (displaced by AI)
Total: $X + ΔX + $Z + ($Y - ΔY)

Net effect: - If ΔX + $Z < ΔY → Total spend decreases (AI deflationary) - If ΔX + $Z > ΔY → Total spend increases (AI inflationary for software)

Tech Fund's observation: The second scenario is playing out. "This continues to accelerate."

繁體中文總結¶

Tech Fund（15 年軟體分析師，具編碼/雲端工程背景）反駁「AI 將顛覆軟體」的敘事，主張企業實際上正在增加軟體支出——而非減少——因為 AI agents 現在也在使用這些工具（與人類並行），透過增強獲得的生產力收益（>$1000 億）遠比冒險替換系統的價值（$5 億）更大，真正被顛覆的是人力服務業（顧問、IT 服務、自由接案），而非軟體供應商。

Jensen 證據點：風險調整後的價值¶

Jensen Huang（Nvidia CEO）的策略選擇很有啟發性： - 專注： 用 AI 提升員工生產力 - 經濟價值： 公司 >$1000 億 - **避免：** 冒險、複雜的軟體轉換 - **潛在節省：** ~$5 億

比例： 200:1 價值創造（生產力）vs 成本削減（軟體替換）

財報電話會議觀察：「其他企業也在這樣做」

含義： 如果世界上最 AI 精通的公司（Nvidia）都不拆掉核心軟體，為什麼其他人要這樣做？

為何軟體替換被高估¶

複雜度現實（經常被「突然冒出的專家」忽略）： - 核心系統：ERP、資料庫、CAD、供應鏈管理 - 時間軸： 典型 2-4 年專案 - 成功率： 最多算一般 - 常見結果： - 財測警告 - 專案失敗 - 營運中斷

「突然冒出的軟體專家」嚴重低估的： - 這些不是即插即用的替換 - 它們運行「業務的骨幹」 - 遷移風險 >> 潛在成本節省

歷史先例： 企業數十年來一直避免痛苦的遷移。為什麼 AI 會突然改變這個計算？

實際模式：AI 增強，而非替換¶

真正的價值玩法：

"盡可能在業務中插入 AI 並提升生產力"

最簡單的實施路徑：

"讓 AI 利用你的核心工具"

觀察到的行為：

"我們現在使用軟體工具的頻率前所未有，因為我們的 AI 也在使用它們。"

支出動態： - 人類使用軟體工具 → 基準支出 - AI agents 也使用軟體工具 → 增量支出 - 結果：「我們在 AI 和軟體上花更多錢，這持續加速」

反直覺洞察： AI 不會減少軟體消費。它增加軟體消費。

錢從哪來：人力服務業顛覆¶

預算重新分配（不是預算削減）：

看空： - ❌ 顧問公司 - ❌ IT 服務公司 - ❌ 自由接案平台

原因：「我們在人力服務上花更少，因為我們現在可以自己做更多」

看多： - ✅ 關鍵軟體工具（嵌入 AI agents） - ✅ 具強網路效應的企業軟體 - ✅ 具安全性/可靠性護城河的 SaaS

原因： - 「營收和 EPS 將從此加速」 - 「這些公司通常將 AI agents 建構進現有工具」 - 「企業非常渴望槓桿這些 AI 能力」 - 「企業——尤其中小企業——沒有深厚的 AI 人才池自己建構」

結構性優勢： 軟體供應商比企業內部建構更快、更可靠地整合 AI 能力。

開源軟體類比：價格不是驅動因素¶

歷史證據：

"免費開源軟體已經存在數十年了"

含義： 如果價格是關鍵因素，開源早在數十年前就贏了。

實際驅動工具選擇的因素： 1. 安全性 2. 可靠性 3. 網路效應

延伸到 AI 時代： - 免費 AI 模型存在（開源 LLM） - 但企業仍為以下付費： - 託管 AI 服務（OpenAI、Anthropic） - 整合 AI 工具（GitHub Copilot，而非原始程式碼模型） - 安全、可靠的基礎設施

結論： 提供「更便宜 AI 替代方案」的 AI 原生新創面臨與開源相同的上坡戰。

資本流動模型¶

傳統企業預算：

軟體工具：$X
人力服務：$Y
總計：$X + $Y

AI 時代企業預算：

軟體工具：$X + ΔX（AI agents 使用工具）
AI 基礎設施：$Z（新類別）
人力服務：$Y - ΔY（被 AI 取代）
總計：$X + ΔX + $Z + ($Y - ΔY)

淨效應： - 如果 ΔX + $Z < ΔY → 總支出減少（AI 通縮） - 如果 ΔX + $Z > ΔY → 總支出增加（AI 對軟體通膨）

Tech Fund 的觀察： 第二種情境正在上演。「這持續加速。」

Key Quotes¶

"Having done tons of coding, cloud engineering, as well as covering the software sector as an equity analyst over the last 15+ years, I'm really not seeing a software disruption here."

"Jensen is focused on boosting the productivity of his workforce, which can easily result into an economic value of >$100 billion for his company, as opposed to doing a set of highly risky and complicated software transitions that might save him $500 million."

"All the sudden experts in software underestimate gravely how complicated it is to rip out and replace an ERP, database, CAD, supply chain management, or other core tool that runs the backbone of a business."

"These are complicated 2-4 year projects with mixed results of success, frequently resulting into earnings warnings and failed projects."

"The real value currently is to insert as much AI into your business as possible and boost productivity. And the easiest way to do is having AI leverage your core tools."

"We're currently using all our software tools more than ever before, as now our AIs are also using them as well. So, we're spending more on AI and software and this continues to accelerate."

"We're spending less on human services as we can do more ourselves now. So, we'd be bearish on companies that provide services such as consulting, IT services, freelancing etc."

"For key software tools, revenues and EPS will accelerate from here in our view, as these names are typically building AI agents into their current tooling."

"It's not like enterprises - and especially SMBs - have a deep pool of AI talent to build this themselves."

"Free open source software also has been available for decades, illustrating that price is not the key factor in selecting a tool. It's about security, reliability, and network effects around software tools."

Personal Reflection¶

Why This Matters¶

This is one of the clearest articulations of AI augmentation >> AI replacement from someone with deep operational experience (coding, cloud engineering, equity analysis). Three aspects make this significant:

Falsifiable via observable behavior

Unlike many AI theses, this is testable NOW: - Are enterprises increasing or decreasing software spend? - Are software vendors seeing acceleration or deceleration in bookings? - Are consulting/IT services firms struggling?

Evidence check (Q4 2025 earnings): - Salesforce, ServiceNow, Adobe: Accelerating growth or decelerating? - Accenture, Cognizant, Upwork: Revenue warnings or expansion?

If Tech Fund is right, we should see divergence: SaaS accelerating, services decelerating.

The 200:1 ratio is the killer argument

$100B (productivity gains from AI-augmented workforce) vs $500M (cost savings from software replacement) is not a marginal difference. It's two orders of magnitude.

Why this matters: - Even if software replacement were easy and risk-free, it's still the wrong move - The opportunity cost of focus on replacement vs augmentation is enormous - This explains why Nvidia (most AI-sophisticated company) isn't doing it

Implication: Any thesis predicting "enterprises will rip out SaaS" must explain why they'd pursue a 200x smaller opportunity.

The "sudden experts" critique is pointed

Tech Fund's credibility comes from 15+ years of doing the work: - Writing code (understands migration complexity) - Cloud engineering (understands operational risk) - Equity analysis (understands enterprise behavior)

The implicit target: AI pundits claiming "SaaS is dead" without having: - Migrated an ERP system - Failed a 3-year software project - Sat through earnings calls where migrations caused warnings

The pattern: Every technology wave produces "suddenly experts" declaring incumbents doomed. Most are wrong because they underestimate switching costs and risk aversion.

The Contradiction with Plur_Daddy¶

We now have direct contradiction on SaaS:

Plur_daddy (Capital Scarcity thesis): - "SaaS software bags" are being sold to fund AI investments - Disruption risk → sell SaaS holdings - Part of broader capital reallocation away from speculation

Tech Fund (Augmentation thesis): - "Key software tools, revenues and EPS will accelerate" - AI agents embedded in tooling → increased consumption - Spend shifting from human services, not from software

These cannot both be correct. Either:

A) Plur_daddy is right: - SaaS gets sold to fund AI capex - Software vendors struggle - Price action: SaaS stocks underperform

B) Tech Fund is right: - Enterprises increase software spend (humans + AI agents) - Software vendors with AI integration outperform - Price action: SaaS stocks outperform (especially AI-integrated)

C) Both are partially right (timing/sector-specific): - 2026: Capital scarcity → SaaS sold (plur_daddy) - 2027+: AI augmentation → SaaS accelerates (Tech Fund) - Or: Horizontal SaaS struggles, vertical SaaS with AI integration thrives

The Human Services Disruption: Underappreciated¶

Tech Fund's most actionable insight:

"We're spending less on human services as we can do more ourselves now."

The structural shift: - Before AI: Enterprises hired consultants/freelancers for specialized tasks - With AI: Enterprises use AI to do tasks in-house, leveraging existing software

Why this is plausible: - Consulting is high-margin, low-automation (until now) - IT services = "bodies in seats" business model - Freelancing platforms = matchmaking for human labor

All three are vulnerable to: - AI agents doing the work directly - Enterprises insourcing capabilities they previously outsourced

Evidence to watch: - Accenture, Cognizant, IBM Global Services revenue trends - Upwork, Fiverr, Toptal booking trends - Consulting firm layoffs / hiring freezes

If Tech Fund is right: These should show stress before SaaS vendors do.

The Open Source Parallel: Brilliant Framing¶

The argument:

"Free open source software has been available for decades, illustrating that price is not the key factor in selecting a tool."

Why this matters: - Every AI disruption thesis assumes price is the main switching driver - "AI will make software free/cheap, so incumbents lose" - But open source already tested this hypothesis—and lost

What actually drives adoption: 1. Security: Enterprises can't risk data breaches 2. Reliability: Downtime costs >> software license costs 3. Network effects: Tools with ecosystem lock-in (Salesforce, Adobe, etc.)

Extended to AI era: - Open source LLMs exist (Llama, Mistral) - But enterprises still pay for OpenAI, Anthropic, Google - Why? Security, reliability, support

Implication for AI-native SaaS: - "We'll undercut incumbents with AI" faces the same problem as open source - Enterprises will pay premiums for security/reliability/support - Network effects (integrations, training, ecosystem) still matter

The SMB Argument: Structural Advantage for Vendors¶

Tech Fund's observation:

"It's not like enterprises - and especially SMBs - have a deep pool of AI talent to build this themselves."

Why this creates a moat for software vendors: - Large enterprises (Google, Meta, Nvidia) can build in-house AI - But 99% of businesses (SMBs) cannot - SMBs are the bulk of SaaS TAM

The vendor advantage: - Software vendors can hire AI talent once - Deploy that talent across thousands of customers - Amortize R&D cost over massive user base

The DIY alternative: - Each SMB hires their own AI team - Builds custom integrations - Maintains over time - Economics don't work

Implication: Software vendors with AI integration have economies of scale advantage vs DIY.

What This Predicts (Falsifiable)¶

If Tech Fund is right, we should see (2026-2027):

Software vendor earnings beats
Salesforce, ServiceNow, Adobe: Guidance raises
Increased seat expansion (AI agents consuming licenses)
Acceleration in AI-adjacent products (Salesforce Einstein, Adobe Firefly)
Services sector struggles
Accenture, Cognizant: Slowing bookings, margin pressure
Upwork, Fiverr: Declining take rates, user churn
Consulting firms: Headcount reductions
Capital allocation shifts
Enterprises announce AI spending increases
Paired with headcount reductions in services/support
But software budgets remain stable or grow
Vendor AI integration race
Microsoft 365 Copilot adoption accelerates
GitHub Copilot becomes standard
Adobe Firefly integrated across Creative Cloud

If plur_daddy is right instead: - SaaS bookings decelerate (capital scarcity) - Software spend cuts to fund AI infrastructure - "SaaS is disrupted" narrative dominates earnings calls

The divergence point: Q1-Q2 2026 earnings season will clarify which thesis is correct.

The Risk to Tech Fund's Thesis¶

Where could this be wrong?

Selection bias: Nvidia ≠ typical enterprise
Nvidia has infinite cash, can afford both AI and software
Most enterprises face harder budget constraints
Jensen's 200:1 ratio may not apply to cash-strapped firms
The "AI agents as users" assumption
Does Salesforce charge extra for AI agent seats?
Or is AI usage included in human seat pricing?
If latter, consumption increases but revenue doesn't
The 2-4 year migration timeline
Assumes migration is the only path to disruption
Ignores gradual displacement (new projects → AI-native tools)
"Nobody upgrades, but new buyers choose differently"
Open source parallel breaks down
Open source lost to proprietary because enterprises paid
But what if AI agents DON'T care about security/support?
AI-to-AI transactions may have different economics
Human services disruption proves software disruption
If AI replaces consultants, why not replace SaaS?
Both are "knowledge work"
The distinction (software = tool, consulting = service) may blur

Investment Implications (If Tech Fund is Correct)¶

Long positions: - ✅ SaaS vendors with strong AI integration (Salesforce, ServiceNow, Adobe) - ✅ Developer tools with AI copilots (GitHub, JetBrains) - ✅ Vertical SaaS with moats (healthcare, legal, etc.)

Short positions: - ❌ Consulting firms (Accenture, Cognizant) - ❌ IT services (IBM Global Services, Wipro) - ❌ Freelancing platforms (Upwork, Fiverr)

Pairs trade: - Long: Salesforce (AI-integrated CRM) - Short: Accenture (Salesforce implementation consulting) - Thesis: AI agents do the implementation, not consultants

Timing: - If capital scarcity (plur_daddy) dominates near-term → wait for entry - If AI augmentation (Tech Fund) already priced in → chase momentum - If both are true at different horizons → staged entry (2026 dip, 2027+ recovery)

The Meta-Question: Augmentation vs Replacement¶

Why does Tech Fund see augmentation while others see replacement?

Possible explanations:

Experience bias:
Tech Fund has 15 years of failed migration projects
Sees risk/complexity firsthand
Newer analysts lack this scar tissue
Time horizon:
Near-term (2026-2027): Augmentation dominates (switching costs too high)
Long-term (2030+): Replacement happens (gradual displacement)
Tech Fund focused on investable horizon, not decade out
Definition of "disruption":
Tech Fund: "Not seeing software disruption" = incumbents survive
Others: "Software disrupted" = growth rates slow from 30% → 15%
Both could be true (slower growth ≠ death)
Base rate neglect:
History: Every tech wave predicts incumbent death (cloud, mobile, etc.)
Reality: Incumbents adapt, integrate, survive
Tech Fund applying historical base rates
Disruptors claiming "this time is different"

Who's right depends on: - Is AI a sustaining innovation (incumbents integrate) or disruptive innovation (new entrants win)? - Sustaining → Tech Fund correct - Disruptive → Tech Fund wrong

Early evidence leans sustaining: - Microsoft integrated OpenAI (GitHub Copilot, Office) - Salesforce integrated Einstein - Adobe integrated Firefly - Incumbents moving fast, not slow

But: Disruption often starts at the low end (SMBs) and moves up. Watch for AI-native startups winning SMB deals.

Cross-Reference: Four Investment Theses in Tension¶

We now have four partially contradictory theses in the vault:

Thesis	Author	Core Claim	SaaS Prediction
Capital Scarcity	plur_daddy	AI capex drains capital	SaaS sold to fund AI
Credit Regime	Andy Constan	Private credit replaces Fed	Asset bifurcation
Exponential Horizon	Andrew Kang	Singularity approaching	All assets moon
AI Augmentation	Tech Fund	AI enhances, not replaces	SaaS accelerates

Compatibility analysis:

plur_daddy vs Tech Fund: - Contradiction: SaaS sold (plur) vs SaaS accelerates (Tech Fund) - Reconciliation: Near-term (sold) vs medium-term (accelerates)?

Andy Constan vs Tech Fund: - Compatible: Both see real economy investment (AI capex) - Diverge on: Whether SaaS benefits or suffers

Andrew Kang vs Tech Fund: - Compatible: Both bullish on AI-driven growth - Tech Fund adds nuance: Not all AI beneficiaries equal (software > services)

The synthesis: 1. Near-term (2026): Capital scarcity → volatility, SaaS pressure (plur_daddy) 2. Medium-term (2027-2028): AI augmentation → SaaS with integration outperforms (Tech Fund) 3. Long-term (2030+): Exponential growth → all productive assets benefit (Andrew Kang)

The key question: Can SaaS vendors survive the capital scarcity squeeze (2026) long enough to capture the augmentation wave (2027+)?

Final Thought: The "Sudden Experts" Problem¶

Tech Fund's most cutting line:

"All the sudden experts in software underestimate gravely how complicated it is..."

This pattern repeats every tech wave: - Internet era: "Nobody will shop online" (wrong) - Mobile era: "PCs are dead" (wrong) - Cloud era: "On-prem software is dead" (wrong) - AI era: "SaaS is dead" (TBD)

The error: Extrapolating current trend linearly without accounting for: - Switching costs - Risk aversion - Integration complexity - Network effects

Tech Fund's advantage: Has personally suffered through migrations that failed.

The counter-risk: Sometimes the "sudden experts" are right (see: Nokia dismissing iPhone).

The meta-question: How do we distinguish: - Hype cycle (sudden experts wrong) from - Paradigm shift (sudden experts right)?

One signal: Incumbent behavior - If Nvidia (most AI-sophisticated) isn't ripping out software → probably hype - If Nvidia starts migrating → probably real disruption

Watch the smart money, not the loud money.