Applied AI vs. LLM Hype: Where the $155B is Actually Going by 2030
The $73.6B Reality Check
Q1 2025: Record $73.6 billion invested in AI and machine learning across 1,603 deals.
But here's what most missed: The shift from building LLMs to integrating AI into workflows.
LLM hype (2022-2024):
- "We built a better GPT!"
- Demo-first, production-never
- Billions raised, little revenue
Applied AI reality (2025+):
- "AI cut our DD from 120 hours to 6"
- Production-first, scale-fast
- Measured ROI from day one
Projection: Agentic AI spending could reach $155 billion by 2030.
The question: Who captures that value?
The Great AI Shift
Phase 1: LLM Land Grab (2022-2024)
The pattern:
- Raise $100M+ to train foundation models
- Showcase impressive demos
- Promise AGI is near
- Revenue: TBD
Winners: Infrastructure providers (NVIDIA, cloud) Losers: Most LLM startups (90%+ will fail)
Phase 2: Applied AI Integration (2025-2027)
The pattern:
- Take existing LLMs (commodity)
- Integrate into specific workflows
- Prove ROI in weeks
- Scale on customer success
Winners: Workflow-specific AI companies Losers: Generic AI platforms
Phase 3: Agentic AI Dominance (2027-2030)
The pattern:
- AI agents run end-to-end workflows
- Minimal human oversight
- $155B market by 2030
- Winner-take-most dynamics
Winners: Companies with production AI + telemetry + trust Losers: Everyone still doing demos
Where the $155B is Actually Going
Bucket #1: Workflow Automation ($75B)
What it is: AI that replaces manual workflows with measurable ROI
M&A examples:
- Due diligence automation
- Data room organization
- Contract review
- Financial modeling
- Integration discovery
Why it wins: Clear ROI, fast payback, easy to prove value
Key players: Workflow-specific AI vendors (not LLM providers)
Bucket #2: Decision Intelligence ($45B)
What it is: AI that augments (not replaces) expert decision-making
M&A examples:
- Deal sourcing and screening
- Valuation modeling
- Risk assessment
- Synergy identification
- Portfolio optimization
Why it wins: Amplifies expert judgment, defensible advantage
Key players: Domain-specific AI (PE, M&A, finance)
Bucket #3: Operational AI ($35B)
What it is: AI embedded in daily operations at scale
M&A examples:
- Real-time portfolio monitoring
- Automated compliance checks
- Integration progress tracking
- Performance dashboards
- Anomaly detection
Why it wins: Always-on value, compounds over time
Key players: Platform companies with telemetry
Total: $155B by 2030
LLM Hype vs. Applied AI: The Comparison
LLM Hype Checklist
❌ "We trained our own LLM" ❌ "Our model is better than GPT" ❌ "Check out this amazing demo" ❌ "Trust us, it's working" ❌ "We'll figure out pricing later" ❌ No customer ROI metrics ❌ No telemetry, no gates ❌ Selling technology, not outcomes
Investment thesis: "LLMs are the future!" Reality: Commodity within 24 months
Applied AI Checklist
✅ "We use existing LLMs (OpenAI, Anthropic)" ✅ "Integrated into M&A workflow" ✅ "120 hours → 6 hours per deal" ✅ "Real-time telemetry dashboard" ✅ "ROI proven in 3 weeks" ✅ 95%+ accuracy with acceptance gates ✅ Selling outcomes (time, cost, quality) ✅ Customer success drives growth
Investment thesis: "Applied AI solves real problems" Reality: $155B market by 2030
The Applied AI Buyer's Checklist
Question #1: Production or Demo?
LLM hype answer: "Here's a demo of our AI answering questions!"
Applied AI answer: "Here's our production dashboard: 847 deals processed, 96.2% accuracy, $11.5K average savings"
Red flag: If they can't show production metrics, walk away.
Question #2: Workflow-Specific or Generic?
LLM hype answer: "Our AI can do anything! Just describe your use case."
Applied AI answer: "We solve due diligence for PE firms. We've processed 1,200+ deals."
Red flag: Generic AI platforms rarely deliver value.
Question #3: Telemetry or Trust Me?
LLM hype answer: "Our AI is highly accurate, our customers love it"
Applied AI answer: "Live dashboard shows 96.3% accuracy on last 2,847 documents processed"
Red flag: No telemetry = no trust.
Question #4: ROI in Weeks or Years?
LLM hype answer: "Over time, you'll see significant efficiency gains"
Applied AI answer: "Payback in 2.3 weeks based on pilot results"
Red flag: If they can't prove ROI in <3 months, they won't ever.
Question #5: Acceptance Gates or YOLO?
LLM hype answer: "Just let the AI run and review outputs occasionally"
Applied AI answer: "Four quality gates: ingestion (95%), analysis (90%), synthesis (95%), validation (100%)"
Red flag: No gates = production disasters.
Real-World Applied AI: Data Room Automation
The Old Way (LLM Hype)
Vendor pitch: "Our LLM reads documents and answers questions!"
Demo: Impressive Q&A on sample data room
Reality:
- Fails on real data (corrupt PDFs, scanned images)
- Hallucinates financial figures
- No audit trail
- No acceptance gates
- Pilot fails, project dies
Investment wasted: $50K-$200K
The New Way (Applied AI)
Vendor pitch: "We automate data room organization with acceptance gates"
Pilot: Process real data room, full telemetry
Reality:
- Handles corrupt files (95% success rate)
- Validates all numbers against source
- Complete audit trail
- Four quality gates
- 120 hours → 6 hours
ROI: 3-week payback, $11K+ per deal savings
The difference: Applied AI integrates into workflows with measurable outcomes.
The $155B Opportunity for Operators
Who captures the $155B by 2030?
The Losers: LLM Startups
Why they fail:
- LLMs commoditize (OpenAI, Anthropic, Google)
- Training costs collapse
- Differentiation impossible
- No moat, no margin
90%+ will shut down or pivot
The Winners: Applied AI for Workflows
Why they win:
- Workflow expertise = moat
- Customer success = compounding
- Telemetry = trust
- ROI = expansion
Winner-take-most dynamics by vertical
How to Ride the Applied AI Wave
Step 1: Ignore LLM Hype
- Don't build your own LLM
- Don't evaluate models
- Don't optimize prompts
- Just use best-in-class APIs
Step 2: Focus on Workflow
- Pick one high-value workflow
- Measure current baseline
- Define success criteria
- Automate with AI + gates
Step 3: Prove ROI Fast
- 3-week pilot
- Measure time/cost/quality
- Show telemetry
- Scale on proof
Step 4: Compound the Advantage
- Add more workflows
- Deepen integration
- Improve accuracy
- Increase lock-in
By 2030: You're riding the $155B wave, competitors are drowning in LLM hype.
Next Steps: Skip the Hype, Deploy Applied AI
Option 1: Self-Guided
- Pick highest-value workflow
- Find applied AI vendor (not LLM platform)
- Run 3-week pilot with telemetry
- Scale on proven ROI
Option 2: MeldIQ Applied AI Sprint
We'll deploy production AI for your M&A workflows:
- Week 1: Baseline + gates
- Week 2: Pilot + telemetry
- Week 3: ROI validation
Explore applied AI solutions →
Option 3: See Applied AI in Production
Watch real M&A workflows automated with telemetry:
The $155B is in applied AI, not LLM hype. Deploy workflows with ROI. Start with operator-grade AI →