The inference market is the fastest growing market in the world & it’s splitting up. Each modality is developing its own inference stack.

NVIDIA’s data center revenue was flat through 2022. Then ChatGPT launched. Three years later : 17x growth.¹

Databases did the same thing. What started as one market fragmented into relational, document, key-value, graph, time series, vector, & others. Each category reflects different workload requirements : real-time transactions vs batch analytics, ACID compliance vs eventual consistency.

The old sales motion asked : what’s your software budget for this category?

The new motion asks customers three questions :

• What’s your software budget?
• What’s your total labor budget?
• What do you want that ratio to be in three years?

That third question shifts a software sale into a strategic planning conversation, the same conversation every board is having right now.

The higher the ratio today, the larger the opportunity. Sales runs 10:1. Support runs 4:1. Engineering runs as high as 25:1. If AI collapses the labor side, the software budget isn’t the ceiling. It’s the floor.

NVIDIA’s latest GPU rental prices on the Ornn Compute Price Index hit $4.95 per hour this week, up from $2.31 in early March : a 114% surge in six weeks.¹

The price spread over prior-generation chips doubled from $0.28 to $1.80 per hour. The new chip is NVIDIA’s B200 (Blackwell); the prior generation is the H200 (Hopper).

Before: Multiple Simple Tools (Human-Centric Design)

Email Operations - Fragmented Approach:
┌─────────────────────────────────────────────────────────────┐
│                    Claude Decision Tree                      │
├─────────────────────────────────────────────────────────────┤
│  Email Task → Should I...?                                 │
│               ├─ draft_email.rb (2 params)                 │
│               ├─ send_email.rb (3 params)                  │
│               ├─ forward_email.rb (2 params)               │
│               ├─ find_and_draft_reply.rb (3 params)       │
│               ├─ read_email.rb (2 params)                  │
│               ├─ archive_emails.rb (1 param)              │
│               └─ safe_send_email.rb (4 params)             │
│                                                             │
│  Result: Decision paralysis, multiple API calls            │
│  Token Usage: ~150 tokens per operation                    │
│  Success Rate: 87% (context lost between calls)           │
└─────────────────────────────────────────────────────────────┘

                             ↓

CRM Operations - Scattered Functions:
┌─────────────────────────────────────────────────────────────┐
│  Company Research → Which tool?                            │
│                    ├─ find_attio_company.rb               │
│                    ├─ create_attio_company.rb             │
│                    ├─ update_attio_deal.rb                │
│                    ├─ enrich_company.rb                   │
│                    └─ validate_company.rb                 │
│                                                             │
│  Problem: Context switching between 5+ tools               │
│  Average: 3.2 tool calls per task                         │
└─────────────────────────────────────────────────────────────┘

After: Unified Complex Tools (AI-Centric Design)

Unified Email Tool - Comprehensive Context:
┌─────────────────────────────────────────────────────────────┐
│                unified_email_tool.rb                       │
├─────────────────────────────────────────────────────────────┤
│  Actions: [draft, send, reply, read, search, archive]      │
│                                                             │
│  Parameters:                                                │
│  ├─ --action (6 options)                                   │
│  ├─ --to, --cc, --bcc (recipients)                        │
│  ├─ --subject, --body, --body-stdin                       │
│  ├─ --from, --query, --folder (search)                    │
│  ├─ --sender, --in-reply-to (threading)                   │
│  ├─ --format [concise|detailed|ids_only]                  │
│  └─ --save-draft, --senders (batch ops)                   │
│                                                             │
│  Result: Single confident decision                          │
│  Token Usage: ~45 tokens (70% reduction)                  │
│  Success Rate: 94% (full context maintained)              │
└─────────────────────────────────────────────────────────────┘

                             ↓

Unified CRM Operations - Complete Workflow:
┌─────────────────────────────────────────────────────────────┐
│                attio_operations.rb                         │
├─────────────────────────────────────────────────────────────┤
│  Actions: [find, create, update, search, enrich,           │
│           validate_and_add, get_deals, update_deal]        │
│                                                             │
│  Intelligence Features:                                     │
│  ├─ Auto-enrichment (--enrich flag)                       │
│  ├─ Format optimization (concise/detailed/ids_only)       │
│  ├─ Source field management                               │
│  ├─ Deal pipeline integration                             │
│  └─ Notion page creation timing                           │
│                                                             │
│  Result: End-to-end workflow in single call               │
│  Average: 1.1 tool calls per task (89% reduction)         │
└─────────────────────────────────────────────────────────────┘

Performance Comparison

Metric Improvements:
┌─────────────────────┬─────────────┬─────────────┬─────────────┐
│ Measurement         │   Before    │    After    │ Improvement │
├─────────────────────┼─────────────┼─────────────┼─────────────┤
│ Token Usage         │ 150/op      │ 45/op       │ 70% ↓       │
│ Tool Calls          │ 3.2/task    │ 1.1/task    │ 66% ↓       │
│ Success Rate        │ 87%         │ 94%         │ 7% ↑        │
│ Decision Time       │ 2.3s        │ 0.8s        │ 65% ↓       │
│ Context Retention   │ Fragmented  │ Complete    │ Qualitative │
└─────────────────────┴─────────────┴─────────────┴─────────────┘

Implementation Principles:
1. Parameter-rich interfaces > Simple function calls
2. Comprehensive context > Modular operations
3. AI ergonomics ≠ Human ergonomics
4. Batch operations > Sequential calls

FFmpeg Analogy: Complex is Better for AI

Human Perspective (Intimidating):
┌─────────────────────────────────────────────────────────────┐
│ ffmpeg -i input.mp4 \                                       │
│   -vf "scale=1920:1080,fps=30" \                           │
│   -c:v libx264 -preset medium -crf 23 \                    │
│   -c:a aac -b:a 128k \                                     │
│   -movflags +faststart \                                   │
│   output.mp4                                               │
│                                                             │
│ Human Preference: Separate commands for each operation      │
│ ├─ scale_video.sh                                          │
│ ├─ set_framerate.sh                                        │
│ ├─ encode_video.sh                                         │
│ └─ optimize_audio.sh                                       │
└─────────────────────────────────────────────────────────────┘

                             ↓

AI Perspective (Perfect):
┌─────────────────────────────────────────────────────────────┐
│ Single command with complete transformation context         │
│                                                             │
│ Benefits for AI:                                           │
│ ├─ Understands full workflow intent                        │
│ ├─ Can optimize across all parameters                      │
│ ├─ No ambiguity about sequencing                          │
│ ├─ Complete error context                                  │
│ └─ Deterministic outcomes                                  │
│                                                             │
│ Result: AI confidently executes complex operations         │
└─────────────────────────────────────────────────────────────┘

These schematics illustrate the paradigm shift from human-centric (simple, modular) to AI-centric (complex, comprehensive) tool design that enables more efficient & confident AI operations.

A reader asked for an update given the macro changes & the stock market behavior. Here it is:

Multiple = -12.6 + 34.1 x Forward_Growth_Rate + 4.2 x Sales_Efficiency

Some quick definitions:

• Multiple is the forward revenue multiple. Take it and multiply your company’s planned revenue (not ARR) next year & you’ll have an estimate of the value of your business if it were traded in the public markets today.
• Forward_growth_rate: how much the business will grow revenues in the next 12 months
• Sales_Efficiency: (change in gross profit) / sales_and_marketing expense. In other words, how much marginal gross profit did the sales & marketing investment last period generate. This can be calculated quarterly or annually.

IBM

We continue to gain momentum with our GenAI book of business growing to over $5 billion inception to date, up by about $2 billion quarter-over-quarter. Approximately 1/5 of this book of business comes from Software and the remaining 4/5 is Consulting.

Accenture

We had another milestone quarter in GenAI, with $1.2 billion in new bookings and approximately $500 million in revenue.

Microsoft

And our AI business has now surpassed an annual revenue run rate of $13 billion, up 175% year-over-year.

Google commoditized its complements : free maps, free email, free browsers, a free mobile OS. They removed every toll booth between the user & search.

Anthropic’s strategy parallels Google’s, a natural extension of the strength of the core product, the model.

“BI became dashboards. And now it is re-expanding to business intelligence.”

— Colin Zima, CEO & co-founder, Omni

Colin describes how AI fuses structured & unstructured data, and why the future of business intelligence isn’t a better dashboard.

A support leader at one of Omni’s customers went through 75 pages of conversation with an AI to identify 10 categories of rep mistakes. The system read support logs, cited specific examples per rep, & suggested concrete changes. Not just a dashboard.