2026-01-29 08:00:00
“We are only at the beginning phases of AI diffusion & already Microsoft has built an AI business that is larger than some of our biggest franchises.”
CEO Satya Nadella captures Microsoft’s Q2 FY2026 earnings in a sentence. The company beat expectations across revenue ($81.3b, up 17%), earnings per share ($4.14 adjusted vs $3.97 expected), & Azure growth (39%). Yet the stock fell 11% after earnings.
“We now expect to be capacity constrained through at least the end of our fiscal year, with demand exceeding current infrastructure build-out.”
CFO Amy Hood said demand is outpacing Microsoft’s ability to build. Azure grew 39%, a slight deceleration from Q1’s 40%, not because demand softened but because Microsoft ran out of capacity to sell. That’s a remarkable constraint for a business generating $32.9b in quarterly revenue.
“Over 250 customers are on track to process over 1 trillion tokens on Foundry this year.”
At a blended average of ~$5 per million tokens across Azure OpenAI models, 1 trillion tokens represents roughly $5b in annual revenue.
“We have 900 million monthly active users of our AI features across our products. There are over 150 million monthly active users of first-party Copilots.”
GitHub Copilot charges $10-20/month. Microsoft 365 Copilot charges $30/month. Even 150 million users generates only $4.5b-6b annually. Microsoft Cloud revenue crossed $51b in Q2, up 26%, so AI-specific revenue remains a fraction of the total.
| Cloud | Operating Margin |
|---|---|
| Azure | 44% |
| AWS | 38% |
| GCP | 17% |
The business generates $38.3b in operating income, up 21%. Margins can absorb the CapEx for now.
Microsoft spent $37.5b in Q2 FY2026 on CapEx, up from $34.9b the prior quarter & $34.2b at Amazon. That spending is accelerating. Each of the three major hyperscalers projects roughly $150b in annual spending, with Meta not far behind at $115-135b for 2026. This very healthy business provides the capital to continue growing.
“Already, we have roughly 10x’d our investment, & OpenAI has contracted an incremental $250b of Azure services … Approximately 45% of our commercial RPO balance is from OpenAI.”
Microsoft’s commercial remaining performance obligation surged 110% to $625b in Q2, with an average duration of two and a half years. Hood’s disclosure means $281b comes from a single customer. That’s substantial risk.
Microsoft’s strength continues to demonstrate two things : insatiable demand for inference & increasing customer concentration risk for even the largest businesses in the world.
Sources:
2026-01-28 08:00:00
In rowing, the coxswain watches everything. How hard you pull on every stroke. Whether you’re in time with the rest of the boat. How your form breaks down when you’re tired. They’re among the most valuable people in a rowing team.
At work, I don’t have a coxswain. So I built a robotic one.
Every night it reviews my meetings & coaches me on how to improve, whether they’re one-on-ones, pitch meetings, or other calls.1
At work, success is subjective. So I built a rubric spanning five dimensions, each scored 0 to 10 : active listening, empathy, questioning effectiveness, communication clarity, & technical depth.
It looked reasonable on paper. Then the real work began. Here’s what the AI told me after reviewing a week of meetings :
Pitch Meeting Analysis (Jan 22) : “Interrupted founder 6 times in first 10 minutes, primarily when explaining technical architecture. Pattern : you ask clarifying questions before they finish establishing context. Recommendation : wait 15 seconds after they pause before responding. Founders need space to demonstrate depth.”
One-on-One Analysis (Jan 24) : “Asked 12 questions in 30-minute check-in, but only 2 were follow-ups to previous answers. Pattern : moving to next topic before exploring depth. This can signal you’re checking boxes rather than genuinely curious. Try : 3 questions deep before moving on.”
Investor Update Call (Jan 26) : “You delivered the portfolio news update in 4 minutes with zero pauses. Empathy score : 2/10. The founder just shared they’re struggling with retention. Your response was immediate pivot to metrics. Recommendation : acknowledge the emotional weight before problem-solving.”
My wife has coached me on this for years. She’s right, of course. But having a machine echo her wisdom every morning has made her advice finally stick.
2026-01-28 00:00:00
Jeff Bezos famously said, “Your margin is my opportunity.”1 Does this maxim apply in software & AI? Yes.
Software companies maintain 76% gross margins yet earn almost nothing. Sales, marketing, & research & development consume it all.
Among 69 publicly traded B2B software companies, the median net income margin hovers near zero.2 Gross margins cluster tightly around 76%, yet almost none of that profit flows to the bottom line.
| Percentile | Gross Margin | Net Income Margin |
|---|---|---|
| 25th | 68.7% | -9.6% |
| 50th | 76.1% | -0.9% |
| 75th | 81.2% | 16.1% |
The bottom quartile loses nearly 10% of revenue. Only the top quartile achieves meaningful profitability at 16%. Palantir, once dismissed as a services company, tops the list at 18% net income margin.
Enterprise & PLG look the same at the bottom line :
| Segment | Median Net Margin | Median Gross Margin | Companies |
|---|---|---|---|
| Enterprise/Mid-Market | -1.5% | 76.7% | 51 |
| PLG/SMB | -5.4% | 69.4% | 19 |
Enterprise software maintains higher gross margins than PLG/SMB, but both segments struggle equally to convert that to net income.3
Go-to-market & engineering consume the entire gross margin. Software companies spend 40-60% of revenue on sales & marketing alone. R&D takes another 15-25%.
Multi-year contracts & positive net dollar retention justify the expense. This is why so many software companies operate at zero net income margin : they’re buying growth. At least, this is the narrative.
But, but, but! Among these 69 companies, there is no correlation between net income margin & revenue growth (r = 0.18, p = 0.136).
Infrastructure is different. Despite intense competition from Google Cloud & Microsoft Azure, AWS has maintained high operating margins over the past decade. In the last few quarters, margins have increased, even as AWS sells CPUs & GPUs similar to those available elsewhere.4
From 2015 to 2025, AWS operating margins have ranged between 25% & 38%. Even after a decade of cloud commoditization, AWS prints money at scale. Where most software companies outspend on sales teams, AWS outspends on data centers. The moat is capex, not customer acquisition, & few other companies have the balance sheet to compete.
Bezos was right. Your margin is my opportunity. In software, that opportunity isn’t price. It’s the race to outspend everyone else.
Still, $8.7 trillion in enterprise value says the race is worth running.
Brad Stone, The Everything Store : Jeff Bezos & the Age of Amazon (Little, Brown & Company, 2013). ↩︎
Software company financials from Koyfin, January 2026. ↩︎
Company public disclosures. Wilcoxon rank-sum test comparing 51 enterprise/mid-market companies vs. 19 PLG/SMB companies. Gross margins differ significantly (p=0.006); net margins do not (p=0.822). ↩︎
AWS margin data from Amazon Q1 FY 2025 Earnings & Statista. ↩︎
2026-01-26 08:00:00
In 1908, 253 American automobile manufacturers competed for the market1. By 1929, just 44 remained. The assembly line didn’t just change how cars were made. It changed who got to make them.
Ford’s Highland Park plant, operational in 1913, slashed the time to build a Model T from 12 hours to 93 minutes2. That 90% productivity gain restructured an entire industry. Manufacturers who couldn’t match Ford’s efficiency faced a simple choice : adapt or exit.
The consolidation was swift. Between 1908 & 1929, 83% of automakers vanished. Some merged. Most failed. The survivors shared a common trait : they adopted Ford’s methods. General Motors, Chrysler & the handful of remaining independents all built assembly lines.
An analogous revolution is happening in software, with important differences. AI coding assistants now reduce development time by 55-81%3. The curve is familiar.
Ford took six years to achieve 90% time reduction. AI coding tools reached 81% in five. The slopes are nearly identical.
What happened to auto industry employment? It grew. Massively. In 1910, US auto plants employed 76,000 workers. By 1929, that number reached 471,0004. Mass production created mass consumption, & mass consumption demanded more workers.
The real explosion came from second-order effects. By 1929, for every one person building a car, seven others had a job because that car existed.
Dealerships, service stations, repair shops & supply chains employed nearly 4 million people. The industry didn’t just create more manufacturing jobs; it spawned an entirely new economy of “enablers” that was 8x larger than the core manufacturing base.
The software industry will follow a different pattern. In autos, capital intensity consolidated power. It’s the opposite in the world of AI.
AI data centers, the assembly lines of software, enable hundreds of millions of developers to build software as if they had the capabilities of an automobile behemoth. Any developer can access state-of-the-art models with a laptop & a credit card.
Easier software creation means more software. More software means more developers, not fewer. We should expect many thousands of new businesses each year as a result, & a similar explosion in second-order jobs.
EBSCO Research : Number of US Automakers Falls to Forty-Four. The number of active automobile manufacturers dropped from 253 in 1908 to only 44 in 1929, with about 80% of the industry’s output accounted for by Ford, General Motors, & Chrysler. ↩︎
Library of Congress : Ford Implements the Moving Assembly Line. Assembly line reduced Model T production time from 12.5 hours to 93 minutes by 1914. ↩︎
GitHub/Microsoft Research : The Impact of AI on Developer Productivity found developers completed tasks 55.8% faster. GitHub’s enterprise study with Accenture showed 67% daily usage & 84% increase in successful builds. Google’s Gemini Code Assist research found developers were 2.5x more likely to complete tasks successfully. ↩︎
Richmond Fed : Wheels of Change. Auto industry employment expanded dramatically during this period as mass production created new job categories in manufacturing, sales, service, & infrastructure. ↩︎
2026-01-22 08:00:00
Talented people get promoted to management. So do talented models. Claude manages code execution. Gemini routes requests across CRM & chat. GPT-5 can coordinate public stock research.
Why now? Tool calling accuracy crossed a threshold. Two years ago, GPT-4 succeeded on fewer than 50% of function-calling tasks. Models hallucinated parameters, called wrong endpoints, forgot context mid-conversation. Today, SOTA models exceed 90% accuracy on function-calling benchmarks1. Performance of the most recent models, like Gemini 3, is materially better in practice than the benchmarks suggest.
Did we need trillion-parameter models just to make function calls? Surprisingly, yes.
Experiments with small action models, lightweight networks trained only for tool selection, fail in production2. They lack world knowledge. Management, it turns out, requires context.
Today, the orchestrator often spawns itself as a subagent (Claude Code spins up another Claude Code). This symmetry won’t last.
The bitter lesson3 insists ever-larger models should handle everything. But economics push back : distillation & reinforcement fine-tuning produce models 40% smaller & 60% faster while retaining 97% of performance4.
Specialized agents from different vendors are emerging. The frontier model becomes the executive, routing requests across specialists. These specialists can be third-party vendors, all vying to be best in their domain.
Constellations of specialists require reliable tool calling. When tool calling works 50% of the time, teams build monoliths, keeping everything inside one model to minimize failure points. When it works 90% of the time, teams route to specialists & compound their capabilities.
The frontier labs will own the orchestration layer. But they can’t own every specialist. Startups that build the best browser-use agent, the best retrieval system, the best BI agent can plug into these constellations & own their niche.
New startup opportunities emerge not from training the largest models, but from training the specialists the executives call first.
Berkeley Function Calling Leaderboard (BFCL) tests API invocation accuracy. TAU-bench measures tool-augmented reasoning in real-world scenarios (paper). ↩︎
Salesforce’s xLAM is a large action model designed specifically for tool selection. While fast & accurate for simple tool calls, small action models struggle with complex reasoning about when to use tools. ↩︎
Rich Sutton’s influential essay arguing that general methods leveraging computation beat hand-engineered domain knowledge. The Bitter Lesson. ↩︎
See DistilBERT, which is 40% smaller & 60% faster while retaining 97% of BERT’s performance. OpenAI’s model distillation enables similar efficiency gains. ↩︎
2026-01-21 08:00:00
Will designers design first in a world where AI can code software immediately, or just describe the design? Will large enterprises pay for premium observability when AI can migrate & monitor open source competitors?
As Michael Mauboussin writes, there’s information in price. These questions are priced in. It’s too early to see revenue erosion, but the market is pricing in the risk.
The median SaaS stock is down 14-17% year to date. 64% of software companies are down. Adobe has fallen 32%, HubSpot 57%, Atlassian 54%.
Revenue growth predicts returns better than margins, profitability, or market cap. Companies growing above 20% are up. Companies growing below 20% are down. Palantir grows 47%. MongoDB grows 21%. Adobe & Salesforce grow less than 10%.
This isn’t a broad market correction. Large caps are holding; small caps are collapsing.
Ten years ago, software crashed too. On February 5, 2016, LinkedIn fell 43% and Tableau dropped 49% in a single trading session. Salesforce lost 13%. The Nasdaq tumbled 3.25%. Investors dumped software in a single afternoon.
The crime was weak forward guidance. LinkedIn projected 20-22% growth when analysts expected 30%. Tableau’s license revenue growth decelerated from 57% to 31% quarter over quarter.
But the selloff reversed within weeks. Nasdaq finished 2016 up 7.5%. SaaS stocks climbed for five more years. No one doubted that enterprises would continue buying CRM software & analytics tools. The products remained essential. Only the price changed.
In 2016, investors questioned valuations. In 2026, they question relevance.
