2026-01-16 08:00:00
This week I chatted with an acquaintance who mentioned a board game. I caught half the title & looked for the full title & Amazon link using my AI in Asana.
The system tried with Gemini & failed. The failover to Claude also failed. Rather than continuously iterating with the AI until it worked, I created a Ralph Wiggum loop.
Geoffrey Huntley coined this pattern. Named after the persistently clueless Simpsons character, the idea is simple : keep pushing the model against its failures until it dreams a correct solution just to escape the loop. The system is deterministically bad in an undeterministic world. Iteration beats perfection.
Now an AI loop runs each night. It finds all tasks with “failed” in them. It creates a plan to debug & iterates until the prompt solves the task.
So far this naive system is working pretty well. There is a risk it might begin to oscillate between two optimal states, but I haven’t observed that in the few days it’s been running. It’s something I’m watching.
AI creates software cheaply; excellence requires iteration. Implicit feedback loops are how you get there.
This self improving loop ensures the system wakes up smarter than it went to sleep. So do I.
2026-01-15 08:00:00
AI is NVIDIA’s third climb up a steep slope.
First came gaming in the late 2010s.
Then cryptocurrency.
Now artificial intelligence.
Each wave pushed revenue growth above 50% & with it, P/E1 ratios surged. P/E ratios rose before the revenue growth materialized.
There’s a four-quarter offset between P/E ratio & TTM2 revenue growth. When you shift revenue growth forward by a year, the correlation with P/E jumps to 0.80.
Investors buy stocks based on where they think the company is headed, not where it’s been. A high P/E today reflects expectations of strong revenue growth tomorrow. During NVIDIA’s climb up each wave, P/E & future growth move in lockstep.
But look at what happens at the peaks.
At the end of each boom, the correlation collapses, falling to zero or even turning negative. P/E stays elevated while revenue growth plummets. The market is slow to reprice.
This is the pattern : during the ascent, P/E & growth are tightly linked. At the peak, they decouple. High P/E persists as growth dissipates.
Today’s AI wave is riding the same mesa. The correlation remains above historical levels. The rapid pace of deal-making & explosive rates of inference growth suggest this roller coaster ride will continue.
2026-01-14 08:00:00
“[Silicon Valley] is the biggest, most volatile petri dish of raw capitalism on the planet.” So what lessons does a successful short-seller living here have? As founder of Crown Capital Management, Scott Fearon has shorted the stocks of over 200 companies.
In his book Dead Companies Walking, Fearon distills three decades of meetings with executives into six failure modes :
Despite their differences, they all failed because their leaders made one or more of six common mistakes that I look for :
- They learned from only the recent past.
- They relied too heavily on a formula for success.
- They misread or alienated their customers.
- They fell victim to a mania.
- They failed to adapt to tectonic shifts in their industries.
- They were physically or emotionally removed from their companies’ operations.
The hairpin turn from SaaS to AI amplifies each.
Learning only from the recent past. Software moves in 20-year waves. Mainframe, then client/server, then SaaS. When these waves come, they create bull markets. As they wane, growth flattens & multiples collapse. SaaS multiples have been flat for three years.
Relying too heavily on a formula for success. Sales motions have changed because of dramatic growth rates in product-led growth & multi-million dollar lands. Many of the rules around efficiencies & quotas no longer apply.
Misreading or alienating customers. Customers want to be AI native. They are willing to pay huge sums for education & solutions. Budgets have exploded with 41% of AI spend net new. Selling the same software application that no longer solves the customer’s pain point is a path to churn.
Falling victim to a mania. The AI hype cycle creates pressure to ship half-baked features. Announcing an AI roadmap isn’t the same as delivering value. This distinction will become increasingly stark as long-running agents enter the workforce in 2026.
Failing to adapt to tectonic shifts. If ever this were true, it’s true today.
Being physically or emotionally removed. Teams who don’t use AI daily miss the pace of change. The technology moves too fast for quarterly strategy reviews. If leadership isn’t prompting Claude or GPT every day, they’re already behind.
Cognitive biases are always hard to see in ourselves. A short seller’s mirror is a useful one at this moment in AI.
2026-01-13 08:00:00
I asked Claude Cowork to read my tools folder. Eleven steps later, it understood how I work.
Over the past year, I built a personal operating system inside Claude Code : scripts to send email, update our CRM, research startups, draft replies. Dozens of small tools wired together. All of it lived in a folder on my laptop, accessible only through the terminal.
Cowork read that folder, parsed each script, & added them to its memory. Now I can do everything I did yesterday, but in a different interface. The capabilities transferred. The container didn’t matter.
My tools don’t belong to the application anymore. They’re portable. In the enterprise, this means laptops given to new employees would have Cowork installed plus a collection of tools specific to each role : the accounting suite, the customer support suite, the executive suite.
The name choice must have been deliberate. Microsoft trained us on copilot for three years : an assistant in the passenger seat, helpful but subordinate. Anthropic chose cowork. You’re working with someone who remembers how you like things done.
We’re entering an era where you just tell the computer what to do. Here’s all my stuff. Here are the five things we need to do today. When we need to see something, a chart, a document, a prototype, an interface will appear on demand.
The current version of Cowork is rough. It’s slow. It crashed twice on startup. It changed the authorization settings for my Claude Code installation. But the promised power is enough to plow through.
2026-01-10 08:00:00
Salesforce, Datadog & Epic are building walls. After two decades of flourishing through open APIs & data portability, the software industry’s largest incumbents are locking down.
Salesforce restricted Slack’s API1 in May 2025, limiting third-party apps to one API call per minute & fifteen messages per request. Datadog deactivated accounts2 for Deductive AI, a competing observability startup. Epic faces a Texas lawsuit3 accusing it of turning patient records into a “gatekeeping tool.”
The pace of software development, as a result of AI, is accelerating, enabling incumbents to broaden their suite & compete with previous partners. Faster execution means more defense from those with established businesses. This is the new normal.
Platform businesses have minted billions in the past. Vlocity ($1.33B acquisition)4 & Veeva ($36B market cap)5 built multi-billion dollar businesses on the Salesforce platform. nCino reached a $2.9B market cap6 serving financial services on Salesforce. ServiceMax sold to GE Digital for $915M7. Klaviyo built a $8.8B business8 with 78% of its revenue from Shopify customers.
But building on top of a system of record offers less stable ground than it once did. When Salesforce throttles Slack’s API, they’re telegraphing where the value is. When Epic locks down patient records, they’re drawing a map. Incumbents don’t build walls around worthless assets. And they don’t build defenses unless they perceive credible & urgent challengers.
AI’s speed enables both startups & incumbents to own a stack end-to-end. That’s offense in an era of walls.
2026-01-09 08:00:00
In 2012, we cared that we used software. Today we care how we use it.
The difference is trajectory.
In the last decade, adopting software was the priority. Moving from on-premise to the cloud or digitizing a manual workflow promised productivity gains. Adoption was the finish line.
Today software is ubiquitous. Every salesperson uses a CRM & every engineer uses an IDE. The edge no longer comes from having the tool but from the specific path & manner in which that tool is used to achieve an outcome : a trajectory through software.
A salesperson creates a lead, enriches the lead, adds in information about the prospect in a particular way. That’s one kind of trajectory. A Q&A session with AI is another trajectory : how do I conduct a research project with AI on post-quantum encryption? What are the leading algorithms? Which companies are implementing them? What’s the timeline for quantum computers to break current encryption? Who are the experts I should talk to?
Tracking a user working through the day like a pinball ricocheting around a machine is tremendously strategic.
First, automation requires trajectories. To automate work, you must first understand the path of that work. In the past we hired consultants to map processes manually. Now AI agents can watch & record & understand these trajectories in real-time. AI learns by observing.
Second, optimization requires repetition. Trajectories provide the dataset for improvement. By analyzing thousands of passes through a workflow, AI identifies success patterns & failures & inefficiencies.
Third, trajectories become the new moat. The higher the resolution of the data, the more differentiated the AI product becomes, which increases vendor lock-in.
Fourth, company leadership benefits from understanding employee trajectories. We think we work together in one way, typically with some aspirational ideas. It’s another to truly understand the workflows in the field.
Fifth, trajectories are the basis for optimizing AI models through reinforcement learning or fine-tuning. Smaller specialized models trained on high-value paths replace massive generalists. Lower inference costs & higher accuracy lead to increased margins.
The strategic nature of trajectories raises the question of whether enterprises will negotiate the rights to their trajectory data when buying AI software, both to capture critical data & prevent lock-in. How those power dynamics play out will determine the pricing power for software broadly.
The companies that master these trajectories will define the future of work.
