Topic
AI transformation
Strategy, rollout, and the operating model that keeps the gains.
Agents at the bank's door
In October, the largest US banks shut out the AI agents their customers had started using to manage money. What they blocked, why it is hard, and a practical roadmap for financial institutions to let agents in safely.
Early October 2026: AI gets a budget
The month in AI, first half of October. What business leaders should take from two weeks of AI news.
The model is eating the harness
Work that AI systems once had to build around the model keeps becoming part of the model. What that pattern looks like, why it keeps happening, and how to build systems that get better with every release.
The AI-native finance team
What a finance department looks like when agents do the routine work: which workflows they handle today, how they connect to the ledger, how the team changes shape, and how to start.
How to measure applied AI
A model's benchmark score says little about how it will do on your work. A practical playbook for measuring AI inside an enterprise: real cases, outcome metrics, repeated runs, one scorecard and a loop that keeps measuring after launch.
The Bending Spoons playbook
Bending Spoons runs more than 50 acquired software businesses on one shared platform with a core team of about 600. What the platform contains, why AI changed its economics, and how any roll-up or private equity firm can build its own.
One view of the business
Private equity owners and management teams often see the same company differently. A playbook for the systems that close the gap, with less reporting work for both sides.
The AI campaign playbook
In 2026 campaigns used AI to draft. In 2028 the first campaigns will run on it. A department-by-department guide to the operation behind a modern campaign.
The Long Lake playbook
A three-year-old AI holding company just closed a $6.3 billion take-private of Amex GBT. How the model works, what it claims, what is still unproven, and what private equity should take from it.
Building an AI roll-up platform
Private equity invented buy-and-build. The AI roll-up is being built mostly outside it. How a private equity firm can run the model, from choosing the vertical to the platform, the people and the exit.
The credit manager's AI playbook
Where AI earns its place across a credit manager, from origination and monitoring to CLO operations and the wealth channel, and what has to be true first.
The private equity AI playbook
What a fund should actually do with AI at each stage of an investment, from diligence through exit, and how to build AI EBITDA that survives the next buyer's diligence.
Beyond cost: AI as a revenue engine
Most portfolio AI plans are cost plans. The larger and more durable opportunity is revenue: capacity that turns into growth, new AI products and services, and new ways to price.
AI across a portfolio, sector by sector
The same technology lands differently in software, services, healthcare, defense, industrials and home services: different work, different constraints, and a different share of the gain the company keeps.
Scaling AI across a portfolio
Most portfolio AI programs can make one company work. Few make the tenth company cheaper than the first. What to share across a portfolio, what each company must keep, and who runs it after the builders leave.
Choosing an AI partner for your portfolio
Lab-owned deployment firms, consultancies, integrators, platform partners or building it yourself. Four questions that separate them, and why model choice matters over a seven-year hold.
The deskless majority
Most of the workforce has never had a keyboard, a login, or an hour to spare for training. The interface problem that kept them off enterprise software is the one this generation of AI removes.
Where AI helps across a university
A guide to the work AI genuinely helps with today, area by area, from admissions and advising through research administration and accreditation, and what has to be true for it to help.
No two institutions start in the same place
Higher education is covered as one market with one story. Five kinds of institution, what leadership in each is actually working on, and where AI tends to help first.
Your advantage is already on campus
Access to capable AI is now ordinary, which moves the question from what an institution can buy to what it already has. Four things a university holds that nobody can copy, and how each becomes a system.
Where voice actually works
Voice stopped being a demo this year. A map of what it is good at now, what it is still bad at, and the places it should not go.
What forward deployed means
A job title that was one company's internal jargon three years ago is now the most contested role in AI. What it actually is, why it came back, and what it should leave behind.
What to build, and what to buy
Enterprises are buying most of their AI and worrying about what they bought. The build-or-buy line does not run between layers of the stack. It runs through every one of them.
Why evals matter
The public benchmarks stopped measuring anything useful this year. The instrument you build for your own work is the one that decides everything else.
Should I train my own model?
Where the model market stands this month, what each class is for, what the industry and the law constrain, when post-training pays, and how Fig thinks about it.
What an ontology is for
The word has escaped into every enterprise AI pitch. Here is what it actually means, why agents made it urgent, and how it becomes the foundation of an AI transformation.
Sovereign AI: the operating playbook
Sovereignty is a capability, not a location. Five rights, a ladder of infrastructure routes, and four proofs to run before production.
The Sovereign Frontier Enterprise
Adoption is universal and impact is concentrated. The difference is what a company owns around the models.