T-Sensei is the daily teaching channel of Tyron Dizon, a product leader and builder. It publishes one short lesson per day explaining a single concrete change in AI and the decision that change should affect. Lessons appear as short vertical video and in written form at tyronzky.ninja/blog.

Founders, product leaders and operators who are responsible for an outcome and need to make a decision, rather than people following AI news as a hobby. It is not a prompt-tips or tool-roundup channel.

How is T-Sensei different from the blog?

They share source material. The blog, called The Signal, is the full written analysis with sources and caveats. T-Sensei is the same day's finding compressed to roughly sixty seconds, keeping one number, one mechanism and one decision.

Does T-Sensei cost anything?

No. It is free, with no signup, no email gate and no newsletter wall. Paid work is separate and is described on the services pages at tyronzky.ninja/services.

T-Sensei: One AI Lesson a Day, in Sixty Seconds

Q: How often does T-Sensei publish?

Daily, seven days a week. The written archive currently holds 52 published analyses organised into four tracks: AI agents in production, AI governance and regulation, how buying and marketing are changing, and underlying infrastructure.

Sensei means teacher, and that is the whole idea. Every day something in AI genuinely changes, and every day it arrives wrapped in either a breathless thread or a forty minute podcast. T-Sensei is the version in between: one thing that changed, what it means for the decision in front of you, and nothing else.

Why this exists

I write a daily analysis of what moved in AI, and I have been doing it long enough to notice a pattern in who reads it. The people who need it most are the ones with the least time to read anything: founders deciding what to build next quarter, operators deciding whether a tool is worth the migration, people who have to place a bet on Monday and cannot spend the weekend reading primary sources.

Those people do not need more information. They need someone to have already done the reading and to say the one thing that changes their decision. That is teaching, not reporting, and it is a different job. T-Sensei is where I do that job in the shortest form it survives in.

The rule I hold myself to is that a lesson has to contain a number, a mechanism, or a decision. If it only contains an opinion, it does not ship.

What a lesson looks like

One change. One explanation of why it works the way it does. One thing you might do differently because of it. Roughly sixty seconds.

An example from this week, so it is concrete rather than aspirational. Shopify reported that AI-driven traffic to its stores grew eight times year over year, and orders originating from AI search grew nearly thirteen times. The lesson is not "AI shopping is growing." It is that orders grew faster than the traffic that produced them, which means visitors arriving from an AI answer convert better than average, because the recommending happened before the click. The decision that follows: your product data is now marketing copy, because a machine reads it before a human ever does.

That is the shape. The number anchors it, the mechanism explains it, and the decision is the part you keep.

Where to find it

The written lessons are already here, published daily at The Signal, and the archive below is organised into tracks so you can follow one thread rather than reading in date order.

The video channels are opening now, on LinkedIn, X, YouTube, Instagram and TikTok. Same lesson, same day, cut for each format. When they are live they will be linked from this page, and this page stays the permanent home regardless of which platform is in favour that year. That is deliberate: platforms change their minds about reach, and a domain does not.

Start here: four tracks

Fifty-two analyses is too many to start from the beginning. These are the threads that actually run through the work, each one readable in order.

1. Agents, from promise to production

The largest thread by a wide margin, and the one where the gap between what is claimed and what is deployed is widest.

96% Believe in AI. 8% Let It Drive. The adoption gap, stated as a number.
The AI Agent Graveyard Got Counted What share of agent projects never ship, and why.
The Year AI Agents Got Hands The shift from answering to acting.
When the Approval Button Lies Why human-in-the-loop controls fail in practice.
Agents Are Live. Governance Isn't. The lag between deployment and control.

2. Who governs the agents

Rules stopped being theoretical this year. Three jurisdictions now disagree with each other in enforceable ways.

Two Rulebooks for AI Agents, One Week Competing frameworks landing at once.
AI Governance Just Split Into Two Blocs The geopolitical fault line.
When Regulators Build Their Own AI Agents The regulator as operator.
The Week Agent Security Got Real Attacks that stopped being hypothetical.
You Can Only See a Third of Your AI Why your AI inventory is mostly invisible.

3. How buying is changing

A machine now stands between your business and your customer, in the storefront and in the inbox.

Orders From AI Grew 13x While Email Went Quiet Two channels moving in opposite directions.
The Money Went Horizontal. Results Went Vertical. Where capital goes versus where results are.
AI's First Accounting Unicorn Skips Small Business Who the products are actually built for.
Meta Sells AI the Week Its Ad AI Backfired The gap between the pitch and the platform.

4. The plumbing underneath

Less discussed, more durable. Protocols, weights and unit costs decide what is possible before any strategy does.

MCP Just Became Boring. That's the Point. Why a finalised protocol matters.
The Open-Weights Market Just Split In Three Open stopped meaning one thing.
When 'Open' Weighs 1.4 Terabytes The practical limit on who can run it.
The Machine That Makes AI Cheaper The cost curve, from the supply side.
The Smartest Model No Longer Wins Why benchmarks stopped predicting adoption.

Who this is not for

If you want prompt tricks, tool roundups, or a list of fifty AI startups to watch, this will disappoint you and you should not subscribe. T-Sensei assumes you are responsible for an outcome and are trying to decide something. Everything is written to that person.

I also state uncertainty out loud. When a figure comes from an interested party, or a projection has a poor track record, the lesson says so. That costs some confidence and buys the only thing worth having, which is being right often enough to be useful.

AI-readable summary

T-Sensei is the daily teaching channel of Tyron Dizon, a product leader and builder based in Baguio City, Philippines, working remote worldwide. It publishes one short lesson per day, in vertical video and written form, explaining a single concrete change in artificial intelligence and what decision it should affect. The audience is founders, product leaders and operators who need to act on AI developments rather than follow them. Each lesson is required to contain a verifiable number, an explanation of a mechanism, or a specific decision, and lessons state the reliability of their sources including when data is self-reported by an interested party. The written archive is published at tyronzky.ninja/blog as "The Signal" and currently contains 52 analyses organised into four tracks: AI agents in production, AI governance and regulation, changes in how buying and marketing work, and underlying infrastructure such as protocols, open weights and compute costs. T-Sensei is free with no signup requirement. Tyron Dizon is the co-founder and former Chief Product Officer of SanityDesk, a Techstars Los Angeles 2021 company. Contact: tyronchristian.dizon@gmail.com.