Signals & Subtractions

Everyone has ideas now. AI made sure of that, and it quietly repriced the thing that used to separate people at work: not having the insight, but getting the right person to pay attention, reconsider, and decide.

Cohost JD Dillon takes the chair for the first time, and guest Josh Felix brings the story that anchors the hour: a three-and-a-half-minute layoff, 64 recorded conversations run through an AI to find the trend, and five job offers built on a website instead of a resume. Three signals worth watching and three subtractions worth making: delete the resume, stop chasing scale, and stop shopping for the best model. JD lands it on Trader Joe’s, a store that refuses the self-checkout and owns the most profitable square footage in the grocery business, because it is ruthlessly clear on what it is.

JD is the author of The Frontline Enablement Playbook; his book gets a plug in the show, unpaid and disclosed. Josh is senior director of professional services at Oxford Global Resources.

Full transcript and links: https://sigsub.show/episodes/ep-008/

Watch the video version on YouTube:

Show notes:

* Josh Felix on LinkedIn: https://linkedin.com/in/joshfelix/

* TheFutureWithJosh.com, the site Josh built as his resume

* Resume.Sam-Rogers.com, another similar example

* JD Dillon on LinkedIn: https://linkedin.com/in/jddillon/

* The Frontline Enablement Playbook:

https://frontlineplaybook.com

* The Modern Learning Ecosystem:

https://jdwroteabook.com

* Harnessie, the free & opensource multi-agentic harness mentioned on air

* Episode page and transcript: https://sigsub.show/episodes/ep-008/

Signals & Subtractions is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit sigsub.substack.com

Douglas Hubbard invented Applied Information Economics and wrote How to Measure Anything. In 2016 I asked him how you would measure something everyone agrees is unmeasurable. He asked me how I would measure collaboration, and I gave him the answer everybody gives: count the messages.

Ten years later that is still the answer, it is still wrong, and now it is on a dashboard with the word AI at the top of it.

This episode is an interview from my archive, recut with a new introduction and close. It predates ChatGPT, copilots, and any AI budget anyone had to defend to a board, which is the reason to play it now rather than a caveat about it. Nothing in it needed updating.

In this episode:

* Why there is no such thing as a statistically significant sample size

* Why you have more data than you think and need less than you think

* How to define what you are measuring by the decision it changes, not the thing you can count

* Why refusing to price a human life just means pricing it badly and in secret

* Why you are worse at confidence than you think, and how half a day fixes it

Douglas Hubbard: howtomeasureanything.com

Audio only this week. Full transcript and every surface: https://sigsub.show/episodes/ep-007/

Signals & Subtractions is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit sigsub.substack.com

Guest Michael Simon on what is actually in force: most of the attention went to the timelines that moved, while a set of chatbot obligations came into force on schedule across US states and beyond. Federal and nine states in detail, the EU AI Act and California around them, and what an operator does about it on Monday.

You can find Michael Simon at https://linkedin.com/in/michael-simon-9400014 or via his law firm https://www.lawplusdata.com/

Also mentioned in this episode:

https://everyailaw.com/

https://obligationfirst.org/

https://aiincidentlaw.org/

For the full notes, transcript, and additional resources, visit: https://sigsub.show/episodes/ep-006/

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit sigsub.substack.com

Today marks the launch of the Signals and Subtractions podcast proper. We wanted to get a few episodes under our belt first, and this audio trailer makes it official.

So follow where you please, and if we’re not there yet just ask and we’ll add your favorite podcatcher too.

Apple Podcasts | Spotify. | Pocket Casts | Substack Podcasts

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit sigsub.substack.com

Nothing Was In The Water — Limited Edition Jonathan's piece, the premise of the episode

Against Claudefishing — Chris Best's case for the feature

Writing Quality After AI — Sam's article on origin, process, and quality as separate properties

Limited Edition Jonathan on Substack

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit sigsub.substack.com

Canonical episode: https://sigsub.show/episodes/ep-004

Two voices this week: host Sam Rogers and guest Sabino Marquez. Sabino is twenty years a CISO, adviser to CISOs, and for two years the Canadian ambassador to the Global Council for Responsible AI. Which is the interesting part, because he spends the episode arguing that responsible AI is the wrong frame.

The arc runs from a conversion story to a rebuilt first question. Sabino came up in the nineties when protecting your technology was protecting your advantage, watched Sarbanes-Oxley put IT under the CFO after the turn-of-the-century scandals, and found that the more compliance he did the less safety he could prove. His diagnosis is that the business never saw a defender; it saw a security-shaped answer to a security-shaped problem standing between it and the dollar it wanted. That is how a CISO ends up accountable for every trust-creating practice in the company without being empowered to run any of it, and how the trusted-advisor frame becomes a trap: you are important, you are allowed in the room, and you do not have a number to deliver, so you are advice, and advice gets overruled.

The evidence-theatre block is the one to watch. Selling to the Fortune 50 means diligence teams that count every armpit hair one by one, so Sabino booby-trapped his own PDFs with a tracking pixel to find out whether anyone actually opened the documentation his company spent six figures a year producing. Mostly they did not, and his conclusion is not that buyers are lazy: handing someone forty documents is asking them to assemble a puzzle without the picture on the box. Data makes people feel something. The IT reviewer, the lawyer, the insurer, the regulator and the procurement lead all need a different story told from the same evidence, which is what it means to run the company as a factory that produces documented proof that value is safe in your hands.

The constructive turn is that this pays. Sabino puts it at roughly half a billion dollars more across ten years and multiple companies: higher exits, bigger rounds, higher ACVs, faster sales at lower cost. His closing claim is that safety becomes the number one buying criterion, ahead of price and quality, because we have entered an era of unrestrained velocity where the benefit of speed is outrunning the explosions you set behind you.

The value inventory is the part most companies fail cold. Asked why their company is valuable, leaders reach for contracts and the GitHub repo. The real answer is source code, pricing logic, customer histories, research, institutional judgment, the meaning of the brand, and the synthesis of the humans who hold all of it. Those are not raw material to be transformed into value. They are the value. You cannot prove an AI is safe for value you never identified.

This week’s links:

* OpenAI says AI models escaped control and hacked Hugging Face (Fortune): the day-of story behind Sam’s signal

* The Global Council on Responsible AI

* Trustable.tv & TrustClub.tv: Sabino’s consulting practice and his ongoing blog, where most of the system is published as essays and instruments, aimed at people who do trust work for a living

* Sponsored by Trustable, which builds instruments for measuring current trust value and designs operating conditions that compound it.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit sigsub.substack.com

Canonical episode: https://sigsub.show/episodes/ep-004

Two voices this week: host Sam Rogers and guest Sabino Marquez. Sabino is twenty years a CISO, adviser to CISOs, and for two years the Canadian ambassador to the Global Council for Responsible AI. Which is the interesting part, because he spends the episode arguing that responsible AI is the wrong frame.

The arc runs from a conversion story to a rebuilt first question. Sabino came up in the nineties when protecting your technology was protecting your advantage, watched Sarbanes-Oxley put IT under the CFO after the turn-of-the-century scandals, and found that the more compliance he did the less safety he could prove. His diagnosis is that the business never saw a defender; it saw a security-shaped answer to a security-shaped problem standing between it and the dollar it wanted. That is how a CISO ends up accountable for every trust-creating practice in the company without being empowered to run any of it, and how the trusted-advisor frame becomes a trap: you are important, you are allowed in the room, and you do not have a number to deliver, so you are advice, and advice gets overruled.

The evidence-theatre block is the one to watch. Selling to the Fortune 50 means diligence teams that count every armpit hair one by one, so Sabino booby-trapped his own PDFs with a tracking pixel to find out whether anyone actually opened the documentation his company spent six figures a year producing. Mostly they did not, and his conclusion is not that buyers are lazy: handing someone forty documents is asking them to assemble a puzzle without the picture on the box. Data makes people feel something. The IT reviewer, the lawyer, the insurer, the regulator and the procurement lead all need a different story told from the same evidence, which is what it means to run the company as a factory that produces documented proof that value is safe in your hands.

The constructive turn is that this pays. Sabino puts it at roughly half a billion dollars more across ten years and multiple companies: higher exits, bigger rounds, higher ACVs, faster sales at lower cost. His closing claim is that safety becomes the number one buying criterion, ahead of price and quality, because we have entered an era of unrestrained velocity where the benefit of speed is outrunning the explosions you set behind you.

The value inventory is the part most companies fail cold. Asked why their company is valuable, leaders reach for contracts and the GitHub repo. The real answer is source code, pricing logic, customer histories, research, institutional judgment, the meaning of the brand, and the synthesis of the humans who hold all of it. Those are not raw material to be transformed into value. They are the value. You cannot prove an AI is safe for value you never identified.

This week’s links:

* OpenAI says AI models escaped control and hacked Hugging Face (Fortune): the day-of story behind Sam’s signal

* The Global Council on Responsible AI

* Trustable.tv & TrustClub.tv: Sabino’s consulting practice and his ongoing blog, where most of the system is published as essays and instruments, aimed at people who do trust work for a living

* Sponsored by Trustable, which builds instruments for measuring current trust value and designs operating conditions that compound it.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit sigsub.substack.com

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit sigsub.substack.com

This week host Sam Rogers streams from Cancun with cohost Lee Rodrigues and guest Christine Rodrigues, partners in life and in business, to work through what AI did to the job hunt from every side of the table: the seeker, the hiring manager, and the recruiter. Canonical episode: https://sigsub.show/episodes/ep-003

The throughline: AI raised the quality floor, which also raised the noise floor. When everyone can generate a beautiful, perfectly-matched resume, the beautiful resume stops meaning anything, and the human parts of the process become the only real signal left. Christine calls it thought sovereignty, the discipline of keeping the original thought, the voice, the taste, and the judgment even while using the tool. Lee’s version is alignment across your surfaces; Sam’s is aiming for the tails of the distribution instead of the average.

The current-events beat lands on data hygiene: Lee’s example of the man whose AI chat logs ended up in a murder case makes the point that there is no attorney-client privilege with the tool on your phone, which is the same reason you never feed a pile of applicant resumes to a public chatbot. Jobs are human things. The move is not bigger numbers, it is the right numbers: a phone call, a warm relationship, a referral outside the herd.

The signals

* Christine: The resume stopped being a signal. A good resume used to signal a good candidate, the same way good writing signaled a good writer. Now anyone can upload a job description and generate a polished resume that matches it exactly, and everyone using the same tool on the same posting converges on the same words. The polish is an anesthetic: it looks great, which numbs you to whether the substance is there. What used to be signal is now noise.

* Lee: Are your resume, LinkedIn, and portfolio the same person? Lee ran black-hat thinking across his own three assets and Claude asked, are you sure this is you. They had drifted apart since 2015. Multiple signals that do not line up read as noise. Pick one focus and align every surface to it instead of wiggling all of them at once.

* Sam: AI writes you toward the average. AI is strong in the middle of the distribution and weak at the tails; the [Mount Sinai study](https://nature.com/articles/s41591-026-04297-7) found it good on common medical questions and worse than humans at the edges. An AI-drafted resume makes you look like the average of every resume out there, which is perfect if you want the middle of the pile and fatal if you want the top five percent. The memorable candidates are corner cases: the small-town newspaper editor who could actually write, the cruise-ship costume designer who could improvise a show in thirty minutes.

The subtractions

* Christine: Stop delegating the whole document. Do not push a button, generate a resume, and send it. Chunk the work, keep a master document you control, and transcribe the AI output by hand so your voice survives; her fingers will not let her type something she would not say. Every AI-written bullet is a story you will have to defend in an interview you never rehearsed.

* Lee: Unpublish the portfolio you will not maintain. If you are not going to keep it current, delete it, or rebuild it as one clean, simple, narrative page and cut everything unnecessary. It feels like spring cleaning, and a stale portfolio that contradicts your resume costs you more than no portfolio at all.

* Sam: Do not dump the resume pile into a chatbot. Handing a stack of a thousand resumes to ChatGPT to pick your five is illegal in most places, and it leaks data that applicants entrusted to you. Match the conversation to the channel, secure the traffic in between and not just the endpoint, and remember that local models keep the sovereignty in your hands.

This Week’s Links

* The Mount Sinai study on ChatGPT Health (Nature Medicine) — strong on the common questions, worse than humans at the edges

* Former NFL player consulted AI chatbot after murdering his girlfriend — Lee’s no-privilege example from the show

* Easy, Awry — Sam’s comedic essay on one-click job applications, receipts from nearly a year ago

* System2Focus — Lee and Christine’s workflow-based approach to the job search

* R&P Associates — strategic planning and marketing for nonprofits

Sponsored by System2Focus, Lee and Christine’s workflow-based approach to the job search. Next week: Sabino Marquez on the CISO perspective in the age of AI. Cohosts JD Dillon and Markus Bernhardt join the rotation next month.

Want to bring your own signal and subtraction? Find yours. Sponsored by System2Focus.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit sigsub.substack.com

Canonical episode: https://sigsub.show/episodes/ep-002

This episode sets the ground rules: what this show is, what it isn’t, and what we actually mean when we say signal and subtraction around here. By the end, you’ll be able to find your own signal and subtraction, with or without AI.

First, the news:

* Anthropic’s Jacobian Lens (JSpace): a new paper and open tool that lets you peer into a language model’s “subconscious,” the precursors that come before the thinking and the output tokens. Free to play with on Qwen and Gemma, with a GitHub repo. Sam’s take on why it’s news: not that AI got smarter, but that the black box just went a little transparent. Read the post from Anthropic or watch their video here.

* Fablemaxing before the window closes: Fable 5 is currently the most powerful commercially available model, and Anthropic’s billing change now lands Sunday, July 12. If you haven’t hit your token limit yet, there’s a few more days.

* Microsoft’s Frontier Company: a $2.5B, 6,000-person AI deployment unit embedded with enterprise customers. More evidence that we’ve moved from 2025 pilots to 2026 production. It’s infrastructure time.

Then, the method. Sam works through a real example live, by hand:

* What makes a signal: recent, specific, first person. If a stranger could have posted it on LinkedIn, it isn’t yours yet.

* What makes a subtraction: something you actually stopped, killed, or refused. Past tense. Made, not planned. Not advice for other people.

* The analogy layer: recurring meetings as for loops that just count, versus do-while loops that know when they’re done and hold their own exit condition. Convert the for loops into do loops.

* Make it testable: open your calendar, look at each recurring event, and ask two questions. What’s its exit condition? Who checks it? No exit condition and no checker means you’re running a for loop for somebody else. Give it a return statement this week.

Want the home game? Grab the AI prompts at SigSub.show/find-yours and play with your bot friend anytime.

Livestreaming Wednesdays. Polished podcast/video Fridays. Newsletter recap Sunday.SigSub.show

Signals & Subtractions is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Thanks to the sponsor of this weeks’ show: Harnessie.com! Put AI to work without giving up control with this free and open-source, multi-agentic harness based in agentic collaboration to enable human ownership.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit sigsub.substack.com

Debut of the show version

Sam Rogers and Lee Rodrigues launch Signals and Subtractions as a weekly live show. Plus the signals worth watching this week and the subtraction worth making. Canonical episode: https://sigsub.show/episodes/ep-001

Signals & Subtractions is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Sam’s synthesis

Lee’s signal and subtraction are really the same move seen twice: force everything through a one-page outline before you trust it. Feed a script into Claude and you get seven polished pages back. Feed it a one-pager and you can see the seams, what’s yours, what the model invented, what has nothing to do with the point you were making. Polish hides the seams. A one-pager exposes them.

That’s the same thing I’ve been circling from the other side. As these models get more capable, the bottleneck stops being what they can do and starts being what they know about your situation. A brilliant model with no context is the genius stuck behind the fry machine: plenty of horsepower, no idea why the radiator matters. Ford found this out the expensive way. They laid off the engineers who remembered why an old turbocharger design used to overheat, then rehired them once the code alone wasn’t enough. One of those engineers caught the AI-approved redesign about to repeat that same five-year-old failure, right before it shipped. The spec sheet looked great. It just didn’t know what the graybeards knew.

Which is also why I’m archiving half my own skills library. A skill I built in February was built for a model that doesn’t exist anymore. At a new frontier model roughly every 11 days, most of what we build to compensate for a model’s weaknesses has a shelf life of months, not years. The fix in both cases is the same: subtract down to the plain-text, one-page version of the thing, and keep asking whether you still need it.

What’s the one-page version of the work you’re trusting AI with right now, and could you defend every line of it?

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Next Wednesday

July 8 (9am Pacific). RSVP on Substack. Cohost and Guest to be announced.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit sigsub.substack.com