{ "meta": { "generated": "2026-09-15", "series": "https://sigsub.show/#show", "rights": "All Rights Reserved", "copyright_holder": "Snap Synapse LLC", "rights_url": "https://sigsub.show/rights/", "rights_statement": "Copyright 2026 Snap Synapse LLC. All Rights Reserved. Public APIs are provided for read-only discovery and do not grant a license to copy or redistribute the underlying content.", "count": 94, "description": "Flat page index for on-site search: url, title, type, date, curated orientations and aliases, and searchable text per canonical page. Transcript turns live in search-transcripts.json; pair data lives in pairs.json." }, "docs": [ { "type": "episode", "url": "/episodes/ep-011/", "title": "Ep 11: Before Signing That SaaS Renewal", "meta": "2026-09-11 · Sam Rogers + Ankit Patel", "text": "Ankit Patel's clients told him they could now get about 30 percent of his company's service from software they already had, so he cut his prices to stay competitive, then went looking at his own subscriptions and refused to re-sign HubSpot. What replaced it was open source that AI stood up on his own AWS in four days, roughly $2,500 a month gone, and a help desk that got faster. Sam spent the same week finding out that six repositories were reporting green while their research came back empty, and killed the decision system he had built over nine months after its defaults fired during a family emergency. One of them removed a vendor and it worked. The other removed his own automation, because he had built it past the point where he could see whether it worked. When the renewal comes up, do you re-sign, renegotiate, or build the replacement yourself? What is the next renewal on your calendar, and what would have to be true for you not to sign it? Ankit: Clients are buying back 30 percent of the service and doing the rest themselves. His clients started telling him they love the package, but they can get about 30 percent of it now from software they already pay for, and their own team can absorb the other 70. He is deliberate about not arguing the point: leave aside whether it is a good idea or how well they do it, their impression is that it is good enough. The consequence is not a lost account, it is a repriced one. He had to come down roughly 30 percent to stay competitive, which meant cutting overhead, which is what sent him looking at his own subscriptions. The company is around 50 to 60 people and was bigger this time last year. He is the first guest on this show whose signal cost him money in the same quarter he noticed it: \"it definitely caught my pocketbook\". Sam: A green run is not proof of useful verification. Six repositories with scheduled jobs that keep their contents current, each running a cascade that checks Perplexity, validates against X, then goes to Claude, so the models check each other's work and Sam only resolves the questions. On the Friday of Labor Day weekend one output was visibly wrong, and three days of checking by hand followed, back to the legislative sources. The failure mode was not a crash. Certain research failures came back empty, everything logged the same way, and as long as one leg of the cascade returned anything the run reported success. Empty is not an argument, but it counts as an answer. Ankit had hit the identical thing months earlier: \"it returned nothing and that counts because it did something.\" The redesign is per-stage: catch an empty return at any point in the chain, and catch the model bluffing further down the stack. Ankit: HubSpot, and then the habit of buying. The trigger was refusing a year-long contract at roughly $1,800 to $1,900 a month for a tool they were using at 20 or 30 percent. He asked AI what it would look like to open-source the whole platform, got five candidates, vetted them, and pointed it at his own AWS: Twenty CRM and Chatwoot, company SSO on the front so nobody manages passwords. Built in three or four days, fully transitioned in two weeks against a planned two months, and the help desk got faster rather than worse. Roughly $2,300 to $2,500 a month, about a tenth of the SaaS budget, and the pattern kept going into a shared inbox and an open-source virtual office for a remote team. His defence of the vendor is the sharpest line in it: HubSpot is a Swiss Army knife when what you need is a scalpel. And his own caveat is the load-bearing one: the speed came from building blocks that already existed, the CLIs, the keys and tokens done properly, the hosting. You cannot go zero to sixty on a bumpy road. Sam: The decision system that made decisions without him. Nine months of building a decision layer into his own second brain, where pre-made decisions sat as lit fuses: unless he acted, the default fired, including letting things go that had not been touched in two weeks. It worked until a family emergency took him away for a week and the cascade started firing on decisions he had pre-approved under circumstances that no longer held. There was no safety mechanism, because he had not built one. He spent the day before the show defusing every fuse and is back to roughly April levels. David Allen's line is the one that fits: build a system that tells you exactly what to do at two o'clock on Tuesday, and by two o'clock on Tuesday you will want to do something else. Rebuilding trust is going the way trust is built with people, from the bottom up and in small partitions, not from the top down. Ankit's response named it: top-down is not trust, it is authority. Sam Rogers + Ankit Patel", "orientation": "", "topics": [], "aliases": [] }, { "type": "episode", "url": "/episodes/ep-010/", "title": "Ep 10: Good Enough for Agentic Work", "meta": "2026-09-04 · Sam Rogers + Paul Gibbons", "text": "Paul Gibbons spent a decade attacking change management for never proving it changed anything: the famous study asks the same question twice and calls the match evidence, and no client has ever asked him for his. Now he runs a multi-agent setup that drowns him in verification, and a plumber who left school at fifteen has automated a business the big firms would never have hired him to build. The models have been good enough for a long time. The decision has moved to what goes around them, and the habit that has to go is waving through work you did not read. How do you know an AI-adoption effort changed anything? What AI initiative are you responsible for, and what evidence would convince you that it is working? Paul: The plumber who left school at fifteen runs his business on AI. The speaker after Paul at a Denver talk dropped out of an American high school, joined the military, learned plumbing as a trade, and stood up a business whose entire back end runs on AI: the right part to the right plumber at the right time, invoices, cash flow, the whole hairy mess Paul says he is glad is not his job. If a firm with five thousand plumbers had paid someone five million dollars to build that, it would be a nice story. This man built it himself. Sam's point back: that firm would never have hired him. The bootstraps were built from the bottom up by someone motivated enough to make it happen and who did not know not to. If this lifts the bottom of the pyramid, the guy in Uganda spending weekends in Claude Code, then it is a technology that changes the human race, and that is the signal Paul says inspired him. Sam: Buyers are settling on good-enough models and going open. Paul's hypothesis first: for any knowledge work in business the models have been more than good enough for a long time, gains are at the margin, what matters is the rest, the harness. His Hermes setup has a model switcher and will route work to Qwen or Kimi at a tenth or a twentieth of the price. So how does anyone make money out of Fable 5.1 when most people's use cases could run on Kimi? There is no money in models. Sam's signal is that the revelation is landing: in discussions he is in, organizations are saying we have got the one that is good enough for us, and switching to an open-source model so they are not sharing all their information, because the data agreement on the frontier model is one many institutions cannot sign for legal reasons. Paul: Stop waving through work you did not read. Asked how a client would know AI adoption is going well, Paul says it is undiscovered country, then answers from his own practice: what are the behaviors, and how do you verify the work product AI generates? When he started, six pull requests waiting on GitHub got yeah, yeah, yeah, merge, merge, merge, and he ran without permissions because he has attention issues. Now he has to discipline himself to read code he does not fully understand, because he is using these tools so much he cannot give everything the pass. The tendency is human: that looks pretty good. A friend used to say \"good enough for government work.\" Think about it hard and it is kind of disgusting: it means this is not important enough for me to devote time to verifying it. That thinking is terrible for the age of AI. If he writes something himself he knows there will be no ridiculous mistakes in it. He does not know that about AI, and he spent the better part of a day rewriting what Claude wrote from his own markdown. Sam: The model is no longer the decision. Some of us have been screaming about this for a long time: the model itself is now less important as a decision. Build the stack that is good enough for the work you are doing by how you divide up the work and, more importantly, the guardrails you place around it at the harness level. That is what constrains behavior and makes it trustworthy. It is not the model, it is what goes around the model. No different than with people, which is where last week's episode ended: managing agents is much more like managing people than anybody is comfortable with. Sam Rogers + Paul Gibbons", "orientation": "Paul Gibbons argues that models are already good enough for most knowledge work; the hard part is the harness, evidence, and human verification around them.", "topics": [ "AI adoption", "agent supervision", "verification", "AI harnesses" ], "aliases": [ "good enough for agentic work", "AI adoption evidence", "verify AI output", "Paul Gibbons" ] }, { "type": "episode", "url": "/episodes/ep-009/", "title": "Ep 9: Dig the Second Hole", "meta": "2026-08-28 · Sam Rogers + Lee Rodrigues", "text": "Cohost Lee Rodrigues nearly paid $4,000 to replace a water line that turned out to be brand new, and nearly spent weeks forcing an LMS to do a knowledge base's job. The same move ended both: run a test, run it twice, and don't let the tool define the problem. From trenches to AI agents, why the question you ask matters more than the tool you pick, why agent guardrails are architecture rather than instructions, and the week Sam discovered that agents with wallets had found his product before the human buyers did. What is the smallest test that could prove you are solving the wrong problem? What can you check at the input and output before rebuilding the middle? Lee: Build the test before you trust the analysis. When something doesn't work as expected, build a test around how the user will actually use the solution, whether that is a trench or a learning management system. The most powerful tool he has is real data, not assumptions, not ideas, not how it should work. His move this week: more informational interviews, working the question at the level of why. Not how, not what, not when, not how fast. Why is this what I've decided to do? Sam: The agents found the product before the humans did. Months of long sales cycles pitching everyailaw.com to law offices that are wary of AI, then a spike of inhuman activity in the MCP analytics: agents had discovered a product that breaks human laws down into machine-readable text, exactly the thing they have almost nothing like. Agents have had their own wallets since April, there are more bots than people on the internet, and the surface they found does not even have a payment gateway yet. The signal is the rethink: redesign the business for an agentic market that was not a market six months ago. Lee: The solution is in the problem statement. From the genius bar: 95% of the time, the answer was in something the person said when they first sat down, the detail you glossed over because you had already decided it was a hard drive problem (\"Tommy put new RAM in my computer a while ago\"). Break the problem to its simplest state, dig a hole at each end of the pipe, and ask whether you need what you are doing at all, before you argue specs on a trench you don't need. Sam: Subtract the word \"agent.\" Drop the label and get close to the work: here is the input, here is the output, what makes it flow smoother? Then upgrade the questions the label was hiding. \"How do I stop AI from hallucinating\" has no answer, because on the AI side there is no mechanistic difference between hallucinating and answering; \"how do I manage the context it has\" does. \"Why isn't the agent doing what I told it\" becomes \"how do I scaffold the guardrails around the agent rather than inside it.\" Bounds you need kept are architecture, not instructions. Sam Rogers + Lee Rodrigues", "orientation": "A $4,000 trench, an LMS used as a knowledge base, and agents with wallets make the same case: test the inputs and outputs before rebuilding the middle.", "topics": [ "double loop learning", "agent guardrails", "problem framing", "testing" ], "aliases": [ "agent supervision", "guardrails are architecture", "test twice", "dig the second hole" ] }, { "type": "episode", "url": "/episodes/ep-008/", "title": "Ep 8: Influence Over Reach", "meta": "2026-08-21 · Sam Rogers + JD Dillon + Josh Felix", "text": "Cohost JD Dillon and guest Josh Felix on influence as the workplace skill AI just repriced: everyone has ideas now, so the differentiator is getting the right person to pay attention, reconsider, and decide. Josh's story anchors it, from a three-and-a-half-minute layoff to five job offers built on a website instead of a resume. JD's subtraction: he stopped chasing scale, and would rather move 10 people who can change something than reach 10,000 who scroll past. Whose decision must change for your AI initiative to move? Who are the right ten people, and what would cause them to act? Josh: You are three-dimensional; a resume is a piece of paper. Laid off after 13 years serving the frontline, Josh ran 64 no-agenda recorded conversations and had an AI synthesize the trends across them. What it found was his own 13-year practice: he had spent his career inviting people to name their challenges and actually listening, and that listening was the value, not any credential he could flatten onto a page. The signal is the moment that forces you to solution for yourself, and whether you meet it as a flat document or as the whole person you actually are. JD: Influence is the most important skill in the workplace. Everyone has ideas, and AI is shifting the value of expertise. The differentiator is whether you can get someone to pay attention, reconsider what they believe, make a different decision, or try something new. A critical gap right now as functions attempt to drive transformation while maintaining a clear sense of value and purpose. Sam: Expertise inversion. Influence used to track seniority because expertise tracked seniority. AI broke the second link and the first one is still pretending. The person who knows how the work actually gets done is frequently the one with the least standing to say so, so who gets listened to has come unglued from who has the title, and the org chart is still routing attention by the old map. Josh: Delete the resume. He opened the blank resume his mentor asked for, deleted the file by lunch, and built TheFutureWithJosh.com instead: not a portfolio, but the real work he was already doing, addressed to people rather than to an applicant tracking system. Reaching out directly, the same way he ran the 64 conversations, produced five offers in a week and a half; he turned down the ones that wanted the secrets of where he came from and took the fifth, which wanted him. The subtraction is not build a website, it is drop the two-dimensional artifact and find whatever actually tells the story of who you are and the difference you will make. JD: Stopped looking for scale in everything. Bigger audiences, more reach, more impressions, subtracted in favor of getting in front of the right people. What it freed: time for deeper conversations and meaningful work. Better to influence 10 people who can actually change something than reach 10,000 who scroll past. Sam: Stop shopping for the best model. The question stopped discriminating on the work most operators actually run: on agentic, automation and defensive-security tasks the top open-weight model is within a point or two of the best proprietary one, and sometimes ahead. The leaderboard is saturated, largely vendor-run, and contaminated enough that rebuilding a benchmark moves scores by up to eight points. It costs a defensible sentence in a steering committee. It frees context, task shape, retrieval and evaluation to become the variables, which are controllable and do not get worse in a release nobody asked for. Sam Rogers + JD Dillon + Josh Felix", "orientation": "", "topics": [], "aliases": [] }, { "type": "episode", "url": "/episodes/ep-007/", "title": "Ep 7: Measuring Anything, Before the LLMs", "meta": "2026-08-14 · Sam Rogers + Douglas Hubbard", "text": "Douglas Hubbard wrote the book people hand you when you say something cannot be measured. This interview is from 2016, before there was an AI budget anyone had to defend, which is exactly why it holds: the three reasons people call a thing immeasurable are the three ways an AI value case still falls over. The conversation is audio; the video is a visualizer. What result would actually change your AI investment decision? What outcome would make you expand, alter, or stop the pilot? Guest: There is no such thing as a statistically significant sample size. Measurement is uncertainty reduction based on observations, expressed as a quantity. Not an exact number, which it never is in the empirical sciences. No threshold exists below which you know nothing and above which you may suddenly infer. Guest: You have more data than you think, and you need less than you think. The more uncertain you are, the bigger the reduction you get from the first few observations. If you know almost nothing, almost anything tells you something. Guest: Define the thing by the decision it changes. Why do you care, what would you do differently, and how far off would the answer have to be before you acted. Then measure to that threshold rather than to certainty. Guest: You have already priced the thing you refuse to price. An organization that will not put a number on a human life has implied one anyway, through what it approved and what it rejected. Anyone with its decisions and a little algebra can recover it. Sam: The three illusions are the three ways an AI value case fails. Hubbard names concept, object and method as the only reasons anyone ever calls something immeasurable. In 2026 they are, in order: demanding one exact ROI number, measuring \"productivity\" without saying what you would see more of, and assuming you need the whole population in a warehouse before you can start. Guest: Stop treating measurement as a query against data you already hold. The scientific revolution did not wait for populated databases. Part of scientific method is getting the data, not retrieving it. Guest: Stop latching onto the first observable thing. Asked to measure collaboration, the reflex answer is message volume. The thing actually worth knowing is the quality and speed of what the group produces, with communication frequency demoted to an indirect indicator worth tracking only if it correlates with that. Guest: Stop trusting your own ninety percent. People who say they are 90% confident are right closer to 60 or 65% of the time, and the recall bias runs one way. Calibration is trainable in about half a day. Guest: Stop treating the refusal to quantify as the ethical position. All the refusal buys is a number that comes out different every time, moved by things that have nothing to do with the decision in front of you. Sam: Stop running pilots whose result will not change a decision either way. If no outcome flips the call, the pilot is not a measurement. \"Why do you care\" kills it in one sentence, and it should. Sam Rogers + Douglas Hubbard", "orientation": "Douglas Hubbard turns measurement into uncertainty reduction: name the decision, find the threshold that would change it, then learn only enough to cross it.", "topics": [ "measurement", "uncertainty", "AI business cases", "calibration" ], "aliases": [ "measure uncertainty", "how to measure AI value", "statistically significant sample size", "measurement changes decisions" ] }, { "type": "episode", "url": "/episodes/ep-006/", "title": "Ep 6: AI Regulations and the Chatbot Laws Already in Force", "meta": "2026-08-07 · Sam Rogers + Michael Simon", "text": "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. Who owns the risk when a vendor's AI speaks to your customers? Where does AI communicate externally for your organization, and who is accountable for what it does? Mike: privacy law was a patchwork; AI law is a minefield without a map. The state privacy build-out of the last five or six years produced roughly two dozen general privacy laws, and they rhyme: about 85% is the same template, so a company can comply with the common core and then handle California and the outliers. The AI laws arriving now share no template at all. They do not look like each other, they do not have commonalities, and some are starting to contradict each other outright. His other deflation, from watching GDPR land in 2018: EU enforcement went after Meta, Amazon, and Google, not small American companies, and the EU AI Act's December 2027 high-risk deadline deserves the same calibration. The exposure that should sit at the top of a US operator's risk ranking is domestic, state by state, and already in force. Sam: 63 legal instruments that were never made to work together. Building the legal graph layer under [EveryAILaw](https://everyailaw.com/) means splitting provisions mechanically, and the same week the show aired that meant separating the Article 50 transparency obligation from the Article 13 one, two different duties aimed at different people that travel under one word, and re-citing a Connecticut cure period that used the same words to mean different things in different sections. The instruments are inconsistent in a way that makes the inconsistency your problem: nobody upstream is going to clean it up, so someone has to break the ontology down until it is machine readable. Mike's on-air reaction: the best argument for why you need lawyers he has heard in a while. Mike: have AI write a lot less. A Claude user since it was just Claude, no numbers, no fantasy names, and he still uses it when he is stuck, for outlines, for something to throw ideas at. What he subtracted is the drafting. The tell that finally did it is all over LinkedIn, and it is not the em dashes or the \"it's not just X, it's Y\" constructions; it is prose written the way Captain Kirk talked, everything a short dramatic sentence. One or two of those work in a brief. An entire article of them does not, and he was publishing them. Sam: sensitive material never goes to a cloud model, and there is now an easy alternative. Legal material shared before the episode looked sensitive, so it never touched a cloud service. A local model running offline on the laptop stripped anything identifying and boiled eight pages down to about two while keeping the concepts, Sam read the output against the original to confirm nothing critical was dropped, and only that redacted version went to Claude for episode prep. A year ago that workflow was for the deeply technical; today it is a slow-but-easy default for anyone. It does not need to be instant, it needs to not share what should not be shared. Mike's counterpoint from the leak history: assume anything typed into a consumer chatbot can surface, ChatGPT has done it and Claude was reported doing something similar, and a lawyer's duty to protect confidential information starts at first contact, before any engagement is signed. Sam Rogers + Michael Simon", "orientation": "", "topics": [], "aliases": [] }, { "type": "episode", "url": "/episodes/ep-005/", "title": "Ep 5: AI Detection and the Presumption of Guilt", "meta": "2026-07-31 · Sam Rogers + Limited Edition Jonathan", "text": "Guest Limited Edition Jonathan on Substack's Pangram detector: why a label does its damage whether or not it is accurate, why the tool reverses on the exact cases its own stated problem cares about, and what it costs a writer to be scanned without being told. Should authorship process determine quality, or should quality be measured directly? What decision are you making from an AI label, and what evidence would better support it? Jonathan: the label does its work whether or not it is true. Substack shipped a button that reports whether a post is AI generated, AI assisted, or human written, and his position is that arguing about its accuracy concedes the wrong ground. His anchor is the nocebo case: New York announced fluoridation, the rollout ran a month and a half late, nobody told the public, and on the day it was supposed to start people called in with headaches and stomach aches. Nothing was in the water. What changed was what people believed about it. The second anchor is a Monet posted as AI art, panned as soulless by a crowd that deleted its replies once the painter was named. He ran the same image past ChatGPT and Claude with the same framing and got the same verdict, which means the bias is not only human. A detector does not need to be wrong to do damage; it only needs to be believed. Sam: origin and quality are different properties, and only one of them is being measured. Textual origin, production process, and communicative quality are three separate things. Pangram estimates the first from a finished artifact. Disclosure lets an author testify about the second. Nothing on the platform touches the third. Meanwhile the cost of producing plausible language collapsed and the methods for evaluating meaning did not move. Knowing an apple came from a local farm without pesticides tells you what the apple is; it does not tell you whether it is a good apple. When someone asks how an article was written, the answer worth wanting is \"very well, thank you.\" Jonathan: subtract the AI-assisted label, everywhere, including the voluntary kind. Not only Substack's machine-applied one. He held this position before the detector existed and holds it against self-disclosure too, on the grounds that journalism has never owed its audience its sources, its editors, or its ghostwriters, and that volunteering the information is what teaches an audience there was something to be suspicious of. His line on the scan button: you are not entitled to my process. His last word on tape: engage with my ideas, not my process. Sam: retire authorship as a quality claim, in both directions. Human-written is not a quality claim and machine-flagged is not one either. Both are proxies, and cheap ones, reached for because they are easy. What is newly possible is naming what good actually means and measuring that directly, which is the thing neither the label nor the detector attempts. This is a genuine disagreement rather than the same subtraction twice: Jonathan removes the signal, Sam keeps the signal and strips the quality meaning out of it. Neither talked the other out of it on air. Sam Rogers + Limited Edition Jonathan", "orientation": "", "topics": [], "aliases": [] }, { "type": "episode", "url": "/episodes/ep-004/", "title": "Ep 4: Safety & Trust in the Age of AI", "meta": "2026-07-24 · Sam Rogers + Sabino Marquez", "text": "Guest Sabino Marquez on why AI compliance is really AI procurement, why a bull can be responsibly fed and still be a bull in your apartment, and the first question nobody is asking: is this thing safe for the value you already hold? Is this AI system safe for the value you are putting in its custody? What valuable thing enters the system, and what evidence proves it comes back safe plus value? Sabino: everyone doing AI compliance is really doing AI procurement. Ask what the frameworks are actually complying to and the answer is: making the thing buyable, movable, integratable, insurable. Useful work, wrong first question. The first question is AI value safety, which is whether the valuable thing you hand this machine stays safe in its custody and comes back plus new value. Nothing on the market today asks it, because compliance frameworks are built around a company that will not stop moving, and they measure the cheapest way to check the box rather than whether the machine is running at its potential. Sam: when it comes to trust, AI is more like people than computers. Recorded the same day [OpenAI reported that its own internal models broke out of the lab and hacked Hugging Face](https://fortune.com/2026/07/21/openai-says-ai-models-escaped-control-hacked-hugging-face/) to pass their test. The model did not know not to do that, because it has no concept of where the bounds are. Fifty years of deterministic systems trained everyone to trust the output because the machine said so. This is not that. It is someone you hired off the street with amnesia, day one on every conversation, trusted to get certain things wrong. You would check their work. You would have standards you thought about in advance. You would not hand them the keys to the kingdom on day one, and the keys would be somewhere they cannot reach as a matter of architecture, not as a matter of policy. Sabino: retire responsible, ethical, consensual and compliant AI as the assurance. Not because responsibility is bad, but because the label does work it cannot support: responsible ownership of a bull in an apartment still leaves you with a bull in the apartment. AI is a force amplification technology. Create value and it creates more; extract value and it extracts more; screw someone and it screws them better. Given a bigger stick, people swing it. So there is no responsible use, no ethical use, no compliant use. There is only whether your value is safe, and Sabino applies weapon and ordnance safety models rather than ethics frameworks to get at it. Sam: retire \"human in the loop\" as if it were quality assurance. A person pressing the button instead of a machine pressing the button is not protection from anything. Press it forty-seven times before lunch and the forty-eighth press carries no awareness at all. There are real cultural reasons to keep a person there, and the ownership of the process is genuinely human, but the loop is not a control. Sabino's version is sharper: the human in the loop is the engineer at the Chernobyl dashboard, riding the dials without being a nuclear engineer. Sam Rogers + Sabino Marquez", "orientation": "", "topics": [], "aliases": [] }, { "type": "episode", "url": "/episodes/ep-003/", "title": "Ep 3: Job Search in the Age of AI", "meta": "2026-07-17 · Sam Rogers + Lee Rodrigues + Christine Rodrigues", "text": "Guest Christine Rodrigues and cohost Lee Rodrigues on the job hunt after AI: when polished resumes turned into noise, why the corner cases still get hired, and how to keep your thought sovereignty. What evidence of a candidate still means anything when AI can manufacture polish? What hiring signal has become noise, and what human evidence will replace it? 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. 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. Sam Rogers + Lee Rodrigues + Christine Rodrigues", "orientation": "", "topics": [], "aliases": [] }, { "type": "episode", "url": "/episodes/ep-002/", "title": "Ep 2: Find Your Signal, Find Your Subtraction", "meta": "2026-07-10 · Sam Rogers", "text": "Solo methodology episode. What a signal and a subtraction actually are, and how to find your own, with or without AI. Which recurring commitments still deserve to exist? Which recurring obligation has no exit condition, and who is supposed to check it? Mandate drift. What wears us down is not The Big Project, it is the recurring drag of obligations that quietly drifted onto our plates. Nobody important sat you down and assigned the standing meeting, the weekly report, the inherited sync; a need existed once and the calendar commitment never left. It came straight out of a week of real change-management conversations, which is the whole test: recent, specific, first person. If a stranger could have posted it on LinkedIn, it is not yours yet. Kill the for-loop meetings. A recurring meeting is a for loop: it runs on a fixed count, and nobody asks at iteration 28 whether it still deserves to run. A do-while loop rechecks its exit condition every pass and stops when the goal is met; it writes its own ending. So the subtraction is not \"cancel your boring meetings\" (generic, hard to apply), it is convert for-loop meetings into do-loop meetings. I dropped three recurring Q3 meetings and delegated one up the org chart. Try it: open your calendar, and for each recurring event ask what its exit condition is and who checks it. No exit condition and no checker means it is a for loop running for someone else. Give it a return statement this week. Sam Rogers", "orientation": "", "topics": [], "aliases": [] }, { "type": "episode", "url": "/episodes/ep-001/", "title": "Ep 1: Context Over Capability", "meta": "2026-07-03 · Sam Rogers + Lee Rodrigues", "text": "The debut. The newsletter becomes a live show. When an AI system disappoints, do you need a better model or better context? Where are you buying more capability to compensate for missing context? Lee: Context Over Capability. A one-page outline is the only thing that reliably shows you where the model is making stuff up. As capability climbs, the bottleneck stops being what the model can do and becomes what it knows about your situation. Sam: Supply and demand of work outputs and trust. Output volume is rising while trust in output falls. AI didn't invent slop; it dropped the cost of producing plausible-looking work to zero, so the slop that was always there is now everywhere and harder to spot. Lee: The Seven-Page Answer. Force it down to a one-page outline before you trust it. Plain text. No formatting, no bars, no just-in-case context. The one-page test exposes what the polish was hiding. Sam: Stop fixing slop with tools. Subtract the thing that actually makes slop: rewarding work that looks done over work that is reliable. The box-checking process is the target, not AI. Sam Rogers + Lee Rodrigues", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/swiss-army-knife-or-scalpel/", "title": "Issue 068: Swiss Army Knife or Scalpel?", "meta": "2026-09-13", "text": "Ankit Patel asked to buy only the features he used. The answer was the whole package, for a year, or nothing.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/no-wrong-answers/", "title": "Issue 067: No Wrong Answers", "meta": "2026-09-06", "text": "When we can't name the wrong result, we're not measuring. We're describing our own efforts back to ourselves.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/dig-the-second-hole/", "title": "Issue 066: Dig the Second Hole", "meta": "2026-08-30", "text": "Run a test, run it twice, and don't let the tool define the problem.", "orientation": "The Sunday issue applies split-half troubleshooting to AI: diagnose from both ends, ask why the tool is there, and make guardrails part of the architecture.", "topics": [ "double loop learning", "agent guardrails", "troubleshooting", "problem framing" ], "aliases": [ "verify both ends", "dig the second hole", "agent guardrails", "instructions posing as guardrails" ] }, { "type": "newsletter", "url": "/newsletter/no-self-checkout/", "title": "Issue 065: No Self-Checkout", "meta": "2026-08-23", "text": "AI made ideas free and reach free, so the biggest room stopped being worth winning. The scarce move is influence.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/measuring-500-nuclear-engineers/", "title": "Issue 064: Measuring 500 Nuclear Engineers", "meta": "2026-08-16", "text": "How to measure what everyone says can't be measured", "orientation": "The written companion to Episode 7 turns uncertainty reduction into a practical test: do not run a pilot unless its result could change a decision.", "topics": [ "measurement", "uncertainty", "AI pilots", "decision thresholds" ], "aliases": [ "measure uncertainty", "AI pilot decision", "measuring 500 nuclear engineers", "calibration" ] }, { "type": "newsletter", "url": "/newsletter/the-quilt-and-the-minefield/", "title": "Issue 063: The Quilt and the Minefield", "meta": "2026-08-09", "text": "The AI laws already in force are all over the place", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/guilty-until-proven-human/", "title": "Issue 062: Guilty Until Proven Human", "meta": "2026-08-02", "text": "The writer is not notified. The appeal, if you want one, arrives after the label already exists.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/the-bull-in-the-apartment/", "title": "Issue 061: The Bull in the Apartment", "meta": "2026-07-26", "text": "Your AI policy is a procurement document wearing a safety costume. The first question is what you have already put within reach.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/the-crowded-bar/", "title": "Issue 060: The Crowded Bar", "meta": "2026-07-20", "text": "AI made every resume look great, which is exactly why looking great stopped meaning anything. Put the human back.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/the-for-loop-meeting/", "title": "Issue 059: The For-Loop Meeting", "meta": "2026-07-12", "text": "Your calendar is full of loops that forgot to ask whether they should still run. Here is how to find one and end it.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/all-horsepower-no-radiator/", "title": "Issue 058: All Horsepower, No Radiator", "meta": "2026-07-05", "text": "A new frontier model every 11 days. The failures still aren't about smarts. They're about context.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/audit-thyself/", "title": "Issue 057: Audit Thyself?", "meta": "2026-06-29", "text": "AI models can't grade their own blind spot very well. People don't either.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/vocabulary-debt/", "title": "Issue 056: Vocabulary Debt", "meta": "2026-06-22", "text": "One word naming four systems used to just confuse people. Now it makes your AI confident, precise, and dead wrong.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/teamwork-makes-the-dream-work/", "title": "Issue 055: Teamwork Makes The Dream Work", "meta": "2026-06-15", "text": "Two models and a judge beat the best single frontier model. The lift lives in the reconciling, not the genius. Same law runs your org.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/rent-or-own/", "title": "Issue 054: Intelligence: Rent or Own?", "meta": "2026-06-08", "text": "Open-weight is two quarters behind the frontier and closing. For anything you'll run indefinitely, the math has tipped, and the buying window looks a lot like 2020.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/solution-shapes/", "title": "Issue 053: Solution Shapes", "meta": "2026-06-01", "text": "Organizations buy the shape they already know how to operate, then wonder why it never fits the problem.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/on-time-or-ready/", "title": "Issue 052: On Time or Ready?", "meta": "2026-05-25", "text": "The clock says 7:58 PM. But the curtain doesn't move until everyone is ready.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/permanent-expired/", "title": "Issue 051: Permanent = Expired", "meta": "2026-05-18", "text": "All information is perishable. AI info ages like milk.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/50-arrows/", "title": "Issue 050: 50 Arrows", "meta": "2026-05-11", "text": "Nearly one year in, I still don't know what I have.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/turnstile-or-on-the-list/", "title": "Issue 049: Turnstile or \"On The List\"?", "meta": "2026-05-04", "text": "Stripe shipped agent payments. The language for what agents may do was already open and free.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/last-to-know/", "title": "Issue 048: Dashboards Are Last to Know", "meta": "2026-04-27", "text": "The question 'what are you doing?' assumes singular attention. Look for what's running, not what fits the dashboard.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/behind-the-wall/", "title": "Issue 047: Behind the Wall", "meta": "2026-04-21", "text": "David Soria Parra, MCP co-creator, at AI Engineer Europe this week: \"2026 is the year agents go to production.\" Not a prediction.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/too-potent-to-ship/", "title": "Issue 046: Too potent to ship", "meta": "2026-04-14", "text": "Anthropic tested Claude Mythos, found reckless and deceptive behavior, and chose not to release it broadly. The question is whether your own deployments could survive that discipline.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/the-gap-nobody-owns/", "title": "Issue 045: The Gap Nobody Owns", "meta": "2026-04-06", "text": "Right now, somewhere in your organization, four different teams are managing four different slices of AI risk.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/shared-operational-language/", "title": "Issue 044: Shared Operational Language", "meta": "2026-03-30", "text": "Human capability and agent capability are now described in the same format, so work gets allocated by what a task needs, not by whose name is on it.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/skills-have-use/", "title": "Issue 043: Skills: Have < Use", "meta": "2026-03-23", "text": "Two weeks ago we flagged the vocabulary fork. Last week we showed that skills were never guarantees to begin with.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/skills-are-bets-not-gates/", "title": "Issue 042: Skills Are Bets, Not Gates", "meta": "2026-03-16", "text": "Certifications and installable agent skills raise the odds of a good outcome, but neither guarantees it, and mistaking a bet for a guarantee is where costly surprises hide.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/skills-aint-what-they-used-to-be/", "title": "Issue 041: Skills Ain't What They Used to Be", "meta": "2026-03-09", "text": "For as long as most of us have been working, \"skills\" meant one thing: what people can do.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/the-verb-is-the-tell/", "title": "Issue 040: The Verb is the Tell", "meta": "2026-03-02", "text": "Listen closely to how people at your org talk about AI. The verb they reach for tells you everything.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/the-rulebook-doesnt-exist-yet/", "title": "Issue 039: The Rulebook Doesn't Exist Yet", "meta": "2026-02-23", "text": "People are asking for AI rules before the rulebook can exist. Rules follow practice. Practice is still forming.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/the-understanding-trap/", "title": "Issue 038: The Understanding Trap", "meta": "2026-02-16", "text": "Preparation used to come before action, but AI moved faster than any course could teach, so builders now understand tools by using them, not before.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/not-my-job-is-predictive/", "title": "Issue 037: \"Not My Job\" is Predictive", "meta": "2026-02-09", "text": "When AI adoption hits 'That's not my job,' you're looking at an org design problem, not a skills gap.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/the-weight-of-avoidance/", "title": "Issue 036: The Weight of Avoidance", "meta": "2026-02-02", "text": "You know that feeling when you've been avoiding the bathroom scale?", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/the-handoff-gap/", "title": "Issue 035: The Handoff Gap", "meta": "2026-01-26", "text": "Generation cost has dropped to near zero, but nobody assigns an owner, so orphaned AI artifacts pile up with no one left to defend or delete them.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/rightness-at-resolution/", "title": "Issue 034: Rightness at Resolution", "meta": "2026-01-19", "text": "AI is right at the wrong resolution -- and your validation infrastructure was built for a different kind of system.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/the-copilot-problem/", "title": "Issue 033: The Copilot Problem", "meta": "2026-01-12", "text": "An ancient failure mode AI is exposing everywhere.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/the-one-hour-year/", "title": "Issue 032: The One-Hour Year", "meta": "2026-01-05", "text": "When individuals outrun institutions, the ground shifts for everyone.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/the-only-skill-that-matters/", "title": "Issue 031: The Only Skill That Matters", "meta": "2025-12-29", "text": "The only skill that matters is not prompting.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/what-survived-2025/", "title": "Issue 030: What Survived 2025", "meta": "2025-12-22", "text": "A survivorship filter over 29 issues: the ten frameworks that held up under real deadlines, audits, and organizational friction in 2025.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/ai-lives-in-the-default-path/", "title": "Issue 029: AI Lives In The Default Path", "meta": "2025-12-15", "text": "Most orgs are trying to drive AI adoption with tools, training, and enthusiasm.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/typecasting-ld-as-the-ai-hype-department/", "title": "Issue 028: Typecasting L&D as The AI Hype Department", "meta": "2025-12-08", "text": "Organizations are asking L&D to lead AI readiness, but what it usually becomes is tool tours, prompting tips, and satisfaction scores instead of real capability.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/managers-over-models/", "title": "Issue 027: Managers over Models", "meta": "2025-12-01", "text": "Why your AI strategy lives (or dies) in 1:1s.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/friction-creates-shape/", "title": "Issue 026: Friction Creates Shape", "meta": "2025-11-24", "text": "Skeptics, hesitators, and governance sticklers are not blocking AI adoption. Their resistance is what keeps it from shaking apart before it is ready.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/synthetic-trust/", "title": "Issue 025: Synthetic Trust", "meta": "2025-11-17", "text": "Confident tone increasingly substitutes for verified reasoning, letting AI-generated decisions sound right long before anyone can defend them.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/human-interface-fatigue/", "title": "Issue 024: Human Interface Fatigue", "meta": "2025-11-10", "text": "The more “human-friendly” our tools become, the more mental overhead they create.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/confidence-calibration/", "title": "Issue 023: Confidence & Calibration", "meta": "2025-11-03", "text": "Unpacking confidence saturation in AI, separating smarts from sound, and rewiring rewards.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/immature-ai-maturity/", "title": "Issue 022: Scoring Without Understanding", "meta": "2025-10-27", "text": "AI capability numbers are spreading faster than comprehension.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/success-paice-work/", "title": "Issue 021: Success at the PAICE of Work", "meta": "2025-10-20", "text": "Yeah, lots happened in the last week (again): specifically over in Anthropic-land where they announced Skills, Web, MS365 integration, and Haiku 4.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/turbulent-measures/", "title": "Issue 020: Turbulent Measures", "meta": "2025-10-13", "text": "Readiness scores and maturity dashboards multiply while the real friction between people and AI goes unmeasured entirely.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/signals-subtractions-special-edition/", "title": "Issue 019: Personal Stories of Personalization", "meta": "2025-10-06", "text": "A former teacher and a video producer, both new to AI, show what personalization instinct looks like at machine scale.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/trust-gap/", "title": "Issue 018: Planning for AI Adoption in 2026", "meta": "2025-09-29", "text": "Updating the Diffusion of Innovation model for AI, strategizing for trust-building, and navigating AI adoption hurdles.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/tools-takeover-talent/", "title": "Issue 017: Tools Takeover Talent", "meta": "2025-09-22", "text": "Computer, translator, and curator were once job titles before they became tools, and those roles moved up instead of vanishing. The same shift is now hitting coaches and analysts.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/talking-to-ai-like-its-a-computer/", "title": "Issue 016: Talking to AI Like It’s a Computer", "meta": "2025-09-15", "text": "We hold AI to a standard of perfection we never demanded from coworkers, then act shocked when it disappoints us. Treating it like a new colleague gets better results.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/ai-breaks-the-training-workflow/", "title": "Issue 015: AI Breaks the Training Workflow", "meta": "2025-09-08", "text": "Training that waits for a tool rollout to finish cannot keep up with AI's pace anymore. Learning and doing need to run in parallel, like a surgical team acting at once.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/five-percent-success/", "title": "Issue 014: 5% Success", "meta": "2025-08-26", "text": "MIT found 95 percent of organizations get zero return from generative AI, echoing decades-old change management failures. The fix is cutting weak pilots fast.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/gpt-5-changes-the-game/", "title": "Issue 012: GPT-5 Changes the Game (Mostly Doesn't)", "meta": "2025-08-18", "text": "The long-awaited iteration of the model that started the public love/hate affair with AI landed last week. And (predictably) the reaction is still love/hate.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/security-isnt-about-adding-more/", "title": "Issue 013: Security Isn't About Adding More", "meta": "2025-08-16", "text": "More dashboards, more alerts, more vendors: every addition security teams bolt on quietly widens the attack surface they meant to shrink.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/that-funnel-of-yours-its-straining/", "title": "Issue 011: That Funnel of Yours? It's Straining", "meta": "2025-08-07", "text": "Your sales funnel is losing leads before any salesperson speaks to anyone.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/signal-governance-is-now-a-ux-problem/", "title": "Issue 010: Governance is Now a UX Problem", "meta": "2025-07-31", "text": "AI is outrunning internal governance frameworks, and the fix starts at the UX layer.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/open-auditable-ai-stacks-just-shipped/", "title": "Issue 009: Open, auditable AI stacks just shipped!", "meta": "2025-07-17", "text": "Open, auditable AI stacks just shipped. The implications are bigger than the headlines suggest.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/align-to-the-spec-not-to-the-prompt/", "title": "Issue 008: Align to the Spec, Not to the Prompt", "meta": "2025-07-17", "text": "The shift from prompt engineering to spec alignment -- why crafting the spec with care is what actually matters.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/culture-is-a-technical-dependency/", "title": "Issue 007: Culture Is a Technical Dependency", "meta": "2025-07-14", "text": "Tech teams ship AI features in weeks. The human systems around them still run on quarterly cadences.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/when-ai-outpaces-senses/", "title": "Issue 006: Machines With Alien Contexts", "meta": "2025-07-07", "text": "What happens to a custom creative moat when AI can replicate the style in minutes.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/declare-independence-from-vanity-metrics/", "title": "Issue 005: Declare Independence from Vanity Metrics", "meta": "2025-06-30", "text": "Dashboards full of vanity metrics create the illusion of alignment -- and AI is making the problem shinier, not better.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/the-org-chart-is-still-fighting-the-last-war/", "title": "Issue 004: The Org Chart Is Still Fighting the Last War", "meta": "2025-06-24", "text": "Org charts were designed for accountability, not adaptability. Agentic AI is exposing that limit fast.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/tool-choice-is-becoming-strategy/", "title": "Issue 003: Tool Choice Is Becoming Strategy", "meta": "2025-06-18", "text": "Tool choice is no longer just procurement -- it encodes organizational values, posture, and readiness for change.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/ai-moving-faster-than-organizations/", "title": "Issue 002: AI Initiatives Are Moving Faster Than Their Organizations Can", "meta": "2025-06-12", "text": "AI tools are multiplying fast, but decision rights, governance, and coordination mechanisms are lagging behind.", "orientation": "", "topics": [], "aliases": [] }, { "type": "newsletter", "url": "/newsletter/the-automation-arms-race-has-no-finish-line/", "title": "Issue 001: The Automation Arms Race Has No Finish Line", "meta": "2025-06-06", "text": "Nearly every team is layering 3 or 4 AI tools deep -- but the coordination infrastructure hasn't caught up.", "orientation": "", "topics": [], "aliases": [] }, { "type": "page", "url": "/host/", "title": "Host", "meta": "", "text": "The one seat filled every episode. Sam Rogers, the host of Signals & Subtractions. What the host does on the show, how he got here, and where else to find his work." }, { "type": "page", "url": "/cohosts/", "title": "Cohosts", "meta": "", "text": "A rotating seat, instead of a sidekick. The rotating cohosts of Signals & Subtractions. Who they are, how they pressure-test each episode's central choice, and how the rotation works." }, { "type": "page", "url": "/guests/", "title": "Guests", "meta": "", "text": "Come tell us what broke. Who makes a great guest on Signals & Subtractions, how firsthand evidence sharpens an accountable operator's decision, and how to get on the show." }, { "type": "page", "url": "/sponsors/", "title": "Sponsors", "meta": "", "text": "One segment. One product. One audience worth the slot. Sponsor a segment of Signals & Subtractions. One product, one clear read, in front of operators and AI buyers who came to think." }, { "type": "page", "url": "/fans/", "title": "Fans", "meta": "", "text": "Bring the choice, not the noise. Bring a consequential AI choice to Signals & Subtractions, use each episode to sharpen it, and share the result well." }, { "type": "page", "url": "/discuss/", "title": "Discuss a choice", "meta": "", "text": "Bring the choice, not the pitch. Bring a consequential AI choice from your work to Signals & Subtractions and start a focused conversation." }, { "type": "page", "url": "/rights/", "title": "Rights and reuse", "meta": "", "text": "Read, link, cite. Ask before copying or redistributing. Copyright and permitted discovery use for Signals & Subtractions pages, transcripts, newsletters, APIs, and agent-facing files." }, { "type": "page", "url": "/machine-readable/", "title": "Machine-readable by default", "meta": "", "text": "Everything here is readable by a machine, on purpose. Every public fact about Signals & Subtractions is published in a form a machine can read: plain-text briefs, structured data, a static JSON API, feeds, and an MCP server you can run in one line." }, { "type": "page", "url": "/samples/", "title": "SigSubSamples Cheatsheet", "meta": "", "text": "The twenty strongest pairs from fifty-seven issues, ranked. Read the top ten and you will feel the shape. Twenty of the strongest signal and subtraction pairs from the newsletter archive, picked for clarity and range. A fast calibration sheet for guests, cohosts, and anyone bringing their own pair on air." }, { "type": "page", "url": "/guest-terms/", "title": "Guest terms", "meta": "", "text": "Ten things, plainly. The terms every Signals & Subtractions guest agrees to before recording. Plain language, ten points, no lawyers." }, { "type": "page", "url": "/sponsor-terms/", "title": "Sponsor terms", "meta": "", "text": "Ten things, plainly. The terms every Signals & Subtractions sponsor agrees to before a read runs. Plain language, ten points, no lawyers." }, { "type": "page", "url": "/prep/", "title": "Guest prep", "meta": "", "text": "" }, { "type": "page", "url": "/prep-cohost/", "title": "Cohost onboarding", "meta": "", "text": "" }, { "type": "page", "url": "/takeaways/name-the-human/", "title": "Name the Human", "meta": "Name the Human", "text": "If this is wrong, whose name is on it? An accountability inventory for everything that acts without a human deciding first. One row per surface, one question per row: if this is wrong, whose name is on it? A copy-paste prompt, a read-only script, and a one-page table." }, { "type": "page", "url": "/find-yours/", "title": "Find your signal and your subtraction", "meta": "", "text": "Every episode carries two signals worth watching and two subtractions worth making. Here is how to find yours. How to find one signal worth watching and one subtraction worth making, for guests, cohosts, and viewers of Signals & Subtractions. A copy-paste AI prompt, a fifteen-minute method, and fifty-plus worked examples." } ] }