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The Signal

The Signal

Autor: Chris and Reuven
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The Signal is a weekly podcast hosted by Chris Grimes and Reuven Gorsht, exploring how AI, automation, and new operating models are reshaping high-stakes industries like mortgage, energy, healthcare, and beyond. Through candid conversations with founders, operators, lenders, and technology leaders, the show digs into what is actually changing on the ground — the workflows being reinvented, the bottlenecks being broken, and the decisions leaders are making as their industries adapt. Each conversation is designed for practical insight: what is working, what is still hype, and what the next wave of industry transformation looks like.Copyright 2026 Chris and Reuven Ekonomia Zarządzanie i przywództwo
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  • AI Finance: The Last-Mile Transformation and ROI

    AI Finance: The Last-Mile Transformation and ROI
    Oct 6 2026
    Applying for a mortgage inside ChatGPT: what lenders loseChris Grimes went looking for a raincoat before a trip to Scotland, typed what he wanted into ChatGPT, and bought one without ever opening a shop's website. Applying for a mortgage inside ChatGPT is the same move with three more zeroes on it, and it is already live. Better now takes a mortgage application without the borrower leaving the chat window, and at the other end of the same business the model companies are pushing into the parts of lending that were supposed to be too complex and too regulated to touch.WHAT CHANGED WHEN THE MORTGAGE APPLICATION MOVED INSIDE CHATGPTThe enterprise end moved first, and quietly. Google announced a partnership with a large financial institution, Microsoft has done the same, and Anthropic is doing it too. Reuven Gorsht's reading of why is unsentimental. Companies valued in the trillions after four or five years have to show a buyer that a dollar in returns three or five or ten, and the strength of the core model alone no longer does that, because most work gets done perfectly well on the cheaper models. So the labs are hiring the people who already have the relationships. Reuven has watched executives from SAP, Oracle and Salesforce move across to companies like Anthropic, arriving with a Rolodex and a familiar face at the office of the CIO.The consumer end moved more loudly. Better's application runs inside ChatGPT, so a borrower starting a mortgage there never opens the lender's own interface. Chris expects a dedicated consumer finance surface to follow, with a connector like Plaid pulling the full picture together so the assistant can give advice against real balances rather than guesses. His worry is not that the labs want to own the application layer. It is that they are dissolving it, folding what used to be somebody's product into something the assistant simply knows how to do.WHY THE ADVICE LAYER IS THE EXPOSED ONEReuven's argument is that consumers take the path of least resistance every time, and that the evidence is already in. He watches Amazon vans on his street while the stores in his local plaza close. He bought his wife a Tesla, a purchase in the mid sixty thousands, entirely through an app, and has never met anybody from the company. Keynote speakers at the New England Mortgage Bankers Association put the same point to the industry more bluntly, which is that the trade says borrowers want a relationship when what they actually want is the lowest rate.Chris has held a version of this since before he left brokering. His view is that the mortgage was never the thing anybody was buying. The house is. The mortgage is the monthly payment you stop thinking about the week after it funds, which makes the advice wrapped around it, rather than the loan itself, the part that is genuinely exposed. Neither host believes people vanish from a purchase this stressful. Both of them get stuck on the same question, which is where exactly those people are supposed to stand once the friction is gone.THE GAP BETWEEN AI SPEND AND AI RETURNThe last stretch of the episode is about money, and it is where both hosts turn sceptical. Reuven cites a presentation from one of the world's largest asset managers on the scale of capital expenditure going into AI infrastructure this year, the gigawatts of data centre capacity still to be built, and what a single investor's portfolio companies are spending with Anthropic annually. The figures are striking, and they arrive with a caveat the episode states out loud, which is that the firm presenting them has a great deal of its own capital deployed in exactly what it is describing.Chris's question is what happens when spending on that scale meets an antitrust regime and a compute shortage at the same time. Reuven's question is simpler and more uncomfortable. He asks what anybody is actually getting back, puts a figure on how much of his own AI output he never uses, and extends it to companies doing the same thing with far larger budgets. WHAT YOU'LL TAKE AWAY- The model companies are entering lending from the borrower's end and the capital markets end at once, which puts the pressure on the middle of the stack rather than on any single product.- What a borrower is buying is the house, so the advice wrapped around the mortgage is the layer most exposed when the friction disappears.- Price and friction are the two things either host says a consumer genuinely chooses on, and which of the two a lender is worse at.- Money going into AI is not the same as value coming out, and the honest version of that gap is easier to hear from somebody describing their own usage than from any vendor.CHAPTERS(00:00) Every Major AI Lab Has Launched a Finance Tool(00:59) Why the Model Companies Need an ROI Story(03:00) Enterprise Partnerships and the App Layer(07:53) What Your LLM Already Knows About You(10:16) Buying Through the Chat Window(14:05) What Borrowers Say They Want Versus What They Pick(16:59)...
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    37 min
  • The Moat Is Not the Software

    The Moat Is Not the Software
    Sep 29 2026
    Scott Larter runs BrokerBot on licensed Canadian property data, and he came to mortgage from the data side, at Opta, Verisk and FCT. Forty-four minutes in, he puts a three to five year clock on his own company's defensibility and names what he thinks is actually holding the door shut. Chris Grimes and Reuven Gorsht push on what is left to defend once a competitive analysis that used to take a team weeks takes twenty minutes and a coffee, and once the same intelligence sits in everybody's pocket. What the three of them land on is narrower than what the industry has been selling, and none of it is the software.Why AI is not a competitive advantage in mortgage techIn 2018 Scott Larter put a big orange booth on a trade show floor in Montreal, knew one person in the room, and was there to find out whether property data had any place in mortgage lending at all. Eight years later he runs BrokerBot, and he is arguing about a different question. What counts as a competitive advantage in mortgage tech now that the cost of building the product has fallen to almost nothing?Scott came to mortgage from the data side, at Opta, Verisk and FCT, which is why he treats a broker's client book as a dataset in an industry that has always treated it as a rolodex. He is talking to Chris Grimes and Reuven Gorsht, who between them have built a lending decisioning platform and a real estate closing business. This page sets out what the three of them worked through and which questions they left open. What they conclude is on the tape.WHY THERE ARE SUDDENLY HUNDREDS OF VENDORSReuven's framing is that the economics of starting a software company have inverted. When he started MoveSnap and when Chris started FundMore, you validated an idea hard first, because you were about to spend at least a million dollars in cash or in sweat equity. Now anybody can build a working prototype in an afternoon, so the constraint has moved.He brings a figure back from a panel of venture investors he sat in on a few weeks before recording. Their position was that they are still funding roughly the same number of deals a year that they always have, while the denominator underneath that number has grown by something like a hundredfold. Scott's experience points the same way from the other side of the table. In his data years he was approached by ten to twenty startups a year looking for Canadian property data to build on, and he is blunt that cheap Canadian property data does not exist in this country.Chris supplies the uncomfortable part. He argues that low adoption among lenders is not a temporary lag, that it has stayed low, and that a proof of concept which never reaches production is not evidence of anything. WHAT COUNTS AS A COMPETITIVE ADVANTAGE IN MORTGAGE TECH NOWReuven's answer begins by taking something away, in one flat sentence that is the most quotable line on the tape. What he says is left afterwards sits in two places. The first is distribution, meaning a network or a community you can actually put a product in front of. The second is the judgment to know which output is any good, which he ties to twenty-five years of knowing which advertisement is off message and which one will get you in trouble with a regulator.Scott gives the clearest worked example. He ran a competitive analysis across the Canadian and US markets and turned it into a deck for his team, and he puts a figure on how long that took against the weeks it used to take. A moat is not a moat if it can be replicated, and that today almost everything can be. You can point a model at a competitor and have it decompose what they are doing in fifteen minutes.He also raises the problem nobody in the industry enjoys looking at. When you feed your own working into a general purpose tool, you may be sharpening the thing a competitor uses against you next quarter. He puts it as a question, because he does not have the answer either.THE END OF THE PURE PRODUCT COMPANYScott asks the hosts a direct question about their own businesses. If a junior person with some AI ability can now build internally, for a salary, what a vendor charges in licensing, does that change how a lender evaluates products like theirs?Chris gives ground immediately and says that it will. Then he draws a line, and where he draws it is the argument of this section. He does not think vendors get wiped out. He thinks the pure product company stops existing unless it owns one very specific problem in a very large space, and that what replaces it is a partner who brings most of the way and then works with the customer on the rest. He puts the split in proportions and calls the result a pivot toward services. Whether he is right is the thing to listen for.Reuven works the same idea through a different industry. He describes replacing an advertising agency brief with a Sunday morning and a model, and is precise about what he did not get, which was thirty years of agency expertise and somebody else carrying the ...
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    44 min
  • The AI Edge: Transforming Non-QM Lending

    The AI Edge: Transforming Non-QM Lending
    Sep 22 2026
    HOW DO YOU AUTOMATE UNDERWRITING WHEN THERE IS NO RULEBOOKHere is the kind of question Sarah Gonzalez was willing to hand to software. In the example she uses, a matrix calls for an 800 FICO on a million-dollar loan and caps the loan-to-value at 75, and the file in front of the underwriter arrives at 76. That one has an answer before anyone reads the file. Almost nothing else in her book does, because almost everything Logan Finance writes is non-QM, and there is no standard guideline set sitting there waiting to be encoded.Gonzalez is Chief Innovation Officer at Logan, and she has had to answer the automation question in production rather than on a panel.CAN YOU AUTOMATE UNDERWRITING WITHOUT AUTOMATING THE CREDIT DECISIONLogan built a tool called Aura. A partner supplies the baseline rules engine, and the rules themselves were written in-house and belong to Logan. That distinction is worth holding onto, because renting an engine and owning the rules inside it is a different asset from renting the whole thing.What Aura does is narrow on purpose. It reads hundreds of pages of guidelines so that an underwriter does not have to page through matrix after matrix, it reports what sits inside the box and what sits outside it, and then it stops. It does not approve anything and it does not decline anything. Gonzalez is strict about that boundary in a way most vendors selling into this market are not.If the tool is already reading the guidelines, does the judgment itself move to the machine within a year or two? WHY SHE DOES NOT START WITH THE ORG CHARTHer panel at the HousingWire AI Summit was about the future org chart, and her answer inside her own organization is that she does not start there. She starts with culture and with the experience she wants the customer or the employee to have, then asks which parts of that work can be automated and which parts require judgment.WHAT SHE WANTS FROM VENDORS, AND IS NOT GETTINGThe sharpest stretch of the conversation is not about her own build at all. It is about everyone selling to her.She arrived at the recording from a week of vendor demos. Her question at the end of each one was not about the model, it was about the implementation timeline and the implementation cost, and the answers kept coming back at six months and a price she thought the work no longer justified.WHAT YOU'LL TAKE AWAY- The automatable surface in non-QM is eligibility, not decisioning, and the two are worth separating in writing before anyone builds anything- A rules engine you rent with rules you own is a different asset from a platform you rent entirely- The honest return on an eligibility engine is underwriter throughput, and hers is 20 to 25%- Eliminating a task and eliminating a person are two separate decisions, and conflating them is a leadership failure rather than a technology one- If a vendor cannot tell you why implementation takes six months, that is the question to keep askingCHAPTERS(00:00) What AI can't do in non-QM lending(01:35) An operator handed the innovation job(03:29) Taking a technology role without a technology background(06:25) Automating a lender with no rulebook(09:22) Will judgment ever move to the machine(11:29) Why she doesn't start with the org chart(12:24) Eliminating task, not judgment(14:01) What the manager's job becomes(16:27) Why vendor AI still takes six months(21:18) Inside Aura, eligibility not decisioning(22:48) A 20 to 25% lift for underwriters(25:07) The right partner at the right costMENTIONED IN THIS EPISODELogan Finance, the non-QM lender where Sarah Gonzalez is Chief Innovation OfficerAura, Logan's eligibility engine, built on a partner's rules engine with rules Logan wrote and ownsHousingWire AI Summit, the Dallas event in August 2026 where the hosts met herThe Mortgage Collaborative, whose summit she had attended the week before recordingIDP and OCR, the document technologies she argues get folded into the word AIClaude, named as an example of what per-seat AI costs a mid-size lender without change management around itRE/MAX, Redfin and Rocket, named by Reuven as evidence the ground is shifting upstream and downstreamABOUT THE GUESTSarah Gonzalez is Chief Innovation Officer at Logan Finance. She has spent thirty years in mortgage, including chief operating and president roles at earlier companies, and now works on where technology genuinely changes how lending gets done rather than where it only adds a step.LinkedIn: https://www.linkedin.com/in/sarahbatangan78/YOUR HOSTSChris Grimes is CEO of FundMore.Reuven Gorsht is CEO of Deeded and The Variable.One builds the tool. One absorbs the friction.LISTEN AND SUBSCRIBECaptivate — https://the-signal.captivate.fm/Apple Podcasts — https://podcasts.apple.com/us/podcast/the-signal/id6805026259Spotify — https://open.spotify.com/show/59UmJswLjpRO5p7vUwr7hVYouTube — https://www.youtube.com/@thesignalreuvenchrisNew episodes every Tuesday.
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    27 min
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