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Free Personalized AI Moderator Trial “If you want to generate alpha, you can’t be sharing all this information.” “That interviewer needs to think and feel and act like you do. It needs to have all the context that you do.” “We’re just making primary research faster, cheaper, and embedded. If I had to summarize the whole thing, that’s what Qualitate is doing.” Sagar Kadakia has spent nearly 20 years in primary research. He joined Tom DelVecchio at ETR in 2011, ran buy-side survey work for six years, and later built research and data products at 7Park Data and CB Insights. In 2023, as ChatGPT and voice capture crossed human parity, he founded Qualitate: an AI interviewer that runs 45–65 minute expert conversations autonomously and is grounded in what he describes as the fastest-growing AI-moderated interview library in the world. Crew Capital wrote the first $1M check when few others wanted to fund a company building its own expert panel. This week, Qualitate announced a $25M Series A led by Insight Partners, with net revenue retention above 300% and run-rate revenue up 10x in nine months. Sagar walks through Qualitate’s two products: a proactive library built using Qualitate’s own capital and questions, and a private channel-check product where clients bring their own questions, interviewer, library, and internal inputs — without that proprietary information being mixed back into the broader system. We also get into why the architecture matters: grounding instead of retraining for every new model, persistent memory so call five knows calls one through four, and transcripts structured so investors can query them for time series instead of simply searching. “You can search through transcript files, but you can’t actually query that data. On Qualitate, you can actually query transcripts.” “We don’t train or fine-tune anything. Because if you do that and the next version comes out, all of that work you just did goes to zero. We ground it.” 💡 This episode is presented by:Fiscal.AI — Delivering Modern Financial Data Infrastructure. 15% discount with code PITCH: ⁠https://fiscal.ai/?via=pitch⁠ Highlights: (00:33) Twenty years in primary research: ETR, 7Park Data, CB Insights (01:40) ChatGPT, voice capture and “continuous expert intelligence” (03:20) $25M Insight Partners Series A, NRR above 300% (04:34) Qualitate’s proactive AI-moderated research library (09:06) Private channel checks: your questions, interviewer and library (12:57) Uploading transcripts and memos into the interviewer (15:15) Human bias vs. AI-moderated interviews (21:08) The $2,000 expert call problem (21:30) One-third to one-quarter of the price; 20–40 calls in days (23:18) Building an expert panel with agents (26:35) Do the first two calls yourself, then hand off 50 (29:07) Structured, queryable transcripts and longitudinal analysis (31:45) Hedge funds moving channel checks to Qualitate (47:12) Continuous intelligence: AI, humans, simulations and synthetics (51:12) Faster, cheaper, embedded primary research (52:23) Sagar’s biggest entrepreneurial mistakes Topics: Expert Networks, AI Interviewing, Primary Research, Channel Checks, Qualitate, GLG, Guidepoint, AlphaSense, Tegus, Snowflake, Insight Partners, Grounding vs. Fine-Tuning, Proprietary Data, Agents, Hedge Funds, Private Equity Diligence Research Sponsors: Oxford Data Plan - The Home of Alternative Data. The Home of Alternative Data Carbon Arc — The Infrastructure for the AI Economy. 30 days free with code PTPM30: ⁠ Pitch The PM: Substack: ⁠ https://pitchthepm.substack.com⁠ LinkedIn: ⁠https://linkedin.com/in/doug-garber-42aa508⁠ Sagar Kadakia: LinkedIn: ⁠https://linkedin.com/in/s-kadakia⁠ Qualitate: ⁠https://qualitate.io⁠ Not Investment Advice.

Carmen Li, CEO of Silicon Data and Compute Exchange, joins the show to explain how compute is becoming a brand-new asset class. A former energy options market maker at DRW who later ran global data integrations at Bloomberg, Carmen was pre-seeded by DRW founder Don Wilson to build the GPU industry benchmarks. Silicon Data is the referee — "the Bloomberg for compute" — while Compute Exchange is the liquidity layer where GPUs, tokens and servers change hands. We get into why token price indexes are falling, recent token & model consumption trends, why GPU residual values are above accounting depreciation schedules, and how neoclouds, their lenders, and compute users can hedge GPU exposure to reduce business volatility. •     "The A100 has been around for six years at this point, and we still see three-year contracts signed on A100s." Residual values are still hovering above five-year straight-line depreciation. •    “we'd definitely be looking at 30 trillion of [GPU] derivatives every year." Topics: Compute as an asset class, GPU indices, CME compute futures, GPU depreciation and useful life, neoclouds, token pricing, AI chip supply chain,  💡 This episode is presented by: Oxford Data Plan–The Home of Alternative Data Not Investment Advice. Highlights: (0:00) Doug introduces Carmen Li, CEO of Silicon Data and Compute Exchange (0:20) From energy options market maker at DRW to Bloomberg Data, and why AI startups' swinging GPU costs made compute an industry-wide hedging problem (4:09) How DRW founder Don Wilson pre-seeded Silicon Data, and building products with no product or sales team (6:10) Two sides of the coin: Silicon Data as "the Bloomberg for compute" and Compute Exchange as "the Nasdaq for compute" (7:49) Licensing indices to CME for H100 and B200 compute futures, and four ETFs filed before the futures even trade (10:22) What makes an index the source of truth: tracking supply-demand, resisting manipulation, staying neutral (12:57) Normalizing 50 B200 server configurations, and what earns a premium or a discount to the benchmark (16:04) Sizing the market: from 1–3 trillion in spot compute to a potential 30 trillion in derivatives notional (18:13) Who hedges first: neoclouds, hyperscalers and the financiers holding GPUs on their balance sheets (21:06) Michael Burry's depreciation bear case vs. the physical market: A100s still signing three-year contracts after six years (22:49) Why older chips stay productive: cooling, floor weight, wait times and the second-hand market (26:20) 300+ neoclouds and why more data sources make an index harder to manipulate (28:48) B200s at ~$5 per GPU hour, the implied forward curve and the shift into contango (31:18) Why an index provider can't give forward guidance, or own the assets. (33:59) Token indices on the Bloomberg Terminal, and a leading indicator for AI revenue (37:03) Why token prices are falling: workflows moving to open-source and open-weight models (38:51) Token pricing has no transparency, and why customers will demand control (41:49) Where to invest in the AI stack, lessons from the internet era and why "AI is a gigantic baby" (45:03) Homegrown silicon, fab capacity and why a hundred chip designers can't create a bubble (48:56) Why compute futures enable price discovery, not a bubble (49:29) Running a data company and a marketplace at the same time (52:47) Carmen's lesson for entrepreneurs: pick something you love (54:08) The vision: the liquidity provider and the referee for every layer of the cake Research Sponsors: Fiscal.AI - Delivering Modern Financial Data Infrastructure. 15% discount with code PITCH Oxford Data Plan–The Home of Alternative DataAlphaSense–Decision Grade AICarbon Arc–The Infrastructure for the AI Economy Pitch The PM Links:📩Early episode drops episode feedback on Substack Daily industry chatter and market color on LinkedIn  Carmen Li Links: Carmen LI on LinkedIn: Silicon Data: https://www.silicondata.comCompute Exchange: https://compute.exchange

My long time confidant, Jarrett Harris, Director of Research at IronAdvisor Insights joins the show to share his latest insights. He is quintessentially mid-western and was my go to source for learning about the on the ground pulse of the industrial economy when I was at Citadel and Millennium. As part of IronConnect, a heavy equipment industry service provider, Jarrett has unparalleled access to equipment dealers, rental companies, and industry executives.  My key takeaways from our conversation:  The industrial economy’s growth is being driven by Data Center related activity from power gen, to heavy equipment, to trucking and even landscaping and coal.  “A second-tier AWP manufacturer's entire inventory being bought out by a national rental house” “I don't think we've ever had a time in my [Jarrett’s] 22 years where all three of those industries [for CAT] were firing at the same time.” “We're having conversations with executives in that component supply chain, I think all of them are looking for an uptick in 2027 volumes. And that was not necessarily the case three or six months ago. “We're hearing about a coal demand resurgence.” “There is a risk that there are projects [Data Center] that developers are proposing in multiple states because they don't know which is going to move forward.”  Stocks: CAT, DE, PCAR, CMI, OSK, TEX, SITE, POOL, BTU, SNDK *Not Investment Advice. 💡 This episode is presented by:Fiscal.AI–Delivering Modern Financial Data Infrastructure. 15% discount with code PITCH (0:00) Introduction/highlights  (0:40) Doug introduces Jarrett Harris who is the axe in equipment-industry channel checks. (1:59) How joining Iron Connect expanded Jarrett’s industry access and dealer network by 10x (4:08) The Industrial economy is being driven by the data center buildout. (5:23) Data center demand expands from construction equipment and power generation into new equipment categories. (6:11) Landscaping, coal, transportation, and flatbed trailers emerge as derivative beneficiaries of the Data Center boom. (9:38) Coal demand begins strengthening as power providers require higher base loads. (12:01) Jarrett says he has never seen all three major Caterpillar end markets firing this strongly at the same time. (12:34) Previously bearish industry contacts become more constructive on the 2027 outlook. (13:36) The risk that data center project backlogs include projects proposed across multiple locations. (15:58) Longer lead times, lighter used inventory, and highly utilized rental fleets keep equipment scarce. (17:34) Jarrett describes an unprecedented buyout of a second-tier aerial equipment manufacturer’s inventory. (21:03) Haul trucks and articulated trucks approach new-equipment pricing as lead times stretch toward a year. (23:59) Trucking combines pent-up replacement demand with stronger data center and energy-related freight activity. (25:53) Component suppliers become increasingly constructive on 2027 truck volumes. (32:59) Trucking demand looks unusually industrial and project-driven rather than consumer-driven. (33:47) Signs of improvement in agriculture collide with cautious dealers still recovering from used-equipment losses. (36:40) A major trailer dealer estimates data centers drive ______________________________________________________________________Research Sponsors:Oxford Data Plan–The Home of Alternative DataAlphaSense–Decision Grade AICarbon Arc–The Infrastructure for the AI Economy ______________________________________________________________________ Pitch The PM Links:📩Early episode drops episode feedback on Substack Daily industry chatter and market color on LinkedIn  Jarrett Harris Links:Jarrett Harris LinkedInIron Connect Highlights:

Prediction markets are surging, but DraftKings is moving in the opposite direction. Is the issue market share? Rising customer acquisition costs? Or simply a reset from last year’s growth expectations? In this episode, Bernie McTernan, CFA, Managing Director and Senior Analyst at Needham & Company, breaks down the rapidly changing prediction-market landscape. We discuss Kalshi’s first-mover advantage, DraftKings’ strategy to replicate its regulated sportsbook playbook in currently unregulated states, Novig’s aggressive customer-acquisition push, and whether prediction markets meaningfully expand the long-term TAM for companies like DKNG. With negative estimate revisions weighing on the stock, elevated short interest, and DraftKings trading at roughly 11x forward EV/EBITDA, the debate becomes increasingly interesting: how much of the bad news is already priced in—and could today’s investments create a much larger earnings opportunity over time? “The stock's probably fifty percent lower than it was and the TAM's doubled.” “Prediction markets aren't just sports. Soon you'll be able to bet the whisper number.” “This is gonna be Robinhood on steroids.” “The numbers are going down, short interest is going up. Why do you think that the investments and lower revisions this year are actually gonna have a benefit next year?” Stocks: $DKNG, $FLUT Topics: DraftKings, Kalshi, Prediction Markets, Sports Betting, FanDuel, iCasino, Novig, Polymarket, Customer Acquisition Cost, Market Share, Regulation, EBITDA, Sportsbooks Not Investment Advice [00:00:00] Introduction to Bernie McTernan, Managing Director and Senior Analyst at Needham & Company.[00:01:40] Kalshi's rumored $40 billion private valuation.[00:02:45] What DraftKings' first NFL week of prediction-market share actually looked like.[00:04:30] The retention data that killed the original bear case on sportsbooks.[00:05:00] Mid-teens to 30–40% share — the playbook DraftKings wants to run again.[00:08:00] Why the prediction-market regulatory battle could end up at the Supreme Court.[00:11:40] The data showing almost no customer overlap between Kalshi and DraftKings.[00:18:00] Hedge fund hit rates and betting the number instead of the stock.[00:20:15] Fast futures, crypto, and the iCasino land grab.[00:21:50] The potential path from $1 billion to $5–6 billion of EBITDA.[00:27:45] Why states legalize gambling — budget deficits, not morality.[00:31:25] A trough multiple on estimates burdened by TAM expansion investments.[00:43:45] Novig's $50 million ad and the Caesars cautionary tale. 💡 This episode is presented by: Carbon Arc — The Infrastructure for the AI Economy.Free trial with code PTPM30:https://www.carbonarc.co/onboarding?flow=professional&billing=monthly&source=lenses Research Sponsors: Fiscal.AI — Delivering Modern Financial Data Infrastructure.15% discount with code PITCH:https://fiscal.ai/?via=pitch Oxford Data Plan — The Home of Alternative Data:https://oxford-dp.com/demo AlphaSense — Decision Grade AI:https://www.alpha-sense.com/Pitch/ Pitch The PM Links: 📩 Subscribe to our Substack for early episode drops, research updates, episode feedback, and the Pitch The PM Job Board:https://pitchthepm.substack.com/ Doug Garber on LinkedIn:https://linkedin.com/in/doug-garber-42aa508 Bernie McTernan, CFA Links: LinkedIn:https://www.linkedin.com/in/bernie-mcternan-cfa-84045a23/ Needham & Company:https://www.linkedin.com/company/needham-&-company/

Chandler Bocklage spent nearly two decades working alongside Steve Cohen, managing a portfolio before leading Business Development at Point72. In this episode, we discuss what separates good investors from great ones, how Point72 develops portfolio managers, why intellectual curiosity matters more than almost anything else, and how AI is reshaping the future of fundamental investing. If you've ever wondered what it really takes to become a successful PM, this is one of the most candid conversations from inside the walls of legendary investment firm Point72. “Alpha decay has been compressing and compressing. There are more and more people chasing the same alphas.” “You have to have different duration, you have to have different catalyst paths, you have to have different thought processes because” “Honestly, I think that's what it really comes down to. The sacrifices…” "We don't think AI replaces analysts or PMs." Highlights: (01:31) The intellectual curiosity that defines Steve Cohen (04:24) The unspoken sacrifices behind being great at this job (04:56) The Kobe, Jordan, and Brady comparison to elite investing (07:48) The origins of LaunchPoint and building a real development path (10:24) Why AI won't replace analysts or portfolio managers (14:41) What actually makes an investment process repeatable (29:47) The talent war, mega-guarantees, and buy versus build (33:12) The biggest mistake of his career — and what it taught him (35:18) Why Steve Cohen stepped back from trading to build the firm Topics: Point72, Steve Cohen, Portfolio Management, AI, Fundamental Investing, Risk Management, Analyst Development ______________________________________________________________________ 💡 This episode is presented by Carbon Arc–The Infrastructure for the AI Economy. 30 Days free with code PTPM30 Research Sponsors:Oxford Data Plan–The Home of Alternative Data. Ping Makay Redd for a trial. AlphaSense–Decision Grade AI. Free trial at https://www.alpha-sense.com/pitch/ Fiscal.AI–Delivering Modern Financial Data Infrastructure. Use code PITCH for 15% off______________________________________________________________________ Pitch The PM Links:📩 Subscribe to our Substack for research updates and new high-conviction episodes from top PMs, and our Job Board: https://pitchthepm.substack.com Doug Garber on LinkedIn for daily market color: https://linkedin.com/in/doug-garber-42aa508  Point72 Links:Chandler Bocklage on LinkedIn: https://www.linkedin.com/in/chandler-bocklage-476518a6/Point 72: https://point72.com/ ______________________________________________________________________

In 2001, Ed Salib walked into TimesSquare Capital Management as the firm's first intern. Twenty-five years later, he's co-PM of the TimesSquare Quality Mid Cap Growth ETF ($TSCM). So what does a 25-year fundamental investor do when one of the market's most debated stocks goes on sale? Ed passed on DoorDash's 2021 IPO, kept it in his research “bullpen,” and bought the 20% sell-off on conservative early-2025 guidance. He got a second bite when the stock fell from the mid-200s to ~$140 this spring on fears it could be "vibe-coded away." In the episode, we dig into why scale and density are the moat Grubhub never built, how DashPass and grocery are lifting order values, why Ed forecasts mid-20s growth through decade-end vs. the Street's high teens, and why the end of an internal investment cycle could surprise investors to the upside on margins. "They gave initial guidance in early 2025 for EBITDA that disappointed and the stock sold off twenty percent. That was our entry point." “We think they can go low to mid twenties versus the Street more like high teens over the next several years till the end of the decade.” “I'm looking at the real-time Oxford data. It has August growing 31%...the Street’s at 21% for the quarter.” We cover:● TimesSquare's consumer filter: durable, needs-based demand, and why "fashion was a four-letter word"● The three-sided marketplace, two-thirds US restaurant share, and why Grubhub lost the suburbs● DashPass, grocery and DoubleDash: how grocery lifts order value and driver economics● Why Ed passed on the IPO, what changed in 2024, and how he sized the 2025 entry● The variant view: low-to-mid-20s growth vs. the Street's high teens● The bear case: cost to grow, Instacart, Uber, PE-backed competition, and Deliveroo/ERP● How Ed sets base, bull and bear price targets● How TimesSquare uses Claude, MCPs and AlphaSense in research Highlights:(0:48) Ed's 25 years at TimesSquare, from first intern to co-PM(3:16) What TimesSquare looks for in a consumer stock(8:08) Why DoorDash is compelling: scale, founder-led, and capital allocation(11:06) Why the business exists and where Grubhub went wrong(13:20) DashPass, 45M members, and grocery(16:09) Carbon Arc data: grocery order values rising from ~$51 to ~$63(18:11) Robotaxis and drones: bull-case upside(20:44) Passing on the 2021 IPO and what changed in 2024(23:50) The 2025 guidance sell-off: "That was our entry point"(25:36) The 2026 AI sell-off and "vibe-coded away" bear case(28:55) Low-to-mid-20s growth vs. the Street's high teens(30:52) Incremental margins, ERP, and EBITDA revisions(32:23) Deliveroo integration and the 2026 investment year(35:25) ODP shows August accelerating to +31% YoY(37:20) DoorDash beyond restaurants(40:01) Valuation at ~22x NTM EV/EBITDA and the bear case(45:29) Economics with merchants and drivers(49:27) Base, bull and bear price targets(52:05) "We're a Claude shop": AI in research(54:47) Security and ring-fencing research 💡 This episode is presented by Oxford Data Plan–The Home of Alternative Data. Ping Makay Redd for a trial (professional investors only) — https://www.linkedin.com/in/makay-redd-122a4364/ Research Sponsors:AlphaSense–Decision Grade AI. Complimentary trial at https://www.alpha-sense.com/pitch/Carbon Arc–The Infrastructure for the AI Economy. 30 Days free with code PTPM30Fiscal.AI–Delivering Modern Financial Data Infrastructure. Use code PITCH for 15% discount Pitch The PM Links:📩 Subscribe to our Substack for research updates and new high-conviction episodes from top PMs, and our Job Board: https://pitchthepm.substack.comDoug Garber on LinkedIn for daily market color: https://linkedin.com/in/doug-garber-42aa508 TimesSquare Links:TimesSquare Capital Management: https://www.tscmllc.com/TimesSquare Quality Mid Cap Growth ETF ($TSCM): https://tscmetfs.com/funds/tscm/ Stocks mentioned: $DASH Not Investment Advice.

In 2019, Ken Griffin, Founder & CEO of Citadel, was looking for some of the best risk-takers on Wall Street. He landed on Rich Falk-Wallace, then a top analyst at Viking, who went on to become a Portfolio Manager at Citadel’s Surveyor Capital. So what comes next after becoming a PM at Citadel at 29? For the past five years, Rich has been building Arcana, a financial technology platform designed to help the world’s top hedge funds and asset managers make smarter decisions, faster. His philosophy is heavily influenced by Steve Jobs: obsess over the details and build products that genuinely delight customers. In this episode, Rich breaks down the secular growth of beta-zero products, the rapid expansion of separately managed accounts (SMAs), the rise of alpha capture, and how human investment signals can complement quantitative systems. We also discuss portfolio construction, product-market fit, and how Arcana is integrating AI across its platform while staying focused on the customer. “How did you convince Stanley Druckenmiller to be your seed investor?” “The problem of portfolio construction is way closer to solved than that last mile of, ‘What’s a good idea?’” “The allocation of dollars in public markets is headed towards beta one or beta zero products.” “SMA-type products are growing massively in every direction. And that comes from allocators of every kind — sovereign wealth funds, endowments…” Topics: Arcana, Citadel, Surveyor Capital, Viking, Hedge Funds, Financial Technology, Separately Managed Accounts, Beta Zero, Alpha Capture, Portfolio Construction, Investment Research, Artificial Intelligence, APIs, MCPs, Product-Market Fit *Not Investment Advice [00:00:27] Rich’s journey from distressed credit and public equities into financial technology. [00:02:20] Why timing, experience, and energy pushed him to make the entrepreneurial leap. [00:04:07] Why domain expertise helps — but nobody has a “right to win.” [00:08:43] What it takes to earn backing and why product obsession matters. [00:11:07] Arcana’s “platform maximalist” approach to software, APIs, MCPs, Excel, and LLMs. [00:14:35] “If you think something is easy, it’s because you’re the buyer.” [00:17:11] Why founders need to forget how hard something is and focus on the customer experience. [00:19:56] Learning to love the incremental process of building. [00:22:38] Finding product-market fit and the shift toward beta-one and beta-zero products. [00:26:52] Why separately managed accounts are growing explosively. [00:28:11] What an SMA is and how it differs from a commingled fund. [00:31:03] How Arcana helps allocators analyze risk, performance, attribution, and repeatability. [00:34:20] Mock portfolios, analyst tracking, and creating better feedback loops for investment talent. [00:40:58] Alpha capture and turning human conviction signals into systematic portfolios. [00:45:45] How Arcana uses AI internally to build software. [00:47:32] Measuring the ROI of AI and token spending. [00:49:58] MCPs, on-platform AI, and giving different investors different ways to access the same insights. [00:54:51] Is Arcana a software company or a data company? Why Rich sees it as both. [00:59:17] Rich’s philosophy of delighting customers and continually improving the product. 💡 This episode is powered by: Fiscal.AI: Delivering Modern Financial Data Infrastructurehttps://fiscal.ai/ Pitch The PM Links: 📩 Subscribe to our Substack for research updates, new high-conviction episodes from top PMs, and our Job Board:https://pitchthepm.substack.com/ Doug Garber on LinkedIn for daily market color:https://www.linkedin.com/in/doug-garber-42aa508 Rich Falk-Wallace Links: Rich Falk-Wallace on LinkedIn:https://www.linkedin.com/in/rich-falk-wallace/ Arcana:https://www.arcana.io/

Daniel Pilling, Co-PM of the Sands Capital Global Growth Fund, has spent nearly 20 years investing across long-only and long-short strategies, including time at Fidelity, Millennium, and Balyasny. Today at Sands Capital, he takes a very different approach: concentrated, deep-dive investing in high-quality growth companies with the potential to compound for years. In this episode, Daniel breaks down why he believes the AI investment cycle is still incredibly early. We discuss $NVDIA, $TSMC, $ASML, memory, AI agents, the return on GPU infrastructure, and why compute could remain supply constrained for a long time. Daniel also explains why Anthropic's growth has been unlike anything he's seen before and how Sands thinks about finding the long-term winners as AI diffuses across the economy. If you're wondering whether the AI trade has gone too far—or whether we're still at the beginning of a much larger cycle—this conversation offers a long-term investor's framework for thinking about what comes next. "I've never seen anything like this." "We're going to be supply constrained in terms of compute for a very long time." “The reason for that is, again, the low penetration and the high ROI of what’s happening.” "Anthropic and agentic AI is incredible. And it's just going viral and the pace of adoption is unheard of." Stocks: $NVDA, $TSM, $MU, $ASML, $AMZN, $GOOGL, $META, $AMD, $ZM Topics: Sands Capital, Artificial Intelligence, NVIDIA, TSMC, ASML, Memory, AI Agents, Anthropic, Compute, Semiconductors, GPU Economics, Long-Term Investing, AI Infrastructure, AI Innovator Fund *Not Investment Advice [00:00:00] Introduction to Daniel Pilling, Co-PM of the Sands Capital Global Growth Fund. [00:01:15] Daniel’s path from banking and multi-manager investing to long-term growth. [00:03:02] How Daniel became obsessed with investing at 12. [00:04:13] Why Daniel left Millennium and Balyasny for Sands Capital. [00:05:14] Sands Capital’s philosophy: concentrated portfolios, deep research, and long-term ownership. [00:07:32] Why memory and NVIDIA remain high-conviction AI investments. [00:09:54] NVIDIA’s market share and why open-source AI could support GPU demand. [00:12:20] Why NVIDIA, Cerebras, Trainium, and TPUs can all win. [00:14:13] The case for a memory shortage as AI agents scale. [00:19:00] Why memory may not follow a traditional cyclical pattern. [00:22:13] AI infrastructure returns and increasingly valuable compute. [00:23:48] Why rising older-GPU prices challenge depreciation concerns. [00:24:23] Anthropic’s growth, Zoom during COVID, and rapid agentic AI adoption. [00:26:46] Why AI compute could remain supply constrained and create an “upside cliff.” [00:28:34] Why Daniel compares AI adoption to electricity. [00:30:42] How AI could make investment research faster and more effective. [00:32:03] Why ASML and TSMC remain key AI infrastructure constraints. [00:34:28] Generating differentiated returns through multi-year views. [00:37:52] Why 99% of daily market information doesn’t matter. [00:39:52] Humility in investing and recognizing when the Zoom thesis changed. [00:43:42] Why AI remains early, underpenetrated, and rapidly improving. [00:46:22] Sands Capital’s AI exposure across semis, memory, cloud, and software. [00:52:56] The case for Meta despite rising CapEx and declining free cash flow. [00:56:45] The fund’s AI exposure and global diversification. [00:58:14] The AI Innovator Fund thesis: low penetration, constrained compute, and AI winners. 💡 This episode is powered by: Oxford Data Plan: Request a Demo  AlphaSense: Request a Demo Pitch The PM Links: 📩 Subscribe to our Substack for research updates and new high-conviction episodes from top PMs, and our Job Board: ⁠https://pitchthepm.substack.com/⁠Doug Garber on LinkedIn: https://www.linkedin.com/in/doug-garber-42aa508  Sands Capital Links:Daniel Pilling on Linkedin: https://www.linkedin.com/in/daniel-pilling-14343116/Sands Capital: https://www.sandscapital.com/

Tom Hardin, formerly known as “Tipper X,” helped the FBI unravel one of the largest insider trading investigations in hedge fund history. The FBI flipped him, convinced him to wear a wire that built more than 20 cases. The hero of the story was Tom’s wife, who stood by him the entire time, allowing him to survive the intense emotional weight. In this episode, Tom walks through how he crossed the line, how easy it was to rationalize small trades as harmless, and how a handful of decisions ultimately destroyed his career. He explains what happened when the FBI approached him on the street, what it was like wearing a wire for two years, and why the $46,000 he made from four trades ended up being the price of his career.  We also discuss the practical lessons investors should take from his story: why who you surround yourself with is the most important decision of your career, why compliance should be treated as a career protector, and how being in a pressured performance situation can change how good people act.  Tom is now the author of Wired on Wall Street "I blew up my career for $46,000."  "Cheating is a choice." “You have to think about who you're surrounding yourself with.”  “If you're even that close to the line, you have to have a conversation with compliance.”  *Not investment or legal advice. Topics: Insider Trading, Tipper X, Hedge Funds, FBI, MNPI, Compliance,Securities Fraud, Risk Management, Investment Research, Wall Street    [00:00:00] Introduction [00:00:30] Tom Hardin’s history as Tipper X and role in the FBI insider trading investigation [00:01:51] The FBI confronts Tom about four trades [00:04:36] Why Tom advises contacting a lawyer before speaking to law enforcement [00:05:03] How fund pressure began shifting Tom’s decision-making [00:10:52] Receiving an acquisition tip and deciding whether to act [00:11:10] Crossing the line by passing the tip to another investor [00:13:17] The need, opportunity, and rationalization behind Tom’s trade [00:14:53] How his boss’s response reinforced Tom’s rationalization [00:16:42] How information-sharing escalated into a $15,000 payoff [00:18:57] Discovering others had already cooperated with law enforcement [00:21:41] Wearing a wire without a lawyer or cooperation agreement [00:24:24] Drawing a line with the FBI and being exposed as Tipper X [00:25:46] How cooperation affected sentencing and Tom’s felony convictions [00:27:58] The line between cooperation and entrapment as an informant [00:31:51] Telling his wife, panic attacks, and her support during the investigation [00:36:03] How running gave Tom structure after his career ended [00:37:03] Insider trading: material, non-public information and breach of duty [00:39:42] Compliance questions around using LLMs in investment research [00:40:38] Why research notes matter when trades are questioned later [00:42:29] Risks from an investor’s research and outside relationships [00:44:05] Expert networks and risks around political intelligence firms [00:46:53] Risks for public company board members with material information [00:47:41] Congressional stock trading and proposed restrictions [00:50:21] The lasting effects of a felony conviction and federal expungement [00:53:33] Why Tom calls compliance the “chief career protector” 💡 This episode is powered by: Fiscal.AI: - Delivering Modern Financial Data Infrastructure AlphaSense: Request a Demo Pitch The PM Links: 📩 Subscribe to our Substack for research updates and new high-conviction episodes from top PMs, and our Job Board: ⁠https://pitchthepm.substack.com/⁠Doug Garber on LinkedIn: https://www.linkedin.com/in/doug-garber-42aa508  Tipper X Links:  Tom Hardin on LinkedIn: https://www.linkedin.com/in/tipperx  Tipper X: https://www.tipperx.com/  Wired On Wall Street Book: https://www.tipperx.com/book Tipper X on X: https://x.com/iamtipperx

My former colleague and banking guru, James Abbott, is living his passion with the launch of Diligence Capital Management (DCM). DCM runs a concentrated, net-long financials strategy alongside a tighter-net long/short financials portfolio. James and his team bring more than 50 years of combined experience in financial services—and a deep understanding of how banks operate, where they underperform, and what it takes to improve them. In this episode, James explains why DCM became actively involved with Eagle Bancorp ($EGBN), how he identified an underperforming bank in need of change, and why he believes the market is still underestimating its earnings power. We also discuss lessons from the 2008 financial crisis and the collapse of Silicon Valley Bank, as well as why spending time inside a business can create an investing edge that is difficult to replicate from the outside. "My goal was to be a portfolio manager just like Peter Lynch."  "The market just doesn't really appreciate what's going on here."  "The deep homework concept... go deeper than anybody else does."  "The company should be earning about $6 a share."  Stocks: $EGBN, $ZION  Not Investment Advice.  ______________________________________________________________________ [00:00:00] Introduction to James Abbott and the Eagle Bancorp investment thesis  [00:02:14] How a Peter Lynch article inspired James Abbott’s investing career  [00:03:46] Early career experiences at SNL Financial and FBR  [00:05:44] Reflections on FBR’s research culture and working alongside Dan Ives  [00:07:03] Lessons from generating positive returns during the 2008 financial crisis  [00:08:07] Moving from the sell side to executive leadership at Zions Bancorporation  [00:09:17] Building a significant ownership stake in Zions through personal investment  [00:10:28] Founding Diligence Capital Management and launching the firm  [00:11:46] The impact of Silicon Valley Bank’s collapse and banking sector contagion  [00:13:43] How market narratives and deposit flows can pressure banks  [00:15:02] Diligence Capital Management’s portfolio construction and leverage approach  [00:16:22] Activist investing through special purpose vehicles and concentrated opportunities  [00:16:37] Why Eagle Bancorp became a high-conviction investment  [00:17:48] Assessing Eagle Bancorp’s earnings power and excess capital  [00:19:09] Concentration risk and the challenges facing Eagle Bancorp  [00:20:47] Commercial real estate exposure and concerns around stale loan-to-value metrics  [00:22:31] Insights into bank credit quality, appraisals, and regulatory oversight  [00:24:35] Recommendations to strengthen Eagle Bancorp’s board composition  [00:27:32] Office loan concentration and portfolio risk management  [00:29:57] The process of engaging management and advocating for change  [00:31:51] Corporate governance reforms and separating the chairman and CEO roles  [00:34:22] Historical governance challenges at Eagle Bancorp  [00:36:09] Proposed board additions and turnaround expertise  [00:37:14] The push for a three-year performance improvement plan  [00:38:48] Market reaction to credit loss reserves and the stock’s recovery  [00:40:27] Why Diligence Capital believed the market mispriced Eagle Bancorp  [00:41:14] The path to achieving $6 per share in earnings power  [00:42:20] Closing thoughts on activism, value creation, and the future of Eagle  ______________________________________________________________________ Pitch The PM Episode Links: Doug Garber on LinkedIn: https://www.linkedin.com/in/doug-garber-42aa508  📩 Subscribe to our Substack for research updates and new high-conviction episodes from top PMs, and our Job Board: ⁠https://pitchthepm.substack.com/⁠ James Abbott on LinkedIn: https://www.linkedin.com/in/james-r-abbott-investor   This episode is powered by: 💡Oxford Data Plan: Request a Demo 💡AlphaSense: Request a Demo

I met Tim Arthurs, the second week when I joined Millennium and knew he was an “A” player. He still has notes from every time he called me on a stock. He’s a process guy. And it has led to his success.  He founded Seaport Research Partners, which has become the fastest-growing independent equity research platform by attracting the top research analysts and empowering them with aligned incentives. Early in his career, he moonlighted as a QB coach for Heisman winner and #1 overall pick Bryce Young teaching him the importance of the right motion and process.   “What have I learned from some of my biggest failures is keep getting up. You're bendable, you're not breakable” "You can't be a big man at night and a little man in the morning" We cover: The Bryce Young story — a Craigslist posting, a dad who lied about his kid's age, and six months of tennis balls and candy wrappers before he ever touched a football. It starts with good habits The MiFID II unlock: how unbundling and vote/rate-card transparency exposed what individual analysts are actually worth — and made an eat-what-you-kill platform possible for the first time The brutal math of the sell side: ~3,500 published analysts in North America, and two-thirds of coverage is "watered-down, check-the-box" — subsidized by banking and syndicate How Seaport recruits the top 1% of the 1%: never a recruiter, 550+ interviews, ~40 offers, 30+ conversions — the clients feed the talent The reference-check questions that actually work: "When did they make you money?" and "What's a 60-minute meeting with them worth?" — asked across 20-30 buy-siders until the trend is undeniable The 3-step analyst checklist: investment judgment (best call, worst call, the consensus view you think is wrong), research edge (what do you produce that clients can't get elsewhere — and what's proprietary in the process), and client franchise (your top 15 advocates who raise their hand no matter what) The salesperson hierarchy: good salespeople are concierge, great ones sell outcomes — "we get paid to anticipate, not analyze" — and the cream of the crop sell feelings: becoming an extension of the client's investment process "You can't be a big man at night and a little man in the morning" Why analysts leave the bulge: "Why do I get comped down 10-15% a year when my franchise wasn't down?" — and what they control at Seaport: coverage, distribution, pricing, input and output The three-stage distribution model: ~400-500 readership, top-100 tactical, top-40 opt-in Proof of concept: #1 global market-share gainer at some of the biggest wallets on the planet, 3 → 30+ analysts in five years, and only one analyst ever lost The next five years: the best 45 analysts in the U.S., replicating the model in Europe and Asia, and filling the void the bulge brackets left Triathlons, 1,440 minutes a day, and the 5% you owe yourself — plus the cause closest to home: the Epilepsy Foundation of Chicago and his daughter Athena _______________________________________________________________ 💡 This episode is powered by Fiscal.AI - Delivering Modern Financial Data Infrastructure Pitch The PM Episode Links: Doug Garber on LinkedIn: https://www.linkedin.com/in/doug-garber-42aa508  📩 Subscribe to our Substack for research updates and new high-conviction episodes from top PMs, and our Job Board: ⁠https://pitchthepm.substack.com/⁠ Links: Tim Arthurs on LinkedIn: https://www.linkedin.com/in/timothy-arthurs-06b3179/ Seaport Research Partners: https://seaportrp.com/ Seaport Global: https://seaportglobal.com/ Chicago Epilepsy Foundation: https://epilepsychicago.org/

Gil Luria and I dig into the bull case after another blockbuster quarter with 93% YoY growth. The reason Palantir wins is their head start on building an enterprise-wide ontology, unconventional usage of forward-deployed engineers in their SaaS model, and customer-aligned, outcome-based pricing. We debate $PLTR’s valuation, future growth trajectory, and AI-driven software budget crowd-out. “Retail investors figured it out first, bid it up all the way to $200. Institutional investors were always caught a step behind, including most of the sell side.” “The bull case is that the stocks’ valuation is now, 50x forward cash flow, not $200, 2% yield, and they're growing 90%, 93% up from 85% last quarter”  “They get to cherry pick customer, deliver results, and win. (8:24) That's when we learned over the last year.” “This is the best software company in the world. Maybe the best company in the world.” Stocks mentioned: $PLTR, $AI, $IBM, $MSFT, $NVDA, $SNOW, $DDOG, $CRWD, $SHOP *Not Investment Advice. Disclosure: The author has a short position in PLTR as of the episode recording; that may change at any time.  ______________________________________________________________________ Highlights: (1:26) Palantir accelerates — 93% U.S. commercial growth and strong government demand. (2:18) How Gil went from valuation skeptic to calling Palantir one of the world’s best companies. (5:13) What makes Palantir different: forward deployed engineers, ontology, AI and outcome-based pricing. (8:26) How Palantir delivers customized solutions at scale. (9:06) Why its engineering talent and brand are hard to replicate. (10:34) Ontology explained — mapping company data to how the business works. (13:29) Palantir’s post-9/11 origin story and original problem. (15:23) A 157% net retention rate and expansion within large customers. (16:42) Why Palantir’s chief revenue officer came from a legal background. (17:48) Gil’s valuation framework: Palantir deserves a premium to software peers. (19:42) Why Palantir customers may be seeing unusually strong AI returns. (21:43) Enterprise AI shifts from structured to probabilistic unstructured data. (23:46) How AI spending is crowding out other technology budgets. (26:48) From GPU hours to tokens to cost per task — AI economics move toward labor. (29:10) Why AI-driven productivity could lead companies to hire more people. (30:29) How AI compressed Gil’s research workflow from weeks to near real time. (35:06) Gil’s AI stack and Microsoft as D.A. Davidson’s enterprise control plane. (37:41) Are companies handing proprietary advantage to frontier AI models? (38:38) Palantir’s sovereignty pitch and risks of relying on one frontier model. (42:45) Why Palantir prefers model flexibility and NVIDIA’s Nemotron models. (43:52) NVIDIA’s strategy: coordinate the AI ecosystem, not just sell chips. (46:08) The Palantir bear case — extraordinary growth eventually decelerates. (48:02) Why slower growth could still support a larger future cash-flow base. (50:35) Why Gil’s Palantir estimates remain close to consensus despite his bullish view. (52:14) The institutional-investor problem: limited revenue disclosure. (53:49) Gil’s belief in Palantir’s mission. (54:04) Doug summarizes the bull and bear cases: ontology, engineering, valuation, and deceleration. ______________________________________________________________________ 💡 This episode is powered by AlphaSense. Use the link here for Complimentary access — https://www.alpha-sense.com/Pitch/💡 Fiscal.AI - Delivering Modern Financial Data Infrastructure Pitch The PM Links: 📩 Subscribe to our Substack for research updates and new high-conviction episodes from top PMs, and our Job Board: ⁠https://pitchthepm.substack.com/⁠Doug Garber on LinkedIn for daily market color: : https://www.linkedin.com/in/doug-garber-42aa508  Gil Luria Links:DA Davidson: https://www.dadavidson.com/ Gil Luria on LinkedIn: https://www.linkedin.com/in/gil-luria-79347a2/ Gil Luria on X: https://x.com/gilluria

She Quit at 29 With No Code, No Co-Founder, No Funding – Now Hudson Labs Is Finance AI that has a “No Hallucination Guarantee” Kris Bennatti (@)  CEO Hudson Labs (Toronto), a former forensic-accounting data scientist who built the first LLM for finance, back in 2019 before anyone knew what to make of it. Her co-founder/CTO Suhas Pai literally wrote the book – O'Reilly's "Designing Large Language Model Applications." "If you try to pull multi-period KPIs beyond four quarters with a generalist tool, you'll get one wrong number 30% of the time." We cover: The leap: quitting a great job at 29 with no funding, no co-founder, and not one line of code written  "those choices are just a reflection of poor risk assessment capabilities" The first hard lesson: the academic papers claiming AI could predict fraud were poisoned by target leakage, the training data contained the outcome. The product she quit her job to build didn't work, and she rebuilt from scratch The forensic risk score: 70+ means a one-in-three chance of an SEC enforcement action within three years and 3x the likelihood of a securities lawsuit  used by investors, D&O insurers pricing risk, and plaintiff-side class-action firms. "We specialize in selling to the enemy." Why generalist LLMs hallucinate on financial numbers: effective context length vs the advertised window, the two-to-three-earnings-calls attention limit, and why hallucination is really LLM memory bleeding into real data The Hudson Labs answer: AI-specific pre-processing of every filing, transcript, and presentation retrieve the whole table, the units, the currency, consolidated-vs-segment and a no-hallucination guarantee, the first of its kind Tone as a screen: find the most stressed-out CEOs, the most confident CFOs, track deflection on analyst Q&A over time  impossible unless tone is stored in the embeddings The soft-guidance problem: why "capex is now expected to be…" slips past generalist AI, and the pathways built to never miss a guidance cue The server useful-life screen: every hyperscaler raising estimates in a follow-the-leader wave and Amazon as the only one to cut The cost story nobody's pricing: a 500-company AI-ecosystem deep dive that cost $200 on Hudson Labs vs ~$13,000 on a frontier-model API at lower accuracy  and why cost efficiency becomes the tailwind as subsidies fade The business today: ~100 hedge fund clients plus Fortune 100s, insurers, and law firms and a new $100/month tier Highlights:  (1:13) Quitting at 29 with nothing but the problem (5:34) The target-leakage discovery — the papers were wrong (9:46) Suhas Pai — the co-founder who wrote the book (13:10) How Hudson Labs differs from the AI-startup wave (15:30) Why generalist LLMs get numbers wrong — context and attention (23:24) The worst strategic decision — and pivoting when short sellers shrank (28:21) Demo: KPIs with verbatim call commentary, no hallucinations (35:30) The infrastructure behind the guarantee (39:34) Tracking Waymo vs Uber when the stats aren't standard (46:39) The forensic risk score — and Meta's rising off-balance-sheet risk (52:00) The server useful-life wave — everyone up, Amazon down (55:14) $200 vs $13,000 — the deep-dive cost math (57:00) The next three years and the new $100/month tier ______________________________________________________________________ 💡 This episode is powered by: Fiscal.AI: Contact Sales Pitch The PM Links: 📩 Subscribe to our Substack for research updates and new high-conviction episodes from top PMs, and our Job Board: ⁠https://pitchthepm.substack.com/⁠Doug Garber on LinkedIn for daily market color: : https://www.linkedin.com/in/doug-garber-42aa508  Hudson Labs Links:Kris Bennatti: https://www.linkedin.com/in/kbennatti/  Hudson Labs: https://www.hudson-labs.com/

"Our numbers are not even half of consensus. Treat the syndicate's number as an extremely optimistic bull case." "We think we're already very optimistic—but consensus is even more optimistic." "Launch is what makes 100% of the value of SpaceX." "Frontier AI is building very high barriers to entry." "Everybody wants to be part of the SpaceX story." Pierre Ferragu, Head of Tech Infrastructure at New Street Research, joins the show to break down SPCX’s sum-of-the-parts valuation and the key debates in the stock. We dig into: Why Starlink has a structural cost advantage over traditional broadband. The tell is that Starlink is already sold out in places: raising prices in capacity-limited markets while adding 0.5-1M subs a month — which is why the V3/Starship ramp is now the gating factor. The Starship launch being the next catalyst. The model needs ~1 Starship launch a week; New Street runs ~1 year behind Elon's timeline — a reminder that Musk's targets have a "world-class track record of being late but delivered" The xAI valuation framework: a ~$575B base value from a $750B-$1T 2030 market at ~15% share (on Anthropic/OpenAI's ~4.6x 2030 revenue) — and why it's a balanced oligopoly, not winner-take-all, so no one gets to run away with it. The spot-vs-planned compute arb: ~$50B/GW (the $15B / 0.3GW Anthropic deal) vs CoreWeave's ~$12B/GW — and why that premium may not last. The setup that should keep you honest: consensus was built on Elon-optimism to sell the deal; the stock will trade quarter-to-quarter on "where there's light" (Starlink subs, Starship launches), through 6-12 volatile months of lockup unwinds and index inclusion.  The Debate: Overly optimistic earnings revisions vs a cost advantage in the space frontier led by the great entrepreneur of our time, Elon Musk This episode was originally a Pitch The PM webinar sponsored by Alpha Sense on June 22nd. Get free access to all of our webinars on our substack Stocks: $SPCX, $TSLA Topics: SpaceX, Starlink, Starship, xAI, Frontier AI, Space Economy, Satellite Internet, Launch Economics, Artificial Intelligence, Infrastructure Investing *Not Investment Advice _____________________________________________________________ [00:00:00] Introduction to Pierre Ferragu, New Street Research, and why the firm is uniquely positioned to analyze SpaceX.[00:03:59] How New Street frames the SpaceX investment thesis for institutional investors.[00:06:32] SpaceX sum-of-the-parts valuation: Starlink, Direct-to-Cell, Launch, and xAI.[00:10:05] Why Starlink has a major cost advantage over traditional broadband.[00:15:49] Starlink pricing, subscriber growth, and global expansion.[00:20:23] Pierre’s firsthand experience using Starlink.[00:23:10] Valuing Starlink and its long-term free cash flow potential.[00:27:36] Why Starship is the most important catalyst for SpaceX.[00:37:22] How SpaceX disrupted the launch industry.[00:44:25] Pierre’s valuation framework for xAI and Frontier AI.[00:53:56] Meta, open-source models, and Chinese AI companies.[00:57:19] Why xAI can command premium pricing for compute capacity.[00:59:36] Pierre’s variant view versus Wall Street consensus.[01:01:04] What investors should watch after the IPO.[01:02:45] Pierre’s outlook for SpaceX shares. _____________________________________________________________ 💡 This episode is powered by: AlphaSense: Request a Demo Pitch The PM Links: 📩 Subscribe to our Substack for research updates and new high-conviction episodes from top PMs, and our Job Board: ⁠https://pitchthepm.substack.com/⁠ Follow Doug Garber on LinkedIn for daily market color: https://www.linkedin.com/in/doug-garber-42aa508

"Our data is powering the infrastructure of this massive new market."  "Building Data-as-a-Service is a very, very hard business."  "difficult problems have big rewards" "You're making decisions on complete gut."  Braden Dennis is the true definition of an entrepreneur. He started a podcast in his college dorm room and now runs an industry leading Fintech. Fiscal.ai has pivoted three times since inception and is now focused on building the latest generation financial data company using delivery speed and rapid product iteration as its competitive advantage. Companies mentioned: Google Finance ($GOOGL), Claude, OpenAI, Bloomberg, Fiscal AI, Perplexity. Thank you to Fiscal.AI, our sponsor of the Wall Street Entrepreneur series! ______________________________________________________________________    Highlights [00:00:18] Introduction to Braden Dennis and Fiscal AI [00:01:44] Braden Dennis's investing background and early podcasting journey [00:02:58] Influential investing books and lessons that shaped his entrepreneurial approach [00:05:27] Overview of Fiscal AI's financial data business and competitive advantages [00:07:29] Origins of Stratosphere and the launch of FinChat [00:08:39] FinChat's viral growth following the release of large language models [00:11:09] Raising seed capital and demonstrating early startup traction [00:11:27] Product-market fit and the importance of rapid iteration [00:13:22] Advice for founders on fundraising and seed-stage milestones [00:15:26] Why the company shifted from a retail investing platform toward data infrastructure [00:16:07] Building proprietary financial datasets and owning the underlying content [00:19:11] Transitioning from software subscriptions to B2B data feeds [00:20:00] Startup decision-making, uncertainty, and operating on imperfect information [00:21:59] Startup moats, competition, and scaling from zero to one [00:24:09] Customer retention, activation, and key business metrics [00:25:52] AI infrastructure, token usage, and building data products at scale [00:28:32] Product roadmap, data coverage expansion, and competitive positioning [00:30:18] MCP integrations and delivering financial data directly into AI workflows [00:31:36] Global data coverage, ownership data, transcripts, and company KPIs [00:34:22] Total addressable market for financial data and long-term revenue goals [00:36:40] Scaling from $10 million to $50 million in annual recurring revenue [00:38:41] Creating a news product focused on signal rather than noise [00:42:36] Managing emotions and maintaining perspective as a founder [00:43:23] Leadership lessons and the risks of impulsive decision-making [00:44:18] Demonstration of Fiscal AI's terminal, API, and investor tools [00:46:37] AI-powered research workflows using Claude, Codex, and MCP integrations [00:49:27] Data ownership, model training, and the value of accurate financial information [00:53:00] Handling adjusted financial metrics and analyst workflows [00:53:58] Building custom investor dashboards and the future of AI-assisted research  ______________________________________________________________________ Pitch The PM Links: 📩 Subscribe to our Substack for research updates and new high-conviction episodes from top PMs, and our Job Board: ⁠https://pitchthepm.substack.com/⁠ Doug Garber on LinkedIn: https://www.linkedin.com/in/doug-garber-42aa508  Fiscal.AI Links: Braden Dennis on LinkedIn: https://www.linkedin.com/in/braden-dennis-881a88b0/   Fiscal AI - https://fiscal.ai/