Future Ventures: Scaling with Clarity – Details, episodes & analysis

Send us Fan Mail

Robert Stanley isn't a healthcare lifer. He spent most of his career in technology — running IT projects for big banks, telcos, and airlines — before moving into home care about a decade ago and building one of Ontario's established providers. That outsider's vantage point is exactly what makes this conversation worth your time. Where insiders see a system to optimize, Robert sees one that's structurally broken: wired to reward visits, procedures, and ambulance rides, with almost nothing that pays anyone to prevent the crisis in the first place. The math behind his urgency is hard to argue with — Canada's population of 82-year-olds peaks around 2051, and demand only climbs from here. 

His idea is called Comprehensive Healthcare at Home: continuously monitor at-risk seniors and send a nurse or therapist when something seems wrong — often weeks before it would lead to an emergency room visit. But what makes CHA different isn't just the technology. It's how he set it up. Robert launched the care company first and treated it as a testing ground, so the technology became part of routines his clinicians already trusted, rather than being added on top. That's the key difference because most healthcare trials fail not because of the idea itself, but because they don’t fit into the busy schedule of overworked clinicians. For entrepreneurs facing a slow, regulated market, this is a lesson in earning the right to make changes. 

Topics Covered 

• The prevention problem — why a system that funds episodic care has no incentive to stop the crisis before it happens. 
• Building care first, tech second — how running a home care company turned product-market fit into a non-question. 
• Ambient monitoring in practice — optical sensors, smart mattresses, and predicting falls up to four weeks out. 
• Data as the long-term moat — why the longitudinal dataset, not the hardware, becomes the company's most valuable asset. 
• The national health record vision — the case for a federated, interoperable record and hospital-at-home at scale. 

Key Insights 

• Prevention is cheaper than almost anyone realizes, and the system ignores it anyway. A urinary tract infection caught early costs roughly $20 in antibiotics; missed, it can escalate to sepsis, a multi-week hospital stay near $20,000, and meaningful mortality risk — yet nothing in the funding model rewards catching it early. 
• The hard part isn't the AI, it's the clinical plumbing. Alerts are useful only if they appear in the workflows clinicians already use. That's why CHA sends its signals through the same electronic record that their teams have trusted for years, instead of asking busy staff to learn something new. 
• Owning the data eventually matters more than owning the device. Today, CHA is essentially an assistive tool with clinicians making every decision. Still, the continuous, anonymized picture of aging its building could become a defensible asset — even an early-warning system for the next pandemic. 

Links 

• Scaling with Clarity Podcast: https://www.linkedin.com/showcase/futureventures-podcast/ 
• CHA Group / CHAH AI Care: https://chah.ai/ 
• Robert Stanley on LinkedIn: https://www.linkedin.com/in/rjastanley/ 
• Future Ventures Corp: https://www.linkedin.com/company/future-ventures-corp 
• Watch on YouTube: https://youtu.be/OInk-HpvUOc 

About Robert Stanley 

Robert Stanley is the Founder and CEO of CHA Group, the parent company of State Home Nursing Care Services and CHA Technology. After a long career in technology delivering large-scale IT projects, he moved into home care a decade ago. He built one of Ontario's established providers before turning his focus to predictive, technology-enabled care. Today, he's pioneering Comprehensive Healthcare at Home, a model that combines AI-powered monitoring with clinical response to keep vulnerable seniors safe, independent, and out of the hospital. 

This podcast has been brought to you by the Capital Intelligence Platform: capital.futureventures.ca

Send us Fan Mail

Greg Miles is the founder and CEO of Milestone Gyno-Mics, a biotech company developing the first AI-powered, non-invasive blood test for endometriosis. He is one of the first bioinformatics PhDs in the U.S., with over 15 years of experience in genomics, diagnostics, and AI. He left a 10-year job at Agilent to focus on this project. His motivation is personal: he lost both grandparents to leukemia that was diagnosed too late, and he watched a loved one struggle for 12 years seeing many doctors before finally being diagnosed with endometriosis by a surgeon. 

This conversation matters because endometriosis affects more than 1 in 10 women, is the leading cause of infertility, and is invisible on imaging more than 95% of the time — leaving most women on a decade-long diagnostic odyssey. Greg's thesis is blunt: endometriosis behaves like cancer (a recent paper found it shares all eight hallmarks), so we should detect it like cancer. The result is a clear-eyed look at why women's health has been underfunded and overlooked, how AI changes the math on early detection, and why the next major breakthrough in medicine may be diagnosis, not treatment. 

Key Topics Covered 

1. The diagnostic gap in women's health — Why early detection exists for breast cancer but almost nowhere else, and what that absence costs women in lost years and lost fertility. 
2. Endometriosis as a cancer-like disease — How endo shares all eight hallmarks of cancer, and why that reframing should change how we diagnose and treat it. 
3. How the AI blood test works — Reading three distinct biological signals from a simple arm draw and using machine learning to turn 20,000+ data points into a single diagnostic signature. 
4. The economics of delayed diagnosis — The roughly $200K-per-patient cost of a 10-year delay, and why insurers, fertility clinics, and employers all have skin in the game. 
5. What investors should actually scrutinize — The four things that separate a real diagnostic from a hyped one, from sensitivity and specificity to the integrity of the held-out data. 

Key Insights 

• Early detection is the key to everything downstream. Catch endometriosis in the first couple of years, and hormone therapy and surgery are far more likely to work; wait, and the disease spreads, recurs, and can close the window to start a family. 
• The "yellow light" problem is the tell. Plenty of companies tout 90% accuracy while quietly parking 30–50% of patients in an inconclusive bucket — and no insurer pays for a test that can't commit to an answer. 
• Repurposed drugs may be the fast track to treatment. Because endometriosis is a systemic, inflammatory disease recently linked to some 600 comorbidities, existing drugs with cleared safety profiles — GLP-1s among them — could leap straight toward endometriosis trials. 

Links 

• Milestone Gyno-Mics: https://www.milestonegx.com/ 
• Connect with Greg Miles: https://www.linkedin.com/in/gregory-miles 
• Scaling with Clarity: https://www.linkedin.com/showcase/futureventures-podcast/

About the Guest 

Greg Miles is the founder and CEO of Milestone Gyno-Mics, a biotechnology company developing a non-invasive, AI-powered blood diagnostic platform for women's health. One of the first bioinformatics PhDs trained in the United States, he brings more than 15 years of experience across genomics, diagnostics, and artificial intelligence, including a decade at Agilent. His mission is to collapse the years-long diagnostic delay women face for conditions like endometriosis — starting with a single blood test.

Send us Fan Mail

Stanley Wei is the Founder and CEO of Pine AI, an autonomous agent platform that doesn't just answer questions — it picks up the phone, fills out the forms, and gets things done on behalf of consumers. Pine negotiates bills, cancels subscriptions, files complaints, resolves disputes, and navigates insurance claims autonomously. Before launching Pine, Stanley held leadership roles at Gore and invested in AI through Hillhouse Capital, watching the space evolve from the early deep learning era through the DALL-E inflection point that convinced him AGI was no longer a question of if, but when. 

This conversation is important because Stanley is developing a seldom-touched part of the AI stack: the unstructured, voice-based interface between agents and the physical world. Most AI products operate in digital spaces, like drafting or querying databases, but Pine trains proprietary voice models on real phone calls. This enables agents to interrupt naturally, manage silences, negotiate, and complete tasks. Understanding this reveals the future of consumer AI and how to compete against giants like OpenAI and Anthropic. 

Key Topics Covered 

1. The Matrix thesis for voice agents — Why the phone call is the channel that lets AI cross from the digital world into the physical one, and why this is fundamentally different from what ChatGPT or Copilot do. 
2. Building an AI company in 2024 vs. now — How model capability went from "100% hike to build an agent" to "everything is possible" in roughly twelve months, and what that means for product velocity. 
3. The unsustainable economics of AI customer acquisition — Why building product is now the easy part, why only Google and Meta own the demand side, and why hitting critical user mass has become survival-level urgent. 
4. Defending against the LLM giants — Why Pine trains its own voice model bottom-up on proprietary phone-call data instead of building on top of OpenAI or Anthropic, and where the foundation labs leave room for vertical specialists. 
5. Running a company without meetings — How Stanley designed Pine to be agent-friendly from the inside out: no code ownership, async by default, agents talking to other people's agents to coordinate work. 

Key Insights 

1. The biggest barrier to consumer AI adoption is not capability — it's trust and education. Most consumers don't yet know what AI can do, don't believe it can do it, and have to be walked through all three layers (pain, solution, proof) before they will pay. 
2. The structural shift in the AI economy has happened on the supply side, not the demand side. Coding agents made it cheap to build, but they did not create new distribution channels — so growth, not product, is now the binding constraint for almost every AI company. 
3. People have found some of the coolest ways to use Pine that the team never planned for. For example, one user who was laid off in 2025 used Pine to make money by buying rental cars cheaply and selling them for more, earning $3,000 on one deal. Another user used Pine to cut down a $5,000 credit card bill to $1,500. The product allows users to do things the creators never expected. 

Links 

Pine AI: https://www.19pine.ai/ 

Stanley Wei on LinkedIn: https://www.linkedin.com/in/stanleywei 

Future Ventures Corp: https://ca.linkedin.com/company/future-ventures-corp 

About Stanley Wei 

Stanley Wei is the Founder and CEO of Pine AI, an autonomous AI agent that takes action on behalf of consumers in the physical world — from negotiating bills to managing insurance disputes. Before Pine, he held leadership roles at Gore and was an AI investor at Hillhouse Capital, where he tracked the field through the deep learning era and the generative AI inflection point. He started Pine in 2024 out of personal frustration with the chores of being an international operator, splitting time between the US, Singapore, and the UK. 

Send us Fan Mail

Stan Christiaens is the Cofounder and Chief Data Citizen of Collibra, one of the companies that helped define the modern data governance category. What began in 2008 as a spinoff from a semantics research lab at the Free University of Brussels has grown into a global platform used by some of the world's largest enterprises to manage data trust, lineage, governance, and increasingly, AI oversight. Eighteen years in, Stan has a vantage point most operators do not — he has watched governance get sidelined every 5 to 10 years by the next shiny technology, and he has watched the same data problems resurface every time. 

This conversation matters because AI has changed the math. Companies that treated data as exhaust instead of as an asset are now discovering that their AI ambitions are bottlenecked by foundations they never built. Stan and Maxim get specific about what those foundations actually look like, why the Chief Data Officer role is at an inflection point, and why the iceberg of unstructured data — roughly 80 to 90 percent of an organization's information — is suddenly the biggest question on every data leader's desk. If you are building, advising, or selling into enterprises right now, this is the conversation about why data discipline is no longer optional. 

Key topics covered 

1. Data as asset vs. data as exhaust — why most organizations are still "growing up" to treat data as an asset, and why fragmentation from every shiny new technology makes the problem worse. 
2. The foundations of governance done well — find it, understand it, trust it, with assigned responsibility, a repeatable process, and a system of record sitting underneath. 
3. The evolution of the Chief Data Officer role — Gartner's five versions from defensive posture to data products to "startup person," with version 6 due, and why the AI moment is the CDO's biggest opportunity. 
4. Data confidence and the AI brain — why five-nines reliability for agents is achievable but requires scaffolding around the model, and why asking an LLM not to hallucinate misses the point. 
5. Selling into enterprise as a founder — why enterprises are slow by design, why they are never greenfield, and how to find the innovation pockets that let you accelerate. 

Three key insights 

You cannot fix data after the fact. Organizations that treat data as a byproduct for years and then suddenly need AI cannot retroactively make that data useful — they have to start treating it as an asset first, which is a cultural shift before it is a technical one. 

The model is not the problem. No matter how smart the frontier LLM gets, it will not make the right decision without the right context at the right time. The work is building the harness around the model — the responsibility, processes, and curated context — not chasing the next model release. 

Patience is the entrepreneur's hardest skill. Plan for a 10-year journey, not a three-year sprint, and make sure your business ambitions and your life at home stay in harmony — because the overnight successes everyone admires were a decade in the making before they looked obvious. 

Links 

• Collibra: https://www.linkedin.com/company/collibra 
• Stan Christiaens on LinkedIn: https://www.linkedin.com/in/stijnchristiaens/ 
• Future Ventures on LinkedIn: https://ca.linkedin.com/company/future-ventures-corp 

About Stan Christiaens 

Stan Christiaens is the Cofounder and Chief Data Citizen of Collibra, which he helped spin out of a computer science research lab at the Free University of Brussels in 2008. Over 18 years, he has built Collibra into one of the defining companies in the data governance category, working with many of the world's largest enterprises on how they organize, trust, and use their data. He is a recognized voice on the intersection of data governance, AI, and enterprise transformation. 

Send us Fan Mail

While most of Silicon Valley argues over whether SaaS is dead, Neeraj Singh is quietly running the experiment that might answer the question. He's the founder and CEO of BigBinary, a 15-year-old remote-first software consultancy, and Neeto, a growing suite of affordable alternatives to the bloated enterprise tools founders begrudgingly pay for every month. NeetoCal goes head-to-head with Calendly at $30 a year — less than a single month of the competition. NeetoSign takes on DocuSign. NeetoForm takes on Typeform. The product list keeps growing, and the marketing budget stays at zero. 

This conversation matters because Neeraj has spent his career doing the opposite of what most founders are told to do. He went remote 15 years before the pandemic forced everyone else into it. He refuses VC money and refuses to "go all in." He treats Slack as a place where nothing important should happen. He keeps engineers on the bench instead of maximizing utilization. He prices like a commodity in markets the gurus call winner-take-all. And he's still in business — profitable, growing, and building. For any founder rethinking pricing, team design, or what sustainable growth actually looks like in an AI-saturated market, this conversation is a different lens on what works. 

Topics Covered 

1. Remote-first before remote was a category — How Neeraj built BigBinary's writing culture by watching colleagues take remote calls between Oracle Tower 1 and Oracle Tower 2. 
2. Why GitHub is the source of truth, and Slack isn't allowed to hold anything important — The tool stack and the philosophy behind it. 
3. Bench time over utilization — Why creative work breaks under 60-hour weeks and what billable-hours culture gets wrong about engineering output. 
4. The 31st scheduling tool problem — Why entering a crowded market is fine, why "race to the bottom" is the wrong frame, and what Henry Ford, Honda, and Samsung teach about followers winning markets. 
5. The real reason SaaS prices keep climbing — Public SaaS companies spend 50%+ of revenue on sales and marketing, then raise prices to fund it; here's the alternative. 

Key Insights 

• Commodity pricing is not a weakness; it's an honest read of the market. If you're the 31st product in a category, you are a commodity by definition. Pretending otherwise and charging luxury prices alongside competitors with deeper pockets is the actual mistake. 
• Your biggest competition is your own costs. Borrowing a tip from Jason Fried of Basecamp: as long as your expenses are less than your income, no competitor can beat you. Most founders worry too much about their rivals when they should be focusing on controlling their own spending. 
• AI hasn't lowered software prices — it's been used to justify raising them. Developers are demonstrably more productive than they were five years ago. The math says prices should fall. They aren't. That gap is the opening for founders willing to take it. 

Links 

• Neeto suite of products: https://www.neeto.com/ 
• BigBinary: https://bigbinary.com/ 
• Neeraj on LinkedIn: https://www.linkedin.com/in/neerajsingh0101/ 
• Future Ventures Linkedin: https://ca.linkedin.com/company/future-ventures-corp 

About Neeraj Singh 

Neeraj Singh is the founder and CEO of BigBinary, a remote-first software consultancy he's been running for close to 15 years with a team of senior engineers based in India. He's the creator of Neeto, a growing suite of affordable SaaS products built to replace the bloated, overpriced tools founders are stuck paying for — NeetoCal, NeetoSign, NeetoForm, NeetoTicketing, and more. A longtime Ruby on Rails open-source contributor, Neeraj writes and operates in public on LinkedIn, X, and through Neeto's customer support — where every reply still comes from him or his team, not AI.

Send us Fan Mail

Hubertus Hofkirchner has spent four decades operating at the intersection of trade finance, technology, and monetary theory. He started his career at Citibank International in Vienna, designed a securities system that rolled out across smaller city banks throughout Europe, and later served as Director at Kreditanstalt Investment Bank. As a serial entrepreneur, he built one of the first online brokerages for Central and Eastern Europe, founded what he believes was the first true options exchange (sold to a British bank in 2008), and famously took over Austrian telecom operator Telering as a loss-making vehicle hemorrhaging hundreds of millions per year — turning it around and selling it to T-Mobile for €1.5 billion in 2005. 

This conversation is important because Hubertus isn't just another crypto supporter promising big changes from the sidelines. He's an Austrian-school economist and ex-investment banker who knows the traditional banking system inside out. Now, he's creating open-source tools to bring back something the world quietly lost in 1913—a peer-to-peer credit system that allows global trade to happen without banks acting as gatekeepers. With the Bitcredit Protocol now live, founders and investors should understand what's being built, why it’s happening now, and what it could mean for trade, investing, and the future of money. 

Key Topics Covered 

1. Why the Fiat experiment is failing — How political money since 1971 has produced asset bubbles, currency manipulation, and a $2.5 trillion trade finance gap that's strangling emerging markets. 
2. SWIFT as a geopolitical weapon — The Swiss trading company that got de-banked over legal Cuban sugar trades, and why hundreds of thousands of European businesses have lost banking access. 
3. The lost technology of bills of exchange — How world trade ran smoothly without internet, intermediaries, or persistent trade imbalances on the gold standard pre-1913, and why the UN's 2017 Model Law revived the legal foundation. 
4. What Bitcredit Protocol actually does — How E-cash technology, Bitcoin main chain settlement, and a decentralized mint network ("wildcats" operating in "cowders") combine to create a self-liquidating currency layer on top of Bitcoin. 
5. Four ways capital can be deployed in the new system — From buying bills of exchange and money-market lending to mints, to providing guarantee capital and running a mint yourself. 

Key Insights 

• Banks should never have been allowed to create money. Money creation belongs with the productive sector — companies shipping real goods through supply chains — not with central banks issuing currency against war bonds or commercial banks expanding credit against equities and real estate. Every major monetary distortion of the last century traces back to this single category error. 
• El Salvador's plan to use Bitcoin isn't fully complete. While making Bitcoin legal money helps store its value, it doesn't cover how businesses extend credit. Without a way to measure trade credit in Bitcoin, the country still depends on the politics of the fiat currency that supports its economy. 
• Bitcoin helps keep energy grids stable instead of causing problems. Research shows that countries with active Bitcoin mining can cut energy costs by 20–30 percent because miners turn on and off based on supply. This means there's no need for expensive equipment that only runs during peak times and often sits idle. 

Links 

• Bitcredit Protocol: https://www.bit.cr/ 
• Hubertus on LinkedIn: https://www.linkedin.com/in/hofkirchner/ 
• Future Ventures on LinkedIn: https://ca.linkedin.com/company/future-ventures-corp 

About the Guest 

Hubertus Hofkirchner is the founder of Bitcredit Protocol, a Bitcoin-native trade finance protocol focused on rethinking trust, credit, and global commerce infrastructure. A serial fintech entrepreneur, he is the former CEO of Austrian telecom operator Telering (acquired by T-Mobile for €1.5 billion) and an economist focused on the intersection of monetary systems and trade finance. He leads monthly seminars at the Hayek Institute in Vienna. 

Send us Fan Mail

Dave Hertig has spent his career watching CEOs up close — first as a business journalist who interviewed more than 100 of them for UBS alone, then as the founder of Boom, where he now focuses on CEO performance under pressure. Along the way, he developed a conviction that became the spine of his work: the people the rest of the company looks up to have blind spots like everyone else, but those blind spots carry outsized consequences. Get the CEO right and the whole system compounds. Get it wrong, and no strategy, market, or product can save you. 

This conversation matters because most of what's written about CEO performance is written for the Fortune 500 — the leaders who get six-month onboarding handlers, custom briefing teams, and curated executive networks. The CEOs running companies between 50 and 1,000 staff are running on a fraction of that support, often with no one in the building willing to push back on them honestly. Dave has built a system to fill that gap, and the principles behind it apply whether you're leading 30 people or 3,000. 

Key Topics Covered 

1. The blind spots that quietly shape every CEO's decision — Why "why aren't more people like me?" is the most common — and most dangerous — pattern Hertig sees in founders and hired CEOs alike. 

2. Hire for your weaknesses, not against them — The Steve Jobs / Tim Cook arc as the definitive case study in pairing vision with execution, and why trying to fix a weakness rarely beats hiring around it. 

3. The adversarial gap — Why genuine pushback is the single hardest thing for a CEO to get inside their own company, and what happens to the people who try to give it. 

4. Accountability vs. outcome in the eyes of investors — The distinction between what a founder can control (input), partially control (output), and never guarantee (outcome) — and why integrity and transparency matter more than hitting the number. 

5. The CEO Sparring System — How a boxing-inspired protected room lets CEOs stress-test Board decks, investor pitches, firing decisions, and press interviews before they go live in the real world. 

Three Key Insights 

Judgment is more important in the AI era, not less. AI can help analyze data, compare options, and speed up decisions. It can even imitate emotional understanding. But it can't develop real emotional intelligence, read a room, or replace the human responsibility that leaders have. The polished answers from large language models make a CEO's judgment more challenging — and more valuable — than ever before. 

Authenticity is not the same as showing everything you are. The strongest CEOs Hertig has worked with put on a "uniform" when they walk into the role — measured, deliberate, strategic about what they say — while still ensuring the job genuinely matches their strengths and what gives them energy. That gap is professionalism, not inauthenticity. 

A progressive CEO wants to keep growing — personally and commercially. The CEOs worth working with are still curious, still hungry for pushback, still aware that their business sits inside a larger system. The ones who have hit a personal ceiling and stopped growing are the ones whose companies stop growing too. 

Links 

• Dave Hertig on LinkedIn: https://ch.linkedin.com/in/davehertig 
• Boom — CEO Sparring System: https://boom.ceo/sparring/ 
• Future Ventures Corp: https://ca.linkedin.com/company/future-ventures-corp 

About Dave Hertig 

Dave Hertig is the founder and CEO of Boom, where he focuses on CEO performance under pressure and works with leaders of medium-sized businesses navigating high-stakes moments. He is the creator of the CEO Sparring System, a boxing-inspired format that gives CEOs a protected room to pressure-test their most important pieces of work before deploying them in the real world. Before founding Boom, Dave spent years as a business journalist and content strategist, interviewing hundreds of CEOs across industries — work that gave him the pattern recognition behind his current focus on identity, judgment, and the human side of executive leadership.

Send us Fan Mail

Omar Sahyoun has spent two decades building at the edge of where consumers, technology, and physical commerce meet. He co-founded TeamBuy and DealFind, two of Canada's earliest daily-deal platforms that collectively scaled past 4 million members. He moved into fintech as a senior operator at Ariel and Purpose Financial, helping bring same-day digital lending into the mainstream. Today, as Managing Partner at Brand-FX, he runs three operating companies — Shoply (SMB commerce for retail and hospitality), Wack Jack (a 15-year-old Canadian consumer marketplace), and Go CXM (a CPG platform serving brands like Monster Energy, with one engagement spanning 30,000 sales reps). 

What I like about Omar's perspective is that he refuses to pick a side in the AI debate. He's not predicting that AI agents will eat retail, and he's not romanticizing the corner store either. Roughly 70% of commerce still happens in a physical location with a real person — that's the number he keeps coming back to —, and his bet is on making that experience smarter, not replacing it. Shoply gives a single restaurant or small retailer the same kind of personalization engine that used to require an Amazon-sized budget. Go CXM helps CPG brands finally talk to the customers their retailers have always controlled. If you're building, advising, or investing anywhere in consumer commerce right now, the conversation is worth your time — Omar has strong opinions about where the AI dollars are being wasted. He's earned the right to have them. 

Key Topics Covered 

1. The 70% problem. Most "future of commerce" conversations skip over the fact that roughly 70% of buying still happens in a physical store. Omar's whole thesis starts here. 
2. What Shoply does for a single restaurant or shop. Average order values up 20%. Table turnover up 40-50%. Repeat visits 2-3x. The numbers Omar shared on what happens when a small operator finally gets the kind of personalization Amazon has had for a decade. 
3. Who owns the customer — Why CPG brands are no longer content to let retailers control the customer relationship, and what tools like Go CXM do to close that gap. 
4. Agentic commerce, honestly — Omar's contrarian view that AI agents are already commoditized, and where the real durable advantage actually sits. 
5. The future of retail real estate — How stores evolve into digitized showrooms, what that means for landlords and logistics, and why face-to-face isn't going anywhere. 

Three Key Insights 

• AI agents aren't the moat — data is. Most of the AI conversation focuses on agents and coders, but Omar argues the winners will be data scientists who can take an LLM and tune it to a specific niche. Without clean, capturable, current data, none of it works. 
• Cohort segmentation isn't personalization. What the industry has called personalization for a decade is really just sending similar messages to broad cohorts. Real hyper-personalization means two customers in the same segment receive genuinely different messages — and that capability is only now becoming operational at scale. 
• SMBs adopt tech that disappears. The hardest part of building for small operators isn't the AI — it's the interface. Owners want to press one button and have it done. "Do it for me" beats "do it with me" almost every time. 

Links 

• Brand-FX: https://brand-fx.com/ 
• Omar Sahyoun on LinkedIn: https://www.linkedin.com/in/omar-sahyoun/ 
• Future Ventures Corp: https://ca.linkedin.com/company/future-ventures-corp 

About the Guest 

Omar Sahyoun is the Managing Partner at Brand-FX, a family-office-backed group that's building three companies in consumer technology, CPG, and retail. He co-founded TeamBuy and DealFind, helping Canada's first daily-deal marketplaces grow to over 4 million members. Omar also held leadership roles in digital lending at Ariel and Purpose Financial. With a background in accounting and management consulting from McGill, he's lived and built businesses in five countries and is now based in Toronto. 

Send us Fan Mail

Andrew Ackerman has sat in nearly every chair around the startup table. He's a serial entrepreneur, an angel investor, a venture capitalist, an accelerator operator at Dream Adventures and Reach Labs (the venture arm of the National Association of Realtors), an adjunct professor at NYU, and the author of The Entrepreneur's Odyssey — a story-driven guide to building startups in the real world. He's invested in more than 70 companies, most in PropTech and ConTech, and is currently building a holding company that buys construction services businesses and rewires their economics with proprietary AI. 

This conversation matters because Andrew has watched the same mistakes kill startups for two decades, and he can name the exact link in the chain where each one snapped. He's also one of the few investors willing to tell a founder their go-to-market is identical to the eleven other decks on his desk that week — and explain why that's not actually their fault. If you're building a company, raising capital, or investing in either, this episode is a masterclass in how to test your assumptions before they bankrupt you. 

Key Topics Covered 

A startup is a chain — one bad link, and you've got nothing. Andrew explains how investors figure out which link to test first instead of guessing at random. 

What it's like to read 1,000 pitch decks in two months. When a dozen near-identical startups show up, Andrew explains how he picks the team that will eat the rest. 

Construction is less productive than it was 50 years ago. We get into the manufacturing comparison that's been quietly making things worse, and why AI is finally rewriting who captures the value. 

The index card trick that kills bad features before you build them. Andrew walks through the prototyping exercise that costs about three bucks and a stolen bank pen. 

Why did Andrew write a story-driven book instead of another bullet-point business read? Plus, the reason Genesis comes before the Ten Commandments — and why founders should care. 

Three Key Insights 

Founders are always fundraising — the only variable is allocation. Spending ten to twenty percent of your time on relationships when you don't need money is the difference between raising on warm intros and grinding through eighty percent of your week on cold outreach when you do. 

The three dimensions of pain determine whether you have a business. Intensity (hammer-on-thumb versus hangnail), prevalence (does anyone else actually have this problem), and frequency (once a lifetime or once a week) — score high on all three, and you have a venture-scale opportunity. Score on two and you're working harder than you need to. 

A new technology or rule change doesn't give you a unique idea — it gives the same idea to a dozen other founders at the same time. Andrew's seen it happen over and over. The team that wins isn't the one that thought of it first. It's the one with a sharper go-to-market, better people, or the guts to do the opposite of what the other eleven decks on his desk are doing.  

Links 

• Andrew's website: https://www.andrewbackerman.com/ 
• Andrew on LinkedIn: https://www.linkedin.com/in/andrewbackerman 
• Future Ventures Corp: https://ca.linkedin.com/company/future-ventures-corp 

Guest Bio 

Andrew Ackerman is a serial entrepreneur turned venture investor who has spent two decades advising startups, accelerators, venture studios, and corporate innovation platforms. He has invested in more than 70 startups, previously helped build Dream Adventures and Reach Labs for Second Century Ventures, teaches entrepreneurship at NYU, and writes extensively on venture capital and innovation. His new book, The Entrepreneur's Odyssey, is a story-driven guide to building startups in the real world.

Send us Fan Mail

John Cowan was about to do what he'd done a hundred times before — make the calls, set the meetings, walk a young founder into the venture capital fundraising process. In a split second, he backed out. He couldn't morally send another kid into a system he knew was rigged against him: four cycles of dilution, ratchets, protective provisions, career death. Six or seven months of thinking later, he came back with an essay called The Theory of Venture Reciprocity, a new investment instrument called the SAFER, and a firm — Nextwave Partners — built to do venture differently. 

This conversation is for founders who've ever felt like they were just performing instead of truly pitching when talking to a VC. John explains how the industry has shifted from focusing on funding new ideas to prioritizing the size of the fund, what founder-friendly capital really looks like in real life, and where he's placing his bets for the next ten years. If you're raising money, advising, or managing investments with $3 million to $50 million in revenue, this hour will be one of the most honest and helpful you'll have this week. 

What we covered 

1. The structural collapse of venture capital. Why the 2009 shift to fee-driven economics turned VCs into venture banks — and what that did to founders. 
2. The SAFER instrument. John's alternative to the SAFE — re-centering the founder-investor relationship around revenue instead of equity. 
3. Killing the Unicorn cult. Why power-law thinking went from observation to ideology, and how Nextwave applies long-tail economics to startup investing instead. 
4. The machine economy thesis. Why John believes we're at the start of a long arc of innovation on par with electrification, and where the real money will be made. 
5. Geographic alpha and coalition funds. How distributed talent is reshaping where capital should flow — and the fund structure John thinks replaces the traditional GP model. 

Three insights worth sitting with 

• Revenue is the clearest indicator of early progress. If a customer is willing to pay for your product, that message is stronger than any pitch deck — and it aligns the goals of founders and investors instead of creating a zero-sum battle over ownership. 
• The Titans of rail didn't get rich on rail technology. They got rich on the economies that formed around it — towns, real estate, industry. The same logic applies to the machine economy: own the infrastructure, not just the software. 
• The next generation of transformative companies will not come out of Silicon Valley. Underserved regions — Florida, the Nordics, Latin America — are producing technical talent that the traditional VC hubs are structurally incapable of touching. 

Links and resources 

• Next Wave Partners: https://nextwave.partners/ 
• John on LinkedIn: https://www.linkedin.com/in/johncowan1 
• Future Ventures Corp: https://ca.linkedin.com/company/future-ventures-corp 

About the guest 

John Cowan is the General Partner at Nextwave Partners, a venture studio and investment firm focused on the era of autonomy and the machine economy. He's a 25-year founder, CEO, and strategist who has raised millions in venture capital across his career and become one of the more vocal critics of the traditional VC model. He's the author of Venture Capital 2.0: Building, Financing and Scaling Startups in the Post Power Law Era, with a second book on financing the machine economy due out shortly.