Web3 Unfiltered

Collins Victory Odabi

Conversations with tech founders, builders, and analysts — cutting through hype to explain what actually works in crypto, blockchain, and decentralised technology.

  1. 3 Sept

    Why AI Won't Fix Your Company's Problems — It Will Amplify Them | Rob Broadhead

    Good architecture rarely makes headlines, but bad architecture eventually becomes impossible to ignore. Behind every app and dashboard sits a set of decisions about infrastructure, data flow, and scalability — and when those decisions are made poorly, organizations spend years fixing problems built into the foundation. Rob Broadhead is a veteran software architect, entrepreneur, and founder of RB Consulting. Over 30 years, he has designed enterprise systems, advised businesses on technology strategy, and helped organizations avoid costly mistakes from poorly planned technology decisions. He also hosts the Devpreneur Podcast. In this episode, Collins Victory Odabi sits down with Rob to explore the most common architectural mistakes companies make, how to turn technology from a cost center into a genuine growth driver, and why AI amplifies whatever problems already exist in a company rather than solving them. In This Episode, We Discuss The most common architectural mistake is building only for today, not for growth. Rob describes companies painting themselves into a corner with systems that work perfectly at launch but require costly rebuilds once growth arrives faster than expected — the fix is asking "what happens if this grows 10x" at the design stage, not after. Pressure-testing assumptions matters more than accepting the first answer. Rob's example: asking a client if only one user will ever access the system, getting a confident "yes," then finding out six months later that families or organizations need multiple logins — a gap that better upfront questioning would have caught. Modernizing legacy systems means more than a straight lift-and-shift. Rob argues that migrating an old system into a modern stack without also adopting new capabilities — mobile access, modern interfaces — wastes the investment; if you're already spending the time and money, take full advantage of what modern technology actually offers. Fundamentals matter more than specific programming languages for developers building long-term careers. Rob has shifted his mentoring advice toward architecture, software development lifecycle, and problem definition, since knowing a particular language is becoming less valuable as AI increasingly handles implementation itself. AI codes like a junior developer — technically capable, but incapable of system-level thinking. Rob argues that without strong guardrails, clear requirements, and human code review, AI-assisted development produces working code littered with redundancy and technical debt, since AI stops at the first brute-force solution rather than generalizing and refining it. About Rob BroadheadRob Broadhead is a veteran software architect, entrepreneur, and founder of RB Consulting, and host of the Devpreneur Podcast (Building Better Developers).🌐 rb-sns.com | 📧 rob@sns.com🎙️ develpreneur.com Key Timestamps / Chapter Markers[0:04] Introduction and episode framing[2:03] From coding in grade school to founding RB Consulting in 2001[6:32] The most common architectural mistakes organizations make[8:53] Aligning technology decisions with long-term business objectives[13:35] Turning technology from a cost center into a growth driver[15:54] Where to start when modernizing legacy technology infrastructure[19:14] What separates developers who thrive long-term from those who struggle[26:50] Why system design matters even more in the age of AI-assisted coding[30:47] The principle behind building systems meant to last for years Connect with CollinsLinkedIn: linkedin.com/in/collins-odabi-266620380Guest enquiries: podmatch.com/member/web3unfiltered Until next time, stay sharp, stay curious, and keep building.

  2. 3 Sept

    Why Traditional Software Development Was Always Obsolete | Nessim Btesh

    For all the progress in technology, internal systems are still broken. Teams rely on spreadsheets, disconnected tools, and manual workflows never designed to scale — and despite years of software investment, the problem persists because the issue isn't the tools, it's how systems are built in the first place. Nessim Btesh is the founder and CEO of AgentUI, an AI-first platform that turns messy processes, spreadsheets, and ideas into fully functional internal tools through simple interaction with AI, instead of writing code or stitching together multiple platforms. In this episode, Collins Victory Odabi sits down with Nessim to explore why so many digital transformation efforts fail despite heavy investment, why he believes traditional software development cycles were always obsolete for most companies, and what treating AI as operational infrastructure actually looks like in practice. In This Episode, We Discuss Most companies don't realize how much technical debt they're carrying until the one developer holding it all together leaves. Nessim describes disconnected tools and siloed databases that never talk to each other as the default state for most organizations, creating operations that quietly become slower and more fragile over time. Digitizing even a single manual process can produce outsized efficiency gains. Nessim recounts joining his family business in 2019, where fully manual operations were digitized from scratch — the resulting growth came simply from collecting data correctly in one place rather than scattering it across systems. AgentUI's core value is standardization plus a security layer, not just AI-generated code. Nessim explains that every project built on the platform follows the same technical documentation and guardrails, meaning a company isn't left stranded if the person who built a system leaves — the AI, not an individual, becomes the consistent builder. Traditional software development cycles were built for regulated industries like banks, not startups, and never should have been applied universally. Nessim argues these rigid workflows slowed down smaller companies for years, and AI is now making that mismatch obvious by compressing weeks of work into hours. Nessim's guiding principle is "build a tool that builds a tool, never build the tool itself." Rather than building an AI that does customer support directly, his team builds an AI that builds the AI that does support — a meta-approach he credits with keeping AgentUI adaptable as needs evolve. About Nessim BteshNessim Btesh is the founder and CEO of AgentUI, an AI-first platform turning manual processes and spreadsheets into standardized, secure internal tools without traditional coding.📱 Instagram: @therealnassim Key Timestamps / Chapter Markers[0:04] Introduction and episode framing[2:09] Programming since age 11 and building AgentUI[3:54] Why digital transformation efforts fail to improve operations[7:53] Treating AI as operational infrastructure, not just a productivity tool[11:33] Why traditional software development cycles were always obsolete[19:23] Why Web3 teams struggle with internal tooling despite technical skill[21:49] What a truly adaptive, self-healing internal system looks like[23:39] The one principle founders should adopt in the AI era Connect with CollinsLinkedIn: linkedin.com/in/collins-odabi-266620380Guest enquiries: podmatch.com/member/web3unfiltered Until next time, stay sharp, stay curious, and keep building.

  3. 3 Sept

    Why "Good SEO Doesn't Always Mean Good GEO" | Cassie Clark

    Search is no longer about indexing information — it's about interpreting, filtering, and deciding what gets surfaced at all. AI systems don't just rank content, they summarize it and present a single version of reality. Once selection replaces ranking, exclusion becomes invisible: things don't appear lower on a page, they simply don't exist. Cassie Clark is a fractional content strategist, CMO of SKANE, and host of the Found in AI podcast. She started the show after finding contradictory advice everywhere about AI search optimization and no one actually testing what works. In this episode, Collins Victory Odabi sits down with Cassie to explore how AI engines actually decide what to cite, why strong traditional SEO doesn't guarantee visibility in AI answers, and the practical framework she uses to help brands stay discoverable. In This Episode, We Discuss AI citations come after the decision has already been made, not before. Cassie compares AI engines to asking your grandma for advice — they verify a brand across its website and social presence first, then surface supporting links only to justify a conclusion they'd already reached. Good SEO doesn't guarantee good GEO (generative engine optimization). Cassie found that smaller brands active across YouTube, Instagram, LinkedIn, and Reddit often get cited over bigger, SEO-strong competitors — and cites a stat that nearly 60% of AI citations come from sites ranked 11-100, not the top 10. Rebranding without consistent new signals can actively suppress a brand in AI answers. Cassie explains that if a company's training data still reflects an old positioning while current content reflects a new one, the mismatch can make it harder for AI systems to surface — the fix is consistent, repeated messaging that overwrites the old signal. Everything is training data now — customer reviews, social comments, even LinkedIn replies. Cassie describes watching the industry flip within a single week from "GEO is just rebranded SEO" to recognizing it as something genuinely different, since brand visibility now depends on signals scattered far beyond a single website. Cassie's practical framework is FSA: Freshness, Structure, Authority. Freshness means updating content every 3-6 months; structure means clear headings, TL;DRs, and schema markup; authority means repurposing the same core message across LinkedIn, YouTube, Reddit, and press releases so it's independently verifiable everywhere. About Cassie ClarkCassie Clark is a fractional content strategist, CMO of SKANE, and host of the Found in AI podcast, focused on how AI search systems rank, cite, and exclude content.🌐 cassieclarkmarketing.com Key Timestamps / Chapter Markers[0:04] Introduction and episode framing[2:50] How a gap in AI search advice led to starting Found in AI[5:00] What changed moving from traditional search to AI answer engines[6:49] How AI systems actually decide what to cite or ignore[8:56] Where bias and exclusion show up in AI search today[15:27] What actually matters in AI search versus recycled SEO noise[18:03] The FSA framework: freshness, structure, authority[22:24] Where AI-driven search is heading, and what builders should prepare for Connect with CollinsLinkedIn: linkedin.com/in/collins-odabi-266620380Guest enquiries: podmatch.com/member/web3unfiltered Until next time, stay sharp, stay curious, and keep building.

  4. 3 Sept

    You Don't Have a Commission Problem — You Have a Data Problem: The Hillel Zafir Case Study

    There's a simple assumption behind almost every compensation system: reward the right behavior, and people will produce the right results. But what happens when a company doesn't actually know what the right result is? A CRM says a deal closed. Finance sees a different number once discounts, returns, and adjustments enter the picture. Which number should determine a commission? In this episode, Collins Victory Odabi uses the work of Hillel Zafir and his company IncentX as a case study for a sharper argument: many companies believe they have a commission problem when they actually have a data problem, and that idea extends far beyond sales compensation into any system built on disconnected data. In This Episode, We Discuss A CRM recording a closed deal isn't the same as the complete economic reality of that transaction. Collins explains that the full picture — invoices, discounts, payment terms, return adjustments — often only becomes visible in the financial system, creating a dangerous gap between what a company thinks it's measuring and what it's actually measuring. People respond rationally to whatever a compensation system actually rewards, even when that produces the wrong outcome. Collins walks through how rewarding revenue volume over margin can make a salesperson closing low-margin deals look like a stronger performer than one generating less revenue but significantly more profit — not because of bad behavior, but because the system was built to reward the wrong metric. IncentX's real value isn't collecting more data — it's connecting incentive calculations back to the specific transaction that created them. Collins argues this traceability turns a commission dispute from an argument over a number into an investigation of the actual underlying economic event: which invoice, which margin, which pricing rule produced the payout. Transparency only matters if you're looking at the right underlying information. Collins pushes back on Web3's long-standing emphasis on transparency and verifiability, arguing a fully transparent system built on bad data can still produce bad decisions — the technology doesn't fix a broken incentive structure by itself. The sharper question isn't "are employees hitting their targets" — it's "are our targets aligned with the outcomes we actually want, and can we prove it from the transactions themselves." Collins argues that if an outcome can't be traced back to the activity that produced it, a company may have a reporting system rather than a genuine measurement system. Key Timestamps / Chapter Markers[0:01] Introduction and episode framing[Segment] When different systems tell different stories about the same deal[Segment] Why people optimize for whatever gets rewarded, right or wrong[Segment] How transaction-level traceability changes commission disputes[Segment] Why transparency alone can't fix a system built on bad data Connect with CollinsLinkedIn: linkedin.com/in/collins-odabi-266620380Guest enquiries: podmatch.com/member/web3unfiltered Until next time, stay sharp, stay curious, and keep building.

  5. 3 Sept

    The Problem Before the Product: What Tiffany Mittal's Utility Ranger Teaches Web3 Builders

    Technological disruption is often framed as beginning with an extraordinary invention — a new blockchain, a new AI model. But sometimes the most interesting opportunities begin somewhere far less exciting: with someone frustrated by a problem everyone else has simply learned to tolerate. In this episode, Collins Victory Odabi uses the story of Tiffany Mittal, a multifamily real estate investor who built Utility Ranger after hitting a wall with utility billing, as a case study in a different kind of innovation — one that doesn't invent new behavior, but makes an existing one work better for the people traditional providers overlooked. In This Episode, We Discuss Not every painful problem is a business opportunity — the useful ones sit in a specific gap. Collins argues the best opportunities are recurring financial or operational losses where existing solutions are too expensive, too complicated, or built for someone else entirely — exactly the gap smaller landlords fell into with utility billing infrastructure designed for large operators. Operational context beats outside expertise when building software. Because Tiffany experienced the utility billing problem directly rather than discovering it as an outsider, she understood exactly where time was wasted, where money disappeared, and what workarounds people were already using — insight that's difficult to replicate without having lived the problem. Growth doesn't only come from raising revenue — it can come from stopping leakage. Collins points to unused software licenses, payment delays, billing errors, and operational duplication as the "boring" problems that rarely make headlines but compound into serious value at scale, exactly the angle Utility Ranger takes by improving landlord cash flow without raising rents. The biggest mistake in emerging tech is starting with the technology instead of the problem. Collins argues the better question isn't "what can blockchain do," but "where are existing systems creating unnecessary friction, and could a different architecture remove it" — a shift he argues applies equally to AI, fintech, and decentralized systems. The best opportunities often hide inside industries where people have stopped questioning "that's just how things work." Collins frames Tiffany Mittal's story as proof that entrepreneurship doesn't require inventing something unprecedented — sometimes it just requires asking why an accepted inefficiency has to persist. Key Timestamps / Chapter Markers[0:04] Introduction and episode framing[Segment] The problem before the product: what Utility Ranger actually solved[Segment] Why operational context is a founder's biggest advantage[Segment] Follow the money leak: growth through stopping loss, not chasing revenue[Segment] Why this lesson applies directly to Web3, AI, and fintech builders Connect with CollinsLinkedIn: linkedin.com/in/collins-odabi-266620380Guest enquiries: podmatch.com/member/web3unfiltered Until next time, stay sharp, stay curious, and keep building.

  6. 3 Sept

    Why "Nobody Knows" How Your Car's Data Is Actually Being Used | Kenneth Chester

    Modern mobility is no longer just about transportation. Cars are becoming data-generating systems, infrastructure is becoming intelligent, and AI is increasingly embedded into how vehicles operate. But every connected system generates data, every digital layer consumes energy, and most people interact with these systems without understanding how they work or what they cost. Kenneth Chester is a nationally syndicated automotive journalist, tech communicator, and CEO of Tech Mobility Productions. With over 30 years in media, he has become a leading voice on the convergence of mobility, artificial intelligence, personal privacy, and climate impact. In this episode, Collins Victory Odabi sits down with Kenneth to explore why most vehicle data collection happens with little to no consumer visibility, why the US has no national framework governing AI or personal data, and why he believes regulation, not awareness alone, is the only real path to consumer control. In This Episode, We Discuss If your car is 10 years old or newer, it's very likely collecting data on you, often without meaningful consent. Kenneth argues nobody actually knows how that data gets used downstream, since the US has no national framework requiring transparency, unlike Europe's "right to be forgotten" protections. "If it's free, you're the product" applies directly to modern vehicles and connected apps. Kenneth cites a real case of a company legally reselling data that was originally obtained through hacking to third-party vendors — a practice he calls flatly wrong, and a sign of how little oversight currently exists. AI training data creates its own bias problem, and Wikipedia's recent AI restrictions are a warning sign. Kenneth points to a tightening feedback loop where AI-generated content gets folded back into the sources large language models train on, compounding existing biases rather than correcting them. Sustainability in mobility isn't one-size-fits-all — it's entirely local. Kenneth contrasts his Midwest utility's roughly 75% renewable energy mix against coal-dependent grids elsewhere, arguing that even genuinely green technologies like small modular nuclear reactors carry their own unresolved trade-offs, like spent fuel storage. Kenneth's core message is blunt: demand regulation, because trust alone hasn't worked. He argues corporations have a poor track record of self-accountability without government oversight, and that individuals currently have no real way to verify whether companies are actually doing what their privacy notices claim. About Kenneth ChesterKenneth Chester is a nationally syndicated automotive journalist, tech communicator, and CEO of Tech Mobility Productions, covering the convergence of mobility, AI, privacy, and climate impact. Key Timestamps / Chapter Markers[0:04] Introduction and episode framing[2:01] Kenneth's 40-year path from auto consulting to national media[3:45] What's actually happening under the hood of "smart mobility"[5:21] What your vehicle's data collection really involves[8:19] How AI is changing data collection in transportation systems[10:49] The hidden climate costs behind mobility's digital infrastructure[15:26] Could decentralized technology address data ownership in mobility?[17:24] What individuals should watch for in an increasingly connected world Connect with CollinsLinkedIn: linkedin.com/in/collins-odabi-266620380Guest enquiries: podmatch.com/member/web3unfiltered Until next time, stay sharp, stay curious, and keep building.

  7. 3 Sept

    Why Personal Branding Isn't Self-Promotion — It's Strategy | Lisa Gibson

    In fast-moving industries like Web3, technology often takes center stage — protocols, tokenomics, infrastructure. But behind every successful project are people who must build trust and credibility in a noisy, competitive environment. Leaders who communicate their vision clearly often gain an edge that technology alone cannot provide. Lisa Gibson is president and founder of Ignite Communications, an award-winning communications executive, bestselling author of Shine the Spotlight on You, and speaker with 30 years of experience. Before launching her firm, she served as Chief of Staff and Head of Communications for Microsoft Canada. In this episode, Collins Victory Odabi sits down with Lisa to explore why personal branding is strategic rather than self-promotional, how to overcome imposter syndrome when sharing your expertise publicly, and how leaders should actually integrate AI into their communications without losing authenticity. In This Episode, We Discuss Personal branding drives more than sales — it drives talent attraction and retention too. Lisa found that leaders she helped amplify on LinkedIn attracted an equally large audience of prospective and current employees as they did customers and investors, reframing personal branding as community-building rather than self-promotion. A strong personal brand starts with 3-4 "brand pillars," not posting everywhere constantly. Lisa's process starts with defining what you want to be known for, aligning it to your real experience and values, then identifying exactly who you need to reach — prioritizing quality and consistency over sheer frequency. Stop watching for likes — validation often shows up in ways you can't see. Lisa describes posts that got minimal engagement leading directly to new clients through private messages, and people telling her in person they'd seen her content despite never once engaging with it online. AI is a genuine ally for communicators, but only when it's trained on your actual voice and never published without a critical human review. Lisa recounts a factual error slipping into AI-drafted content when she rushed without fact-checking — and argues strategic thinking, creativity, and crisis judgment remain distinctly human skills AI can't replace. The biggest lesson from her time at Microsoft was Satya Nadella's growth mindset: the willingness to try things and fail fast. Lisa credits that mindset directly with giving her the confidence to leave a 30-year corporate career and figure out, largely from scratch, how to build her own consulting business. About Lisa GibsonLisa Gibson is president and founder of Ignite Communications, an award-winning communications executive and bestselling author of Shine the Spotlight on You, helping leaders build personal brands and communicate with clarity. Key Timestamps / Chapter Markers[0:04] Introduction and episode framing[2:28] From 30 years in corporate communications to founding Ignite Communications[4:13] Why leaders should invest in building a personal brand[6:44] The core steps to building a strategic, authentic personal brand[9:44] Overcoming imposter syndrome when sharing your expertise[15:29] Becoming a credible voice in a rapidly evolving industry like Web3[18:35] Is AI a friend or foe for communications professionals?[23:08] The biggest leadership lesson from her years at Microsoft[24:58] What led her to leave corporate life and start her own firm Connect with CollinsLinkedIn: linkedin.com/in/collins-odabi-266620380Guest enquiries: podmatch.com/member/web3unfiltered Until next time, stay sharp, stay curious, and keep building.

  8. 3 Sept

    Why Michael Storm Believes Bitcoin Is Just Another Fiat Currency | Michael Storm

    Decentralized identity promises to remove central authority through blockchains and consensus. But even in decentralized systems, we still see validators, governance structures, and token-based power dynamics. Are we eliminating control, or just redistributing it? Michael Storm is a longtime student of economic theory, law, and human systems, with a strong focus on anarchism and voluntarism — the idea that authority itself is largely an illusion, and that most human interactions already operate without it. In this episode, Collins Victory Odabi sits down with Michael to explore what he means by "authority is an illusion," why he believes large systems inevitably drift toward centralized control regardless of scale, and why he argues Bitcoin, despite its technology, is functionally a fiat currency and a collectible rather than a genuine break from centralized money. In This Episode, We Discuss Michael distinguishes between authority you trust and authority that forces compliance. The state, he argues, claims the second kind purely through force and numbers — codifying political and social opinions into "law" that isn't law in the natural sense, unlike gravity, which no vote can repeal. Voluntary coordination breaks down not because it doesn't work, but because of scale and biological limits on trust. Michael points to research suggesting humans can only meaningfully track relationships with roughly 60-150 people — beyond that threshold, irresponsible actors go unnoticed and management systems inevitably emerge to compensate. Bitcoin is fiat currency by the actual economic definition, not a break from it. Michael argues fiat means an instrument of exchange without intrinsic store of value — and that Bitcoin behaves exactly like a collectible, valuable among people willing to trade it but useless without a market of buyers, unlike gold's inherent utility. Every Bitcoin transaction is permanently ledgered, which Michael argues undermines the claim that it offers protection from government. Wallets can be traced and seized even without identifying the person directly, meaning the appearance of anonymity and freedom from central control is, in his view, largely illusory. A stateless justice system already exists in miniature, and Michael's example is Judge Judy. Two parties voluntarily bring a dispute to an impartial arbiter who rules based on evidence — a working, everyday model, he argues, for how disputes could be resolved without a monopolistic state judicial system. About Michael StormMichael Storm is a longtime student of economic theory, law, and human systems, focused on anarchism and voluntarism as an alternative framework for understanding authority and coordination. Key Timestamps / Chapter Markers[0:00] Introduction and episode framing[2:13] Michael's early disillusionment with the state and money[4:52] What "authority is an illusion" actually means[7:59] Why large-scale systems keep defaulting to centralization[12:31] Can large-scale voluntary systems function without structure?[18:38] Is code-based governance different from traditional authority?[22:15] Why Michael argues Bitcoin is a fiat currency and a collectible[34:30] Can a fully voluntary system realistically scale, and can it deliver justice? Connect with CollinsLinkedIn: linkedin.com/in/collins-odabi-266620380Guest enquiries: podmatch.com/member/web3unfiltered Until next time, stay sharp, stay curious, and keep building.

See All (45)

Ratings & Reviews

About

Conversations with tech founders, builders, and analysts — cutting through hype to explain what actually works in crypto, blockchain, and decentralised technology.

Information

  • Creator
    Collins Victory Odabi
  • Years Active
    2026
  • Episodes
    45
  • Rating
    Clean
  • Copyright
    © Collins Victory Odabi
  • Show Website
    Web3 Unfiltered