10X AI with Julius Neil

Julius Neil Buenconsejo
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🎙️ Host, 10x AI Podcast | AI Systems & Future-of-Work Strategist I interview founders, scientists, operators & creators who are using AI to: ✅ Automate workflows ✅ Scale businesses ✅ Future-proof careers ✅ Multiply human potential We uncover practical frameworks and ethical insights so people can work smarter, build faster, and lead confidently into the future.

  1. 5d ago

    How a Behavioral Intelligence Model Predicts AI Adoption Success

    𝗟𝗲𝗼𝗻 𝗠𝗼𝗿𝗮𝗹𝗲𝘀 𝗼𝗻 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲, 𝗔𝗜 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝘄𝗵𝘆 𝗽𝗲𝗼𝗽𝗹𝗲 𝗰𝗼𝗺𝗲 𝗯𝗲𝗳𝗼𝗿𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 Julius Neil Buenconsejo speaks with 𝗟𝗲𝗼𝗻 𝗠𝗼𝗿𝗮𝗹𝗲𝘀, 𝗣𝗿𝗲𝘀𝗶𝗱𝗲𝗻𝘁 𝗮𝗻𝗱 𝗖𝗵𝗶𝗲𝗳 𝗘𝗻𝗲𝗿𝗴𝘆 𝗢𝗳𝗳𝗶𝗰𝗲𝗿 𝗮𝘁 𝗗𝗡𝗔 𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿, about how behavioral intelligence helps organizations improve hiring, team dynamics, and AI adoption. They also dig into AI governance, ethical use, and why understanding human behavior matters more than simply adding new technology.In this episode, Leon shares how DNA Behavior uses behavioral profiles, style matching, and team benchmarks to improve fit across roles and departments. He also explains why AI works best as an enabler when it is guided by governance, human oversight, and respect for individual strengths. 𝗞𝗲𝘆 𝘁𝗼𝗽𝗶𝗰𝘀   • Leon Morales introduces DNA Behavior as a behavioral intelligence platform that uncovers over 4,000 insights from a natural behavior discovery process.   • Style match explained - how behavioral compatibility can affect collaboration, communication, and team performance.   • Why poor role fit creates real business costs, including cost of poor quality (COPQ) and project failure.   • How onboarding and hiring can be improved by matching people to the work they are naturally wired to do.   • The link between behavioral mismatch, stress, burnout, and career stagnation.   • Why AI governance matters - not just tool access, but structure, access control, confidentiality, and cross-functional oversight.   • Ethical AI concerns around sensitive data, overreliance, and the risk of using AI in deeply personal matters. 𝗧𝗶𝗺𝗲𝘀𝘁𝗮𝗺𝗽𝘀 00:00 - Leon Morales introduces himself and DNA Behavior 00:36 - What DNA discovery measures and why behavioral intelligence matters 01:56 - Why the title Chief Energy Officer fits behavioral work 03:59 - Style match and how compatibility affects teamwork 06:36 - Translating behavioral mismatch into ROI and cost of poor quality 08:36 - Matching roles to strengths during onboarding and hiring 14:26 - Team forming, storming, and performing in behavioral terms 19:33 - Three steps for evaluating career fit and finding direction 30:09 - Why companies fail when they throw AI at problems without governance 36:34 - Building a practical AI governance framework 41:17 - Why people come before numbers in decision-making 43:11 - Can perfect data still lead to bad decisions? 44:36 - AI as an enabler, not a replacement for human judgment 47:03 - AI anxiety, confidential data, and trust 48:07 - Behavioral testing, digital scan, and AI transparency 53:31 - Predicting AI adoption success through team behavioral analysis 56:19 - Lessons from startup teams and hiring people too similar to the founder 60:34 - What a behaviorally intelligent AI strategy looks like 63:46 - Ethical AI, AI addiction, and the limits of chatbot relationships 72:06 - Behavioral data as competitive advantage or ethical risk 73:03 - How financial advisors can become better human coaches 75:08 - If Leon had 100,000 dollars to invest in AI, he would build a behavioral operating system 75:55 - Rapid-fire: the tools, habits, and skills Leon uses most 81:12 - How to connect with Leon and DNA Behavior

  2. Oct 21

    AI Voice Cloning & Cybersecurity: Can You Trust What You Hear? | Robert Siciliano

    Human Cyber Risk, AI Deepfakes, and the Strategic Human FirewallThis episode explores why cybersecurity is less about technology alone and more about human behavior, trust, and decision-making. Robert Siciliano, founder of ProtectNowLLC, explains how AI has supercharged social engineering and why employees need to become a “strategic human firewall” instead of relying on compliance checklists.We discuss practical defenses against deepfakes, voice cloning, phishing, multi-factor authentication attacks, and insecure habits like password reuse. The conversation also covers security theater, the limits of training metrics, and how to build lasting security awareness through dialogue and real-world examples. Key topicsThe episode centers on the idea of the “human blind spot”—people naturally trust what feels familiar, which attackers exploit.Traditional phishing training is not enough because AI now enables high-precision impersonation through voice cloning and deepfake audio, video, and text.Robert introduces the “triple A protocol” as a break-the-fake playbookRobert explains how 2FA can still be compromised through man-in-the-middle attacks, especially when criminals pose as IT or leadership.The conversation distinguishes compliance from real security, arguing that metrics and checkboxes often create security theater rather than genuine protection.He shares hands-on experiments involving ATMs and secondhand devices to show how both analog and digital systems can leak sensitive data.Timestamps(0:00) Introduction to Robert Siciliano and 30 years of security awareness training (1:23) Why employees are the weakest link in a company’s security chain (2:24) Why dialogue works better than lecture-based security training (3:32) How AI has changed phishing, impersonation, and deepfake fraud (4:30) The triple A protocol and out-of-band verification (6:22) How to verify suspicious financial requests in a corporate setting (7:46) Keeping sensitive data inside approved internal AI tools (8:44) Why enterprise AI agents need CIO, CTO, and CISO oversight (9:53) Why humans are part of the problem in cybersecurity (11:11) How two-factor authentication gets compromised through social engineering (12:44) The “human blind spot” and why trust overrides suspicion (14:32) Why manufactured urgency bypasses rational thinking (16:01) Password reuse, dark web exposure, and why employees underestimate risk (18:16) Deepfake calls, voice cloning, and AI face overlays in real fraud (19:39) AI as a double-edged sword for cyber risk and infrastructure (20:46) Why bad actors already use jailbroken LLMs for harmful purposes (21:55) Buying an ATM and uncovering a data breach (24:08) Secondhand devices and the discovery of compromised logins (26:35) Returning to basics: locks, passcodes, self-defense, and home security (27:29) iOS vs Android from a security perspective (29:27) How to spot a skimming device or hidden camera at an ATM (30:48) Why phishing metrics can hide deeper employee risk (33:04) What security theater looks like inside enterprises (35:23) Why compliance does not equal security (37:00) How behavior change shows up in better phishing performance (38:13) The “kitchen table effect” and making security part of daily life (39:19) Training people to become harder to manipulate (41:08) What makes strategic human firewall training different (42:41) Why AI makes seeing no longer equal believing (44:26) The AI-powered attacks companies are not preparing for (44:50) What CEOs, CIOs, and security leaders should teach first (45:50) The oxygen mask lesson: secure yourself first (46:53) Social engineering as an attack on human trust (47:26) A 60-second explanation of human cyber risk for executives (48:54) Rapid-fire insights on AI tools, habits, and future trends

  3. Oct 14

    AI Hackers Never Sleep: The Coming Cybersecurity Crisis | Taha Mubashir, Inovia

    AI Hackers Never Sleep: The Coming Cybersecurity Crisis | Taha Mubashir, Inovia AI is changing cybersecurity forever. As AI agents become more autonomous, cyberattacks are getting faster, software is being built at unprecedented speed, and traditional security tools may struggle to keep up. So, who protects us when hackers operate at machine speed? In this episode, Julius Neil sits down with Taha Mubashir from Inovia to explore the future of AI cybersecurity, autonomous AI hackers, and the next generation of cybersecurity startups. Drawing on his experience in venture capital, software investing, and enterprise technology, Taha explains why artificial intelligence is creating both unprecedented cyber risks and massive opportunities for founders and investors. In this conversation, we explore: AI-powered cyberattacks: How AI agents could automate hacking, discover vulnerabilities, and make sophisticated cyberattacks more accessible.The future of cybersecurity: Why machine-speed attacks require machine-speed defense, and how autonomous AI security agents could transform enterprise security.AI agent security: The emerging risks of prompt injection, memory poisoning, excessive permissions, agent identity, and autonomous agents accessing sensitive systems or financial wallets.The next wave of cybersecurity startups: Opportunities in AI security, autonomous penetration testing, AI endpoint detection and response (EDR), AI security operations centers (SOC), identity management, and AI firewalls.Vibe coding and software security: Why rapidly generated AI code creates new security challenges, and why enterprise software still needs strong engineering and security reviews.The next big infrastructure opportunity: How AI agents could reshape data infrastructure, observability, logging, and the way software systems consume information.What makes a startup win: Why founder quality, speed of execution, go-to-market strategy, and distribution can matter as much as the product itself.The future of venture capital: How to identify promising AI and cybersecurity companies in a rapidly evolving technology market.How to use AI to become 10x more effective: Taha’s perspective on AI tools, learning faster, automating low-value tasks, and focusing on work that creates the greatest impact.Taha also shares his views on AI cyber insurance, agent-to-agent trust, AI supply-chain security, the limitations of fully autonomous systems, and why human judgment and relationships remain important in an AI-driven world. The big question: As AI makes both software development and cyberattacks more accessible, will cybersecurity be able to evolve fast enough to protect the next generation of AI-powered businesses? Whether you're a startup founder, cybersecurity professional, CTO, technology investor, or AI enthusiast, this episode offers a practical look at the risks, opportunities, and businesses shaping the future of AI security. 🎙️ Guest: Taha Mubashir, Inovia 🎙️ Host: Julius Neil If you’re interested in artificial intelligence, cybersecurity, AI agents, venture capital, enterprise software, and the future of technology, this episode is for you. Subscribe for more conversations on AI, startups, cybersecurity, investing, and building 10x businesses.

  4. Oct 7

    Why Smart CEOs Are Using AI More and Trusting It Less

    Why are top CEOs utilizing artificial intelligence more every single day, yet trusting it less? In this episode of the 10X AI podcast, Julius Neil sits down with former 29-year utility CEO and leadership expert Barry Moline to uncover the real contradictions of the AI era. Discover why relying blindly on LLMs is a massive trap, how infrastructure giants leverage predictive analytics, and why many customers actually prefer interacting with AI bots over humans. Barry also shares game-changing insights on why you should stop viewing your career as a traditional ladder and treat it like a rock climbing wall instead. Whether you are an entrepreneur, a corporate leader, or a professional looking to 10X your workflow, this conversation breaks down the practical strategies you need to master leadership, human context, and innovation in the age of AI. In this episode, you will learn: Why smart leaders use AI more but trust it lessThe real reason customers prefer AI agents over human customer serviceHow to spot "AI slop" and protect your business contextWhy your career path is a rock climbing wall, not a ladderThe 5 essential leadership principles for the next decade#ArtificialIntelligence #Leadership #AIStrategy #FutureOfWork #CEOMindset #10XAI #TechTrends Timestamps 00:00 - Barry Moline’s background in utilities and leadership 03:09 - The three levels of AI adoption: skeptics, cautious users, and targeted adopters 05:35 - How AI saves time but increases the risk of mistakes 06:04 - Why AI agents work well for routine customer service tasks 09:05 - Why utilities were early AI users for prediction and failure analysis 12:58 - Why many customers prefer AI for simple service requests 16:03 - Human judgment matters more as AI gets more capable 20:18 - How to spot AI slop by recognizing what feels off 23:59 - When AI-assisted writing turns into low-quality content 25:38 - Why editing AI output into your own voice still matters 27:29 - Using AI to turn long conversations into proposals and outlines 31:58 - Why people distrust AI: unfamiliarity, errors, and the wild west phase 35:40 - How Meta’s algorithm and AI relate in the Facebook settlement 43:43 - The risk of cognitive outsourcing and why kids need judgment skills 45:32 - Whether AI will replace math and what that means for learning 49:57 - How AI could be used to break into highly secure systems 54:14 - Why limiting AI in classrooms could help students build first principles 59:24 - Why careers are better viewed as a rock climbing wall than a ladder 62:53 - The leadership capability that will matter most: growth mindset 67:27 - Barry’s five leadership principles 73:59 - The AI tools he uses daily 74:46 - The AI task that has 10x’d his productivity 75:36 - The human skill AI cannot replace: context 77:35 - A practical way to ask AI how it can and cannot help with a job 78:38 - The AI and innovation resource Barry recommends 79:42 - AI trends to watch: deepfakes, rogue nation use, electricity dependence, UBI, and productivity 81:54 - What 10x means to Barry personally: talking something into existence 82:54 - How to contact Barry Moline for speaking or consulting Music licensed through Soundstripe. Code: RKBMRLM4ABSWBLA3

  5. Sep 30

    AI Won’t Replace Lawyers. It Will Change What They’re Paid For

    What happens to lawyers when AI can write, review, analyze, and even help design contracts? In this episode of 10X AI with Julius Neil, Julius Neil sits down with Dácio, a Brazilian lawyer and cross-border business consultant, to explore the future of AI and the legal profession—and why AI may change what lawyers are paid for rather than simply replace them. AI can already draft contracts in seconds. But the most valuable part of legal work may happen before the contract is ever written: understanding the business, evaluating risk, conducting due diligence, negotiating, designing strategy, and making judgment calls. Dácio explains why the future of legal AI isn't just about automating contracts. AI could become an extension of our thinking—helping entrepreneurs analyze scenarios, challenge assumptions, identify risks, and even recognize when an entire contract structure no longer fits the way a business has evolved. But there is one thing Dácio believes should never be delegated to AI: Final judgment. In this conversation, we explore what happens when AI moves from being a legal copilot to becoming something closer to a business architect. In this episode:Will AI replace lawyers?What will lawyers be paid for when AI can write contracts?How AI is changing legal work and contract automationWhy judgment and due diligence still matterCan AI design better contracts than humans?How AI could help entrepreneurs make better business decisionsThe difference between rigid contracts and flexible negotiationDácio's “oak vs. bamboo” approach to negotiationWhy strategy often comes before the contractHow AI could help small entrepreneurs operate with greater strategic capabilityWhat CEOs should consider before expanding into Latin AmericaWhat should never be delegated to AI?The future of AI, law, business strategy, and decision-makingThe bigger question isn't simply whether AI will replace lawyers. It's whether AI will fundamentally change what valuable legal work looks like. And as Dácio puts it, AI can structure the possibilities—but the judgment remains human. Guest: Dácio — Brazilian lawyer & cross-border business consultant Host: Julius Neil Buenconsejo Podcast: 10X AI with Julius Neil #AI #ArtificialIntelligence #LegalAI #AIandLaw #FutureOfLaw #Lawyers #ContractAutomation #AIContracts #LegalTech #BusinessStrategy #AIforBusiness #Entrepreneurship #AIDecisionMaking #10XAI Music licensed through Soundstripe.Code: MNLOWF7TMB3OBIPL

  6. Sep 23

    Why Middle Market Companies Have the Biggest AI Advantage

    In this episode, Julius Neil Buenconsejo sits down with John Rudd, an investment banker, consultant, restructuring leader, and AI operator who now helps middle-market companies build practical AI capability. John explains why AI is less about flashy tools and more about organizational speed, data readiness, and choosing the right problems to solve.We discuss why large enterprises often move too slowly to capitalize on AI, why middle-market firms may have the biggest edge, and how leaders can avoid “agentic circus” behavior by tying AI work to real business outcomes. Key topicsJohn shares his career path from banking and distressed assets to restructuring, agile consulting, Accenture, and finally AI through LightSight.We discuss why the book title, The Age of the Speedboat, reflects the agility middle-market companies can use to outmaneuver larger “aircraft carrier” organizations.John explains the central AI advantage for middle-market firms:Faster decision-makingMore manageable dataLower compute costsGreater ability to course-correct quicklyWe discuss why many large enterprises struggle with AI because their scale, layers, and matrixed structures slow execution.John explains the “Penske file” analogy from Seinfeld as a way to describe leaders who pretend to have answers instead of admitting uncertainty and co-creating solutions.We discuss why many organizations claim to have an AI initiative but lack a meaningful strategy, metrics, or business purpose behind it.John breaks AI implementation into three layers.We discuss how to distinguish real AI value from “success theater,” where companies report adoption metrics that do not change the business.John explains that transformation fails so often because culture, middle management, and overreliance on top-down change can stall progress.John shares why data quality is always a first-stop issue, and why clean, consistent data matters more than being fully perfect on day one.We discuss the “wrong problem trap,” where companies chase AI because competitors are doing it instead of identifying bottlenecks and high-value use cases.We discuss how CEOs should frame AI to boards. Timestamps 00:00 — John Rudd’s background from banking to AI 05:33 — Why writing the book started with blog content and patterns 06:54 — Why the middle market is the core of the book 09:28 — Why large-company scale can become a liability in AI 15:17 — The Penske file analogy and fake certainty 18:26 — Why many companies claim AI initiatives without real substance 19:42 — Three AI layers: tools, SaaS efficiency, and custom differentiation 22:50 — The “agentic circus” and AI everywhere, strategy nowhere 25:17 — What metrics should actually matter in AI programs 27:02 — How to use automation to remove bottlenecks and expand expert capacity 29:28 — Why AI must fit continuous innovation, not one-time projects 33:57 — A bad metric example: licenses and training instead of business impact 36:52 — Three questions CEOs should ask their steering committee 39:20 — Why 88% of transformations fail 42:39 — Why traditional change management is not enough anymore 47:17 — Why “sandbags are not a strategy” 50:33 — Data quality traps and fixing data going forward 52:48 — The wrong problem trap and why low-hanging fruit matters 54:33 — The success theater trap 70:14 — Where to find the book, The Age of the Speedboat Music licensed through Soundstripe. Code: IOS6MVSJMPOBI46X, 6DCAGIZUYJCSMJOR

  7. Sep 16

    Will AI Replace Project Managers? What Changes in the AI Era

    Are you worried about how artificial intelligence is reshaping the future of project management and corporate risk? In this episode of the 10X AI with Julius Neil podcast, Julius is joined by Mariam Petrosyan, an operational risk leader in banking and a fellow member of the prestigious University of Oxford AI for Business cohort. Tune in as Mariam shares her 15 years of transformation experience to break down what project managers must do to stay relevant in 2026 and beyond. Discover why AI isn't here to replace project managers, but rather to shift them away from manual administration and into high-value strategic leadership. Key Takeaways You'll Discover: The Death of Administrative PM Work: How AI handles meeting summaries, initial plans, and status updates so you can focus on human judgment, negotiation, and relationships.The Dangers of the "AI Halo Effect": Why a polished, well-formatted AI response can be dangerously misleading in high-stakes operational risk and compliance environments.Mastering Prompt Engineering: Why treating prompts like a structured brief (defining roles, context, constraints, and outputs) is the secret to getting executive-ready results.The Agile vs. AI Trap: Why organizations must avoid blindly forcing AI into every workflow overnight, and how proper governance prevents data leaks and accountability gaps. Whether you are a seasoned project manager, a banking professional navigating digital transformation, or simply curious about real-world AI adoption, this episode provides actionable frameworks you can start using today. Timestamps: 00:00 - Mariam’s background in banking, engineering, and operational risk 02:14 - AI shifts project managers away from manual information gathering 03:33 - PMs become more strategic: asking better questions and coordinating action 04:47 - Where AI fits in operational risk and what it should focus on first 05:08 - Key AI risks in regulated banking, including hallucinations and the halo effect 07:33 - Why prompts must be specific for risk and transformation work 08:21 - Defining role, context, and output clearly in a prompt 10:26 - How Mariam’s organization is exploring AI adoption 11:43 - Why risk, compliance, and legal teams must be involved early 14:43 - Moving from individual AI use to analysis and workflow integration 17:13 - Why Mariam applied for Oxford’s AI for Business diploma 20:20 - Continuous learning and how past experience becomes the foundation for new skills 24:05 - How AI can turn project data into executive-ready communication 26:31 - Prompt engineering as structured briefing for project managers 27:59 - The biggest mistake PMs make: using AI without context or fit 31:33 - A sample prompt for a complex banking transformation project 35:16 - Five AI roles around a project table: admin, PMO, analyst, junior dev, QA support 37:24 - What AI can be trusted with most and least 38:08 - Accountability stays with the project manager, not the AI 40:53 - How AI helps translate complexity for executives and stakeholders 42:55 - What happens when companies adopt AI faster than governance can keep up 45:10 - Why AI governance will matter as much as cybersecurity governance 47:21 - AI adoption and regulation in Armenia 49:36 - Quick-fire answers on trust, verification, and blind trust in AI 51:38 - Where project managers should start learning about AI 53:10 - Managing projects and AI-powered teams will both matter 54:08 - Everyday AI tools, biggest misconceptions, and what can never be automated 56:34 - Mariam’s view on agentic AI as the next major trend Music licensed through Soundstripe. Code: 5EBDZQCZ8JWF6JFG, JSMYSMPZIWVOFETG

  8. Sep 9

    AI’s Hardest Problem Isn’t What It Can Do — It’s What It Leaves Alone

    Why do most enterprise AI implementations fail? In this episode, we sit down with DataChi founder and product marketing leader Charif Mutaki to unpack the real bottleneck of artificial intelligence in business. Discover why the future of work isn't about maximizing tool automation, but understanding what intelligence systems should actively leave alone. We explore the shift from basic AI agents to autonomous virtual teammates (VTMs), how to solve the hidden costs of sales process friction, and why deciding what not to automate is the ultimate competitive advantage for modern organizations. Key Takeaways Covered in This Episode: The Virtual Teammate Paradigm: Why autonomous VTMs differ fundamentally from standard reactive AI agents.The Danger of Data Noise: How knowing what not to tell a sales rep prevents cognitive overload and protects high-stakes accounts.Enterprise Integration Strategy: How modern teams deploy smart overlays without adding clunky tech stacks.The Human Element: Why the final negotiation, judgment, and deal-closing process will always belong to humans. If you are a business leader, founder, or enterprise strategist looking to scale revenue without burning out your team, this conversation redefines how you should look at AI adoption. Timestamps 00:00 - Introduction of Charif 02:53 - Why “virtual teammate” is different from tools or assistants 04:04 - Shaping the new VTM product paradigm 05:49 - Selling the rep’s day, not just the product 06:19 - Prospecting, account timing, and pre-call research 07:41 - Writing sequences, follow-ups, and CRM updates 10:29 - Agent versus teammate and why autonomy matters 11:30 - Product as productivity capacity, not replacement 12:07 - Chief of staff structure inside the team 12:51 - Overlay integration with legacy systems 15:30 - Sales playbooks for prospect research and prioritization 16:59 - Dynamic adaptation to quota gaps and target pressure 18:26 - Turning detected signals into suggested actions 24:07 - Adapting to company size, from SMBs to enterprise 27:32 - Trust, autonomy, and risk on small versus big accounts 29:51 - The real hard problem is deciding what not to say 37:55 - Making inbound and outbound warmer and more informed 39:49 - The sonar module scanning for useful changes 43:07 - The meaning behind “more human than humans” 44:13 - What cannot be automated in sales 45:16 - Why all repetitive tasks should be automated 46:35 - Who owns AI mistakes and why they still happen 51:07 - Naming teammates and making them easier to remember 53:12 - Should AI teammates be evaluated like employees? 54:10 - Why the system should be allowed to say “I don’t know” 55:39 - Using smaller models for simple tasks 56:06 - Using multiple LLMs for strategic questions 57:50 - Why the AI teammate model beats a traditional sales hierarchy 59:43 - Sales gets the strongest ROI today 60:40 - Five-year vision: employees bring their AI tools with them 62:26 - Data quality as the constant that matters most 63:24 - Daily AI tools: a general assistant and DataChi alpha 64:03 - AI already 10x’d reading and summarization 64:44 - The skill AI can never replace: knowing when to say nothing 64:58 - The most annoying thing to automate forever 65:57 - The biggest misconception about AI 66:15 - How to 10x yourself starting today 66:53 - Advice to his younger self 67:44 - The End of Work as a lasting reference 69:13 - The trend he expects to matter most 69:39 - What 10x means personally Music licensed through Soundstripe. Code: XFPMEJAEPWRXYQQC, UFWZGOLC1Q0SYFOU, RLRYSQ3W9VYGZJTV

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About

🎙️ Host, 10x AI Podcast | AI Systems & Future-of-Work Strategist I interview founders, scientists, operators & creators who are using AI to: ✅ Automate workflows ✅ Scale businesses ✅ Future-proof careers ✅ Multiply human potential We uncover practical frameworks and ethical insights so people can work smarter, build faster, and lead confidently into the future.

Information

  • Creator
    Julius Neil Buenconsejo
  • Years Active
    2025 - 2026
  • Episodes
    52
  • Rating
    Explicit
  • Copyright
    © 2026 Julius Neil Buenconsejo
  • Show Website
    10X AI with Julius Neil