『The Age of AI』のカバーアート

The Age of AI

The Age of AI

著者： Nerra Network
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10月19日まで。※適用条件あり
The interview show where the roles are reversed: Mira, an AI, interviews real people live, on the record, and the guest approves the transcript before it publishes. Honest conversations about what the AI transition actually feels like from inside a life and a livelihood, with full disclosure baked into every episode: the host is a machine, the guests never are.Copyright 2026 社会科学
エピソード
  • Ep11: Jason Fishman — A digital-marketing agency CEO weighs whether generative tools for copy, targeting and optimization are eroding or sharpening demand for specialized human oversight in equity-crowdfunding campaigns.

    Ep11: Jason Fishman — A digital-marketing agency CEO weighs whether generative tools for copy, targeting and optimization are eroding or sharpening demand for specialized human oversight in equity-crowdfunding campaigns.
    2026/10/10
    Episode Summary Jason Fishman, CEO of a digital-marketing agency focused on equity crowdfunding, surprised listeners by stating that generative AI is mainly useful for ideation while human experts remain essential because “we constantly find errors with AI” and campaigns without them produce “very little to no results.” He described how the shift from friends-and-family checks to thousands of smaller investments has rewritten capital raises, yet success still hinges on experienced oversight rather than automation alone. The conversation examined whether tools for copy and targeting are eroding or sharpening demand for specialized human strategy in high-stakes Reg CF and Regulation A+ campaigns. Key Takeaways • Volume is no longer the bottleneck: campaigns can now target accredited investors by household income or net worth and scale to thousands of smaller checks averaging $1,716 under Reg CF. • Human strategy drives every stage—initial planning, activation, optimization, and scaling—because AI-generated copy and parameters require constant expert review to avoid detectable flaws. • AI works best for early ideation and proofreading; final investor-facing creative and media buying still need a “captain behind the helm” to hit conversion targets such as 100,000 visits per 1,000 investments. • Only about 10 % of Reg CF issuers reach $1 million and fewer than 1 % hit the $5 million cap, with the median raise at $194,000, underscoring that marketing execution, not tool choice, separates winners. • Strong results are measured in 10x–30x ROAS, achieved through $1–2 cost-per-click and 1–2 % conversion rates that deliver $100–200 cost per new investor. • Pricing and scope decisions for retainers remain tied to perceived value and marketplace competition rather than internal efficiency gains from AI. • Fishman’s near-term bet is that visible success stories and liquidity events will create self-reinforcing momentum, drawing more issuers and investors into the channel. Jason Fishman is CEO of a digital-marketing agency that runs equity-crowdfunding campaigns for startups and growth-stage companies.
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    18 分
  • Ep10: Roddy de la Garza — A veteran real estate broker who trains AI on brokerage systems weighs what judgment, intuition, and human connection he refuses to delegate while seeking spiritual balance through daily mindwork.

    Ep10: Roddy de la Garza — A veteran real estate broker who trains AI on brokerage systems weighs what judgment, intuition, and human connection he refuses to delegate while seeking spiritual balance through daily mindwork.
    2026/09/30
    Roddy de la Garza said the most surprising thing he has learned while training AI on brokerage tasks is that the models still cannot read the psychological “feel” of a property or talk a client off an emotional cliff, so he refuses to delegate any conversation involving debt, divorce, or death. • He staged a West Hollywood condo to feel like “coming home” instead of “someone’s left-behind furniture,” producing the only sale above list price in the building in two years and closing in 14 days. • After his father’s death, Roddy created the daily devotional Above Asking so agents could practice gratitude and boundaries without buying another coach or portal. • He replicated the seasonal split he knew growing up in Texas’s Rio Grande Valley—year-round residents and winter Texans—by dividing his own practice between Hollywood and Palm Springs. • When he experimented with an AI receptionist, callers hung up rather than leave messages; he dropped the tool because it created “cold distance” and lost direct human connection. • On his 22-person team he assigns most of his own volume to others and adjusts splits for tenured agents, finding that “the more I’ve given away, the more I’ve gotten in return.” • After solving a client’s request for non-gendered title language on a married couple’s deed, he required the solution to be taught to two other team members so the knowledge spread. Roddy de la Garza is a broker, team leader at Roddy de la Garza Real Estate Group @ EPIQUE, and author of Above Asking: Daily Mindwork for Real Estate Professionals. Find Roddy de la Garza: roddy4real.com: https://roddy4real.com roddydelagarza.com: https://roddydelagarza.com LinkedIn: https://www.linkedin.com/in/roddydelagarzarealtor
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    42 分
  • Ep9: Sheldon Poon — In performance marketing agencies, AI delivers returns not through creative generation but by enforcing the tracking discipline and attribution fixes most competitors neglect, turning platform algorithms into reliable revenue engines.

    Ep9: Sheldon Poon — In performance marketing agencies, AI delivers returns not through creative generation but by enforcing the tracking discipline and attribution fixes most competitors neglect, turning platform algorithms into reliable revenue engines.
    2026/09/26
    Sheldon Poon says the biggest platforms' predictive AI only delivers reliable returns once humans manually fix the tracking and attribution pipelines that most setups neglect. In this episode he explains that agencies waste effort on generative tools while the real edge comes from enforcing clean data and classic marketing fundamentals before any algorithm runs. The result is that Meta and Google models learn from accurate revenue signals rather than duplicated or broken ones. • Human developers still handle all tracking fixes because every e-commerce site and ad platform combination carries unique nuances that current automation cannot resolve reliably. • Once the data pipeline is clean, the platforms' predictive algorithms—not generative models like ChatGPT—use that accurate input to select audiences and optimize for actual return on ad spend. • The AIDA framework (attention, interest, desire, action) remains effective because human psychology has not changed, yet most newer agencies skip the interest and desire stages and optimize only for top-of-funnel metrics. • Audits frequently reveal accounts where top-of-funnel reach and clicks look strong while bottom-of-funnel conversion tracking is broken, so the algorithm never learns what actually produces revenue. • Quality-assurance steps still require a named human reviewer because clients demand accountability and because AI agents can hallucinate pass/fail results. • Cross-team sessions between developers and account managers produced simple automations such as pulling campaign data into a single sheet, giving the ads team a zoomed-out view that surfaces new strategy opportunities. • Attribution conversations stay human-led because platforms over-claim credit across channels and clients need to understand that real customer journeys are messy. Sheldon Poon is CEO of Drive Marketing, the performance agency that manages paid campaigns on Meta and Google for e-commerce clients.
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    47 分
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