Cryptonized! with Mark Fidelman

Guest: Nikhil Vimal  
Host: Mark Fidelman

Starting an e-commerce company used to mean assembling a small army: researchers, marketers, content creators, media buyers, customer-support reps, and operations people. AI has changed that equation.

In this episode, Mark Fidelman sits down with AI strategist, entrepreneur, and author Nikhil Vimal to map out how they would build an e-commerce business today. Their playbook starts with AI-powered market research, moves through content creation and campaign execution, and closes the loop with monitoring systems that learn from every result.

The bigger opportunity is a connected system of AI agents that can watch competitors, spot cultural moments, create and repurpose content, re-engage customers, and improve over time—with a human responsible for every important approval.

Mark's take: Founders who use AI for isolated tasks will save time. Founders who build a learning system around it will build an advantage.

## What You'll Learn

- Why AI agents can replace much of the early infrastructure once required to launch a direct-to-consumer brand
- How to automate competitive research across promotions, ads, creators, partnerships, and adjacent industries
- How AI can turn market intelligence into seasonal campaigns, product images, video, copy, and content calendars
- Why "clipping" and content repurposing can turn 20 original assets into 100 distribution-ready pieces
- How to react quickly to viral cultural moments without forcing a founder to live on social media
- Why every agent still needs a human owner who approves consequential work
- How observability and monitoring help the system learn from wins, misses, and customer behavior
- Where AI voice agents fit into 24/7 customer support
- Why Shopify, Perplexity, Claude, ChatGPT, HubSpot, Buffer, and Zapier are practical places to begin
- What Zapier still does well: visual workflow design, collaboration, connection monitoring, and operational visibility

## Episode Chapters

- **00:00** — Welcome and Nikhil Vimal's background
- **01:42** — The challenge: build an e-commerce company with today's AI tools
- **02:12** — AI agents as an on-demand marketing and growth team
- **04:34** — Step 1: continuous research and market analysis
- **06:02** — Learning from winning ads and adjacent industries
- **07:16** — Step 2: campaign preparation and generative content
- **08:22** — Why a human must own every AI agent
- **09:08** — Step 3: execution, outreach, UGC, and content calendars
- **09:52** — Scaling content through clipping and repurposing
- **10:53** — Using AI to react to viral moments in real time
- **13:32** — Step 4: observability, monitoring, and continuous improvement
- **14:54** — The central lesson: build systems, not scattered automations
- **15:54** — AI voice agents and the customer-support opportunity
- **17:35** — Where a new e-commerce founder should start
- **19:19** — The practical starter stack
- **20:25** — Why Zapier still matters in the era of connected LLMs
- **23:00** — Final lessons and a preview of agentic AI at enterprise scale
- **24:18** — Where to find Nikhil and his forthcoming book

## The Operating Model

1. **Research:** Track competitors, adjacent categories, creators, ads, promotions, and market shifts.
2. **Prepare:** Convert that intelligence into campaign concepts, product visuals, video, copy, and calendars.
3. **Execute:** Publish, distribute, follow up with customers, recruit creators, and repurpose the best material.
4. **Observe:** Measure results, identify why something worked or failed, and feed those lessons back into the next cycle.
5. **Approve:** Keep a named human accountable for the decisions that can affect customers, pricing, reputation, or revenue.

## Tools Mentioned

- Perplexity for research and market analysis
- ChatGPT and Claude for preparation, execution, and connected workflows
- Shopify for commerce infrastructure and educational resources
- Zapier for visual automation, workflow orchestration, and monitoring
- HubSpot for marketing and customer operations
- Buffer for content calendars and publishing workflows
- Notion, ClickUp, and Asana for project and workflow management
- Midjourney, Higgsfield, and Seedance for image and video creation
- Meta Business Suite and Marketo for marketing operations and analytics

## About Nikhil Vimal

Nikhil Vimal is an AI strategist, entrepreneur, investor, advisor, and author who has spent roughly 15 years working across startups, e-commerce brands, agencies, and large enterprises. His forthcoming book, *The Essential Pivot*, focuses on navigating change and separating signal from noise.

Connect with Nikhil on [LinkedIn](https://www.linkedin.com/in/nikhilvimal) and visit [nvimal.com](https://nvimal.com).

## Closing Thought

AI can help one founder operate with the reach of a much larger company. But the advantage doesn't come from collecting more tools. It comes from connecting the work, measuring what happens, and making the system smarter every time it runs.

That's the operating model to build around.

------------------------------
Want all of my insights in one place, buy our book at www.agentized.com/book (Release Nov 2026) 

Host: Mark Fidelman
Guest: Aksel Bedikyan, marketing research and analytics practitioner based in Montreal, Canada

Episode description

How is AI changing marketing analytics—and what remains valuable when AI can write code, analyze data, and automate reports? Mark Fidelman talks with Axel Benikian about AI agents, the changing role of data science, and practical ways marketers can get more insight from the data they already have.

Axel explains how his team uses AI to automate recurring analysis and presentation work, while emphasizing that useful insights still depend on access to good data. His advice to businesses: map your existing data first, use AI to explore what it can tell you, and turn to custom research when important questions can't be answered with public or existing data.

Key takeaways

• AI is broadening access to data science. Coding may become less central to some roles, while more people use AI to build apps, models, and tools for business questions.
• AI can automate repetitive analytics work. Aksel's team uses Claude to help generate analyses and recurring PowerPoint reports, including brand trackers.
• Start with the data you already have. Map your internal data and connect it to the questions you want to answer before investing in new data collection.
• AI has limits when it comes to proprietary information. LLMs can help analyze available data, but they don't automatically have access to a company's private customer or business data.
• Custom research still matters. Interviews, focus groups, ethnography, and surveys can fill gaps when existing data can't answer new questions.
• Learn by doing. Aksel recommends experimenting with AI tools and sharing what you learn with others.

Episode timestamps

• 00:27 —Aksel's background in aerospace engineering, consulting, and market research
• 02:36 — Will AI replace data scientists?
• 05:25 — AI agents: risks to jobs and opportunities for innovation
• 07:05 — How Aksel's team applies AI to marketing analytics
• 09:54 — Automating recurring analyses and PowerPoint reports
• 11:46 — How marketers can make better use of existing data
• 15:13 — When AI needs to be supplemented with custom research
• 15:47 — Staying curious and learning through hands-on experimentation

Links

• Mark Fidelman's book, Agent Shock: agentize.com/book
• Aksel Bedikyan: OG Insights · Find Axel on LinkedIn

Stop Chasing AI Tools: What Marketers Should Actually Do With AI

AI marketing is moving fast and the constant stream of new tools can leave even experienced marketers feeling behind. In this episode, Mark Fidelman talks with marketing educator Will Francis about how to put AI to work without getting swept up in the hype.

Will shares why he recommends starting with one primary AI assistant, how desktop apps can help marketers work directly with their files, and why he prefers practical workflows over complicated agent hierarchies. He also walks through his own AI-powered dashboard, explains how he uses voice dictation throughout the day, and discusses where human judgment still matters.

The conversation also covers Will's AI for Marketing community, which he said was planned to launch in mid-October at the time of recording.

Key takeaways

• Start with one main AI workspace instead of trying every new tool.
• Look for useful outcomes—like organizing documents or creating a daily briefing—before building a complex AI setup.
• Treat AI agents as assistants that need direction and review; left unchecked, they can produce "work slop."
• Connect AI to the systems and data you actually use if you want a more complete picture.
• Make room for human judgment, strategy, and leadership—and step away from AI during the day.

In this episode

• 00:04:20 Will's background and his practical approach to AI education
• 00:07:29 How to avoid getting overwhelmed by AI tools
• 00:10:30 Will's tool recommendations for marketers
• 00:12:30 Why he's skeptical of complex, always-on agent systems
• 00:18:02 Practical ways CMOs can get started with AI
• 00:22:09 AI as a sounding board—and the continuing role of human judgment
• 00:25:15 Will's daily AI workflow and custom dashboard
• 00:30:30 The AI for Marketing community and workshops
  
  Sign up for my book AgentShock 

About Will Francis

Will Francis is a marketing educator and creator with 25 years of experience, including work at MySpace, advertising agencies, and his own agency. He creates practical, no-hype content to help people use AI for marketing.

Learn more: willfrancis.com

AI Marketing Podcast – AEO (Answer Engine Optimization) with Elina Panteleyeva

Guest: Elina Panteleyeva, Founder of ShowUpWithAI
Host: Mark Fidelman
Topic: How to rank in AI answers (AEO) and capture high-intent leads from ChatGPT, Claude, Gemini, etc.

Episode Overview

In this episode, Mark and Elina break down Answer Engine Optimization (AEO)—how to become the brand that AI agents and chatbots recommend first.

Elina shares:

• Why AI-driven leads convert 4–12x better
• How to optimize your website, content, and off-site signals for AI
• What's changing from traditional SEO to AEO/GEO
• Real-world case studies of brands exploding inbound leads in 15–60 days
• Why agentic commerce is the next big wave—and how to get ahead of it

Timestamps & Segments

[0:02:46–0:04:52] Warm-up & context

• Light banter about calls and busy schedules.
• Mark mentions bringing a company public (Exascale Labs) and dealing with heavy legal sign-offs on marketing.
• Sets the stage that this is an AI + marketing focused audience.

[0:04:52–0:08:59] Guest intro & origin story

Your setup and question:

• 0:04:41 Mark: "All right, so we're going to talk about the AEO… I'm going to be the voice of the listener and walk through it as if you were talking to me for the first time."

Key points:

• 0:07:39 Elina's background:
  • Founder of ShowUpWithAI
  • Worked at an AI startup, got laid off in 2023.
  • Bootstrapped an e‑commerce brand to seven figures.
  • Noticed people using ChatGPT to choose doctors, therapists, services.
• Realization:
  • "There's no page two of chat."
  • She spent hundreds of hours studying documentation, signals, and how LLMs evaluate brands.
  • That research became ShowUpWithAI

[0:08:59–0:12:13] Why AEO matters now

Your question:

• 0:08:59 Mark: "Okay, so why should anyone care about AEO?"

Key points:

• People coming from AI/chat are 4–12x more likely to convert.
  • They've already discussed their problem with an AI.
  • They trust the model and are now ready for a solution.
• AI is always recommending someone—you or your competitor.
• It's still early:
  • Most businesses aren't optimizing for AI yet.
  • Results can come within 30–60 days.
• Elina: AEO is "the biggest ROI you can get in 2026."

Your follow-up question:

• 0:10:13 Mark: "What if 1,000 plumbers are doing this? How does ChatGPT know which one to recommend?"

Key points:

• Right now, 1,000 plumbers are not doing it, which is why starting now matters.
• LLMs care deeply about recommendation quality:
  • If they recommend bad options, users switch models (Claude, Perplexity, etc.).
• Models use specific signals different from traditional SEO:
  • On-site: technical optimization, schema, phrasing, blogs, structure.
  • Off-site: Reddit threads, YouTube, reviews, third‑party mentions.

[0:12:13–0:15:05] Signals, platforms & AI-optimized content

Your question:

• 0:12:13 Mark: "Is that primarily how you rank… or is there something else you have to do to make sure you're top of mind, especially locally, for ChatGPT, Gemini, or Claude?"

Key points:

• It's industry-dependent:
  • Some spaces are Reddit-heavy.
  • Others lean on YouTube, Trustpilot, or specific review sites.
• Local AEO:
  • Google Business Profile + reviews are critical.
• Website & content structure matter a lot:
  • Blogs should be AI-optimized, not "typical" blogs.
  • Key elements:
    • Summary at the top
    • FAQs at the bottom
    • External citations to credible sources
    • Internal linking: blogs → service pages; services → blogs
• There are "100+ more" technical and structural factors behind the scenes.

Your next question:

• 0:13:51 Mark: "So it's no longer the link-sharing game with SEO or trying to get links in high-traffic sites?"

Key points:

• Big difference vs. traditional SEO:
  • Backlinks still help, but…
  • AI can infer trust from unlinked brand mentions.
• Example:
  • Someone mentions your brand on Reddit without a link.
  • If your website is properly set up, AI can:
    • Recognize the entity
    • Attribute that mention back to your site and brand
    • Treat it as a credibility signal

[0:15:05–0:18:07] Search behavior shift & Google's AI overview

Your question:

• 0:15:05 Mark: "Do you have any breakdown of how many people are doing Google searches vs AI now… it used to be like 99/1?"

Key points:

• Tracking is tricky:
  • People find you via chat/AI then go Google your brand.
  • In Google Search Console, you'll see a spike in branded searches if AEO is done right.
• Behavior changes:
  • People still use Google, but:
    • They rarely scroll far.
    • They definitely don't hit page two.
  • The new game:
    • "How do you get into that Google AI overview box?"
    • That's where the real battle is now.

Your commentary:

• You share your own behavior:
  • You now start most product/service discovery via ChatGPT/Claude/Gemini, not Google.
  • You trust AI to synthesize reviews, source data, and filter out "gamed" SEO.
• Strategic takeaway:
  • If you're paying 30% to Amazon vs 0% on your own site, AEO is crucial.
  • Do "whatever you can" to rank high in AI results.

Your next question:

• 0:17:06 Mark: "Is there any software that tells you where you rank in these AI systems? With SEO there's a lot—what about AEO?"

Key points:

• There are tools for AI visibility tracking.
• The real bottleneck:
  • Implementation is like a 30-hour/week job:
    • Ongoing optimization
    • Technical/content updates
• You must define the prompts/queries you want to rank for:
  • Tools need a list of target prompts.
  • If you choose the wrong prompts, the data is misleading.

[0:18:07–0:20:30] Prompt strategy & why "just use an agent" doesn't work

Your clarification/question:

• 0:18:07 Mark: Paraphrased – So you basically have to reverse engineer prompts back into FAQs, summaries, ranking signals, etc. And if you don't, it gets hard unless you focus on a few phrases.

Key points:

• Elina's tip:
  • Scrape Reddit for your industry.
  • Use AI (e.g., Claude) to extract pain points and questions.
  • Those questions are likely the same queries people ask AI models.
• This gives you a starting list of prompts to optimize for.

Your provocative question:

• 0:18:46 Mark: "Why shouldn't I just have an AI agent do all this for me? Why wouldn't I get 100 agents to post as fake customers on Reddit?"

Key points:

• Reddit hates self‑promotion—especially from new accounts.

• There's a case study:

  • A casino tried something similar.
  • Result: their entire domain got banned.

• Platforms now aggressively detect:

  • Bot-like behavior
  • Coordinated fake posts

• Beyond Reddit:

  • You must know:
    • How to script YouTube videos so AI will cite them.
    • What to say, when to say it, and how to structure them.
    • How to format blogs and web pages exactly for AI.

• If you simply tell an LLM:

  "Optimize me for AEO,"
  you'll get a basic, shallow implementation that everyone else has—
  not the nuanced, multi-signal strategy that actually moves the needle.

[0:20:30–0:23:27] Case studies & ROI

Your question:

• 0:20:30 Mark: "Do you have any examples of companies or individuals you've helped and what the results were?"

Key case studies:

1. Eight-figure franchise brand
   • Start: ~9% visibility for their target prompts.
   • After ~15 days: 55% visibility.
   • Lead impact:
     • Went from 1–2 inbound leads per week
     • To 3 inbound leads on a single Saturday (within ~20 days).
2. 20‑year-old marketing agency
   • Was getting ~1 lead every other day.
   • After AEO work:
     • 4–5 inbound leads per day.
     • And from industries they'd never seen inbound from before.

Your follow-up question:

• 0:21:56 Mark: "How long did it take these companies to set things up so leads started showing up?"

Key timing details:

• Franchise:
  • Started ~20 days before the results she cites.
  • Saw big movement in 15–20 days.
• Agency:
  • Saw strong inbound increases in ~35–40 days.
• Elina's positioning:
  • She typically says 30–60 days to see increases.
  • If your customer LTV is > $10k, AEO is a "no‑brainer."
  • AEO work builds durable assets:
    • YouTube videos
    • Content
    • Brand signals
  • Those assets pay off beyond AI search itself.

[0:23:27–0:24:28] AEO vs SEO: where should budgets go?

Your question:

• 0:23:27 Mark: "Should people stop spending money on SEO and focus everything on AEO? What do you tell companies that hire you?"

Key points:

• Elina:
  • Her company focuses on AEO, and good SEO is a byproduct.
  • Contrast: traditional SEO shops slap on "AEO" at the end.
• Her thesis:
  • AI is here to stay and expanding.
  • Agentic commerce is coming:
    • Personal agents that know you better than you know yourself.
    • They'll make purchasing decisions automatically.
  • If AI/agents can't find you, you're not even in consideration.
• Therefore, primary focus should be:
  • Optimizing to show up in AI search as the recommended option.

[0:24:28–0:26:41] Agentic commerce & future state

Your commentary:

• You agree agentic shopping is inevitable.
• Likely path:
  • Starts from LLMs → evolves into persistent agents.
  • They'll look at:
    • What people like you have bought.
    • High‑rating clusters.
• Implications for sellers:
  • Need to provide great products and service.
  • Need strong public signals: reviews, mentions, satisfaction.
• You tie this into your upcoming book:
  • "Agent Shock" – coming out next month.
  • This AEO focus is one of the core recommendations.

Your question to close the AEO topic:

• 0:25:27 Mark: "Is there anything we didn't cover that we should have?"

Key points:

• Elina reiterates:
  • Best time to start was 3 months ago, second best time is now.
  • This isn't fake scarcity; someone in your industry is already being recommended by AI.
  • ROI is currently very high and very fast, like SEO in the early 2000s.

[0:26:19–0:27:21] Future episode idea: ChatGPT ads vs AEO

Your suggestion / question:

• 0:26:19 Mark: You propose a future episode on:
  • ChatGPT ads
  • How to take advantage of them
  • How they interact with organic AEO
• Note that you're just starting to see ads and know nothing about pricing or mechanics yet.

Elina: Open to coming back for that topic.

[0:27:21–0:29:07] Post-show wrap-up

• You explain your goals with the podcast:
  • Learn what's happening in your industry.
  • Meet potential customers, especially people needing compute / data centers.
  • Talk about:
    • AI infrastructure
    • Data center boom vs actual deployments
    • Security topics like the Hugging Face hack
• Quick tangent on:
  • Novel data center ideas (e.g., ship in the water using the ocean for cooling).
  • The gap between announced and actually built data centers.
•  

What happens when an AI agent stops answering questions and starts running the business?

In this episode of AI Marketing, Mark Fidelman talks with veteran marketing automation expert Gary Henderson about Luna, the AI agent his company built to handle podcast outreach, customer service, community management, content creation, social media, direct messages and marketing funnels.

Luna has contacted approximately 700 podcasts, booked Gary on more than 20 shows and opened conversations with dozens more. She also replaced a seven-person concierge team, now resolves roughly 95% of customer-service issues without human intervention and communicates with customers around the clock in their preferred language.

Gary explains how he went from managing a 12-person operation to running the business largely by himself, with AI handling much of the execution.

In this episode:

• How an AI agent booked more than 20 podcast appearances
• Why AI agents are becoming operators, not just assistants
• Which marketing and customer-service tasks can already be automated
• How to give agents enough freedom to act without losing control
• Why the perfect marketing funnel may soon disappear
• How AI will personalize every customer journey
• Why niche creators may outperform traditional advertising
• How professionals can stay employable as agents and robots become more capable

Mark and Gary also discuss the larger shift ahead: businesses will soon move from individual AI tools to coordinated fleets of agents capable of completing entire workflows. The people and companies that learn to direct those systems will have an enormous advantage. Those that wait may discover the tide has already reached them.

About AgentShock

AgentShock: Surviving and Profiting from the Rise of AI Agents and Robots, by Jas Dhillon and Mark Fidelman, is a practical guide to staying one rung ahead as AI advances from simple tasks to workflows, agent fleets and physical robots. It explains what is coming, what it means for your career and business, and how to turn disruption into opportunity.

Learn more and get the book at Agentized.com.