By playing, you agree to uListen's Terms and Privacy Policy and YouTube's Terms of Service.

How I make $18K/mo with a niche podcast (STEAL THIS)

Starting this podcast wasn’t an easy decision. It takes up nearly half my time. And for months, it wasn’t making any money. But fast forward to today, and the podcast is now profitable and central to my creator business.

Aakash Guptahost

Jun 29, 20251h 37mWatch on YouTube ↗

Summaries, chapters, transcripts, and FAQs on this page are made by uListen, not YouTube.

CHAPTERS

Jump to publisher timestamps

Chapter summaries

80 episodes in: the “suddenly exponential” podcast growth inflection
Aakash sets context on the show’s trajectory, explaining why early profitability was low despite monetization channels. He frames his approach as investing upfront until the audience curve turns upward, and shares the key subscriber and listenership milestones reached by episode 80.
- •80+ interviews with notable product leaders; what he’s learned from the run
- •Growth philosophy: invest early to reach an exponential inflection point
- •Milestones: from ~4.7K YouTube subs at episode 50 to ~11.1K at episode 80
- •Improved profitability from better advertisers and increased AdSense
- •Thank-you and reset on where the show stands today
How the podcast grew: consistency, better trailers, and smarter quality tradeoffs
He breaks down the practical levers that moved growth: publishing cadence, stronger trailers, and avoiding diminishing returns on production upgrades. The theme is incremental improvement (1% better each episode) without letting costs balloon.
- •Publishing twice weekly builds listener habit; aims for consistent day/time
- •Trailer upgrades: tighter clip selection + higher investment in visuals/sound
- •Diminishing returns on gear; shifting focus from perfection to leverage
- •Still room to improve (producer, set design, lighting), but cost-aware
- •Interview guides and research are improving, but not the primary growth driver yet
Lessons from 80 episodes: product management is changing fast (AI, efficiency, scrutiny)
Aakash shares how the podcast reshaped his view of modern PM work, especially compared to earlier eras. He argues the pace of change is accelerating due to efficiency pressures and AI, forcing PMs to adopt new skills and artifacts.
- •2025 PM differs meaningfully from 2015/2020; change velocity is higher
- •Efficiency and ROI scrutiny: PM vs engineering ratios, role clarity
- •AI as a platform shift (like mobile/cloud) altering PM practice
- •New PM skills: AI prototyping, AI evals, AI PRDs, AI strategy
- •PM orgs adapting toward clearer value delivery expectations
What content wins: recency beats timelessness on the podcast
He contrasts what performs well in podcasts versus newsletters. Surprisingly, “timeless” episodes underperform while “what’s new” content (especially AI PM) spikes, suggesting recency is a major demand driver in audio/video formats.
- •Timeless interviews can underperform (example: classic PM prep content)
- •Recency and novelty perform better in podcast listening/viewing behavior
- •AI PM-focused episodes outperform and ‘soar’
- •Newsletter can succeed with more evergreen topics; channel-specific strategy
- •Implication: choose topics based on how audiences consume each medium
How the podcast fits the business: time allocation, coaching, and sponsorship reality
Aakash explains tradeoffs of running multiple content products and how the podcast affects his broader business. He describes coaching as a research/feedback loop and admits sponsorships are a pragmatic choice tied to cost of living and lifestyle goals.
- •Podcast now ~50% of his time; requires team support to protect quality
- •Choosing focus: one business vs multiple platforms; he runs several streams
- •Coaching is priced for feedback and research rather than max revenue
- •Sponsorships are a necessity at current lifestyle stage (higher COL)
- •Forecast: podcast profit share could exceed 50% in 2–3 years
How podcasts make money: AdSense, sponsorships, exclusivity—and early tradeoffs
He outlines the main podcast monetization channels and the strategic choice between growth and profitability. Sponsorships can slow growth but fund production and make the podcast financially viable sooner, which mattered for his circumstances.
- •Primary revenue sources: YouTube AdSense, sponsorships, exclusivity deals
- •Sponsorship types: paid ads vs paid guests (he avoids paid guests)
- •Early-stage choice: self-edit + no sponsors vs sponsors to fund a team
- •Sponsorships can reduce growth rate but increase early profitability
- •Podcast created partly to meet inbound advertiser demand he couldn’t fulfill elsewhere
Dream employer: why he’d pick OpenAI (and how he actually uses AI models)
If forced to stop content and pick one company, Aakash chooses OpenAI for learning, scale, and upside. He also shares his day-to-day multi-model workflow and why he often prefers alternatives to ChatGPT despite believing OpenAI will win overall.
- •Would choose OpenAI: consumer scale, data flywheel, leadership, investment
- •Personal workflow: prompts across multiple models, then blend outputs
- •Often uses Grok and Gemini 2.5 Pro more than ChatGPT for speed/writing
- •OpenAI’s distribution advantage vs competitors’ quality-without-distribution
- •Financial upside framing: PPU vs traditional RSUs; high expected return
Live demo: using Gemini 2.5 Pro to tailor a resume to a job description
Aakash demonstrates an AI-driven resume rewrite workflow, emphasizing prompt quality and relevance filtering. He shows how to tailor bullets and positioning to match what recruiters and ATS systems seek, especially when a candidate lacks a key domain signal.
- •Claims Gemini 2.5 Pro is best-in-class for resume tailoring (as of May 2025)
- •Prompt structure: persona + target job + constraints + explicit output request
- •Add critical context (e.g., missing gaming background) and how to compensate
- •AI produces rewritten summary and bullets mapped to the job’s requirements
- •Outcome: reduces manual tailoring from ~15 minutes to ~1 minute + light edits
AI prototyping as the new PM superpower: PRDs in minutes, prototypes in hours
He argues PM work is moving from long documents to fast, iterative artifacts that generate buy-in. The core method: write a concise PRD with strong context and output formatting, iterate with feedback, then convert directly into prototype instructions.
- •AI prototyping is changing PM: closer to pixels, faster iteration cycles
- •Two prompt essentials: missing context + desired output format
- •Example PRD: Apple Podcasts adding video with offline/storage constraints
- •Iterative prompting beats accepting first draft; refine metrics and stories
- •Convert PRD into concrete instructions for tools like Bolt.new
Hands-on build: creating a Bolt.new prototype and iterating toward Apple-like UX
Aakash walks through generating a working interactive prototype, spotting tool confusion, and correcting it with clearer guidance and reference screenshots. He highlights that PMs must read code comfortably to collaborate with AI coding tools and debug issues.
- •Bolt generates multiple screens: episode list, download modal, settings
- •Prototype targets exec concerns: storage management + quality preferences
- •Iteration loop: add screenshots and specific UI feedback to align with Apple
- •Tools can error (imports) and sometimes self-fix; PM should still inspect
- •PM skill shift: reading code becomes essential in AI-assisted prototyping
Why 2025 is a great time to be a PM—and how career switchers can break in
He pushes back on headlines that AI will replace PMs, emphasizing PM as a people-coordination and strategy role. For emerging PMs (e.g., analytics backgrounds), he proposes finding a unique edge via domain adjacency or being the customer, often via internal transfers.
- •PM work is ~75% people coordination; hardest area for AI to replace
- •Future PMs may be best positioned to prompt AI coding/design tools
- •PM population is adaptable; role shifts from PRDs to evals/prototypes
- •Break-in edges: internal adjacency (product analyst → PM) or domain expertise
- •Career switch cost: sometimes a level down; larger companies may match comp
AI PM skills roadmap for new grads: build a product, ship fast, add agents
Aakash lists the most important AI PM skill categories and then gives a practical learning plan: build in public. He recommends creating a real product in a competitive market, shipping iteratively, collecting feedback, and integrating AI features/agents to learn by doing.
- •Top AI PM skills: strategy, basics, PRDs, prototyping, evals, agents
- •Use V0/Lovable → export to Cursor/Windsurf to start building quickly
- •Pick a market you personally understand and pay for; solve real pain points
- •Ship features rapidly; learn prioritization, bloat control, and growth loops
- •Design an AI agent workflow (e.g., wearable data → training plan via SMS)
From MVP to first paying customers: beta loveability + launch mechanics
He offers a concrete path from “viable” to “lovable” by focusing on a tight beta group and daily iteration. Then he details a launch playbook centered on building pre-launch community, executing Product Hunt/Hacker News tactics, and mobilizing supporters on launch day.
- •Start with a small beta group to reach Minimum Lovable Product (MLP)
- •Aggressive iteration: daily texting/users + daily shipping for ~30 days
- •Build pre-launch audience across platforms and recruit launch supporters
- •Launch tactics: Product Hunt assets, HN story framing, app-store readiness
- •Momentum brings users/feedback; then improve reliability and growth loops
Zero to 250K followers: platform sequencing, swipe files, and high-effort content
Aakash explains how he’d restart as a creator from zero by focusing on one platform and one product offer, then expanding only after reaching clear milestones. He gives a repeatable system: study what works, build a swipe file, experiment across formats, and double down on what fits both audience and creator strengths.
- •Start with one platform + one product (e.g., LinkedIn + free newsletter)
- •Choose where your audience already is; avoid “dead zones” like FB/IG reach
- •Build swipe files from creators you admire; replicate structure, not content
- •Experiment daily; track what performs and what you enjoy, then triple down
- •Scale platforms by milestones: ~10K then add next; sequence for PM audience
LinkedIn as a career accelerator: inbound jobs, comp jumps, and posting tactics
He connects LinkedIn presence to concrete career outcomes, including being discovered for roles with significantly higher pay. He then shares a system for junior PMs: prioritize high-effort posts, use AI for style iteration (without copying), and build relationships through genuine engagement.
- •LinkedIn presence can drive inbound opportunities and salary upside
- •Example: comp jump from regional scale to national scale via inbound discovery
- •Professional posting strategy: weekly/weekend posts to avoid “too online” vibe
- •High-effort content wins: edited copy + strong hooks + strong visuals/research
- •Engagement strategy: thoughtful comments → DMs → mutual network flywheel
Newsletter success stack rank + PM job search advice + sprint prioritization foundations
He ranks drivers of newsletter growth (timing, marketing distribution, copywriting, research, consistency, network) and downplays luck. He then shifts to career execution: resumes should emphasize measurable impact over soft-skill claims, and international US job seekers must rely on targeted networking and referrals—not mass applications—before closing with sprint prioritization fundamentals rooted in strategy and roadmap alignment.
- •Newsletter stack rank: timing first; distribution/marketing second; luck last
- •Copywriting + research reduce unsubscribes; consistency builds habit
- •Resumes: emphasize hard impact; prove soft skills via credible evidence
- •US PM job search: avoid volume applying; use small-market targeting + referrals + work products
- •Sprint decisions: strategy → roadmap → backlog → sprint themes; manage stakeholders via shared priority list

Publisher timestamps

Get more out of YouTube videos.

Read along on your phone, search it beside the video, or ask your favorite AI app what was said.