CHAPTERS
80 episodes in: the podcast finally hits the exponential growth curve
Aakash shares that after investing heavily upfront, the show is now entering the “suddenly exponential” phase of growth. He compares subscriber growth between episode 50 and 80 and frames the strategy as deliberately delaying profit to build momentum first.
- •80+ podcasts recorded; growth milestones: ~4.7K to ~11.1K YouTube subs
- •“Invest upfront” mindset to reach an inflection point
- •Early-stage growth feels flat until it isn’t (Jack Butcher curve)
- •Shift from growth-only to now generating meaningful monthly profit
How the show grew: consistency, better trailers, and spending smarter on production
He breaks down the practical levers that improved distribution: shipping cadence, stronger packaging via trailers, and a more ROI-driven approach to production quality. The theme is focusing on what moves audience behavior without overspending on marginal gains.
- •Publishing frequency: 2 episodes/week to build audience habit
- •Trailer upgrades: better clip selection, SFX/VFX, more iteration cycles
- •Production quality has diminishing returns (logistic curve)
- •“Make each episode 1% better” without cost explosion
- •Improved research/interview guides still a major growth lever
What 80 interviews taught him: PM in 2025 is changing fast, and content must be timely
Aakash summarizes learnings across product management and content creation. On the PM side, he argues the pace of change is accelerating due to efficiency pressure and AI; on the content side, he learned recency often beats “timeless” topics in podcast performance.
- •PM role shifting due to AI + efficiency scrutiny + ROI expectations
- •New must-have skills: AI prototyping, AI evals, AI PRDs, AI strategy
- •Platform shifts like mobile/cloud: AI changes PM fundamentals
- •Podcast performance favors “what’s new” over evergreen (often)
- •Newsletter audience may prefer more timeless content than podcast listeners
How the podcast fits the business: balancing newsletter, coaching, sponsorships, and lifestyle
He explains the operational tradeoffs of running multiple creator-business lines. The podcast now consumes a large share of his time, while coaching functions as research/feedback and sponsorships support current lifestyle needs.
- •Podcast takes ~50% of his time; requires team/process efficiency
- •Creator strategy choice: one business vs multiple platforms
- •Coaching used as low-priced feedback + research loop
- •Sponsorships are not ideal personally but fund higher cost of living (LA)
- •Belief that podcast profit share could exceed 50% in 2–3 years
How niche podcasts make money: AdSense, sponsorships, and platform deals
Aakash outlines the core podcast monetization paths and the tradeoff between early growth and early cashflow. He also clarifies his stance on sponsorships vs paid guests and why he chose monetization earlier than some creators do.
- •Primary revenue: YouTube AdSense, sponsorships, exclusivity contracts (e.g., Spotify)
- •Sponsorship formats: paid ads vs paid guests (he avoids paid guests)
- •Early sponsorships can reduce growth rate but improve early profitability
- •His constraint: podcast time competes with higher-earning newsletter work
- •Podcast created partly to satisfy advertiser demand he couldn’t otherwise fulfill
Sponsor break #1: Miro + AI Evals course
Two ad reads: one for Miro’s collaborative workspace and one for a live course on AI evaluation. Both are framed as productivity/workflow accelerants for PMs and engineers.
- •Miro as an all-in-one collaboration workspace with templates and integrations
- •Miro AI positioned as a way to synthesize research into PRDs quickly
- •AI Evals course by Hamel Husain + Shreya Shankar promoted
- •Course emphasizes systematic evaluation frameworks for production AI
- •Discount code and cohort timing mentioned
Dream employer: why he’d choose OpenAI (and his real multi-model workflow)
He answers a hypothetical about returning to a single company and picks OpenAI. He also shares he doesn’t rely only on ChatGPT and describes how he compares outputs across multiple frontier models.
- •Would choose OpenAI for consumer distribution + data flywheel potential
- •Uses multiple models (Grok, Gemini 2.5 Pro, Claude, ChatGPT) and blends outputs
- •Preference notes: Grok for speed/search/tone; Gemini for writing tasks
- •OpenAI advantages: investment, leadership, talent density, Jony Ive signal
- •Comp structure mention: PPU vs traditional RSUs; potential financial upside
AI resume transformation demo: tailoring resumes with Gemini 2.5 Pro + strong prompts
Aakash demonstrates a workflow to quickly customize a resume to a job description using Gemini 2.5 Pro. He emphasizes providing context (constraints, recruiter biases) and using explicit prompting to get a targeted, ATS-friendly result fast.
- •Claims Gemini 2.5 Pro is best for resume tailoring (as of late May 2025)
- •Prompt structure: role + resume + job description + constraints + desired output
- •Example: tailoring for Epic Games role, compensating for lack of gaming background
- •Shows output improvements: summary reframing + bullet rewrites aligned to JD
- •Time compression: ~15 minutes manual work reduced to ~1–2 minutes + light edits
AI prototyping revolution: PRDs as fuel for prototypes, not documents for reading
He argues AI prototyping is redefining product management by pulling PMs closer to pixels. The PRD becomes a fast, iterative input to generate prototypes, and prompt engineering hinges on giving missing context and specifying the output format.
- •“Days of PRD-only are over”: prototypes communicate more effectively
- •Gemini 2.5 Pro recommended for fast PRD drafting/iteration
- •Two prompt pillars: (1) context AI lacks, (2) required output format
- •Example PRD: adding video podcasts to Apple Podcasts with offline/storage constraints
- •Iterative refinement: critique output, tighten scope, select 1–2 prototype journeys
Sponsor break #2: Linear + AI PM certification on Maven
Two more ad reads: Linear as the unified product execution system, and an AI PM certification program. Both are positioned as tools to reduce coordination overhead and improve PM capability in modern teams.
- •Linear pitched as consolidating planning, tracking, and feedback workflows
- •References notable Linear customers (e.g., OpenAI, Vercel, Brex, Cash App)
- •AI PM certification on Maven promoted (cohort-based, 8 weeks)
- •Emphasis on learning how top teams “keep shipping”
- •Discount code and link provided
Live prototype build: using Bolt.new (plus iteration and debugging) to ship a demo fast
Aakash walks through generating a working prototype from PRD instructions and iterating toward an Apple-like UI. He highlights that PMs must be comfortable reading code, guiding the tool, and managing scope to reach a demo that earns executive buy-in.
- •Paste PRD/prototype instructions into Bolt.new to generate an interactive app
- •Prototype focus: download quality + storage management to address key exec concern
- •Iterate with screenshots and UI references to match Apple Podcasts look/feel
- •Tools comparison idea: try alternatives like Lovable.dev and V0
- •Deploying prototype for sharing (e.g., Netlify) to strengthen PRDs and reviews
Why 2025 is a strong time to become a PM: people work + adaptability
He responds to concerns about layoffs and AI replacing PMs with a bullish thesis: most PM work is human coordination and shared understanding. He argues PMs will evolve into orchestrators who prompt AI tools across coding and design.
- •~75% of PM work framed as “people work” (alignment, coordination, communication)
- •AI less likely to replace cross-functional negotiation and shared context building
- •If tooling advances, PM becomes the natural prompter/orchestrator
- •PM talent pool is high-performing and has shown adaptability to shifts
- •Hiring data: rebound from lows; long-term steady growth expectation
AI PM skills roadmap for new grads: build real products with AI tools
Aakash lists core AI PM competencies and recommends learning by building. He suggests creating and shipping a small business/product using prototyping tools, then expanding into code editors to develop real-world stories and taste for prioritization.
- •Core skills: AI basics, AI strategy, AI PRDs, AI prototyping, AI evals, AI agents
- •Project approach: pick a market you personally understand and improve an existing product
- •Use tools (e.g., V0/Lovable) then export to Cursor/Windsurf to build further
- •Practice PM fundamentals: user problems, prioritization, bug vs feature tradeoffs
- •Add AI/agents into the product to learn workflows end-to-end
From MVP to first paying customers: beta love → launch momentum loops
He advises an early-stage founder on getting first paying customers by turning MVP into an MLP with a small beta group. Then he outlines a launch playbook focused on daily public building, community setup, and coordinated launch-day distribution.
- •Start with 3 trusted users; sprint to “minimum lovable product” quickly
- •Daily shipping + daily feedback loops (texts/check-ins)
- •Pre-launch marketing: post progress daily across channels
- •Build a launch support group (~600 people) to amplify on launch day
- •Launch tactics: Product Hunt assets, Hacker News angle, mobilize friends/family
Growing a personal brand from zero: one platform + one offer, then scale
Aakash shares a structured creator growth strategy: pick a single platform and a clear product (often a newsletter), then iterate content types using a swipe file. He also explains when to expand to additional platforms based on follower milestones.
- •Start with one platform (e.g., LinkedIn for PM audience) and one product (newsletter)
- •Build a swipe file from top creators; experiment across formats (polls, infographics, videos)
- •Double down on what you enjoy creating, not just what the algorithm rewards
- •Cadence suggestion: daily LinkedIn + weekly newsletter (or weekly for “professionals”)
- •Scaling rule of thumb: add platforms at ~10K, ~100K, ~1M milestones
LinkedIn as a career accelerator: inbound jobs, higher comp, and posting mechanics
He argues LinkedIn presence directly impacts compensation and opportunity by creating inbound demand and credibility. He then gives tactical advice on optimizing your profile, writing high-effort posts, and building a network through thoughtful engagement.
- •Personal example: Epic (~$275K) to Affirm (~$800K) enabled by LinkedIn visibility
- •Inbound recruiter/exec messages driven by consistent presence and positioning
- •Profile optimization: headline, About, keywords, recommendations, skills, photo
- •Posting advice: high-effort content; teach or tell strong stories; write to “you 6 months ago”
- •Growth via community: thoughtful comments, repeated engagement, then DMs and mutual support
Newsletter success factors: stack ranking what matters (and why ‘luck’ is overrated)
Aakash ranks drivers of newsletter growth and retention, prioritizing timing and distribution skill over purely content quality. He argues newsletters are a grind and credits execution more than randomness.
- •Ranked: timing #1; marketing/social prowess #2; copywriting #3
- •Research ability reduces churn by adding unique value
- •Consistency builds habit and repeat reading
- •Network helps via recommendations and cross-promotion
- •Luck dismissed as a meaningful driver vs sustained effort
Breaking into PM (especially US roles): stop spamming applications and build leverage
He gives blunt feedback on job search strategy: volume applications without networking or work products won’t work, especially with visa constraints. He recommends narrowing targets, moving closer to job hubs if possible, and creating referrals before applying.
- •US PM roles are hard; visa sponsorship constraints shrink the market drastically
- •Thousands of apps signals broken approach: poor fit and low-leverage tactics
- •Use “small market recruiting strategy”: target intersection of fit + visa sponsors
- •Network before roles open; aim for multiple internal contacts and strong referrals
- •Apply early with a work product; mobilize network to reach recruiter/hiring manager
MBA-to-PM roadmap and sprint prioritization: strategy → roadmap → themed sprints
He closes with practical PM fundamentals: build real products during school, network deliberately, and focus on strategy-driven prioritization. For sprint conflicts, he emphasizes alignment on strategy and roadmap rather than debating individual features ad hoc.
- •MBA candidates: build a product now to generate real interview stories
- •Consume PM content intentionally; shift leisure time toward product learning
- •Get internships (even local startups) and network into target companies with work samples
- •Sprint decisions should flow from strategy → roadmap → backlog → sprint themes
- •Stakeholder management: co-create ranked priorities; explain tradeoffs transparently
