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TikTok's Gen Z Search Takeover, MCP Data Leak, Jack Ma's Comeback

Plus: AI Agents Paying Bills, Startup Pitch Defence Strats, Infrastructure That Never Dies

Author

Alastair Preacher
September 19, 2025 • Estimated Reading Time: 16 minutes

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What’s cooking this week? 🥘 

What's cooking this week? 🧑‍🍳

TikTok quietly becomes Gen Z's Google (with geopolitical drama), OpenAI adds full MCP support (cue immediate data leak), and the attention economy gets industrialised at 3,000 podcasts per week. Meanwhile, Jack Ma's back (so back) and Chrome gets vertical tabs (finally).

  • 📰 Not Boring → TikTok's search supremacy, AI workflow middleware, attention economy automation

  • ⌚️ Productivity Tapas → Agent-building agents, conversation recall tools, customer feedback insights

  • 🍔 Blog Bites → From bootstrapped exit to VC compliance play, startup idea defence tactics, Instagram's instant upload hack

  • 🎙️ Pod Shots → AI as infrastructure's fourth pillar (why this changes everything about building software)

Let's go 🚀

📰 Not boring

📱 TikTok: Google for Vibes, but With Geopolitics

TikTok has quietly become Gen Z’s default search engine, with answers wrapped in aesthetics rather than hyperlinks. Washington and Beijing have now opted for the uneasy compromise: you can’t stop the vibes, so you re‑route the ownership to Oracle and friends.

🤖 AI for Work: Memory, Agents, and Glasses on Your Face

  • OpenAI adds full support for MCP in ChatGPT; update Jira tickets, trigger Zapier workflows, or combine connectors for complex automations

  • Claude app now has memory and file creation for Team and Enterprise customers

  • How people actually use ChatGPT vs Claude — ChatGPT for writing vs Claude for code (although personally I prefer Claude for writing…)

  • AI agents can now make secure payments thanks to Google's new AP2 protocol

  • Amazon looks like it's making its own display-enabled smart glasses. They definitely have a van driver user base ready to go

  • Why we haven't seen a Stripe-sized company in analytics - and how OpenAI's $1.1B Statsig acquisition could change that

AI here is less novelty, more infrastructure. OpenAI is morphing into workflow middleware (with the predictable data‑leak teething issues), Anthropic is pushing memory as the killer differentiator, and Google is laying rails for agents to pay each other. Even Amazon’s smart glasses fit: AI seeping into everyday work — literally strapped to the faces of van drivers. Probably. And now, OpenAI is betting a billion dollars that AI can finally crack the analytics market, turning data into a new kind of enterprise gold.

🎨 AI for Attention: Ads, Spam, and Scale

  • Upload an image and get lip-synched talking video using Veo3 on Higgsfield

  • New startup Inception is creating 3,000 podcasts a week at $1 each (that’s 20 listeners to break even)

This is where AI collides with the attention economy. Veo3 is already being used in ad production, cutting costs and ramping up creative churn. Inception industrialises content: 3,000 podcasts a week isn’t curation, it’s flooding the zone and hoping the economics hold. The question isn’t whether this works — it’s how much more noise audiences can handle.

🌀 Everything Else

  • Jack Ma Returns with a vengeance to ‘Make Alibaba Great Again’

  • Spotify veterans launch AI-powered learning platform - Oboe

  • Gemini overtakes ChatGPT in US iPhone App Store; Perplexity climbs to #28, Grok at #62

  • Google Chrome is finally getting native vertical tabs

Jack Ma’s comeback tour feels more about optics than innovation: Alibaba’s competition is ByteDance and Shein, not the absence of its founder. Oboe shows there’s still space for specialised AI tools tackling specific jobs. And Gemini’s leapfrogging ChatGPT is the reminder that in consumer AI, distribution trumps tech — especially once the products start to look commoditised and the differences blur.

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⌚️ Productivity Tapas: Time-Saving Tools & GPTs

  • Latte by latitude: An Agent to help you build Agents (how meta!) using natural language

  • Convo: Realtime agent to help recall past conversations and prompt live in chat

  • Enterpret: Create actionable insight from customer feedback with AI

    Remember. Product Tapas subscribers get our complete toolkit - 460+ personally tailored, time-saving tools for PMs and founders. Your shortcut to efficiency and what's hot in product management 🔥

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🍔 Blog Bites - Essential Reads for Product Teams

Strategy: From Bootstrapped Exit to VC-Backed Compliance Automation

Girish Redekar is a two-time founder helping fast-growing technology companies build trust through automated security compliance. On OpenSource CEO he recently explored the journey from building a bootstrapped startup to creating a venture-backed compliance automation platform. It’s got some great nuggets on the two funding models and for those in the startup world in general. Read the full article here.

💡 "The real challenge was that this problem is typically solved as a service, with people. From the start, my co-founder and I were clear that we didn't want to build a services company; we wanted to build a product company."

Key Takeaways

• Bootstrapped vs VC Learning: Bootstrapping forces founders to learn every aspect of the business but creates bottlenecks; VC funding allows hiring experts earlier but requires different preparation and mindset

• Product-Market Fit Strategy: Conducted customer interviews before writing any code using "The Mom Test" principles; Worked with design partners to validate the solution before broader market entry; Focused on productising what was traditionally a service-based solution

• Go-to-Market Approach: Identified they were "harvesting demand" rather than generating it, shaping entire GTM strategy; Leveraged urgency of compliance needs to drive strong inbound marketing; Expanded across multiple channels: organic search, performance marketing, events, and LLM-based search

• Fundraising Execution: Brought Series A investor onto team for insider VC perspective; Structured raise in three buckets: research (investor mapping), narrative (story crafting), data preparation; Set clear timeline of 6-8 weeks to create urgency and avoid endless conversations

• Scaling Strategy: North Star metric: number of happy customers (combining logo count with NPS above threshold); Evolved ICP from founders/heads of security to CISOs and GRC heads as they moved upmarket; Now serving 3,000+ customers across 75 countries with multiple market segments

• AI Integration: Identified 15 internal AI use cases with centrally guided but locally executed implementation; Using AI agents to make compliance upkeep nearly autonomous; Helping customers adopt AI safely while protecting against AI-based attacks

Girish Redekar, Open Source CEO

Strategy: Why Every Great Startup Idea Gets Torn Apart (And How to Fight Back)

Andrew Chen’s latest essay covered the phenomenon of "anti-pitches" - the lazy one-liners skeptics use to instantly dismiss startup ideas. He reveals how founders can navigate these predictable criticisms by strategically reframing their pitch to avoid common traps. Read the full article here.

💡 "The Best Anti-Anti-Pitch is a Good Pitch... almost every new product idea can be pitched in five or ten different ways. Your ability to pick a pitch out of all the different permutations gives you a lot of leeway to go in whatever direction you want."

Key Takeaways:

• Common Anti-Pitch Patterns: AI ideas get dismissed as "just another GPT wrapper" or "too crowded"; Social apps face "network effects are too strong" criticism; Hardware ideas trigger inventory and China copycat concerns; Dating apps are "too hard to monetise" with constant churn

• Response Strategies: Ignore when criticism comes from irrelevant sources (dinner party randos, click-hungry journalists); Accept then address when you have genuine innovation to counter the concern; Deny carefully by redirecting to better comparisons rather than just saying "we're not like X"

• The Pitch Maze Concept: Work backwards from predictable criticisms to craft better initial pitches; Test different framings - team-focused, customer-need focused, feature-focused; Navigate towards responses where you have stronger counter-arguments

• When to Listen vs Push Back: Pay attention when criticism comes from ideal customers or industry experts; Distinguish between lazy skepticism and legitimate market concerns; Remember that 99% of startups fail, so skeptics are usually statistically right

• Reframing Tactics: Avoid accepting the critic's framing (puts you in a defensive hole); Redirect comparisons to more successful companies you want to be associated with; Use timing and innovation as differentiators ("first AI-native version")

• Learning Opportunities: Don't just "show up and throw up" with endless pitching; Extract real data from industry-specific criticism; Balance optimism with genuine market feedback

Andrew Chen, Substack

Product: Why Instagram's "Instant" Upload Trick Became Their Secret Weapon

Tom from Strategy Breakdowns explores how Instagram's co-founder Kevin Systrom solved mobile photo sharing's biggest pain point in 2010 through clever perception hacking rather than technical improvements. The team applied Gmail's predictive loading principles to create the illusion of instant uploads by starting the process before users even hit "share".

💡 "Mobile experiences fill gaps while we wait. No one wants to wait while they wait" - Kevin Systrom

Key Takeaways:

• Predictive User Behaviour: Instagram began uploading images the moment users selected filters and moved to captions, betting on high user intent to complete posts

• Two-Stage Upload Process: Heavy image data transmitted during caption writing, with only metadata processed when users hit "Share"

• Strategic Risk Management: Team accepted occasional wasted bandwidth from cancelled uploads in exchange for dramatically improved user experience

• Perception Over Reality: Focused on making the app feel fast rather than just being technically faster than competitors

• Natural Pause Exploitation: Hid computational work during moments when users were distracted (writing captions, selecting filters)

• Instant Feedback Loops: Comments and likes render immediately on devices before server confirmation, maintaining the illusion of speed

• Strategic Constraints: Limited uploads to 640×640 pixels vs competitors' 1,536×1,536 resolution to optimise processing pipelines

• Compound Growth Effect: Faster UX led to higher retention, more content creation, and accelerated user acquisition

• Mobile-First Philosophy: Prioritised instant gratification over pixel-perfect quality, perfectly suited for mobile consumption patterns

• Industry Standard Creation: Netflix pre-loads episodes, YouTube buffers ahead, Facebook pre-fetches predicted links

• Universal Application: Whether B2B or B2C, anticipating and delivering what users need next creates competitive advantages

• Growth Catalyst: Speed became Instagram's primary differentiator during crucial early growth phase (1M users in 3 months)

Tom, Strategy Breakdowns

🎙️ Pod Shots - Bitesized Podcast Summaries

Remember, we've built an ever-growing library of our top podcast summaries. Whether you need a quick refresher, want to preview an episode, or need to get up to speed fast - we've got you covered.

Check it out here

🎯 AI as the Fourth Pillar: How Infrastructure Never Dies, It Just Gets Layered

The infrastructure world is experiencing its most dramatic transformation in decades, with AI emerging as a fundamental new layer that's reshaping how we build and think about software. In this comprehensive discussion, Andreessen Horowitz's infrastructure team explores how AI models are becoming the fourth pillar of infrastructure alongside compute, networking, and storage – and why this represents the biggest disruption to software development in a generation.

Why this matters for product leaders: Understanding infrastructure shifts is crucial for product strategy. As AI becomes core infrastructure, product teams need to grasp how this changes development capabilities, team structures, and what's possible to build. The shift from "programming logic" to "programming with intelligence" fundamentally alters product development timelines, feature possibilities, and competitive dynamics. Whether you're building AI-native products or traditional software enhanced with AI, these infrastructure changes directly impact your product roadmap, technical architecture decisions, and go-to-market strategy.

The conversation features insights from the A16Z infrastructure team, including partners who've been at the forefront of infrastructure investing since the firm's early days, witnessing everything from the pre-cloud era through today's AI revolution.

🎥 Watch the full episode here:

📆 Published: August 2025

🕒 Estimated Reading Time: 5 mins. Time saved: 44+ mins! 🔥

🏗️ Defining Infrastructure in the AI Era

Infrastructure fundamentally encompasses "what makes software work" – the technical tools and systems that engineers use behind the scenes to build applications. The formal definition centres on the technical buyer: if it's used by developers, data scientists, analysts, or cybersecurity professionals to build other software, it's infrastructure.

"Infrastructure is basically what makes software work," explains one partner. "At the simplest possible level, if you want software, infra is what engineers are using behind the scenes to make all this possible."

This technical buyer distinction separates infrastructure from enterprise software. Whilst vertical SaaS might serve flooring companies or marketers, infrastructure serves the "nerds behind the scenes" – the technical professionals building the systems that power everything else.

Key Takeaways:

  • Infrastructure serves technical buyers: developers, data scientists, DevOps professionals

  • It's the "stuff you use to build the stuff" – tools for creating applications

  • Technical buyers tend to be centralised, unlike vertical market buyers

  • The definition encompasses compute, networking, storage, and now AI models

But AI isn't just another infrastructure layer – it's fundamentally changing the nature of programming itself. In the full article, we explore how this "fourth pillar" is creating the biggest disruption to software development in a generation, plus practical insights on defensibility, team structures, and what this means for your product roadmap.

That’s a wrap.

As always, the journey doesn't end here!

Please share and let us know what you would like to see more or less of so we can continue to improve your Product Tapas. 🚀👋

Alastair 🍽️.

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