Intelligent Artifice, formerly the Mobile User Acquisition Show

Ravi Mehta built a working product in about four or five hours. Then he spent six weeks making it good enough to ship. That gap between a one-shot build and something with craft is the whole conversation. When AI can build almost anything, what is left for a product team to be good at?

Ravi is a former CPO at Tinder and co-created Reforge's AI Strategy course with Brian Balfour. He now spends about half his time as a fractional CPO and writes the Top 1% Builder Substack. In this conversation we cover why AI will not end slop, what makes a moat when everything is crawlable, and how teams should decide what to ship when output has exploded.

Slop, taste and craft. Ravi defines slop as the accumulation of unexamined defaults, and says AI will not eliminate it, only make it prettier. The antidote is taste and judgment: your opinion about what is good, paired with the craft to make something that meets that bar. He explains the difference using a restaurant critic who has taste but cannot cook.

Throw more away. A lot of teams are drowning in unshipped features, specs and PRDs nobody reads. Ravi argues the next phase is curation, and that the best AI teams should ship only the best of what they build, the way Apple always has. He also explains why running an A/B test on everything leaves a product feeling like a Frankenstein.

Latency, not velocity. Big companies produce more output but with high latency. Ravi's litmus test is how long it takes to change the copy on your most important button, which takes some companies weeks. He also explains why trading talent for tokens is a mistake, and what a leader should do with the extra capacity AI creates.

From the Tinder days. Ravi walks through how Tinder used Plus, Gold and Platinum tiers plus Boosts and Super Likes to approach a price for every customer, and why price discrimination only works when it feels fair, using the Coca-Cola vending machine and Uber surge pricing as examples.

Video Chapters:

00:00 Introduction
02:43 Is a personalized price for every customer closer with AI?
06:28 Why price discrimination has to feel fair: Coca-Cola and Uber
08:06 Which moats survive in AI
13:35 Taste is a skill, and slop is unexamined defaults
19:28 Taste versus craft
20:16 When an AI feature earns its place: Descript's eye contact
22:51 Who pays for the tokens?
24:33 Drowning in prototypes: why AI teams need curation
29:25 Building Copy Paste and a launch trailer made in Claude Code
33:49 Minimize latency, not maximize velocity
38:00 Where should the extra capacity go? Talent for tokens
41:24 A/B testing and understanding customers
42:49 Wrap-up and where to find Ravi

Topics covered:

- Whether AI makes a personalized price for every customer possible
- Why price discrimination has to feel fair: Coca-Cola, Uber, airlines
- Which moats survive in AI: people, proprietary data, brand, switching costs
- Taste, craft and why AI creates prettier slop
- When an AI feature earns its place: the Descript eye contact example
- Why teams should curate and ship less
- Building Copy Paste and making a launch trailer entirely in Claude Code
- Minimizing latency instead of maximizing velocity
- Trading talent for tokens, and where extra capacity should go

Learn more:

Episode page: https://mobileuseracquisitionshow.com/episode/how-to-build-product-taste-and-become-a-top-1-builder-with-ravi-mehta/
Ravi on LinkedIn: https://www.linkedin.com/in/ravimehta/
Ravi's website: https://www.ravi-mehta.com/
Top 1% Builder Substack: https://blog.ravi-mehta.com/
Ravi's free workshop: https://luma.com/9x2hnhd0
Newsletter: https://intelligentartifice.kit.com

Gabe Kwakyi has 16 apps live, all built with AI.

The gap between building an app and getting results is the whole conversation. When anyone can ship an app in a day, what is actually hard?

Gabe co-founded and ran the ASO and ad agency Incipia, co-authored a book on advanced app store optimization, and later led Lingvano, a sign language learning app. Today he and his partner run Rhythmic Apps, a studio of apps that includes Ink, a journaling app, and Togella, a couples' question app. In this conversation we cover what happens when you go from one app to a portfolio, what traction really looks like, and what is working and not working in ChatGPT ads and web-to-app.

Traction is a threshold, not a finish line. Gabe's starting signal is $10K in ARR. He lists the benchmarks he looks for: a trial conversion rate of 10 to 20%, a trial-to-subscription rate of 30 to 50%, day one retention of 20 to 40%, a cost per install under $2 or a cost per trial under $25. Even with two apps at that level, he says he would not claim product market fit.

The bottleneck moved. Before AI, the bottleneck was developers and expertise. Now one person can one-shot an entire app in about a day, which moves the bottleneck up the funnel to distribution, positioning, messaging and intuition. He also explains why organic App Store downloads are being squeezed, and why he calls viral success stories success bait.

ChatGPT ads and web-to-app. Gabe has been testing ChatGPT ads. His read is that it is not there yet as a direct response channel, even though CPMs, CPCs and CTRs look decent, and he explains how context hints replace keywords and audiences. In web-to-app, one app he works with had a fraud rate of 80% or more on trials, a challenge he says AI alone will not solve.

Video Chapters:

00:00 Introduction
02:46 Why Gabe started his own app studio
05:05 What changed: people now manage agents that write software
06:27 The aha moment: building a full app with AI
08:27 A portfolio of 16 apps: which ones are working
11:36 What product market fit looks like: the benchmarks
14:25 Does app store optimization still work? The organic squeeze
19:03 AI moved the bottleneck from building to distribution
23:06 Is it really easy to go viral with AI creative?
28:21 ChatGPT ads: context hints and what is not working yet
33:51 Web-to-app: attribution and the fraud problem
36:28 Managing 15 apps: where the time goes
38:28 Wrap-up and where to find Gabe

Topics covered:

- Why Gabe started an app studio after leaving a CEO role
- Why people now manage agents that write software
- Running a portfolio of apps and what counts as working
- Benchmarks for traction: trial rates, retention and cost per install
- Why organic App Store downloads are being squeezed
- Why AI moves the bottleneck from building to distribution
- Why viral success stories are success bait
- ChatGPT ads and context hints
- Fraud in web-to-app and how to manage a portfolio of apps

Learn more:

Episode page: https://mobileuseracquisitionshow.com/episode/how-a-two-person-app-studio-launched-16-apps-with-ai-with-gabe-kwakyi/
Gabe on LinkedIn: https://www.linkedin.com/in/gabrielkwakyi/
Rhythmic Apps: https://rhythmicapps.com/
Ink: Daily Journaling Prompts: https://apps.apple.com/us/app/ink-daily-journaling-prompts/id6761030956
Newsletter: https://intelligentartifice.kit.com

Should a high-value user see a high price? When I asked Jacob Rushfinn, he answered, "Sometimes, but I wouldn't get caught up in the price. Price is just one variable." For some of the highest-intent segments his team has seen, a lower price doubled conversion. What the user sees around the price matters as much as the price itself.

Jacob is the CEO and co-founder of Botsi, which trains a custom model for each subscription app client to decide which offer each new user sees. He started with beta testing and private clients last year and now works with apps doing more than $100 million in ARR. He also writes retention.blog and hosts the Price Power podcast. In this conversation we cover how to move from classic price testing to personalized offers, and what you can do today without a machine learning model.

The limits of price testing. Testing a baseline price, then your biggest markets, then higher and lower variants works, but going country by country is slow. Raising prices can also lift revenue up front while the total number of subscribers drops. Jacob explains why that pushed him toward personalizing the offer.

Signals before the paywall. Device data such as country, OS, device type and language. First-party data from quiz and onboarding answers. Acquisition source, down to the ad network, campaign and Apple Search Ads keyword. Predicted LTV is layered on top so the model optimizes for long-term revenue, not just conversion.

A check you can run today. The share of users who cancel in the first seven to 14 days after subscribing is highly predictive of renewals at one year. It needs no machine learning model.

Inside Apple's rules. Botsi does not generate price points on the fly. Teams pre-select a handful of products, Apple approves them, and five to ten products can combine into a large number of offers. One example is the same price, with the monthly plan behind a see all plans page and the annual plan shown up front.

What it does to your ads. Jacob says he has not seen a real negative impact on ad performance. When a team told him their ads stopped being profitable after a price test, his read was that half of their users had been shown a bad price.

Video Chapters:

00:00 Introduction
02:38 Jacob's method for price testing
05:19 Packaging: the monthly to annual ratio
06:52 Why one price for everyone stopped making sense
09:28 The signals behind a personalized offer
12:10 Layering in predicted LTV
12:55 Price is just one variable: the whole offer
15:12 A check you can run without machine learning
17:54 When a lower price doubles conversion for high-intent users
19:23 How much data you need to personalize offers
20:59 How AI changed the approach
26:28 How personalized offers work within Apple's rules
30:02 Is personalized pricing fair to users?
34:24 How personalized offers affect media buying
41:12 Where to find Jacob and his work

Topics covered:

Why price testing alone stops working as you scale
- The signals used to personalize an offer before the paywall
- Why a lower price doubled conversion for some high-intent users
- A no-machine-learning check: cancellations in the first 7 to 14 days
- How personalized offers work within Apple's rules
- How personalization affects media buying and ad performance

Learn more:

Episode page: https://mobileuseracquisitionshow.com/episode/how-to-personalize-paywalls-for-subscription-app-price-testing-with-jacob-rushfinn/
Jacob on LinkedIn: https://www.linkedin.com/in/jacob-rushfinn/
Botsi: https://www.botsi.com/
retention.blog: https://retention.blog
Price Power podcast: https://podcasts.apple.com/ng/podcast/price-power/id1844494362
Newsletter: https://intelligentartifice.kit.com

When I interviewed Steve P. Young in 2019 the topic was getting featured by Apple. Today he runs App Masters, an agency that started in app store optimization, and his focus is paywalls, onboarding and monetization, both for clients and for a growing portfolio of apps he buys and operates himself. This conversation covers what changed, and the numbers behind it.

Video Chapters:

00:00 Introduction
03:01 Why an ASO expert moved to paywalls and monetization
06:40 The tentacle strategy: building an app around a keyword a giant can't own
09:55 How a $2.99 app became an $8,000 to $10,000 a month app
11:35 What to build when anyone can build an app with AI
14:50 Viral formats vs viral content: the before and after feature
18:35 How to buy an app and how it gets financed
22:37 The real numbers: profit by month and why distribution matters
25:11 The hard paywall test that proved Steve wrong
28:11 Running an agency and his own apps: the team and giving back
30:50 Spending on his own apps vs client budgets
32:26 Wrap-up and where to find App Masters

Topics covered:

Why an app store optimization expert moved to paywalls, onboarding and monetization
- The tentacle strategy: finding the keyword a giant like Calm can't own
- How a $2.99 one-time app became an $8,000 to $10,000 a month app with no additional downloads
- How he financed an app purchase and what the monthly numbers looked like
- The hard paywall test: 13% vs 6% trial activation, 35% trial-to-paid both ways
- Why health and fitness has the highest revenue per install

Learn more:

Episode page: https://mobileuseracquisitionshow.com/episode/from-aso-to-paywall-optimization-mastering-app-monetization-with-steve-p-young/
App Masters: https://appmasters.com/
Steve on LinkedIn: https://www.linkedin.com/in/stevepyoung/
Newsletter: https://intelligentartifice.kit.com

Most creative strategists treat audience research as a box to check before the real work starts.

Karina Tukanova, freelance creative strategy consultant, treats it as the actual work.

Karina is a freelance creative strategy consultant working with health, wellness, fitness, and lifestyle apps. She made the jump from performance marketing manager to creative strategist right around the iOS 14 shift, back when creative strategist wasn't even a defined role.

In this episode, Karina breaks down how she found an entirely untapped audience for a stretching app by scrolling TikTok, Instagram, and Reddit instead of running customer interviews, why the comments section is the most underrated research source most teams ignore, how she uses AI as a second brain to store and surface insights, and how AI uncovered a hidden script formula running across hundreds of UGC ads that would have taken forever to spot by hand.

We also get into why she thinks Meta's push for constant net-new creative volume (the Andromeda era) isn't sustainable on a human level, why handing 100% of ad production to AI just produces slop, and where she thinks AI genuinely earns its place in a creative workflow versus where a human still has to sit in the loop.

Timestamps:
00:00 Intro
02:27 The iOS 14 shift and the rise of creative strategy as a role
04:12 The stretching app that found a new audience in older users
07:21 How to surface real audience insights without endless customer calls
08:39 Why ad and organic comments are an underrated research goldmine
09:39 Using AI as a second brain to store and surface creative insights
10:44 How AI spotted a hidden script formula across hundreds of UGC ads
13:22 What makes UA and creative teams work well together
16:17 Deciding what's worth making before you spend anything
18:34 The Andromeda craze and the myth of 100 new concepts a week
20:19 Fighting Meta's sameness penalty with visual variety
22:18 Where AI should and shouldn't touch ad production
24:31 Advice for bridging the UA/creative skills gap
26:44 Closing: how to find and work with Karina

Connect with Karina on LinkedIn: https://www.linkedin.com/in/karinatukanova/

About RocketShipHQ:
Newsletter: https://intelligentartifice.kit.com
rocketshiphq.com
Full episode and transcript on the Mobile User Acquisition Show:
https://mobileuseracquisitionshow.com/episode/how-to-win-with-deep-customer-research-for-mobile-ad-creative-strategy-user-acquisition-without-focus-groups-or-calls-with-karina-tukanova-freelance-creative-strategy-consultant/

Most telehealth founders think AI's job is to write the ad copy faster.

Vivek Girotra doesn't. He used it to figure out what his company actually stands for before he wrote a single line.

Vivek is the co-founder of SurpassMD, a metabolic health telehealth company that launched with GLP-1 medications and plans to expand into HRT, TRT, longevity treatments, and peptides. Before founding SurpassMD, he spent over a decade leading marketing teams on the agency and client side.

The compound is the same for everyone. What isn't the same is the brand, the trust, and the full experience wrapped around it. That is where Vivek thinks AI actually earns its keep.

In this episode, Vivek breaks down how he used AI to interview himself into a brand mission, how he coded platform, clinical, and operational compliance directly into his AI workflow, why his first batch of social creatives took 15 rounds of revision, and why a nurse's unscripted hospital video beat anything his team generated with AI.

If you are building an AI-native company inside a regulated industry, or trying to figure out where AI should and shouldn't touch your brand, this episode is the most practical breakdown available.

Vivek is offering Intelligent Artifice listeners 20% off their first SurpassMD subscription (any duration, US only). Use code ROCKETSHIP at checkout.

Episode page
rocketshiphq.com
Newsletter
Full episode and transcript on the Mobile User Acquisition Show:
https://mobileuseracquisitionshow.com/episode/he-left-the-cmo-track-to-build-an-ai-native-health-brand-with-vivek-girotrafounder-ceo-surpass-md/

Noom's onboarding funnel runs 120 to 140 screens. Some 3-screen funnels convert above 10%. "The length does not matter. Bigger is not always better," as Jacob Rushfinn put it. Part 2 of the FunnelFox panel gets tactical on how web-to-app funnels actually get built.

Part 2 of the FunnelFox webinar panel, with Rocketship HQ as partner. Shamanth Rao moderates Jacob Rushfinn (CEO, Botsi), Andrey Shakhtin (CEO, FunnelFox), Patrick Stuart-Constant (CEO, Sociaaal), Mike Gadd (VP Customer Success, Singular), and Elise Zareie (Head of Paid Social, Lingokids). If Part 1 was about the shift, Part 2 is about the mechanics.

Elise Zareie on why longer funnels convert better in weight-loss and health but fail on kids apps, where a parent just wants to hand the phone over. Andrey Shakhtin on Noom's 120 to 140 screen funnel as the industry ceiling. Mike Gadd on why your MMP, your ad network, and Google Analytics will never agree, and why that is fine once you pick a source of truth. Jacob Rushfinn on cohort-quality guardrails: ROAS alone gets gamed, so you need activation rate, D1, D7, and trial-cancel rates on the same chart. Patrick Stuart-Constant on a Liquid Death-inspired ad that mocked confused users and ran profitably for months.

Video Chapters:
00:00 Recap of Part 1
00:45 Jacob: start with 10 to 15 screens, not 100
02:00 Elise on why funnel length is vertical-specific
04:00 Andrey on Noom's 120 to 140 screen funnel
07:00 Platform hell: MMP, ad networks, and Google Analytics
09:30 Mike on picking a source of truth, using SKAN as a check
11:00 Andrey on optimizing for ROAS over conversion rate
11:45 Down-funnel accountability for UA teams
12:30 Jacob on cohort-quality guardrails (activation, D1, D7)
14:20 Mike on tying campaign data to attribution and events
15:30 Elise on creative-quality health checks
17:00 Quick-fire: paywall and pricing wins
18:30 Quick-fire: standout creative wins
20:00 Closing advice from all 5 panelists

Topics covered:
- Funnel length: when longer converts better, when it kills conversion
- Platform hell: MMP as source of truth, SKAN as check
- ROAS over conversion rate as the campaign KPI
- Down-funnel accountability and cohort quality guardrails
- Creative wins from social listening and negative comments
- Paywall wins: intro offers, multi-page paywalls, paid trials

Learn more:
- Original webinar recording: https://www.youtube.com/watch?v=OYPDZlwLhNA
- FunnelFox (webinar host): https://funnelfox.com
- Rocketship HQ (partner): https://rocketshiphq.com
- Jacob Rushfinn (Botsi): https://www.linkedin.com/in/jacob-rushfinn/
- Andrey Shakhtin (FunnelFox): https://www.linkedin.com/in/andrey-shakhtin/
- Patrick Stuart-Constant (Sociaaal): https://www.linkedin.com/in/patrick-stuart-constant/
- Mike Gadd (Singular): https://www.linkedin.com/in/mike-gadd-27a16427/
- Elise Zareie (Lingokids): https://www.linkedin.com/in/elise-zareie/
- Shamanth Rao (Rocketship HQ): https://www.linkedin.com/in/shamanthrao/

82% of top-grossing apps now take payment outside the app store. Patrick Stuart-Constant's team at Sociaaal ships 4,000 video ads a month, scaling to 10,000. The shift to web-to-app monetization isn't coming. It already happened.

Part 1 of a two-part panel from a FunnelFox webinar, with Rocketship HQ as partner. Shamanth Rao moderated the conversation with Jacob Rushfinn (CEO, Botsi), Andrey Shakhtin (CEO, FunnelFox), Patrick Stuart-Constant (CEO, Sociaaal), Mike Gadd (VP Customer Success, Singular), and Elise Zareie (Head of Paid Social, Lingokids) on how paid acquisition teams design the path after the click.

Andrey Shakhtin on why off-store attribution beats SKAN on speed, and how 2-5% payment fees unlock room to iterate. Patrick Stuart-Constant on scaling ad volume as a search space problem, and why cheaper AI creative grew his team instead of shrinking it (Jevons paradox). Elise Zareie on why web funnels only work with dedicated ownership inside the UA team. Mike Gadd on why the first renewal is the metric most teams miss. Jacob Rushfinn on the $1M revenue threshold before web funnels pay off, and the 10K to 20K monthly loss to budget for the learning phase.

Video Chapters:
00:00 Introductions and format
02:40 The one shift teams have not priced in
07:50 Andrey on the 82% off-store payments finding
09:52 Mike on measurement across web and app, platform hell
11:45 Elise on creative: lift-and-shift vs TikTok-native
15:54 Patrick on the search space, 4K to 10K ads a month
18:07 Jevons paradox: cheaper ads, bigger creative team
19:30 Elise on where AI helps and where taste is still human
21:39 What separates web-funnel winners from teams that quit
25:27 Andrey on iteration pace correlating with revenue
27:00 Who should not try web funnels: payments complexity
28:42 Jacob on the $1M threshold and the learning-phase budget
29:36 Common learning phase mistakes and how to short-circuit them
32:00 Part 2 drops next week

Topics covered:
- The shift to web-to-app monetization and off-store payments
- AI creative production at scale, 4,000 to 10,000 ads a month
- Web funnels: dedicated ownership vs side project
- The $1M revenue threshold for going off-store
- Learning phase mistakes on acquisition, funnel, and paywall
- Measurement complexity across web and app

Learn more:
- Original webinar recording: https://www.youtube.com/watch?v=OYPDZlwLhNA
- FunnelFox (webinar host): https://funnelfox.com
- Rocketship HQ (partner): https://rocketshiphq.com
- Jacob Rushfinn (Botsi): https://www.linkedin.com/in/jacob-rushfinn/
- Andrey Shakhtin (FunnelFox): https://www.linkedin.com/in/andrey-shakhtin/
- Patrick Stuart-Constant (Sociaaal): https://www.linkedin.com/in/patrick-stuart-constant/
- Mike Gadd (Singular): https://www.linkedin.com/in/mike-gadd-27a16427/
- Elise Zareie (Lingokids): https://www.linkedin.com/in/elise-zareie/
- Shamanth Rao (Rocketship HQ): https://www.linkedin.com/in/shamanthrao/

Most mobile UA teams think the algorithm is something you set and surrender to.

Itai Kafri doesn't. He engineers the signal.

Itai leads product growth at Voyantis, a predictive LTV and signal engineering platform for mobile apps. He has been in mobile user acquisition since before Meta introduced oCPM, back when advertisers controlled every bid manually. Since then, he has spent years figuring out what actually replaced that control.

The answer is not the model. It is the signal strategy built on top of it.

In this episode, Itai breaks down signal engineering for mobile UA: how to send predictive LTV signals to automated channels like Meta Advantage Plus, Google UAC, and TikTok in a way that actually moves performance. Why timing matters more than accuracy. Why the channel needs rank, not dollar precision. And the three questions every growth team should answer before investing in a predictive LTV media strategy at all.

If you run paid user acquisition for a mobile app and you are thinking about PLTV, value-based bidding, or tROAS optimization, this episode is the most practical breakdown of signal engineering available.

Video Chapters:  

00:00 Introduction
00:36 Pre-2014 vs post-2014: how automation changed acquisition
02:42 The seven-day conversion window ceiling
04:11 Why marketers and CFOs define a "good user" differently
07:00 Predictions, confidence, and the timing tradeoff
11:11 What actually feeds a prediction: the marathon analogy
16:26 Why proxies like "level 5" break down
17:42 How much data you need to build a model
20:15 Value-based bidding and the accuracy misconception
23:12 Managing outliers without breaking the strategy
25:11 Real client story: Google Search Partners going haywire
28:11 Whales vs outliers, and why hyper-whales backfire
32:03 Signal engineering vs RTB
34:05 User-level data, privacy, and reading the user mix back
38:57 How Black Friday and seasonality hit signaling
40:48 Why casual gaming is tricky for PLTV
41:16 Three questions to ask before investing in a prediction model
45:32 Where to find Itai and Voyantis

Topics covered:

- Signal engineering for mobile user acquisition
- Predictive LTV and value-based bidding on Meta, Google, and TikTok
- Confidence thresholds, latency trade-offs, and signal timing
- How automated channels use signals: Advantage Plus, UAC, PMax
- When D7 ROAS is already your best signal and PLTV will not move the needle

Learn more:

Episode Page : https://mobileuseracquisitionshow.com/episode/how-to-win-with-signal-engineering-itai-kafri/

Connect with Itai on LinkedIn - https://www.linkedin.com/in/ekafri/

Voyantis gap analysis - https://www.voyantis.ai/signal-engineering-gap-analysis

82% of top-grossing apps now take payment outside the app store. Patrick Stuart-Constant's team at Sociaaal ships 4,000 video ads a month, scaling to 10,000. The shift to web-to-app monetization isn't coming. It already happened.

Part 1 of a two-part panel from a Funnelfox webinar, with Rocketship HQ as partner. Shamanth Rao moderated the conversation with Jacob Rushfinn (CEO, Botsi), Andrey Shakhtin (CEO, FunnelFox), Patrick Stuart-Constant (CEO, Sociaaal), Mike Gadd (VP Customer Success, Singular), and Elise Zareie (Head of Paid Social, Lingokids) on how paid acquisition teams design the path after the click.

Andrey Shakhtin on why off-store attribution beats SKAN on speed, and how 2-5% payment fees unlock room to iterate. Patrick Stuart-Constant on scaling ad volume as a search space problem, and why cheaper AI creative grew his team instead of shrinking it (Jevons paradox). Elise Zareie on why web funnels only work with dedicated ownership inside the UA team. Mike Gadd on why the first renewal is the metric most teams miss. Jacob Rushfinn on the $1M revenue threshold before web funnels pay off, and the 10K to 20K monthly loss to budget for the learning phase.

Video Chapters:
00:00 Introductions and format
02:40 The one shift teams have not priced in
07:50 Andrey on the 82% off-store payments finding
09:52 Mike on measurement across web and app, platform hell
11:45 Elise on creative: lift-and-shift vs TikTok-native
15:54 Patrick on the search space, 4K to 10K ads a month
18:07 Jevons paradox: cheaper ads, bigger creative team
19:30 Elise on where AI helps and where taste is still human
21:39 What separates web-funnel winners from teams that quit
25:27 Andrey on iteration pace correlating with revenue
27:00 Who should not try web funnels: payments complexity
28:42 Jacob on the $1M threshold and the learning-phase budget
29:36 Common learning phase mistakes and how to short-circuit them
32:00 Part 2 drops next week

Topics covered:
- The shift to web-to-app monetization and off-store payments
- AI creative production at scale, 4,000 to 10,000 ads a month
- Web funnels: dedicated ownership vs side project
- The $1M revenue threshold for going off-store
- Learning phase mistakes on acquisition, funnel, and paywall
- Measurement complexity across web and app

Learn more:
- Original webinar recording: https://www.youtube.com/watch?v=OYPDZlwLhNA
- FunnelFox (webinar host): https://funnelfox.com
- Rocketship HQ (partner): https://rocketshiphq.com
- Jacob Rushfinn (Botsi): https://www.linkedin.com/in/jacob-rushfinn/
- Andrey Shakhtin (FunnelFox): https://www.linkedin.com/in/andrey-shakhtin/
- Patrick Stuart-Constant (Sociaaal): https://www.linkedin.com/in/patrick-stuart-constant/
- Mike Gadd (Singular): https://www.linkedin.com/in/mike-gadd-27a16427/
- Elise Zareie (Lingokids): https://www.linkedin.com/in/elise-zareie/
- Shamanth Rao (Rocketship HQ): https://www.linkedin.com/in/shamanthrao/