The Superwall Podcast · 2025-10-03
Dan Vykhopen - This app makes $2m/year using this ridiculously easy strategy
Dan Vykhopen, founder of Massive (AI that fills out job applications, ~$200K/month, tiny team), on validating with one viral LinkedIn post, a 40-50K waitlist that barely converted, grinding to ~$20K MRR via Hacker News and manual outreach, and why audience-targeted hooks ('Stop using LinkedIn' vs 'Indeed') matter more than views: an 11M-view video made only ~$20K MRR. Also: 80/20 copying of proven formats, UGC not scaling for him, doubling revenue via pricing, PrepAI's grey-hat fake job-offer videos, and audience/market lessons from Headway, Minutes AI and Masterschool.
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Numbers mentioned
| Subject | Metric | Value | Quote |
|---|---|---|---|
| Massive | revenue | USD 200K/month approximate | “So right now, we're at about 200k a month.” |
| Massive | arr Host's intro and question; consistent with Dan's ~$200K/month | USD 2M approximate | “there's a consumer app that's quietly pulling in $2 million in ARR that you've probably never heard of” |
| Massive | count Job applications submitted by the AI; earlier also described as 'tens of millions' | 10M applications approximate | “I don't know the exact number, but it's definitely more than 10 million applications at this point.” |
| Massive | funding raised Raised from VCs for the pre-pivot product | USD 3M | “We'd raised $3 million from a bunch of VCs” |
| Massive | team size Founder plus one full-time engineer, plus a handful of VAs and ops people | 2 people | “It's me, it's a full-time engineer, and I have a handful of like VAs and like ops people.” |
| personDan Vykhopen's LinkedIn network before the viral post | followers | 500 connections approximate | “At that point, I had like maybe 500 connections.” |
| Massive | views Validation LinkedIn post; also ~5,000 comments | 8M impressionsper post approximate | “We got, I think, like 8 million impressions, 5,000 comments, and people were really, really excited.” |
| Massive | count Comments on the validation LinkedIn post | 5,000 commentsper post approximate | “We got, I think, like 8 million impressions, 5,000 comments” |
| Massive | count Waitlist sign-ups in the first 2 months before launch | 40K–50K waitlist sign-ups approximate | “We got, I think, 40 or 50k waitlist sign-ups in the first 2 months of just playing around with this core idea” |
| Massive | conversion rate Waitlist -> signed up at launch; essentially no one paid | 0.5% approximate | “only about like half a percentage of the waitlist converted to even signing up” |
| Massive | paying users First paying users from Hacker News and manual outreach after launch | 100–500 users approximate | “And that's how we got our first like 100, maybe 500 users to go pay and give us feedback.” |
| Massive | mrr | USD 20K approximate | “from April to December, were just like manual grinding to I think it was maybe 20k MRR” |
| Massive | views More views from the 'Tinder for jobs' framing vs longer descriptions; also raises creator reply rates | +2–3 x estimate | “It increases reply rates from creators, it helps us do like two to three times more views, probably.” |
| Massive | views Organic influencer + UGC views now | 3M–4M views/week approximate | “I think we do maybe 3 to 4 million views per week roughly” |
| Massive | views Earlier view level, which converted about as much as today's 3-4M | 500K views/week approximate | “So when we were doing like 500k views, we were actually probably converting as much.” |
| Massive | count | 3–5 viral videos/day approximate | “cuz right now we're getting three, four, five viral videos a day kind of thing” |
| Massive | views 'Stop using LinkedIn to apply to jobs' hook; converts very highly when done well | 1M viewsper video approximate | “"Stop using LinkedIn to apply to jobs," and we'll get a million views on that. If it's done well, it'll convert very highly.” |
| Massive | views 'Who's still using Indeed?' video, within the first 4 days of restarting the UGC program | 11M viewsper video approximate | “This did roughly 11 million views, but almost no conversions.” |
| Massive | mrr MRR attributed to the 11M-view Indeed video; conversions only came between ~3M and 3.5M views | +USD 20K approximate | “Like 20k MRR, it's not a lot for 11 million.” |
| Massive | time to milestone From restarting the UGC program (after running one for ~a year) to the 11M-view video | 4 days approximate | “this happened within the first like 4 days of like restarting that UGC program” |
| Massive | growth rate Revenue doubled from pricing changes (no detail given) | +100% approximate | “We doubled our revenue from pricing changes.” |
| Massive | growth rate Revenue increase from 'doing ads in a specific way' (no detail given) | +50% approximate | “We increased revenue 50% by doing ads in a specific way” |
| YOGABODY | price Stretching course Dan bought from ads | USD 100 approximate | “It was like a stretching course. It's like 100 bucks.” |
| YOGABODY | ad spend | millions of dollars a month estimate | “they're probably spending millions of dollars a month” |
| YOGABODY | count Ads Dan saw before converting | 6 ads approximate | “So I think I saw six ads before I actually converted.” |
| Dupe | views Same discovery format, early inauthentic-feeling attempt (creator Alyssa Home Finds) | 382 viewsper video | “The same format, 382 views versus 1.7 million.” |
| Dupe | views Same format after weeks of iteration | 1.7M viewsper video | “The same format, 382 views versus 1.7 million.” |
| Headway | revenue | USD 200M–300M/year approximate | “$2-300 million a year, 30% margins, and two times year-over-year growth on straight paid” |
| Headway | margin | 30% approximate | “$2-300 million a year, 30% margins” |
| Headway | growth rate | 2 x/year approximate | “two times year-over-year growth on straight paid, on performance marketing, on paid ads” |
| Neuro Gum | count TikTok Shop affiliates, on top of in-house affiliates/UGC creators | 100K affiliates approximate | “They have almost in-house affiliates, UGC creators, and they have another like 100,000 on TikTok shop” |
| Neuro Gum | revenue | nine-plus figures estimate | “they were doing maybe nine-plus figures” |
| TurboLearn | downloads Sensor Tower, read loosely off screen; may combine two apps; revenue ~100K | 170K downloads approximate | “It says like 100k over here, combining these two with like 170k downloads, and they're targeting a younger audience.” |
| Answers AI | mrr | USD 10K–15K approximate | “So they're like 10, 15k MRR.” |
| Minutes AI | revenue With ~30K downloads; ~3x TurboLearn's revenue on 1/5-1/6 the downloads | USD 300K/month approximate | “Minutes AI, and they're doing 30k downloads and 300k a month” |
| Minutes AI | downloads | 30K downloads approximate | “Minutes AI, and they're doing 30k downloads and 300k a month” |
| Masterschool | revenue Before the Maestro AI launch | USD 50M/year approximate | “So they were doing decent, right? Like $50 million a year.” |
| Masterschool | growth rate Revenue tripled in less than a quarter after the Maestro AI launch | +3 x/quarter approximate | “three times revenue in less than a quarter” |