New Dating Apps Launch with AI Claims. Investors Buy the Pitch, Not the Product.

Match Group (MTCH) and Bumble (BMBL) must be feeling a curious mix of flattery and vindication. Three dating apps launched within days of each other in mid-July, and every single one claims to solve the same problem: shallow swiping, endless scrolling, and the failure to facilitate actual dates. If this sounds familiar, it's because it is—and that repetition tells you more about the industry's current state than any single product feature.

Better In Person, Lovector, and Find Our Way all went live between 15 and 17 July, each positioning itself as the antidote to dating app fatigue. Better In Person emphasises facilitating real-world connections through what it describes as 'advanced AI for personalised date planning'. Lovector promises to match users based on their 'philosophy of life' and 'the way you see the world', augmented by a rating system where members evaluate each other post-date.

Find Our Way centres its proposition on gamification, claiming to use AI to reduce superficial swiping. Three apps, three slightly different feature sets, one identical pitch: we're not like the other apps.

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The AI matching mirage

Every one of these launches leans on artificial intelligence as a core differentiator, yet none offers specifics about how their AI functions differently from the recommendation engines that have powered dating platforms for years. Better In Person's 'advanced AI for personalised date planning' is typical of the category: it signals technical sophistication without defining what makes it advanced or how it improves on existing matching algorithms.

The claim becomes more questionable when you consider that Match Group's affinity-based matching on Hinge and Bumble's interest-based filters already use algorithmic matching informed by user behaviour and preferences. According to Match Group's Q1 2025 earnings call, Hinge's algorithm analyses over 200 signals per user profile. What, specifically, are these new entrants doing that incumbents with vastly more data and engineering resources cannot?

Find Our Way's gamification approach—described as using AI to reduce superficial engagement—presents a similar problem. Gamification isn't new to dating; Bumble introduced question prompts in 2018, and Hinge's 'Most Compatible' feature has used algorithmic prompts since 2019. The meaningful question isn't whether an app uses AI—it's whether the AI produces measurably better matches, faster connections, or higher satisfaction.

Moving beyond digital: the unfulfilled promise

Better In Person's stated emphasis on facilitating offline meetups positions it within a well-established tradition of apps claiming to prioritise real-world connection. Thursday, which launched in 2021, built its entire model around encouraging same-day dates. Filteroff introduced video-first dating in 2020 with the same promise. Both are still operational, but neither has achieved the scale or member growth to threaten the incumbents.

The contradiction is structural. Dating apps generate revenue through subscriptions and in-app purchases, which require sustained engagement. An app that successfully moves people offline quickly cannibalises its own revenue model.

Match Group disclosed in its Q4 2024 earnings that average revenue per paying user (ARPPU) increased 8% year-over-year, driven in part by longer subscription durations. That's not an accident. The business model rewards time on platform, not time on a date.

Lovector's post-date rating system introduces another variable. Members can rate each other after meeting, ostensibly to build trust and accountability. The mechanic echoes Lulu, a now-defunct app that let women rate men, and aspects of The League's member approval process. Without details on how Lovector moderates ratings or prevents retaliatory scoring, it's unclear how this feature improves on the trust and safety mechanisms incumbents already deploy—and those mechanisms, according to BMBL's Q1 2025 regulatory disclosures, already consume roughly 15% of operating budgets.

Investor appetite meets market reality

That three dating apps secured enough funding to launch within the same week suggests continued investor belief in the category, even as public market valuations remain depressed. Match Group trades at roughly 9x forward earnings as of mid-2025, down from peaks above 40x in 2021. Bumble's valuation has followed a similar trajectory. Yet venture investors continue to back new entrants, betting that niche positioning or novel features can carve out defensible market share.

The evidence for that bet is mixed. According to data from Sensor Tower, dating apps outside the top 20 by revenue collectively capture less than 8% of total category spend. The dating market isn't winner-take-all, but it's close. Smaller apps can survive—and occasionally thrive—by serving specific demographics or geographies, but breaking into the top tier requires either acquisition by an incumbent or distribution advantages that these launches don't appear to possess.

What's more telling is that none of these new apps are attempting business model innovation. All three follow the freemium subscription playbook that has defined the industry for a decade. There's no mention of alternative monetisation, community ownership structures, or data-sharing models. The innovation, such as it is, remains confined to feature sets and user experience—the most easily replicated parts of the product.

The pattern suggests an industry where founders are optimising for fundraising narratives rather than user outcomes. AI-powered matching and offline-first positioning sound differentiated in a seed round pitch, but they're table stakes in a market where every app makes the same claim. Until a new entrant can demonstrate that its AI produces materially better results, or that its offline features genuinely change time-to-date metrics, these launches will remain another data point in the long history of dating apps that promised to be different but ended up being more of the same.