92% of Grindr Developers Report Productivity Surge Thanks to AI

Approximately 70% of Grindr’s code is now written by AI, CEO George Arison told Bloomberg — a statistic that would put a pit in any human developer or software engineer’s stomach. 

But Arison made it clear that experienced human developers are still highly valued at Grindr, especially when they can adapt to such a quickly changing environment. “Since mid-2025, Grindr’s Product Engineering team has been working on an aggressive journey to embed AI into every layer of how we build software,” Grindr announced. 

The app’s 65 engineers incorporated into their daily work tasks Claude Code, Cursor, and Firebender over a six-month period, and later described exactly how these AI tools changed the way they work. 

Overall, 92% of 50 surveyed engineers at Grindr said their productivity has increased by 1.5 times or more. And more than half (58%) say they’re moving through their tasks two to three times more quickly than they did before using AI. And they’re not only moving quicker, but, importantly, they’re moving with more confidence. 

But speed and confidence inevitably lead to mistakes, and Grindr hasn’t shied away from acknowledging the roadblocks its engineers have faced since incorporating AI into their daily workloads. 

AI Engineers Have Increased Productivity  

Nowadays, it’s not uncommon, and even expected, for developers to run one to five AI agents simultaneously throughout the work day; 94% of Grindr engineers say they do just that during the average development session. Sixty-four percent use at least one AI agent in some capacity at work. 

The time spent moving between tasks seems to be an unavoidable part of any workflow, but Grindr says that its engineers have cut down on context switching with the help of AI. And since time is money, Grindr saves both when engineers delegate certain tasks to AI agents. 

As “one of engineering’s biggest hidden costs,” context switching is, as an area of work, ripe for automation. 

About 94% of Grindr engineers run one to five AI agents in parallel.

Grindr also saw improvement in debugging times, as, naturally, “AI can analyze a codebase, surface relevant files, and identify root causes faster than any manual search,” Grindr said. 

The more time saved, the faster engineers can, as Grindr put it, “test more ideas, ship more often, and recover from mistakes quickly.” That’s clear value for the company and for the user.

The increased efficiency is easy to see: The average time it takes engineers to complete tasks has been cut nearly in half by AI, according to Grindr. 

AI Efficiency Shouldn’t Replace Entry-Level Workers 

Since incorporating AI more heavily into their daily workflows, engineers seem to delegate to AI agents like they’re interns, which allows engineers to turn their minds to more big-picture tasks. 

The goal is for these AI agents to not only pick up the slack, but to remove pesky busywork from an engineer’s workload. As Grindr mentioned, AI tools — when used in the right way — can pave the way for engineers to make real, meaningful change. 

But therein lies a potential snag. It’s neat when we can automate our more monotonous tasks, but in automating them, we lose the experience that comes with doing them. After all, you can’t write a novel if you don’t know how to read. 

“When companies stop hiring entry-level people it’s short-term thinking at the expense of investing in the future,” MIT research scientist, Frank Nagle, said in his report on the impact of generative AI use on software developers. 

Tech’s “Isolating Effect” Can Be Good and Bad for Developers

In 2025, Nagle of MIT’s Initiative on the Digital Economy studied how the open-source platform GitHub changed the way software developers worked. He found that developers who used GitHub Copilot increased coding activities by 12.4%, while they decreased the time spent on project management 24.9%. 

Using AI allowed them to delegate their non-technical tasks so they could focus more on coding — a bright side for developers in management who would rather spend their time creating than putting out personnel-related fires.  

But Nagle also highlighted a potential downside: Developers using Copilot reduced their peer collaborations by nearly 80%. 

If you’re okay with having automatons for employees, this may sound like a win. But there’s a lot lost when workers no longer find value in their own community. “If no one has to ask their colleagues for advice, is that good or bad?” Nagle wondered.

It’s important to remember what Nagle calls the “isolating effect” of technology — and nothing can grow in isolation. 

Grindr’s Enthusiasm Hasn’t Caught Up With Reality 

What’s stopping Grindr from becoming the “AI-native” environment it dreams of being is, well, the fact that it wasn’t originally built to be AI native. 

Forty-two percent of Grindr’s surveyed engineers want to use more AI agents, but aren’t sure they have the “muscle.” Twenty-eight percent say they don’t have the tools or screen space they need to run a bunch of AI agents simultaneously. 

And, yes, 20% of engineers aren’t convinced that going all-in on AI is the right move. Many would rather temper AI use with good ol’ human moderation.

“The next frontier is fully agentic automation — bug fixing, SDK updates, UI-to-code generation, and enhanced code review handled end-to-end by agents,” according to the app. “We’re early here, but that’s the direction.”

“Grindr is becoming an AI native or an AI first company.”

And Grindr is full-steam ahead. “Grindr is becoming an AI native or an AI first company,” Arison told Bloomberg. This bold transition allows Grindr to do something some old-school brands can only dream of: Evolve. “[AI is] taking a legacy 15-plus-year-old company and converting it into an AI company as if it were built in the last two years.” 

The fact that Grindr has already delegated 30% of coding tasks to Cursor AI proves that its dreams of becoming AI native are still alive — and AI automation has become part of the company’s routine. 

In his study, Nagle also found that developers’ time spent coding never returned to its pre-AI baseline. “This gives some indication that people will change the way they work, and [this change] will stick around in the long term.”