Major League Soccer is now running auto-generated recaps from its in-house system, called “Created by MLS Generative AI,” with a disclaimer that content is not reviewed by editorial staff. The soft launch crept in under the radar, but fans noticed new blurbs on game pages and hit them hard — and loud — across social media.
The tension mirrors a broader debate in sports media: leagues and outlets are stepping up their use of AI to chase scale while fans wonder about accuracy, tone, and the shrinking of human sportswriting.
What MLS Shipped and How Its New System Works
The new summaries sound like densely packed wire copy. They draw from the match data — goals, cards, substitutions — to sew together a few paragraphs moments after the whistle blows for full time. At the bottom of each article is a feedback form, a tacit admission that mistakes may make it through without human intervention.
The league quickly posted back-to-back recaps in the early going for Inter Miami vs. Atlanta United and Orlando City vs. Vancouver Whitecaps. A third recap from LA Galaxy vs. FC Dallas popped up in search results before disappearing, so it seems MLS is iterating in real time on publication rules and quality standards.
Why Many Fans and Writers Are Pushing Back on MLS AI
Supporters on Reddit described the move as “gross” and said they were troubled by its lack of human review. Other readers were cool to the tone, noting lines like “Orlando started brightly” as formulaic or stilted. To fans used to the club-specific nuance and local color of their home announcers, the summaries rang hollow — technically correct in parts but emotionally vacant.
Top soccer journalists echoed those concerns. “If you are running Fox Soccer or Yahoo Sports and such, ask someone — anyone — to go cover a match occasionally,” columnist Tom Bogert wrote, adding that nefarious actors might actually be able to afford people who aren’t very good at pretending they are disinterested. The larger fear: Automated blurbs could flood young sportswriters who have typically cut their writing teeth on game recaps.
Why Automating Those Recaps Is So Tempting
There’s a clear business logic. Recaps are highly organized, data-rich and time-sensitive — exactly the type of information prime for automation. The Associated Press, which has automated corporate earnings and minor-league sports for some time, said it did so for the speed and consistency advantages the approach brings. Data companies for soccer, such as Stats Perform, create templated copy from Opta feeds, and clubs use much of the same set of offerings across the world for their match centers.
Regular summaries are just one area where MLS has been working AI into its workflow. Throughout the season, if AI were a cult — and between us it is most definitely not a cult — then MLS wasn’t being very coy about its recruitment tactics: reports from Sports Business Journal showed the league had increased personalized, AI-generated content on its app, and showcased startups using AI to translate game broadcasts, track player statistics, and speed up video processing. The league’s thesis is that machine-generated content can scale coverage and customize experiences for millions of fans.
The Risk Of Mistakes And Credibility Gaps
Speed can’t be at the cost of trust. Unedited AI articles are capable of hallucinations, misunderstanding context or a subtle mistuning of the tone, which can be alienating. The sports world has experienced the pitfalls: a widely publicized local-news experiment generated robotic high school recaps with ungainly syntax and factual flubs that drew scrutiny from journalism watchdogs like Nieman Lab.
Reputational damage is real. Front Office Sports, a major sports publication, was crucified for AI-bylined content and then stripped the bylines clean; the incident became a legend throughout media. They not only crave facts, but perspective and accountability as well, a feeling that the person behind the keyboard understands the club.
What Good AI Looks Like in Sports Coverage and Workflows
Human-in-the-loop editing is the baseline. Even a single passing glance from an editor or team reporter will catch botched attributions, fix clunky phrasing and add a line of context — this was the first start for that homegrown academy player; that tactical shift changed the match — that gets missed when you keep things raw. Clear labeling helps; so does having a visible corrections workflow (so fans know where to send feedback about errors and how they’ll be addressed).
There is real upside, if MLS navigates carefully. AI can immediately pull shot maps, trends in expected goals and strategies on subs, while human writers bring the tale to life with notes from the pitch and interviews. Used this way, automation complements, not replaces, journalism — it provides supporters more of what they come for: insight with personality.
The Bottom Line for MLS: Guardrails, Clarity, and Trust
Releasing AI-generated summaries untouched by human hands and any clear editorial standards was always going to draw the fire of a fan base that craves genuine in this age of slick professionalization. The solution isn’t to scrap the experiment but to put guardrails on it — human oversight, clear labeling and integration with beat writers who have intimate knowledge of the clubs.
If AI-generated content is going to win over supporters, MLS has to make it feel like soccer, not software. That begins by keeping people on the ball.
