The NFL draft and artificial intelligence: two perennial buzzwords meeting at the precipice of American athletic destiny and silicon-fueled speculation. For decades, the most highly anticipated job interview in the country—also known as the NFL Draft—has relied on a mix of raw athletic numbers, grainy college highlight tapes, and an army of clipboard-toting scouts whose nostrils can supposedly smell "intangibles." In 2025, however, the game has changed. AI is not just in our smart speakers, search engines, and self-checking grocery aisles—it's making mock drafts.
This year, USA Today dove headfirst into the swirling data pool of AI predictions, wrangling four of the most popular chatbots—Microsoft Copilot, Meta AI, Grok, and ChatGPT—for a kinetic, bot-powered round of first-round NFL draft projections. Each algorithm, bristling with code, its ego stoked by training regimens that would make the Combine look like a stroll in the park, was asked a simple question: Who’s going where in the 2025 first round?
Human writers, perhaps fearing a Skynet scenario involving football, looked over the AI picks and made sure each one was at least plausible—no AI-driven dreams of the Detroit Lions taking a punter at number one, or the New York Jets, in a fit of digital irony, drafting a quarterback.
The result? An NFL mock draft more sci-fi than SportsCenter, but with enough consensus picks and bold wildcards to satisfy both the tape-grinders and the analytics nerds.
For anyone slouched in a barcalounger on Thursday night, this might not be the most thrilling selection—Ward’s blend of arm talent, mobility, and competitive fire was obvious to (human) scouts throughout the season—but the unanimity is both eerie and reassuring. In a time when AI can generate Shakespearean sonnets about spaghetti, it’s nice to know it can also agree on a good quarterback.
Why such consensus on Ward? The AIs largely cited his poised performances against top-tier defenses, his knack for late-game heroics, and that ever-nebulous “NFL readiness.” While the scouts may wax poetic about leadership vibes and “it factors,” the bots stuck to the numbers and tape, and still landed on the same golden-armed Floridian.
Meanwhile, Grok and ChatGPT zigged: they eyed Travis Hunter, the Colorado polymath who seamlessly toggles between wide receiver and cornerback. Hunter has been simultaneously haunted and hallowed by draftniks, who see in him both Deion Sanders swagger and Tyreek Hill quickness—a versatility that makes even machine learning models a little giddy.
In the trenches, Missouri’s Armand Membou and Michigan’s Mason Graham became frequent darlings of the AIs. Is there a bias in the code for linemen who dominate at the point of attack and light up advanced metrics? Almost certainly. The algorithms gushed over Membou’s footwork and Graham’s disruptive force up the middle, with Graham a top-five lock for both Copilot and Grok and a trendy choice for others.
Curiously, the AIs also sprinkled in a few deep cuts and potential risers—players like Shavon Revel Jr., Maxwell Hairston, Grey Zabel, and Jahdae Barron. Whether these picks result from brilliant pattern discovery or a rogue training set is anyone’s guess, but it brings a chaos element vintage mock drafts sometimes lack.
Some picks, like Cam Ward’s consensus number one, drew nods of approval. Others, such as wild round-one love for positions rarely taken early—a guard here, a nose tackle there—met the raised eyebrow of football realism. For every bot that had Alabama’s Tyler Booker storming the podium, a human was waiting with a skeptical smirk, armed with a knowledge of recent draft history and, possibly, a souvenir mug reading “Never Draft a Guard Early.”
Still, several AI justifications aligned closely with human logic. Flexibility, athletic traits, and proven production stayed at the forefront of both robot and reporter reasoning. Where the AIs struggled, perhaps, was in processing off-field considerations, system fits, and the complex “network effect” of coaching, scheme, and team culture that so often derails or elevates prospects.
In the AI predictions, certain positions—fullbacks, anyone?—remained extinct. Others, such as quarterbacks beyond the big three, faded into the background noise. The bots, so focused on maximizing value and minimizing risk, sometimes missed the emotional arcs that make draft night so gripping. There’s no “hometown hero” logic or sticky-fingered GM trading up for his college roommate’s cousin. The math rules, the algorithms churn, and the picks emerge—structured, tidy, quietly ruthless.
But every so often, the algorithms lurch into uncharted waters. A random run on interior linemen, an out-of-nowhere offensive tackle, or the kind of bold reach only a gambler (or, let’s face it, a Houston Texans front office circa 2023) would countenance.
Ultimately, the robots have not yet fully replaced Todd McShay and Daniel Jeremiah. But considering where AI mock drafts were just a few years ago—random name generators and feverish Madden simulations—it’s clear the machines have caught up fast.
Where AI falters isn’t in the obvious (tight end rankings, edge rushers, the safe quarterback pick). It’s in the swirl of the human drama—players rising and falling for reasons invisible in a stat sheet. The unidentified stress fracture, the cryptic interview answer, the uncle with TikTok dirt. Bots don’t vibe with those vibes.
And let’s not even broach the high art of draft night trades, a chaos element that shreds even the best-laid mocks—algorithmic or otherwise.
For fans, AI-powered mock drafts are both a curiosity and a barometer—how different is the latest bot’s mock from your favorite beat writer? Did the bot see something everyone else missed? The influx of these synthetic predictions won’t spell the end for water-cooler debates; it might just fuel more.
In the future, don’t be surprised to see draft night green rooms dotted with iPads running real-time AI mocks—just don’t expect Bill Belichick or John Lynch to surrender their draft cards to a chatbot. Not just yet.
As for USA Today’s experiment—it’s a signpost, not a spoiler. For all the predictions and counter-predictions, 2025’s true story is unwritten. The best AI models will get some picks right, some spectacularly wrong, and most of them somewhere in between. The drama lies in the difference.
So, pour another cup of optimism (or a stiff drink if your team’s war room is historically suspect). Tune into the draft. The future is part code, part chaos. And just maybe—when your team’s name gets called, you can lean over, nudge your neighbor, and smirk: “The AI called it.”
Or, if it didn’t—just blame the algorithm. It’s 2025, after all.
Source: USA Today NFL mock draft 2025: Rounding up AI predictions for the first round
The Robots are Mocking (Drafts)
This year, USA Today dove headfirst into the swirling data pool of AI predictions, wrangling four of the most popular chatbots—Microsoft Copilot, Meta AI, Grok, and ChatGPT—for a kinetic, bot-powered round of first-round NFL draft projections. Each algorithm, bristling with code, its ego stoked by training regimens that would make the Combine look like a stroll in the park, was asked a simple question: Who’s going where in the 2025 first round?Human writers, perhaps fearing a Skynet scenario involving football, looked over the AI picks and made sure each one was at least plausible—no AI-driven dreams of the Detroit Lions taking a punter at number one, or the New York Jets, in a fit of digital irony, drafting a quarterback.
The result? An NFL mock draft more sci-fi than SportsCenter, but with enough consensus picks and bold wildcards to satisfy both the tape-grinders and the analytics nerds.
Cam Ward: The Unanimous Algorithmic Darling
Let’s not bury the lede. With astonishing consistency rarely seen outside of synchronized swimming or presidential debates, every AI platform agreed on one thing: Cam Ward, quarterback from Miami (FL), sits perched atop the virtual draft board. Microsoft Copilot, Meta AI, Grok, and ChatGPT all picked him number one.For anyone slouched in a barcalounger on Thursday night, this might not be the most thrilling selection—Ward’s blend of arm talent, mobility, and competitive fire was obvious to (human) scouts throughout the season—but the unanimity is both eerie and reassuring. In a time when AI can generate Shakespearean sonnets about spaghetti, it’s nice to know it can also agree on a good quarterback.
Why such consensus on Ward? The AIs largely cited his poised performances against top-tier defenses, his knack for late-game heroics, and that ever-nebulous “NFL readiness.” While the scouts may wax poetic about leadership vibes and “it factors,” the bots stuck to the numbers and tape, and still landed on the same golden-armed Floridian.
The Edge Rusher Scramble: Abdul Carter and Travis Hunter
Where the AI divide begins is at the second pick—a battlefield where explosive edge rushers and Swiss Army knife defenders wage analytics war. Microsoft Copilot and Meta AI both fell hard for Abdul Carter, the edge terror from Penn State, whose relentless pass-rush repertoire either terrifies offensive coordinators or delights data models, depending on your perspective.Meanwhile, Grok and ChatGPT zigged: they eyed Travis Hunter, the Colorado polymath who seamlessly toggles between wide receiver and cornerback. Hunter has been simultaneously haunted and hallowed by draftniks, who see in him both Deion Sanders swagger and Tyreek Hill quickness—a versatility that makes even machine learning models a little giddy.
The Polarizing Stars: Shedeur Sanders, Armand Membou, and Mason Graham
Travel further down the virtual big board, and the picks get spicy. Shedeur Sanders, another Colorado star and son of Coach Prime, gets snapped up early in several mocks. AI’s love for this gunslinging quarterback wasn’t universal—Meta AI passed on him, favoring the ironclad tackles and relentless edge threats—but Sanders’ on-field improvisation and big-game nerve have kept him in multiple bots’ top fives.In the trenches, Missouri’s Armand Membou and Michigan’s Mason Graham became frequent darlings of the AIs. Is there a bias in the code for linemen who dominate at the point of attack and light up advanced metrics? Almost certainly. The algorithms gushed over Membou’s footwork and Graham’s disruptive force up the middle, with Graham a top-five lock for both Copilot and Grok and a trendy choice for others.
Running Backs and Wild Cards: Ashton Jeanty, Tetairoa McMillan, and Friends
Running backs rarely go early in modern drafts, where positional value is king and passing is the new running. The AIs, however, each took a swing at bucking this trend. Boise State’s Ashton Jeanty found himself lurking in the mid-to-late first round across several mocks, praised for his three-down ability and breakaway speed. By comparison, Arizona receiver Tetairoa McMillan rocketed up some lists, as did Alabama’s dynamic Jalen Milroe—though opinions swirled wildly on draft spots.Curiously, the AIs also sprinkled in a few deep cuts and potential risers—players like Shavon Revel Jr., Maxwell Hairston, Grey Zabel, and Jahdae Barron. Whether these picks result from brilliant pattern discovery or a rogue training set is anyone’s guess, but it brings a chaos element vintage mock drafts sometimes lack.
What the (Human) Experts Said: Sifting Fact from Fiction
For all the sophistication of machine learning, there’s still an unquantifiable magic in draft night’s unpredictability. USA Today’s human editors, saddled with the unenviable task of fact-checking their AI colleagues, peppered each pick with the context only years in the trenches can provide.Some picks, like Cam Ward’s consensus number one, drew nods of approval. Others, such as wild round-one love for positions rarely taken early—a guard here, a nose tackle there—met the raised eyebrow of football realism. For every bot that had Alabama’s Tyler Booker storming the podium, a human was waiting with a skeptical smirk, armed with a knowledge of recent draft history and, possibly, a souvenir mug reading “Never Draft a Guard Early.”
Still, several AI justifications aligned closely with human logic. Flexibility, athletic traits, and proven production stayed at the forefront of both robot and reporter reasoning. Where the AIs struggled, perhaps, was in processing off-field considerations, system fits, and the complex “network effect” of coaching, scheme, and team culture that so often derails or elevates prospects.
Who Got Left Out? The Collateral Damage of Code
Amidst the celebration over AI’s gridiron insight, there’s one unavoidable truth: Not everyone can make the first round. Even with 32 picks, hearts are broken—college stars, fan favorites, and program icons left waiting for day two (or three, or undrafted free agency).In the AI predictions, certain positions—fullbacks, anyone?—remained extinct. Others, such as quarterbacks beyond the big three, faded into the background noise. The bots, so focused on maximizing value and minimizing risk, sometimes missed the emotional arcs that make draft night so gripping. There’s no “hometown hero” logic or sticky-fingered GM trading up for his college roommate’s cousin. The math rules, the algorithms churn, and the picks emerge—structured, tidy, quietly ruthless.
The Consensus and the Chaos: Can AI Actually Beat the Mock Draft Gurus?
So how did the AIs stack up to the old guard—the Mel Kipers and mock draft czars who’ve mined this cottage industry for decades? It’s a mixed bag. The consensus picks—Cam Ward, Abdul Carter, Mason Graham—look eerily similar to what you’d find in almost any human-etched mock draft in 2025. The justifications, brimming with confidence in combine metrics and game tape, sound familiar, borderline plagiaristic of ESPN primetime.But every so often, the algorithms lurch into uncharted waters. A random run on interior linemen, an out-of-nowhere offensive tackle, or the kind of bold reach only a gambler (or, let’s face it, a Houston Texans front office circa 2023) would countenance.
Ultimately, the robots have not yet fully replaced Todd McShay and Daniel Jeremiah. But considering where AI mock drafts were just a few years ago—random name generators and feverish Madden simulations—it’s clear the machines have caught up fast.
What AI Still Can’t Predict (Yet)
Let’s get real for a moment. NFL General Managers aren’t going to crowd around a Surface tablet loaded with the latest chatbot and let it run the war room. There’s too much behind-closed-doors subterfuge, too many late-breaking injuries, too much old-school intuition.Where AI falters isn’t in the obvious (tight end rankings, edge rushers, the safe quarterback pick). It’s in the swirl of the human drama—players rising and falling for reasons invisible in a stat sheet. The unidentified stress fracture, the cryptic interview answer, the uncle with TikTok dirt. Bots don’t vibe with those vibes.
And let’s not even broach the high art of draft night trades, a chaos element that shreds even the best-laid mocks—algorithmic or otherwise.
Will AI Shape the Future of Draft Analysis?
Put simply: it already is. AI is extraordinary at processing millions of data points, identifying historical patterns, flagging statistical outliers, and even writing dry-but-persuasive prospect blurbs. Teams will increasingly use these tools to supplement their human scouting, balancing abstract instincts with the unblinking gaze of the algorithm.For fans, AI-powered mock drafts are both a curiosity and a barometer—how different is the latest bot’s mock from your favorite beat writer? Did the bot see something everyone else missed? The influx of these synthetic predictions won’t spell the end for water-cooler debates; it might just fuel more.
In the future, don’t be surprised to see draft night green rooms dotted with iPads running real-time AI mocks—just don’t expect Bill Belichick or John Lynch to surrender their draft cards to a chatbot. Not just yet.
AI’s Gridiron Crystal Ball: A Waypoint, Not the Final Fate
The NFL Draft remains the sport’s last great mystery box—a place where broken models, bad luck, and freakish breakthroughs flip the script year after year. AI has intruded on this sacred chaos with cold calculations and confidence scores, but the heart of the draft beats on: walk-ons, late bloomers, and the overlooked still have their day.As for USA Today’s experiment—it’s a signpost, not a spoiler. For all the predictions and counter-predictions, 2025’s true story is unwritten. The best AI models will get some picks right, some spectacularly wrong, and most of them somewhere in between. The drama lies in the difference.
So, pour another cup of optimism (or a stiff drink if your team’s war room is historically suspect). Tune into the draft. The future is part code, part chaos. And just maybe—when your team’s name gets called, you can lean over, nudge your neighbor, and smirk: “The AI called it.”
Or, if it didn’t—just blame the algorithm. It’s 2025, after all.
Source: USA Today NFL mock draft 2025: Rounding up AI predictions for the first round
Last edited:
