What happens to a space when you don’t know what is real and what is fake, especially when all that matters is truth and authenticity? You stop paying attention.
If AI is a hammer, everything is starting to look like a nail. Before I dive in, let me get this out of the way: I’m one of those nauseating “AI guys.” I’m the person at the bar who can’t shut up about the latest model release; the one who makes you instinctively shift a stool away. That said, I carry a fair amount of skepticism, especially when it comes to how much appetite there really is for AI-generated content in the outdoor industry.
This post is about that tension, the push and pull between opportunity / quality and fatigue / slop. We’ll look at what your feed might soon be saturated with, why ad costs could climb as more brands lean on generative tools, and where AI can actually add value instead of just flooding the zone with filler. This post is for both consumers who scroll these feeds and the marketing heads responsible for filling them.
The Multi-Modality of AI Content
It’s no secret that “ChatGPT-style” text is infiltrating nearly every written medium, emails, Twitter/X threads, LinkedIn posts, blog copy, you name it. What we haven’t seen in force (yet) is the same wave of generative AI content across visual and audio platforms like TikTok, YouTube, or Instagram. Google’s Veo-3 gave us a glimpse earlier this year with those quirky eight-second clips (yes, the talking Yetis…see below for the one I made for this post), but for the most part our feeds remain dominated by human-made photos, reels, and shorts.
That’s starting to shift. Entire companies are now being built with the sole purpose of generating, distributing, and monetizing AI-created media. The tools are finally catching up, video models that can spit out short-form content in seconds, voice models that sound uncannily natural, image systems that can pump out ad creative at scale. The question is no longer if this will hit your feed, but when — and what that does to how consumers engage with outdoor brands and media.
Ethics, the Outdoor Industry and Photoshop
It’s worth pausing on the history and cultural etiquette of content in the outdoor space. While an entire book could be written here, the short version is this: there has always been a line between retouching for aesthetics and altering the underlying story of what was captured. Over the past 20 years that line has blurred, thanks to increasingly powerful digital workflows that make it easy to enhance a photo or clip far beyond the moment as it really happened. Adjusting levels or tweaking color is one thing. Stitching multiple exposures into an HDR sky, or superimposing riders for “better light,” is another.
The unspoken rules were clear: you didn’t cross into fakery. Stretching an air in post, digitally cutting and pasting an athlete into a scene, or speeding up footage just enough to make someone look faster — these were seen as a kind of digital doping. And if you got caught, your credibility was toast. The outdoor community prized authenticity above all. If you weren’t real, you weren’t part of the tribe.
Of course, this gets tricky. A photo or video is never pure reality, it’s a reflection of a moment, a ghost of time and light captured on a sensor manipulated by the elements of a lens. Still, the farther you drift from the essence of that moment, the more audiences feel cheated. People don’t want CGI waves with a surfer composited underneath, cartoonish avatars skiing in B.C., or AI-generated riders smashing a World Cup course. What we crave, what inspires us, are real humans doing real things in wild, unpredictable places. That’s the cultural foundation outdoor media is built on.
Which raises the question: if the appetite is for authenticity, why would AI-generated content even be considered?
The Problem
Content sells. Everyone knows this. It’s why athletes land sponsorships, why helicopters fly every spring in Alaska, and why media teams are flown across the world for product launches. Great content moves gear. It’s the backbone of modern advertising. Content is king. But it’s also expensive, logistically gnarly, and time-consuming to produce.
To put numbers on it: a single photo trip to Alaska can cost a brand well into six figures once you account for athlete contracts, heli time, guides, media staff, and production. Even something “simpler”, like a week-long product shoot in Moab with a small crew, can run $15–50K. That’s before editing, distribution, or ad spend (and probably ignores real permits).
AI promises to flip that math. Instead of tens of thousands of dollars and months of planning, companies can crank out endless variations of content in seconds, at a fraction of the cost. For brands, especially small ones, under constant pressure to “feed the algorithm,” that efficiency is intoxicating.
And there’s another layer: personalization. As production costs collapse, marketers can target narrower and narrower demographics. Instead of one polished global campaign, they can generate dozens of versions tuned to your zip code, browsing history, or last purchase. Over time, the ad won’t just feel like it was made for “people like you” — it will be made for you.
My Hypothesis
A lot of generative AI content will be rejected by the market. Distribution costs will rise. And our feeds may become less usable.
Humans Want Human Story: Yuval Harari was right in Sapiens: people are story-driven creatures. And the stories we care most about are human-centered ones. Sport is a uniquely powerful medium for authentic storytelling — the athlete against the mountain, the skier chasing a storm, the climber holding on by fingertips. But those stories lose their punch if they’re over-augmented or digitally fabricated. The outdoor industry, in particular, relies on authenticity. If audiences sense fakery, the spell is broken.
Costs Will Rise: In the advertising ecosystem, we — the humans — are the product. The size of a given demographic doesn’t change much. So as more content competes for the same eyeballs, the price of distribution climbs. Production costs may plummet with AI, but the fight for attention only gets more expensive. The only exception? If your AI-generated content is so uncanny that it organically cuts through the noise. But that circles us back to point one: people respond most to authentic, human-driven stories.
Your Feed = Possible Trashcan: There’s a real risk that as AI floods the space, feeds get sloppy. Paid or otherwise, the sheer volume of low-effort content could overwhelm the system. Algorithms will likely adapt — they already do a decent job of filtering out bad content — but there’s still a non-zero chance your TikTok, Instagram, or YouTube feed starts to feel unusable, more slop than signal.
So, is the genAI wave a bust? Not necessarily. There’s a near-term middle ground — a nuanced way for brands to use these tools without alienating their audience. And beyond that, there’s a bigger picture to explore: not just where AI content is now, but where it’s heading. Let’s look at a few examples of what’s acceptable, what isn’t, and how brands can navigate the line.
Good Use of AI in Content
Not all AI-driven content is bad. In fact, there are several use cases where generative tools make a lot of sense — reducing cost, improving efficiency, or creating entirely new consumer experiences without undermining authenticity. A few examples:
Product-Centric Content: If there’s no athlete involved, AI is relatively safe. Product photography and video are prime candidates. Imagine you’re selling a coffee gadget: you start with a handful of studio-quality photos, then use AI to create thousands of variations — different backdrops, seasons, or environments — without needing to fly a crew around the world. This unlocks big cost savings with minimal downside. I’d bet many companies are already quietly doing this.
AI + Automation for Campaigns: Smart brands won’t just create AI assets; they’ll automate the feedback loop. By wiring tools like n8n into engines such as Google’s Veo-3 or Gemini 2.5 Flash, a campaign can self-optimize — analyzing which ads or shorts are performing, doubling down on winners, and quickly iterating on losers. This kind of automated “creative A/B testing on steroids” is already possible today.
Wardrobe Changes: Athletes often shoot in pre-production gear that later gets shelved or tweaked. Instead of scrapping expensive footage, AI can swap out colors or kits without altering the essence of the trip. Photoshop has long done this, but generative tools make it faster, cheaper, and cleaner. In this case, the audience isn’t misled — the athlete, the action, and the place remain real.
Augmented Reality for Consumers: Upload your photo and see how a jacket or pair of pants would look on you before buying. This isn’t far from dropping a couch into your living room via a Wayfair app, but it’s a natural and valuable use of AI for outdoor retail. It empowers consumers without crossing into fakery.
Over-the-Top, On-Purpose Fakery: Brands like Pit Viper thrive on absurdity. In their case, leaning into obvious AI-generated chaos — aliens skiing moguls, talking yetis selling goggles — fits the brand. Done well, it can be hilarious. Done poorly, it bombs. This lane is narrow, but for brands built on parody, it’s a logical fit.
AI as a Creative Partner: Finally, not all AI use needs to be consumer-facing. Using it as a creative partner, to brainstorm campaign directions, test messaging, explore KPIs, or riff on early-stage ideas, is entirely reasonable. The key is remembering what these systems are: next-token prediction engines. They can spark ideas, but they shouldn’t be treated as gospel (or lead the charge…yet).
Closing Thoughts and Predictions
The point of this post isn’t to dunk on AI or to glorify it, it’s to get us thinking about what comes next. The world we’re stepping into really is different. In the time it took me to write this post, I could have spun up ten different AI models, each generating a dozen drafts in parallel, then used another AI to pick the “best” one and publish it. That’s not science fiction — it’s how some coding models already work. When you can produce at scale this fast, you don’t need every version to be good. You just need one. And if the cost and time are an order of magnitude lower, you win in the current attention economy.
But here’s the rub: just because we can doesn’t mean we should. Humans crave authenticity. In outdoor sport especially, what moves us is the real thing — athletes, landscapes, stories that inspire us to get out there ourselves. If everything turns into AI slop, people will feel it. We may self-police this on taste alone, but the gray areas — the wardrobe swaps, the “optimized” feeds, the parody campaigns — will make us all scratch our heads, and I clearly don’t have a crystal ball.
My (likely wrong) prediction? AI won’t replace outdoor storytelling; it will reshape the pipeline behind it. The winners will be the brands that use AI to cut cost and friction without eroding trust. The losers will be the ones that chase scale at the expense of authenticity. In a culture built on being real, that’s still the quickest way to get left behind.
Ultimately, AI might change how content is made, but it will never replace why we care.
Thanks as always for reading. If you liked this, please share it with a friend or three. Also, I’m going to keep reminding my audience to check out my new app – Rali – a much improved version of Strava aiming at privacy and the sports Strava has ignored. If this is interesting, head over to the website and put your name on the waitlist!
At the bottom of this post, I’ve included a short list of companies that have recently launched tools in the generative space — products that could change the way we consume photos, videos, and audio in the very near future.
- Inception Point AI – AI podcast network. They claim they can produce a full podcast using AI for under $1.00 and so long as they get 20 listeners its a net breakeven. As a result, they’ve already outputted over 5,000 pods.
- PhotaLabs – AI photo company. They take your everyday photos and enhance them (ie, add focus where it was absent, open eyeballs that are closed, make jeff have hair etc).
- *Veo3 – Already touched on this, you put in a prompt and Veo3 creates a video. Google also added a way to stitch AI outputted videos into a longer story, which sounds easy but is technically challenging to reuse elements. This is a frontier they are actively working on and will likely be overcome soon.
- *NanoBanana – Google’s own image model (Gemini 2.5 Flash Image) which has been yielding incredible results. Upload a photo. Tell it what you want. Be wowed at the results. “Photoshop is Dead” has been the mantra around this model.
- NotebookLM – Over a year old; give it content of any kind and two “podcast hosts” will output a piece of audio content discussing your prompt + colleteral.
- Remaker – Image to video, image retouching.
- *ElevenLabs: Far and away the best audio AI on the market. If you want to create a realistic voice for any purpose, from customer support to video narration, ElevenLabs will blow your socks off.
- *ChatGPT: Though there were other image models before GPT 4o, this was the one that largely shocked the world realizing you could output and edit videos simply using a prompt. Similar to NanoBanana, with slightly not-as-good results, but crazy high distribution.
- Adobe – The old 10,000 pound gorilla has already added tools to Premier and Photoshop that let you extend a clip past what was captured or completely shift an image to the user’s liking.
*=api access is available, meaning these aren’t just companies outputting content, but solutions that offer others the ability to create content at scale and automatically.