AI Images That Look Real: What Happens to Your Photography Next?

AI image generators are making images that look like photographs, and it’s pushing you to ask what part of your work is skill, taste, or just access to a tool like Photoshop. That question hits even harder when a prompt can produce something that passes at a glance, whether it’s going on your website, a client deck, or a social feed.

Coming to you from Blake Rudis with f64 Academy, this plainspoken video treats today’s AI panic as a rerun of older art fights, not a one-off crisis. Rudis starts in the 1800s, when painters dismissed photography as mechanical and “too easy,” like the camera was doing the thinking. That complaint sounds familiar now, only the target has shifted from lenses and film to datasets and prompts. The useful part is how he keeps the focus on behavior: who feels threatened, who adapts, and who tries to freeze the definition of “real” at the moment they learned it. You end up watching the current argument with a little more distance, even if you still feel the heat when you see AI images getting traction.

Then the video lands on a concrete turning point: the release of the Kodak Brownie camera in February 1900 and what it did to exclusivity. Suddenly families could make pictures without training, without a darkroom, and without begging someone else to operate the gear. Pros didn’t shrug it off, they worried that ease would flatten the craft into “anyone can do this,” which is basically the same fear you hear today about automated image-making. Rudis doesn’t romanticize the old gatekeeping, but he also doesn’t pretend the pressure wasn’t real when the market got flooded with competent results. The tension he sets up is practical: when the tool becomes common, you have to separate “I can operate the tool” from “I can make something worth revisiting,” and those two skills don’t move together.

The video also jumps to the early 2000s, when digital capture and Photoshop got framed as cheating, with film positioned as the last honest method. If you started on film, you probably remember how quickly people stopped debating outcomes and started policing process. Rudis admits he was part of that resistance, which keeps the point grounded instead of smug. He frames the pattern as predictable: a new tool arrives, the prior generation calls it inauthentic, serious makers redefine their role, then the tool becomes normal and the fight moves on. That arc forces you to ask where you are in it right now, especially if your gut reaction is to defend the old lines instead of testing new ones.

Where it gets sharper is when Rudis pulls algorithms into the same frame as authenticity. He isn’t only talking about AI versus cameras, he’s talking about what platforms reward when the goal is quick reaction and constant scrolling. AI images often aim for instant punch and fast comprehension, which can do well in that environment, while human work can be slower, stranger, or more personal in ways that don’t read in half a second. That creates a real tradeoff: do you chase the kind of impact that performs immediately, or do you build a body of work that’s harder to copy and harder to forget, even if it travels slower. He hints at how some high-end fine-art shooters are already moving away from hyper-real looks toward more expressive choices, which is a risky move if you’ve built your identity on realism. Check out the video above for the full rundown from Rudis.