AI Product Packaging Challenges

The tension between what a technology can do and how it’s packaged is a thing I’ve grappled with for the majority of my career as a designer. This isn’t a new problem. It’s been around at least as long as humans have been making and selling technologies for use by others.

One of my favorite examples of tech product packaging is Atari 2600 cartridge package design.

If you were a nerdy kid growing up in the late 1970s, the toy you wanted for Christmas was the Atari 2600 Video Computer System.

Atari 2600 games were incredibly simple by today’s standards, having to fit on 2-8kb ROM cartridges that would run on an 8-bit i.19 MHz processor with 128 bytes of RAM. The graphical performance of the 2600 was significantly more limited than the games found in arcades and corner stores.

In order to compensate for a lack of graphical sizzle, Atari’s graphic designers employed incredible product art to capture the imagination of kids looking to bring their arcade experience home.

I suspect Atari’s graphic designers understood that they couldn’t lead with game graphics as they weren’t compelling enough on their own. Turning the cartridge boxes over reveals the stark reality of what could be achieved with the Atari 2600’s low-cost hardware.

One of the things I love about these package designs is that I think they were the best solution given the limitations of the technology they were promoting. The box art was exciting and made reference to arcade game cabinet art. The 2600 system was positioned as an entertainment platform and Atari branding was instantly recognizable at stores, making it easy for parents to recognize and find.

I love these because that they’re juuust on this side of a very fine line where aspiration runs in advance of reality. These games were still fun to play even if they only superficially resembled the arcade games they were inspired by, and a kid’s imagination could do a lot of heavy lifting to compensate for the lossiness of the experience relative to the real arcade games. The console’s gameplay captured the spirit of the original game and the box art sealed the deal.

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One of the dangers of creating products that don’t live up to their promise is that it can be easy to set expectations that you can’t fulfill. When this happens, the results can be catastrophic.

Just about a year after these cartridges were released, Atari found itself on the other side of this line when it released Pac-Man. At the time, Pac-Man was one of the most popular arcade games and kids were very familiar with how it worked. Atari’s management had failed to understand the importance of being able to follow through on the expectation players would have of the game, and did not give the game’s sole designer enough time or freedom to figure out whether it was possible for the Atari 2600’s aging hardware to do a credible job with Pac-man. The experience delivered did not match customer expectation and the Atari brand suffered a significant blow.

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More of Atari 2600 Pac-Man story on hackaday.com

As an aside, someone eventually did find a way to make a credible Pac-Man for the Atari 2600, but it took more than 20 years for the better game to arrive. Amazing what creative humans can accomplish when given the time and space. This effort even managed to win the admiration of the game’s original designer.

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This is all to say: how a product or technology is packaged relative to what it delivers matters greatly. It can often determine whether a product or technology will ultimatelty be successful or not.

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Turning back to the present, I wonder how AI products will eventually be packaged and what shapes they will have once we fully figure out what they’re good for.

After having spent some time building with Cursor and Claude, I’m struck by the contrast between how powerful these technologies are and how little their capabilities are apparent at first glance.

When the primary interface for accessing AI capabilities is through text-based interfaces, it requires that users be curious enough to be willing to poke around and feel their way through the tech, much like early PC users had to feel their way around text-based adventure games.

With a technology that can accept and understand natural language as well as today’s LLMs can, text-based interfaces can get you a helluva long way. But this approach comes at a cost of discoverabilty. It doesn’t explicitly communicate how it should be used and what it’s good for.

In the long run, how AI capabilities are packaged and surfaced will have a profound impact on how readily they are adopted. I think this will be one of the biggest challenges for the designers of AI products.

In some interesting ways, today’s AI product packaging problem feels like the inverse of the Atari 2600 game packaging problem. The package leads with the 8-bit screen art, but the experience of playing looks and feels like the artist’s rendering.

The job for Atari’s packaging designers was to find ways to make the best of a very limited set of technologies by generating a sense of drama and excitement. The limitations of the underlying hardware defined the upper boundaries of how the games could reasonably be packaged and sold.

By contrast, the open-ended nature of AI is both its strength, and its weakness. When you have a technology that promises to do so much, the trick becomes finding ways to show how it can be used to solve some important subset of problems that humans care about today — many of which may be rather mundane.

Given this, I think it’s likely that today’s general purpose AI interfaces will eventually need to give way to domain-specific interfaces. The kinds of affordances needed for architects and mechanical designers (for example) will likely be different than the kinds of affordances that software devlopers will need to have.

And just as software developers are bootstrapping the tools and workflows they want to have to facilitate agentic engineering, making AI work for other domains such as architecture, construction, mechanical design will require developing the interfaces, affordances, and workflows to enable skilled practitioners to figure out how to best incorporate AI into their own ways of working.

This feels like a very different conception of product packaging than traditional desktop software for professionals. It might be that the packaging job around AI will be to build out a critial mass of easy-to-use core capability so that practitioners can see the utility of AI-enabled workflows, and then offer ways for them to take it the rest of the way.

So in some ways, we may be strangely back to the Atari model where AI is the platform, and it’s accessed through well-packaged “cartriges” that are good at doing specific jobs.

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One thing that’s going to be interesting to see is what the impact of aspirational messaging around AI will have on the development of AI-enabled products and services. I wonder whether the belief in the unlimited promise of AI might actually present challenges for productizing the tech. When you’re promising so much, it can be easy to overlook the smaller things that could turn out to be very useful.

Even if foundational AI progress were to stop today, I think AI’s natural language understanding and pattern recognition are more than enough to fundamentally reshape how we interact with computers (and how they interact with us). It will be interesting to see whether we’re willing to give ourselves the time and space to fully explore this, or whether the pressure to deliver the future will tempt us to take shortcuts that outpace our understanding of how to best productize these powerful technologies.