AI's Heavy Freight

Back in the late 2000’s, I was brought on by a former client to design an API infrastructure startup named Apigee, which is now part of Google Cloud. Apigee gave me a front-row seat to see the power of good framing and the impact it can have on the long-term success of a technology.

Apigee’s executive team practiced and refined the company’s pitch every day. My job during the early stages of the company was to take the pitch and turn it into an identity, marketing website, applications, and sales materials. We continually refined the messaging and product as the API infrastructure market grew, and this continued until Apigee was well-established and on its way to IPO.

Apigee’s leadership understood the framing game:

1. Figure out what problem you’re solving and why it’s important.
2. Find a framing for this problem that makes sense. Communicate why people should care and how they will benefit.
3. Work to get the market to accept your framing of the problem.
4. Ensure the product you create is a better fit for the problem you have framed than the products your competitors offer.
5. Adjust the framing and product based on market needs, the competitive landscape, and public perception.
6. If you do these things better than your competitors over a sustained period, you’re going to win.

Things get much tougher if your framing is off:

• If you have a good product, but your framing is off, then you’re likely to struggle with being successful in the long run.
• If you have great framing but your product doesn’t meet the expectations you’ve set, you are vulnerable.
• The more out of sync your framing is with what the world needs, the harder it will be for the world to accept what you’ve made.
• If you fail to adjust your framing or your products based on shifts in the wider landscape, then you’re likely to be overtaken by someone who can do both better.

There are indications that the computer industry may be in the process of learning some hard lessons around AI’s problem framing.

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Steep Grade

If you want to get a sense about how AI’s problem framing is being received out in the wider world, this essay by Nilay Patel, is worth a watch.

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John Gruber at Daring Fireball lends his support:

Something is profoundly off in the computer industry when it comes to software broadly and AI specifically. It’s up for debate what exactly is off and what should be done about it, but the undeniable proof that something is profoundly off is the deep unpopularity surrounding everything related to AI. You can’t argue that the public always turns against groundbreaking technology. The last two epoch-defining shifts in technology were the smartphone in the 2000s, and the Internet/web in the 1990s. Neither of those moments generated this sort of mainstream popular backlash. I’d say in both of those cases, regular people were optimistically curious. The single most distinctive thing about “AI” today is the vociferous public opposition to it and deeply pessimistic expectations about what it’s going to do.

Both authors make it clear that there’s something amiss with AI’s framing and what’s being produced with AI, despite there being genuinely useful things created with it.

The high-level pitch of AI is that soon, it’ll do all the things. This is a hard pitch to swallow if you do a thing you’ve worked hard to be good at it, and someone tells you that thing won’t be nearly as valuable in the not-too-distant future. Understandably, the people on the receiving end are going to get angry and be distrustful of the people who make this pitch. When companies lay people off in preparation for the day when AI will do all the things, then people will take this as evidence that the pitch is true, and they will increasingly distrust the things made by the people who made the pitch.

This is the heavy freight that AI is currently carrying, and unless the computer industry can find a way to reframe AI and the problems it chooses to solve, I expect people to become angrier and the grade the computer industry is traveling on will get steeper. This is not a good long-term recipe for success, especially if you’re betting on a technology that can only get smarter if people are willing to feed it with more of the things that they care about.

I think Patel and Gruber both did an excellent job calling attention to the problem, but I didn’t get the sense that they had solid ideas about what the computer industry might pursue instead of automation.

So I’d like to explore whether there might be a different framing around AI, and I’d like to propose some rough ideas around how the computer industry might start to shed the heavy freight of its current AI framing — or at least shift it in ways that can be demonstrably beneficial to humans.

Let me start by zooming out to the pandemic.

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Brittle real-world systems are the more pressing existential problem.

One of the things that was shocking about the Covid pandemic was how quickly it brought into focus what’s really important. It showed us just how brittle, fragile, and interconnected the systems we rely upon are.

During the early stages of the pandemic, solid real-time information was very hard to come by. People created grassroots systems for tracking the spread of Covid and communicating out situational awareness. This is software at its best — helping people be more informed and connected in a dynamic situation that’s evolving in real time.

One of the great lessons of the pandemic is how important it is to have humans who are good at making decisions based on messy and incomplete information in unprecedented circumstances. The number of lives saved and lost came down to the decisions people made based on their understanding of what was at stake.

Another lesson of the pandemic was how important the running of the real world is. When the normal functioning of the world was at risk, we discovered who was an essential worker, and who was not. I remember the shock of recognition when I realized all the essential people that contribute to the supply chain that delivers toilet paper to grocery shelves were orders of magnitude more important and vital than the people that do my job.

The pandemic affirmed the utility of software, but also affirmed the importance of humans as creative problem solvers who can act on information, experience, and intuition.

When the pandemic hit, we were dealing with an event that had significant existential repercussions and it was remarkable to see how we collectively threw everything at the problem we could. One example was people rapidly developing cheap open source ventilators in an effort to shore up an overwhelmed hospital system.

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In order respond to the next crisis effectively, we need to have a surplus of extremely skilled, resourceful, and creative people who can come together on very short notice and use all of their problem-solving skills and experience to find out-of-the-box solutions for out-of-the-box situations.

I think a healthy society needs to have a reserve of these people at the ready. There’s no way to know in advance what kinds of skills and talents might be needed. The recipe will change depending on what kind of crisis we’re dealing with. Many of the kinds of people needed when a crisis hits might not directly interface with computers when providing some vital service in a time of need.

Automating or accelerating what we already do with software won’t really help us to build in the kinds of resilience we’re going to need to have in order to deal with the world’s increasing environmental, economic, and societal pressures. Without some care, we might even find that speeding things up will destabilize things further.

Also, it’s hard to square the idea that superintelligent AI will be a benefit to humanity if people believe (or see) that it’s going to result in them being less valuable and useful at an industrial scale.

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Enabling human creativity is a far better framing than making computers more intelligent.

If AI really is being made for the benefit of humanity then perhaps the job for AI should be to focus on generating a stockpile of creative resources and capabilities so that we can mitigate the worst effects of the next inevitable destabilizing event.

The resources to generate in this case aren’t computer resources. They’re human capacities. Our ability to imagine and invent from as many perspectives as possible is the thing we need to develop and grow.

Computers are amazing tools — and I think AI can be an amazing tool — but the shape of a tool determines its function. If AI is going to be a good tool, one that enhances people rather than degrades, replaces, or disenfranchises them, then we need to take a very hard look at what work this tool is supposed to perform for us.

Sir Ken Robinson’s talk on creativity suggests a way forward:

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Robinson defines creativity as “The process of having original ideas that have value”. He characterizes creativity as a ruthlessly squandered resource that should be cultivated and encouraged. He asserts that fostering the faculty of creativity is the best way to ensure that we’ll have the collective wherewithal and diversity of thinking to solve tomorrow’s unforeseen problems.

The job for AI then isn’t just to make computers smarter. The application of AI needs to be in the service of making people smarter, more creative, more inventive, and better able to pool their collective problem-solving capabilities so that we can improve the world we live in.

People will lean into the tools and technologies that allow them to change things for the positive, but in order for this to happen, they need to have AI tools that enable people to solve the kinds of problems that really can have a positive impact on the human condition.

Deepmind’s Alphafold https://deepmind.google/science/alphafold/is an excellent example of this, so I think there’s ample evidence that AI can be used to create incredibly important applications with a wide downstream benefit for people.

If the computer industry wants to start shifting the framing of AI, then it needs to care about what kinds of things we choose to make with AI. We need many more Alphafolds, and we need them fast enough to be able to credibly shift the narrative around AI before people decide they’ve really had enough.

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Thoughts on shifting AI’s framing

With the gargantuan resources in play around AI, it’s going to be very hard to change AI’s trajectory in a meaningful way. It might be that we’re living in a world where the computer industry has lost the ability to determine its own future and direction.

Once enough people buy into the idea of AI being a race where the victor takes all, is it even possible to choose a direction that’s selective around where we make use of AI and where we don’t? I honestly have no idea. Sometimes the train can’t be stopped when the freight is too heavy.

However, I hope the computer industry will decide to frame AI differently now that it’s becoming apparent there’s a significant misalignment between how AI makers see the tech and how the rest of the world sees it. I think this is the prudent play to make, but it would require a change in ambition, focus, messaging, and outcomes.

With this in mind, here is a list of incomplete thoughts about how we might go about shifting the framing around AI. Hopefully you’ll find some food for thought here.

1. Recognize that the framing we choose for AI determines the kinds of products we’ll make.

If you’re an AI maker, and you’ve convinced the market (and yourself) that AI is a zero sum game up for grabs between the good guys and the bad guys, it’s unlikely that you’re going to spend much attention figuring out what today’s tech is really good for. Instead, you’re going to try and deliver the thing with existential implications because that’s what the market now expects, and that’s where the big incentives are. Right now, this feels like a case of target fixation on a massive scale. The only way out of target fixation is to look at a different target.

2. Adopt a much narrower problem framing

The more AI can be used to solve legitimate problems that people have, the better the pitch. The more AI enables people to solve the problems they care about, the stronger the pitch. Given that the vast majority of problems people face are messy and have real-world dimensions and repercussions, there’s always going to be a limit to how much AI can do on its own. The more honest and realistic the pitch, the easier it is to deliver against expectations. A narrower problem framing may also buy the computer industry some much-needed time. If we’re making things that we don’t fully understand because they aren’t constructed in a way that makes them open to inspection, this is an incredibly shaky foundation upon which to build. It’s hard to trust the assertion that this technology is going to be the thing that changes everything when even the makers of it can’t really anticipate what it will do with each new generation.

3. Commit to make things that add to people rather than take away.

This requires that we take full responsibility for the benefits and harms around AI. Our eyes need to be wide open here. Damage done whether we notice it or not is still damage. This also means that we need the same kinds of regulatory structures and safeguards in place that we see in the medical industry to ensure that AI is being developed in accordance with the public good. One of the challenges that comes with building a foundational technology that’s used to make other things is that the makers of the technology will be judged by the things people make with it. The more safeguards there are in place to ensure the tech is being used for the greater good, the more likely it will be used for the greater good.

4. Demonstrate genuine interest in fostering human creativity.

We need to treat the study of enabling humans to be more creative as a first class problem — and this isn’t just about being more creative with computers. This is understanding the rich tapestry of creativity, invention, and expression writ large. The more we can understand what creativity is and how to foster it, the better we’ll understand what subset of problems can be aided with the help of computer-enabled things. We’ll also be able to recognize what sorts of things prevents humans from reaching their full creative potential. You’ll recognize that we’re taking this subject seriously when people are able to create entirely new classes of useful, interesting, beautiful, and functional things — not just build the same things faster and with less care. You’ll also be able to see it when the next generation of creators can start to make inroads on problems that have stumped humanity for generations.

5. Figure out which problems are software shaped.

Solving software shaped problems well can make a huge difference in the lives of people. Attempting to make non-software shaped problems amenable to software solutions will certainly cause harm, and will further reinforce the collective perception of AI as being harmful.

6. Conserve what already works well.

If you’re a company that already produces software that does something genuinely valuable, take good care to ensure that what you add to your products is genuinely aided by the inclusion of AI. People can tell the difference between a feature that’s added for their benefit versus a feature that benefits the company that makes the software. Once you put those capabilities in the software, it’ll be very hard to remove them, so take care. If the AI-enabled capabilities you’re adding require that you treat your customers (or the things they make) like food, this will not result in shifting the framing of AI in a positive direction, and it will likely degrade the goodwill you’ve built up over years.

7. Give people greater control and agency.

One of the easiest ways to take fear away is when people have a sense of control over their conditions. There’s a big difference between helping someone do a thing and doing the thing for them. So much of today’s AI usage seems to be focussed on having AI do the thing. There are huge opportunities for AI to augment how people do things, but this is a much harder problem to solve. It requires having a deep understanding about what problems people have, how they want to work, and what they need. This understanding can’t be generated by sifting through the extremely weird slice of the human condition that finds its way online. The digital design for a building or a website does not contain any record of whether it is well-suited to the problem it is supposed to address.

8. Give people the ability to own and shape the fundamental technologies they use.

We have more than 60 years of software not being organized in this way. Having worked for one of the world’s premiere CAD software companies I can tell you that people are tired and frustrated by having so little control over the software they rely upon to do their jobs. Huge opportunities here. Enable professionals to make their software fit the way they work and you’ll see the benefits of AI-enabled software compound over time.

9. Create software that people can shape to fit their way of thinking.

If we spend the next decade focused on the problem of making software fit people with the same vigor and enthusiasm that we spent making people fit to software over the last two decades, we’ll be in a much better place. Surely with the help of AI we should be able to move twice as fast as we did before. Software that can understand what people are asking for is an incredibly useful capability to build upon.

10. Build fundamental AI technologies so that they can be understood.

We don’t yet know how to do this, but it’s hard to see how AI can be made to reliably work in high-consequence real-world contexts without the ability to definitively verify the results and the chain of reasoning. The more black box a technology is, the less people can and should trust it when lives and livelihoods are at stake. If the AI technologies we currently have can’t help us get to ground truth, then it’s likely that we haven’t discovered the right fundamental AI building blocks.

11. Give people the information and tools that help them make smarter decisions over time.

When humans get to decide the things that are important to them, they feel a sense of agency. Making good decisions requires integrating information from multiple sources, only some of which may be online. We need to invest in tools and technologies that can help people become better, more thoughtful decision makers. The pattern-seeing abilities of LLMs are a remarkable capability, but the ability for foundational models to make these patterns understandable to humans feels significantly less developed. The ability for AI to accurately communicate in pictures, animation, 3D models, immersive experiences in real-time is still in its infancy. Huge opportunities here.

12. Create incentives for people to make better use of AI — and their own potential.

The hard problem of figuring out what AI is good for has to start with an admission that it won’t be good for everything. For example, we now make refrigerators that can notify us when our milk needs to be replaced. It’s hard to imagine that the talented people who worked on this couldn’t have made better use of their gifts on more pressing problems. What people make with AI will color what people think about it. If we want to change the perception of what AI is good for, we must incentivize people to make good things that really can make a difference.

13. Treat all of these things as first-class problems to solve today, not something we’ll eventually do when computers are superintelligent.

Assuming that computers will one day be smart enough to do all these things is feels like an awfully big IOU to write given the costs incurred to deliver today’s AI. I’d wager that unless we can figure out how to do the things I’ve described above, I doubt we’ll be wise enough to build the guardrails in place to ensure that we get to enjoy the best outcomes from our superintelligent friends should they arrive. Perhaps the way we’ll be able to tell that AI is in alignment with us is by the things we have it help us make. If what we make is something that disenfranchises a huge swath of creative people, that’s a pretty good sign that the companies that make AI are misaligned. However, if the result of AI is that it helps us build up the reserves of creative capacity to help us mitigate (or even prevent) the next destabilizing event, we’d have good evidence that the makers of AI are in alignment with the greater needs of humanity.

14. Have humility to travel farther.

If we’re going to set out to do a big thing that is paved with unknowns, approaching the problem with humility may be a necessary precondition for success. To make something novel means accepting the possibility of failure. The problem of AGI could be significantly harder than AI boosters are willing to admit. Perhaps building AGI will be more like building a cathedral that takes generations to construct. I don’t know nearly enough about AI to have a well-formed opinion about whether this is true, but I do know that when you’re making something new, it’s always better to underpromise and over deliver.