While GPTs may prove useful in their current form, they’re part of a grander plan. The exact path is yet to be seen, but I believe OpenAI tipped their hand with a very specific choice in how GPTs are designed.

Conversation starters

Here is a common model for building a custom chatbot on the GPT-4 APIs:

1. A user navigates to your bot. (e.g, a feedback coach web app)
2. The bot introduces itself, and asks you an initial question.
3. The user responds to the question, and the conversation goes from there.

OpenAI GPTs do not support this model.

Specifically, unlike many API-powered bots, GPTs are not allowed to do Step 2: they cannot have the first word. A GPT author can provide a blurb describing the GPT, or provide suggested inputs to the user, but GPTs sit silently until you send them some kind of initial message. GPTs force a flow like this:

1. A user navigates to your bot. (e.g. a feedback coach GPT)
2. A user types something – anything – to the bot.
3. The bot responds to the… whatever it was, and the conversation goes from there.

This was initially baffling to me as a developer. When we built the Feedback Wizard, a LLM-backed structured coaching experiment, it was clear that the Wizard should start the conversation by asking the user the first question. This is how a lot of software works: you start it up, and it asks you something.

• “What do you want to find?”
• “Where do you want to go?”
• “On a scale of one to ten, how would you rate your pain?”
• “Shall we play a game?”

This “computer starts with a question” model seems especially well-suited for building chatbots. For example, the whole point of the Feedback Wizard is to ask you questions. Attempting to build a GPT version of it under the constraint that the GPT can’t start the conversation was annoying.

Meanwhile, GPTs are set up to offer “conversation starters” – snippets of text users can tap to get things rolling, which force developers to think about how the GPT might be queried. When putting together the Feedback Wizard GPT, I felt pushed into writing conversation starters like “Can you help me frame some feedback for a co-worker?” – entry-points that felt redundant, given that the user has already selected a feedback coaching tool. However, while doing this, it struck me why OpenAI designed GPTs to be written in this backward-feeling way:

GPTs have to wait for user input before saying anything, because that will make them useable as building blocks of an Everything Engine.

The Everything Engine

ChatGPT is powerful, but has many limitations. No company can build, by itself, an Everything Engine: one text box that you can type into for any problem. OpenAI experimented with plugins to expand ChatGPT’s capabilities, but plugins competed with one another in-context, and the business model was questionable. Worse, there was too much friction to finding and selecting the plugin you wanted to use.

While GPTs may seem like they have similar problems, they offer a clear evolutionary path to a low friction, scaleable, and potentially highly profitable user experience. In a world where many useful GPTs exist, and those GPTs are written to respond to user input rather than start conversations with people who select them, ChatGPT can incrementally become an Everything Engine simply by routing requests to the best GPT for the job.

Let’s say you ask ChatGPT, “Can you help me with this math problem?” it could offer to send your query to the Khan Academy GPT, built by learning experts. If you ask, “How are the markets today?” it could sub into Yahoo Finance’s stock market GPT, equipped with realtime market APIs. If you ask an Everything Engine “Where should I go on vacation?” it might leverage a travel expert’s lovingly crafted GPT for helping you consider your options.

Just joking, it’ll offer the Tripadvisor GPT. Or whoever the highest bidder was. We won’t see sponsored GPT results for a while, but an Everything Engine would be the most compelling ad opportunity since web search results pages. If it works, the incentive for OpenAI to accept payment for GPT promotion will be immense.

LLMs aren’t great at math, but Wolfram is.

Admittedly, OpenAI might retain enough of their not-for-profit DNA to resist the urge to monetize the Everything Engine like this. Or maybe Sam Altman will be fired again on the path to building it. But even if they don’t do it, Google will.

If I’m right – if a model and ecosystem like GPTs can be used to build a compelling Everything Engine – then an ad-supported Everything Engine is coming. I’m more than willing to pay $20/mo for ChatGPT Plus, but most people won’t. But they will visit a free website where they can type into a text field, and it will answer their questions with ads.

Right now, that website is Google for most people. But no matter how much work Google puts into their Quick Answers and Bard, no one company is going to be able answer everything on its own. Serving the long tail of queries in one place – creating an Everything Engine – will require an ecosystem of developers and content providers.

In the coming years, if we do start to see the emergence of the Everything Engine, well… we will live in interesting times.

Thanks to Adam Lisagor, Jenn Cooper, Chris Parrish, and Paul Kafasis for feedback on this article and the ideas behind it.

In 2010, my co-founder Nigel and I started Steamclock. The vision was to build products for clients, and use those profits to fund our own product development. Which worked! Mostly. We’ve built a client business that’s been growing and profitable for a decade straight, which has funded a lot of our own product exploration.

As we grew, though, a flaw in our plan became clear: trying to build your own products within a client services company puts both businesses at a disadvantage. Specifically, a focus disadvantage.

There are fundamental differences between building a product for a client and building a product startup. They justify different mentalities, approaches, and habits. When you have other revenue coming in, you can get away with hobby products – such as building a polished, well-loved app that gets a million downloads, yet loses money. For example.

These exploits may feel partially successful because they’re fun and educational and popular, but they distract from doing what’s best for the product or services business. While hobbies can be rewarding and informative, by definition they can’t support a talented team, iterating and improving towards something truly great.

Me personally, I like to build with a talented team, iterating and improving towards something great. That’s how we do our client work, and that’s what I want to do for product. But trying to do both in a combined team makes it hard to do either well.

So, last year, we started the process of splitting our client services and product development work into separate businesses.

From one perspective, it’s a simple change: despite our many product experiments, Steamclock is fundamentally a client services business. Given that, this change is less about splitting out a product business, and more about founding a new one.

From another perspective, it’s a complex change: I’ve been focused on our client services business for over a decade. Despite a lot of progress in giving our teams ownership and independence, I’ve still been a bottleneck for some key processes like hiring, marketing, and strategic planning. To focus in on product I knew I needed to push to the next level of delegation and handoff – but after 12 years I found myself a bit burnt out.

After some angst and coaching, I devised a plan:

1. Take 3 months to systematically delegate, teach, coach, and document
2. Take a sabbatical for the following 3 months, to recharge and plan
3. Return and see what we’d learned

You know – an experiment. While I knew it was needed, I expected this process to be unpleasant and difficult. I was worried I’d come back to a bunch of firefighting and un-delegating.

I was happily wrong.

Preparation

As a leader, you do two kinds of work:

1. Work that’s clearly visible to your team. This is the easiest to hand off.
2. Work where your team has no context for how and why you do it, or even that you do it at all. These are harder to hand off.

A big benefit to teams with a habit of transparency is that there isn’t a lot of hidden work. I’d been doing a few invisible tasks – “when we do strategic planning, this is how I prep it,” or “every quarter I run this analysis on the P&L” – but for 80% of my job, the parts of my work that needed delegation were familiar and legible to the team.

Once we had a map for me being away, a key question remained: how often should I check in? We considered a weekly or monthly checkin or review, but that would diminish the value of the experiment. Instead, we devised a very short list of emergencies I might be pulled in for – e.g. a surprise cashflow problem or the departure of a key leader. Then, we worked to set the team up with the context, docs, and explicit authority to handle everything else.

While I expected this prep work to be a grind, it was actually… fun? We have an excellent team, and the time-boxed nature of the mission felt very purposeful. I iteratively handed off my tasks, spent less and less time working in the business and more time writing docs and coaching (which I enjoy), and after 3 months of prep the team was ready to rock.

Sabbatical

Since I am me, I knew that – without some structure – my sabbatical would quickly devolve into Allen unofficially working. I also knew that, after 12 years of full-time work, I had more side-projects I wanted to do than could conceivably be done in 3 months. I know going on a 3-month break is a huge privilege, so I didn’t want to fork it up.

To give myself some guardrails, I chose a theme for each of the 3 months:

• Month 1: Do nothing
• Month 2: Try new things
• Month 3: Prepare to return better than I left

I was not great at doing nothing. I did a lot of forest-walk thinking, reading AI papers, and completing projects around the house. While I didn’t do nothing, I successfully stayed out of my work email and Slack – and got no emergency pages from the team. Month 1 was a success.

The “Trying new things” month was glorious. I tried new instruments, technologies, fitness routines, restaurants, books, habits, movies, priorities, games, and mindsets. Not just things I’d been meaning to try, but stuff outside my comfort zone. It was joyous. Since that month, I’ve kept up the new mindset and it’s been wonderful.

I also got a lot out of the preparation month. I started going to new meetups, reading books about products and startups, and getting clear on my mission. I love product work, so I was happy to be digging into exploring and talking to potential customers, before my sabbatical even ended.

Return

While I feared returning to a list of fires, everything pretty much… worked. In my absence, our team delivered good work, kept clients happy, and retained everybody. The preparation paid off. In contrast to the bad old days when I was the only person with enough context to make big decisions and keep things moving, in recent years we’ve developed excellent project management, operations, and Directors of Engineering and Client Services that support and execute for our clients. It’s pretty great.

Of course, there were some gaps in my absence. But having proven out what I was actually needed for – as opposed to what I was doing out of momentum – it’s been a lot easier to work through filling them with further experimentation. It’s a big level-up to go from “I worry that blog posts won’t move forward without me around” to “How do we get blog posts moving again, now that I’m not running them?”

Meanwhile, I’ve gotten my wish: I’ve been focusing more and more on product work. I’ve pursued product research questions like “How could team communication get far better?” and “How do effective leaders manage their time and attention?” Recently, I’ve led prototyping and customer validation on a tool that helps product teams work with LLMs. I’ve been learning more than I have in years, and am excited to ramp further on product work in 2024.

The lesson, at least for me, is that even when a big change seems risky, there’s a path to doing it well. In retrospect, that may seem obvious. Over the years I’d already reinvented my job repeatedly – from founder, to manager, to leader of managers – but this felt like the biggest and most daunting change yet. Still, the ingredients have always been the same: a team, a plan, and some experimentation.

And now, for more of that.

Well, not for no reason. Our minds are wired to learn.

However, without a bit of steering, this curiosity can get a little… time-wastey. Learning something – especially if that thing reinforces our existing preconceptions – will give you a nice little dopamine hit, whether or not the information is useful.

Greek triremes were often fitted with bronze rams to destroy enemy ships’ oars.

Cool, right? A fun fact. Dopamine received, thank you. This loop even works even if the “fact” isn’t actually true.

Polling released earlier today demonstrates that your political party’s views on climate are increasingly accepted, putting mounting pressure on legislators.

Heck yeah. Fave, repost, resume scrolling.

In the modern era of anything and everything all of the time, we’re drawn to habits that sate our curiosity with these little dopamine loops. Which is fine in moderation! But not like, super great.

Luckily, there’s one weird trick that can buck that trend. It helps you build meaningful expertise, meet interesting people, foster your reputation, and spend less time scrolling and more time actually learning.

You should have a research question.

Research sounds boring, get me out of here!

No, wait, I promise it’s more fun than it sounds. When I say “research”, I don’t mean a formal scientific hypothesis with a methodology, bibliography, and, like… apparatus and stuff. Although, I guess if that’s your bag, go nuts? When I say research, though, I just mean intentionally going in-depth on a specific question or topic. Taking a thing you’re curious about, and going beyond a Google search, Wikipedia page, or one-off question to a friend. Digging in. Going on a little journey.

By occasionally picking things to go deep on, you balance out the otherwise broad information diet we all get by default by being on the internet, consuming media, and just kind of being a modern human.

Of course, you already have some subjects and interests you spend more time learning about. The idea behind a research question is just to be a little more intentional about that. Pick up a specific question or two for a few weeks, and level up your understanding. Repurpose some of the time you would otherwise be scrolling news or memes or dunks.

Questions of Interest

Anything that matters to you can make a good research question – as long as it’s deep or obscure enough that you can’t simply get a clear answer from Google or ChatGPT.

For example, a few years back I was frustrated at how unclear it was who I should vote for in our local Vancouver elections. So I dug in, learned a bunch of interesting things about the parties and our government, helped tens of thousands of people learn how they might want to vote, and got to meet the Mayor. It was time well spent.

Research questions don’t need to be of great societal import to be worthwhile. I once found myself thinking about cereal. Well, that happens a lot – cereal is delicious – but I was feeling frustrated that I didn’t know which cereals are actually okay for you, and which cereals are just empty sugars with healthy branding on the box. So I spent some time investigating, gathering some data, and sharing what I learned. Now I eat more Shreddies, and less Honey Nut Cheerios. That bee is a charlatan.

Intentional research is especially powerful for investigations that relate to your work. Earlier this year, I was growing frustrated with OmniFocus, the app I use to manage my tasks and todos. Instead of diving into making a competing app like I would have done in the bad old days, I posed a research question: “How do other leaders manage their time and attention?” Then I contacted a few folks I know, had interesting conversations, recorded what I learned, and asked those people if they had a suggestion for somebody else I should talk to. It was amazing.

Not only did my little research project level up how I manage my own time, improve my understanding of how to support others, help me avoid the misery of building an unprofitable todo app, and give me something to write about, it led to me connecting with 25 people that I respect. While it was nominally a research project about productivity, in retrospect the connections with interesting people were even more worthwhile than what I learned.

Most people under-estimate how worthwhile it is to contact a handful of people you respect with a question. Of course, books are the classic way to immerse yourself in a topic, but there are a lot of topics that are niche enough – or current enough – that the only way you’ll fully understand it is talking to people. “What is working at Stripe like?” “Where do the best engineering managers look for jobs nowadays?” “How does your team iterate LLM prompts and chains without breaking things?”

Not only will talking to others help you rapidly learn about a topic, you’ll often be asked questions in turn – questions you’ll be well prepared to answer because you’ve been researching this very topic! That is, you’ll be talking to smart people about interesting questions. Awesomeness achieved.

Of course, reaching out to people can also be a bit uncomfortable. At first, I suffered great hesitation when I considered emailing somebody about my little question. Even after I got a response or a chance to talk, I often wouldn’t systematically take notes, or remember to ask my interviewee if they knew anybody else who might want to chat about the topic. Even then, if I did take good notes, I often wouldn’t turn those notes into a coherent doc or article I could share with anybody else.

But as with anything, practice makes progress. You build the muscles you use.

So consider it. Your question can be personal – “What kind of company would I enjoy working at?” It can be as serious as Patrick Collison’s collection of “interesting questions” of humanity-level import, or as trivial as The Pudding’s investigation into how K-Pop group composition has changed over time”.

The point is to build a habit of sometimes diving deep. Picking a research question that might motivate you to read, meet new people, write about what you learned, or even just go overboard about something for the pure joy about it.

It’s a habit I cherish. And I suspect – if you give it a try – you will too.