Keynote
Introducing Self-Determination Theory and Human Flourishing
The speaker opens by explaining his shift from AI to psychology, introducing self-determination theory (SDT) based on decades of research. He reads foundational passages describing humanity's capacity for curiosity and agency, contrasted with the potential for apathy and alienation, setting up the tension between optimal and non-optimal human functioning.
Eudaimonia vs Hedonia and the Power of Motivation
The speaker distinguishes hedonic pleasure from eudaimonic flourishing—the full actualization of one's capacities—as the deeper measure of living well. He explains that authentic motivation, as shown in research, drives not just productivity but vitality, self-esteem, and overall well-being, going beyond typical business-book framings like Dan Pink's Drive.
The Four Pillars: Autonomy, Mastery, Relatedness, and Purpose
The speaker outlines SDT's core motivational axes—autonomy, mastery, relatedness, and purpose—focusing his talk on autonomy and mastery. He connects this to clinical research on depression, noting that behavioral activation, which builds a sense of accomplishment, mirrors the same principles that drive flourishing in non-clinical populations.
Flow, Dark Flow, and the Risks of AI-Induced Addiction
Drawing on Csikszentmihalyi's concept of flow, the speaker defines it as a state where skill matches challenge in a goal-directed system with clear feedback, using motorcycle racing as a personal example. He warns of 'dark flow'—an addictive, superficial version of flow exploited by casinos and, increasingly, by AI coding tools, referencing Rachel Thomas's article on 'breaking the spell of vibe coding.'
Real-World Warnings from Developers About AI Dependency
The speaker shares cautionary anecdotes from developers like Armin (creator of Flask) and community members who describe how AI coding agents create a dopamine-driven illusion of productivity that later collapses under scrutiny. He highlights examples of vibe-coded projects that stalled or failed to deliver real results despite feeling productive, tying this to broader industry skepticism like Uber's new token budget restrictions.
AI's Dual Potential: Decaying or Supporting Autonomy and Mastery
The speaker argues AI can either erode or enhance autonomy and mastery depending on how it's used—citing 'illusion of control' scenarios where users blindly accept AI suggestions versus using AI to genuinely learn and build skill. He cautions that companies selling AI tools and employers focused on output metrics have little incentive to protect users' psychological well-being, urging individuals to take responsibility for how they engage with AI.
Historical Visionaries of Human-Computer Augmentation
The speaker traces a lineage of computing pioneers—Ivan Sutherland's 1963 Sketchpad, Douglas Engelbart's 1968 Mother of All Demos, Kenneth Iverson's APL notation, Bret Victor's interactive learning tools, and Chris Lattner's programming language work—who all shared the mission of deeply connecting humans with computers to augment intelligence and creativity rather than replace it. This historical context frames AI as a potential continuation of this augmentation tradition rather than a departure from it.
Answer.ai's Mission to Augment, Not Replace, Human Creativity
The speaker describes his personal and organizational mission at answer.ai to build AI tools that augment human creativity rather than perform tasks entirely for users. He contrasts this philosophy with typical AI marketing, which emphasizes doing work 'for you' rather than helping you understand and create.
Live Demo: Learning Recursive Language Models with Solveit
The speaker demonstrates Solveit, a dialogue-based tool designed for augmented learning, by walking through his own recent exploration of a recursive language models (RLM) paper. He shows how he used Solveit to question figures, generate concrete examples, spawn sub-agents, write and debug code, and ultimately reimplement RLM functionality himself, achieving deep understanding rather than passive output generation.
Live Demo: Rebuilding a Styling Framework from Julia Evans' Blog Post
Continuing the Solveit demonstration, the speaker shows how he used the tool to actively engage with Julia Evans' blog post on moving away from Tailwind CSS, building his own components, color palette, and typography system step-by-step with AI assistance. He emphasizes that the AI didn't write the code for him but helped him experiment and verify his own design choices in real time.
Closing Reflections: Using AI to Write the Talk and Flourish
The speaker reveals that this very talk was created using Solveit, with AI helping him organize research and track narrative progress rather than writing content for him. He closes by announcing early access to Solveit for conference attendees and reiterating his hope that others will use AI to support genuine learning and flourishing rather than passive output generation.
Cheers. Thanks for that. It's nice to be back in my hometown. It's good to see you all. I have a bit of a unusual talk I wanted to give today. The first half of it's about psychology rather than AI and hence the title which is about growing on purpose and the work that makes you.
It's such a critical moment in our history right now and the the work that we're all doing is changing. And I wanna share with you some key findings from the last fifty years of research about how your work makes you. And so then you can make informed choices about the work that you choose to do.
And in particular, I wanna draw on this this paper. It's not just a paper. It's this was a review paper at the end of thirty years of research representing hundreds and hundreds of experiments that that led to this huge overarching thing called self determination theory or STT. But I I just wanna read you the first two paragraphs of this and and I want you to have a think about it.
The fullest representations of humanity show people to be curious, vital, and self motivated. At their best, they are agentic and inspired, striving to learn, extend themselves, master new skills and apply their talents responsibly. That most people show considerable effort, agency and commitment in their lives appears in fact to be more normative than exceptional.
In other words, this appears to be how humans are born to be, suggesting some very positive and persistent features of human nature. The very next paragraph continues, yet it is also clear the human spirit can be diminished or crushed and that individuals sometimes reject growth and responsibility. Examples of both children and adults who are apathetic, alienated and irresponsible are abundant.
Such non optimal human functioning can be observed not only in our clinics but also among the millions who for hours a day sit passively before their televisions, stare blankly from the back of their classrooms or wait listlessly for the weekend as they go about their jobs. So here we have an interesting bifurcation of the observations about the nature of of human flourishing and the fullest representations of humanity that we observe.
There's been thousands of years of history and more recently, many decades research looking at this difference between eudaimonia and hedonia. So hedonia is where we get the word hedonics or hedonism. There's nothing wrong with it per se. It's that frictionless, pleasant ease, pacifisty, and kind of easy pleasures.
Eudaimonia on the other hand is what it turns out that these fullest representations of humanity are about, fully actualizing your capacities. That turns out to be what it means to to live well. And as I say, there's there's hundreds of experiments, there's randomized controlled trials, there's bucket loads of research behind this.
This is not just a a crazy idea somebody randomly came up with. So one of the sub pieces of of SDT, self determination theory, a key sub piece is around motivation. Now, why is motivation important? Interestingly, when I've read about motivation in books like, Dan Pink's Drive, which is a great book and some of these ideas come from his, the research in that book, Tends to talk about like how do you get people to do stuff for you, you know, how do you get people how do you get workers to be productive?
But it turns out actually motivation is much, much, much more important than productivity. It turns out that the research shows people whose motivation is is authentic have more interest, excitement and confidence and yes, that does manifest as enhanced performance and persistence and creativity but it also has enhanced vitality, self esteem and general well-being.
So motivation is key to this kind of flourishing, this eudaimonia. SDT, has three particular axes and then I've added on one more which is very commonly seen to create these four, which is it comes from autonomy, mastery, relatedness and purpose.
Relatedness is all about connecting with other human beings, and feeling supported and part of a group and purpose is all about what you're doing something for. Is there something worth doing? I'm not gonna talk much about those two today. They're very important but they're rather orthogonal to the points I wanna make. So I'm gonna focus on autonomy and mastery.
Interestingly, there's another few decades of research from a completely different part of the research community that have looked at the opposite question, which is, rather than what helps achieve human flourishing, it's for those who are very much not, the very much not, which is those with, clinical depression, how do we pull them out of it?
And interestingly, the research shows something very similar which is perhaps the most effective action, even versus, antidepressants, cognitive behavioral therapy, and so forth, is this thing called behavioral activation, which is basically the same thing, helping patients to engage with actions which bring a sense of accomplishment.
So from both angles, you know, going from kind of, yeah, I'm fine, I guess, to I'm thriving, or going from Jesus, life's getting me down to getting by, the same actions from very different parts of the research community show to be very effective. You've probably heard about flow and flow fits in here a lot.
This is particularly the work of Cheksham Mahaly. And I wanted to be careful to define flow here because flow is so key to this this sense, that leads to flourishing. So flow should be a sense that one's skills are adequate to cope with the challenges at hand in a goal directed rule bound system that provides clear clues as to how well one is performing.
So one of the greatest experiences in my life was getting really good at riding a motorcycle fast around the Philip Island Circuit and that is exactly that. Very goal directed rule bound action system, very clear clues as to how I was performing, and and that sense I'll never forget, you know, of extraordinary flow.
Interestingly, however, also talks about, junk flow or dark flow, which is something that can look a lot like flow that is very bad, which is you can get addicted to a superficial experience that maybe flow at the beginning, but after a while becomes something you become addicted to instead of something that makes you grow.
And there's a lot of research and studies around this. And in fact, the way gambling establishments, the way casinos are set up is specifically designed to capture this kind of dark flow to give you what's called an illusion of control and to to to create this kind of addiction. So Rachel Thomas, I really encourage you to read this article if you have a chance, talked about breaking the spell of vibe coding in which she noted how certain kinds of interactions coding interactions with an AI can absolutely harness this kind of dark flow.
So you get this kind of positive flow when you have a high level of challenge and a high level of skill. So, it's it's it's interesting and a bit scary to note how it's quite possible to end up, with this kind of, pulling the slot machine lever version of flow if not careful.
So interestingly, a lot of people are now saying, oh, that's happened to me or that's happened to my friends, people who have previously been extremely positive about about agents and using AI encoding and so forth. I think, Armin was one of the particularly interesting ones.
He you might know him as the guy who created Flask, been a very important software developer, and, of course, also George Hots who, created the commerce self driving AI system and the original iPhone hacker and so forth. I've got some quotes from from Armen here, though, that I thought was interesting. He said, when when you know, for months, he he was in this situation where the dopamine hit from working with these agents is so very real, saying you feel productive, you feel like everything's amazing, and you go deeper and deeper in this belief that it all makes perfect sense, but it's decoupled from any external validation.
And so it's kind of starting to see this this these concerns. I saw this, like, two days ago from a guy who's been working on this kind of new GPU functional programming system who was saying, like, how cool it was that he went from naught to 95% in his most recent project in five hours and then realized like, oh, fifteen hours later, I'm still not there.
And I keep finding problems and I actually don't know if there's still problems and so actually at this point, don't even know where I stand. But getting the first 95% done in five hours sure felt good, but did I actually achieve anything by using AI other than that dopamine anticipation? So I think it's very encouraging that, thoughtful people in our community are reflecting and sharing their reflections.
In fact, one of our own community members, put this on up on our Discord the other day and was asking for feedback from from our community. He was saying talking about the product he works on. It's genuinely interesting. The problem requires deep domain expertise, but it's hard for us to verify because we've got 200,000 lines of of kind of Vibe coded software at this point.
And he he's actually realized the pace we've moved at has slowed down as models get better and token speed spend increases because we generate more and more code with less and less careful engineering. Debugging failures, he told us, you know, is super painful. And interestingly, again, I guess, this kind of dark flow idea, most of his colleagues feel they're making great progress.
But then when they have quarterly meetings with management, they get a reality check when they have to show what have you shipped, what's the accuracy, how many clients have you signed, and they suddenly realized, the results actually weren't good. So, you know, I'm I'm not gonna go, too deep into negativity. We've all seen it.
It's in mainstream, newspaper articles nowadays, Wall Street Journal, you know. I think just today, you know, Uber is now saying they're putting a strict budget on token use because they're not seeing the ROI. And so rather than, dive into some kind of, like, AI negativity, I instead actually wanna point out something that is you can go in two totally different directions with AI, and I'm looking at two of those key motivation platforms of autonomy and mastery.
And it's certainly true that AI can decay those things. So I'm sure anybody who's kind of done a lot of agentic work and vibe coding has been in that situation where we have what psychologists call an illusion of control. The agents asking you like, hey, do you wanna go with a distributed system here or would you rather use green threads since with a polling loop or whatever whatever?
And you're like, I don't know what everything that means, a or b, a. So this is this is something that actually decays your economy. On the other hand, AI can be used to support your growth. It can be teaching you things. It can be trying things.
It isn't necessarily creating more outputs more quickly, but it's definitely something that can happen. So ditto with mastery. Right? Mastery is not in in STT. It's not about creating more outputs, creating more products. It's about creating this genuine ability to craft something.
It's effortful and involves learning from that effortful work. So with AI, you can tackle more complex tasks and you can focus on learning those under underlying foundational principles and master your craft or not. Right? You could focus on outsourcing more and more to AI more and more quickly with less and less effortful practice, getting less and less learning.
So AI is neither good nor bad for you, for your psyche. But warning, the people getting you to use AI don't care about your autonomy and mastery. They care about your outputs and so they're gonna put you in the decay world all the damn time.
The people who are selling you the AI models, platforms, harnesses, and your bosses at work who need to be able to show their quarterly token maxing metrics. So you need to look after yourself in this world. So I wanna show what it looks like to have amazing mastery over a computer and how that's changed over a period of time.
So this is Ivan Sutherland. I've done done this at two x back in 1963. 1963. And he's shown how he's able to create a direct interface between himself and a computer where he's drawing with a light pen. He's you don't see it with one other hand. He's pressing buttons to set constraints as he's drawing.
He's using it directly against one of these think this is one of these fancy vector monitors, and he's showing the the interviewer here how he can create an arc, for example, using these constraints by drawing directly on the screen, and he can adjust it. This is an extraordinary level of deep connection between the human and the computer. You might have seen this, the mother of all demos.
This was 1968 that this happened. Very similar idea. So in the mother of all demos, Douglas Engelbart introduced for the first time the mouse, hypertext, real time collaborative editing, video conferencing, word processing, screen windowing, and dynamic file linking.
This quote was in 1962 towards the start of this project and what the demo was in 1968. And his goal was the same, augmenting the human intellect so that the entity to be produced will exhibit more of what could be called intelligence than an unaided human could. We've amplified the intelligence of the human by organizing his intellectual capabilities into higher levels of synergistic structuring.
So you see, it's very similar between what Sutherland was doing, what Engelbart was doing. This this was this was their mission, was to amplify and augment human intelligence. One of the most underappreciated, most extraordinary people in the history of computer science is Kenneth Iverson.
I mean, not that underappreciated. He got the Turing Award, But he designed APL. APL is a new notation or was a new notation for representing computation and mathematical thinking. This is from his Turing Award presentation paper.
And if you don't know APL, it won't look very familiar, but what he's showing here is he's proving some characteristics of the inner product in his new notation. And one of the really interesting things about this, you ever get into APL, and I strongly recommend it, is it turns out that this generalizes in a much deeper way than normal mathematical nomenclature and that the inner product in APL can actually is a is an operator that can combine any two functions.
It's not necessarily multiplication and addition. And so suddenly, he's proved a whole class of features about a whole class of functions, many of which never been looked at by a mathematician before, just through notation. And this can go a really long way. Some of you might have seen this very famous single line of code in APL, which is a complete implementation of Conway's gain of life. So here in this video, that, life function is being applied over these two characters and off it goes.
Again, it's the same thing. Right? Iverson was passionate about creating this connection between between the human and and supporting the human's thinking, notation as a tool of thought. Perhaps most mind blowingly, Brett Victor, who spent a couple of years as he described being a hermit living on a train and he came out the end of those two years of hermit hood having built the most extraordinary and inspiring array of real world demos showing about how to understand climate, how to understand electricity, how to understand how to how to build games, how to build graphics, how to understand waveforms.
And he shared this all with the world. This is his coding environment whereas he changes it graphically. This is his amazing game playing demo where he actually created a time machine for his code. If you haven't seen this, please watch everything Brett Victor's done. It's incredibly inspiring and all of it, you'll see it's all of it's the same thing.
It's creating this connection between the the human and the computer that they're working with so that they can craft. This is this is all effortful craft that he is supporting. Chris Latner, this is his playground system. He's created a whole amazing hierarchy from from LLVM, PLAANG, Swift, Playground, MLIR, Mojo, you know, at every level trying to improve this ability for humans to to connect to and work with their computers.
My argument is that, actually, we're still on this chain. We we we can continue working in in along this history from the mother of demos in a really deep and powerful way. AI is a marvelous way to connect more deeply with our computers and achieve this fullest representation of humanity.
So this has actually been kind of my mission for the last thirty years and very dramatically for the last ten. And at answer.ai, it's all of my focus, this idea that we should be seeking to augment human creativity, not to replace it. It's interesting to see hopefully, this resonates with you, but also, hopefully, you see this is almost never what you actually see as being marketed when somebody's trying to sell you a piece of AI.
It's like it's it's gonna summarize this for you. It's gonna write this for you. It's gonna do this for you. It's all about being done for you. So I wanna quickly demo something we've built to give you a sense of what it looks like to have a tool that that is specifically designed for this augmenting human creativity and understanding indeed. And so I'm gonna do it.
I'm gonna just show you a couple examples of actual dialogues I've gone through with the help of AI in the last three days. So one was I wanted to learn about recursive language models. So probably a lot of you have know about recursive language models. They've been kind of taking over the world. So I we've got this system called Solveit, and so I should show you solveitsolve.it.com.
And I loaded the paper, recursive language models paper into Solveit, and you could just read it in the normal way, piece at a time, but make sure you understand it. And so here, I looked at figure one. I was like, well, I don't know what this is or this is or this is. And so normally, I might just skip over it.
But here I can just say, hey. What's this figure? And it's tells me, like, okay. These are the three evals that the RLM authors are doing here. And and interestingly, it's like, It's going from a constant to a linear to a quadratic complexity, which is not actually captured in the original papers. That's really helpful information.
Now I don't work very well at an abstract level. I need things to be concrete, so I could ask for an example of each task. This is much easier than going into the papers and trying to dig them out and so forth. And so here, I'm getting little examples. It's like, okay. I get it. Right? So I always tell people, don't move on when you're learning a new thing or working on something until you you get it.
So I was like, okay. I get it. So then there's another figure where they describe it. I didn't fully understand this figure, to be honest, at first, and so I just it it tells me what the the key pieces are. It's very handy. So I I when I'm reading this, I'm thinking like, okay.
I want to push beyond this. I wanna understand it but try and go a bit past it. So thinking I'm like, okay, I wonder if we can replicate, all of the features of an RLM right now. So I kind of had a hypothesis about how to do that and I kind of checked with the AI like, I think we can try this ourselves and it clarified some key points about, where sub agents fit. And, as it turns out, Solveit has a sub agent. So I said, yeah, you've got a sub agent.
Why don't you try it? So in this case, it spawns an agent, and tells it to use Python to solve the complex square root. And we can also write code here. Right? So I can then try it myself and I can compare. I'm like, oh, cool. Okay. So I kind of confirmed. I'm in an environment where I can actually do the same things that the RLM paper did.
So another thing I mentioned as I read papers is often it's very easy to skip over citations. Right? But here, it says, well, here's recent recent work. Alright. Well, okay. I don't know any of these, so I shouldn't keep moving. So I said, like, stop. Can you please go and read all those papers for me and tell me basically what they are and why they're here?
I can decide whether to click on those links, read them all myself, and I would have kind of test my understanding at this point. So anyway, to skip ahead a little bit, I'm thinking like, okay, let's let's try it.
So they've got their table of results here for four different tasks. So I'm kinda thinking like, I'm not sure that this thing called an RLM even exists. It just feels like a normal tool loop that happens to have particular tools in it, and I seem to have the same tools right now. So I think we can be an RLM, can't we?
I said, let's try it. Let's try this code QA thing. I've never heard of this before. It tells me where to find the data. And so I start so it writes some code for me, which actually didn't work, but then we can it can help me debug it. And then I can test it and I then I could experiment and look at this data carefully, make sure I understand what this eval is and how it works.
And then I just tell it like, okay, solve it. Go ahead, you know, go ahead and just solve this right now. And so this is one of the things in one of the evals and it goes ahead and tries to do it and it says I think the answer is B and they check, oh, it is b. No. No.
Tell me how you did that. And then okay. Let's try another eval. And answer is C. Yep. Answer is C. And so this is very interesting. So I did this for a few different, tasks including the hardest quadratic tasks. So I downloaded another of these datasets, went through them, and, yeah, discovered that solve it correctly solved every one of the tasks. And so I've now not only do I understand RLM, I've reimplemented it. This is all in the space of a couple of hours. And in fact, discovered that this is a much more powerful platform than even RLM is.
Another example was I was looking at Julia Evans, who's a fantastic writer, always really interesting and she I'm very, I'm not a fan of Tailwind, and I was keen to see how she had done this she had this article called Moving Away From Tailwind, which had a particular structure. This was the structure she had, and I decided, okay. I wanna go through her blog post.
So I loaded the blog post, as you can see, into Solvit and I started reading it. And again, so the red is me asking questions as I go through the article. And so she said she used something called Tailwind preflight as her starting point for her styles. And I like, well, are there other options? So I went and grabbed it.
And actually, one of the cool things about Solveit is it's because it's in a browser, I've got actual styles HTML here. So I'm actually modifying my environment as I go. And so I can see see this happening, and then I can try it out. So I'm trying out layers. I've never really I'd never done anything with layers before, so I make sure I understand how they work by again, I'm actually using them. So before I actually used RLM, you know, rebuilt RLM inside a dialogue, here I am rebuilding Julia's styles inside of dialogue.
She had a section about components. And again, same thing. I started creating the components myself, as you can see, with the help of AI to make sure I understand. So here I've got a badge component, for example. And, you know, this is all real. Right? I'm creating actual code. I'm seeing it actually, running, button components.
Then very interested in colors. I kind of had some ideas about how to create a a new color framework. So I kind of bit of a discussion as you see with the AI. But again, the AI, didn't write it for me. Right? I kind of came up with this idea of what I think I wanted to do.
And then I tried it. And you can see I've created my own new color palette that I'm very happy with. And I thought, oh, cool. I'll try and map those to kind of semantics now. It's like like, okay. Which should danger be? So, like, it helps me show me what it looks like, different colors on different backgrounds, inverted ones.
It helps me create these full swatches so can see how my color palette looks. Did a similar thing with font sizes. I had an idea of how I wanted to create nice topography. So again, kind of write the code, talk through it, and have a look to see. And I'm like, oh, they all look pretty good.
Happy with that. I kind of prefer these tighter line heights than normal. So I'm really experimenting with different graphical styles to what's fashionable nowadays. So, you know, you get the idea. Right? So you won't be surprised to hear this whole talk was written in Solvit as well.
But when I say this whole talk was written in Solvit, Solvit created none of the narrative, none of the slides. Right? I asked it for examples of research. I then read the papers. And I was getting pretty tired last night, so I was I was then kind of pasting in my slides and saying, like, here's where I'm up to.
And it helps me keep track of, like, okay, this is where you're at on your narrative. As I read through the STT paper, same thing. Right? I put it in here and I was checking my understanding of it by asking as I went. So I'm gonna wrap it up there, but I I just whether you use this particular tool or some other tool, it's not so important, but I just wanted to give you some examples of of, like, how I work with AI.
It doesn't do my work for me, and at the end of every day, I feel honestly energized, excited. I'm learning more things all the time, and it seems to be working. We're building stuff that no one ever built before. I've got a strictly speaking, solve solve it is not available at the moment. It's been beta tested for the last two years by 3,000 people. But for this conference, we've given you a special code that you can use before it's officially released.
And I've also popped there links to each of the four dialogues, and it's quite cool. If you're on Solveit, you can click on any one and it'll open and solve it, and that's how you should read them. So if you wanna read any of these dialogues about STT or about creating a graphics framework, a styling framework, or about, recursive recursive language models, don't just read it.
Right? Open it, ask questions, and engage, and then write some code. And so, yeah, my hope is that by thinking of AI this way, by focusing on creating thinking about yourself as hopefully being the fullest representation of humanity, This is a period of time where you will feel like, you and the people around you are truly flourishing, and that's what I hope for all of you. Thank you.
People
- Armin Ronacher
- Brett Victor
- Chris Lattner
- Dan Pink
- Douglas Engelbart
- George Hotz
- Ivan Sutherland
- Julia Evans
- Kenneth Iverson
- Mihaly Csikszentmihalyi
- Rachel Thomas
Technologies & Tools
- APL
- Flask
- LLVM
- MLIR
- Mojo
- Python
- Solveit
- Swift
- Tailwind CSS
- Tailwind Preflight
Concepts & Methods
- Behavioral Activation
- Conway's Game of Life
- Dark Flow
- Eudaimonia
- Flow
- Hedonia
- Illusion of Control
- Self Determination Theory
Organisations & Products
- Answer.ai
- comma.ai
- Discord
- Turing Award
- Uber
- Wall Street Journal
Works
- Breaking the Spell of Vibe Coding
- Drive
- Moving Away From Tailwind
- Recursive Language Models
- The Mother of All Demos
AI is transforming the work we do — so what does the work we choose do to
us? Jeremy Howard steps back from the technology to draw on fifty years of
psychology research, from self-determination theory to what makes work
genuinely sustaining, and asks how AI engineers can grow on purpose:
making informed choices about the work that, in the end, makes them.
