DIY Software, Where Betting Markets May Take Us, & What Makes Art

Exploring where betting markets may take journalism, how DIY software will change consumer and enterprise apps, and what else should be programmed by the masses.

Edition #25 of Implications.

• This edition explores forecasts and implications around: (1) Where betting markets may take news, (2) DIY software revolutionizing consumer and enterprise applications, (3) true art standing out, (4) the decentralization and community programming of money, media, and more, and (5) some surprises at the end, as always.

• We have Part III of Insights for Modern Product Leaders.

• If you’re new, here’s the rundown on what to expect. This ~monthly analysis is written for founders + investors I work with, colleagues, and a select group of subscribers. I aim for density and provocation vs. frequency and trendiness. We explore the implications of what’s happening to ignite discussion, socialize edges that may someday become the center, and help all of us connect dots.

• If you missed the big annual analysis or more recent editions of Implications, check out recent analysis and archives here. A few recommendations based on reader engagement:

  • When is augmented reality going mainstream? A review of progress.

  • Transcending prompt-based creativity with the “controls era” ahead.

  • The more edgy implications of the transformation of entertainment.

  • Making sense of rapid displacement and “the best model” rat race.

Assertions & Implications

Lets jump into five assertions with implications worth noting — all around the theme of unexpected developments of new tech and things top of mind for me.

• DIY Software will revolutionize apps for consumers AND the enterprise.

• The next wave of investigative journalists will break news via betting markets. Is truth best surfaced with incentives?

• True art lies outside the distribution curve of what’s been done.

• Insights for The Modern Product Leader Playbook, Part 3

• Bonus section: thoughts on Bitcoin as an elaborate and decentralized form of Universal Basic Income (UBI), and more.

DIY Software will revolutionize apps for consumers AND the enterprise.

Replit (am an investor) has been blowing my mind lately. With a simple prompt, I was able to make an app (simple but functional!) for my kids to track their book list and daily reading goals. Describing my idea with a sentence kicked off a form of “AI product management,” and then, with a click of a button, Replit wrote the code, developed the logic, created a database, and deployed server space. Within two minutes, I had a v1 to start playing with. Wow. There has been much discussion of AI code reviews, GitHub co-pilot, and no-code application builders for the enterprise, but what are the implications of agent-assisted software development for consumers? What if there are billions of people making apps for themselves? What “picks and shovels” tools, APIs, security services, and other hosting and deployment tools will prove essential in such a world? I have the same questions for the enterprise: Will the cost calculation of building your own internal tools start to merit homegrown solutions to workflows and enterprise functions as opposed to the usual “find a SaaS product to solve every need?” Will new “orchestration layer” homegrown technology stitching together various AI models into intricate workflows replace whole swaths of Enterprise SaaS products?

The next wave of investigative journalists will break news via betting markets. Is truth best surfaced with incentives?

Many of us spent the last year focused on betting markets like Polymarket and Kalshi, with a keen eye on the gaps between these anonymous financially-driven projections and professional political polling. Of course, the betting markets proved far more accurate, which begs the question of why and whether the implications will spread far beyond politics. One illustrative story is of an independent researcher in Europe who leveraged “neighbor polling” (small polls where people are asked who their neighbor is voting for, as opposed to their own voting intentions) to discover a gap between those results and the official polling of registered voters. This researcher gained enough conviction to reportedly bet $30M on Trump winning the election. In essence, this was an independent researcher who leveraged proprietary insights to influence the news “Trump is going to win” through market dynamics as opposed to news outlets. With the rise of decentralized betting markets, will the people who break the news (or perhaps even make the news?) start profiting from the news? Will we see a new cohort of investigative journalists and researchers become wealthy news sleuths by sourcing proprietary insights and breaking the news via betting markets as opposed to mainstream news outlets that are funded via advertising? Does any of this constitute “insider info” if those placing bets to break news are, in some way, a part of the story they are breaking? Needless to say, we are in the early days of betting markets. However, as we consider ways to restore “truth” and trust in the news, I wonder if betting markets can be part of the solution? Perhaps there will be a news site with a series of claims (headlines and articles) where people can “bet” on the validity of these claims. And then an independent bipartisan group of people can surface and source facts that, collectively, serve as the arbiter of who wins and loses the bets? While there seems to be no disincentive for spreading false information these days on social media, people act differently when money is on the line. The general population may benefit from bottom-line-driven fact checking and the use of betting markets to suss out the truth of what is really happening.

True art lies outside the distribution curve of what’s been done.

What is art? As a builder of products for creative people, someone who spends a lot of time working with leaders in the AI space, and as a board member at the Museum of Modern Art, I do think a lot about art in the age of AI. There is a general misunderstanding that “AI can make art” when, in reality, AI is a tool - much like a camera or brush - that someone can use to make art. And, like most photographs or brush strokes, AI is most often creating something “average” and often common. The potential of a tool to make art is up to its user. Much like any kid with crayons can draw a tree or anything else they’ve been exposed to, GenerativeAI can conjure up a tree if you ask for one, but it will come from the commonality of attributes across the trees in the model’s training data. Under the hood, large language models (LLMs) and generative media models are trained on massive amounts of data to help predict a sequence of words or pixels based on the prompt you provide. So, if you prompt for a tree, you’ll essentially get the average representation of a tree. You won’t get the most exotic tree that resembles one-of-a-kind trees that lie outside the distribution of trees…unless you sufficiently describe such a rare variation of tree. Which takes ingenuity. The point I am trying to make is this: If AI is trained on the masses of data, then the responses and capabilities of these models “emerge from the center of the distribution of inputs,” as one friend framed it. These models are designed to give you “what you would expect.” This is the opposite of art, which engages you by being what you didn’t expect. Which brings us back to the question, what is art? Art exists outside the distribution of what’s been done. Once you copy art, it is less art. But when you’re inspired by art and inject your own humanity and unique perspective, you’re liable to make art. I am reminded of producer Rick Rubin’s view on artists: “Artists allow us to see what we are unable to see, but somehow already know…the artist’s perception reminds us of who we are and who we can be. The personal is what makes art matter. Our point of view, not our drawing skills or musical virtuosity or ability to tell a story.” With this context, as the tools change and skills become more accessible to more people, the unique point of view we each bring to our work — much more than the tools we use — is what makes art. Art comes from the edges, outside the distribution curve of what’s been done (aka the data).

Insights for The Modern Product Leader Playbook, Part 3

Continuing this monthly collection of insights I share with or learn from modern product leaders I admire. I’m pulling out 3-5 of these from my collection for each edition of Implications, given many of you are founders or leaders of various functions or teams across the creative and technology industry. Pretty sure I’m done writing books, so figured this was an optimal place and audience to share these insights…

• Collapse the talent stack. A lot of the magic I’ve observed in teams over the years happens when the talent stack is collapsed - when a designer also codes, when an engineer has a growth hack skill set, when a product leader is great at copy. Vertically integrating interdisciplinary collaboration in a single human is f’n awesome, and you should transcend traditional org structures of teams and make exceptions for who you hire when you encounter an opportunity to collapse the talent stack.

• For early-stage products, density of usage matters more than number of users. For consumer products - or any product with a network effect - one lesson and observation is just how critical small pockets of engaged users are for organic growth. Small pockets of highly engaged users help a small number of people discover the full value and utility of a product far faster than depending on mass adoption. Facebook went college campus by college campus with lots of intentionality, so as to ensure small pockets of high engagement. Pinterest also did this with small networks of middle-American moms. And I remember Periscope thriving with > 50% DAU in a small group of people within Silicon Valley. Most recently, a new AI-driven news app called Particle launched, after an extensive public beta designed to demonstrate high density of real usage. In fact, I’d argue that any product that relies on achieving scaled use in order for users to feel successful is doomed to fail. Startups, given their reliance on small pockets of deeply engaged users, have an inherent advantage here. Big companies tend to get lost in large numbers and fail to optimize for dense nodes of users. The best path is high density of usage in small pockets, and any company in growth mode should (ironically) focus on density of usage pocket-to-pocket over total number of users growing person-to-person. A pocket of dense usage demonstrates both engagement and the network effects required to grow.

  

• When you love what you do, you get a lot more done. Initiative outperforms experience. Your job as a leader is to align people with tons of initiative most closely with their area of genuine interest. When it comes to hiring - especially for early stage ventures and bold turnarounds - initiative outperforms experience. This means you don’t want to be a resume snob, you want to hire people who have demonstrated, over the course of their lives, the willingness to take initiative in anything that matters most to them. If they loved sailing in college, they started a sailing club. If they are an avid reader, they co-chair a book club. These are all signs. Also, I’ve always firmly believed that a labor of love always pays off, just not necessarily the way you’d expect. Hire accordingly.

Assortment Of Findings & Shout-Outs

• Dystopia or Utopia? No surprise, I like when technologists take the pen and think out loud about some of the tensions around new technology. On this topic, Vinod Khosla recently sent me his essay aptly titled “AI: Dystopia or Utopia?” He managed to transcend the general myopic soundbites by breaking down  the major areas of concern and opportunity. Most naysayers believe the negative implications are inevitable. But he points out that they are a choice. I would also add that humans tend to evolve to account for and protect against the negative implications of new technology rather than fall victim to them. We created traffic laws and issued drivers licenses to protect against the danger of cars, etc… The accommodation of change is part of the human condition. Vinod also addresses some of the concerns I also share around rising income inequality and AI leaving groups of humans behind. The Norway "transition funds" analogy is one I often cite and I wonder how we can get politicians to grasp this. Capitalism certainly needs to evolve. As a father, I think about this often as a preventative measure against civil unrest from inequality (probably one of the actual risks we should be most focused on!). Check out his essay.

• Making it easier to bring your product to the enterprise. Just a bit of a shout-out to WorkOS, a company I have watched evolve a bit over the years that helps startups start selling to enterprises with just a few lines of code. So many products I have supported over the years have struggled to scale by engaging enterprise customers, and WorkOS is finally change the game with a complete user management solution along with SSO, Directory Sync (SCIM), and Fine-Grained Authorization (something many enterprise customers require!). Much like products like Stripe, etc, it is self-serve and easy to integrate in minutes with modular and easy-to-use APIs. They now power hundreds of high-growth companies including Perplexity, Vercel, and Cursor.

• What other centralized parts of life will become decentralized and programmed by the people? With the rise of Bitcoin, there is no shortage of hot takes. But one consistent voice in this arena is Balaji, who noted recently “All media became social media. All money becomes cryptocurrency. I was struck by the theme of things once controlled by small groups of people in back rooms becoming programmed by the masses, whether it be those programming a news channel being replaced by social media and community curation, or the small group debating federal reserve policy becoming replaced by a decentralized and programmable form of money. This prompted the question of what other parts of life are poorly centralized today? Perhaps healthcare, where we struggle to access our own data and await very lengthy FDA processes to get ahold of new drugs and treatments? Perhaps education, where we conform to a curriculum as opposed to the curriculum becoming tailor made (perhaps using AI tutors) for each student? We also can’t always assume that “programming by the people” or the wisdom of crowds is always better. One might also argue that mainstream media becoming social media programmed by the people could exacerbate the less wonderful natural human tendencies of people, like being captivated by conspiracy theories and being manipulated by algorithms designed for engagement more than truth seeking.  No doubt, this theme of “collective programming” of things that were once centralized will be a dominant theme of the decades ahead.

Ideas, Missives & Mentions

**Finally, here’s a set of ideas and worthwhile mentions (and stuff I want to keep out of web-scraper reach) intended for those I work with (free for founders in my portfolio, Adobe folks…ping me!) and a smaller group of subscribers. We’ll cover a few things that caught my eye and have stayed on my mind (including an unorthodox take on Bitcoin and universal basic income, data provocations related to toys, and more). Subscriptions go toward organizations I support including the Museum of Modern Art. Thanks again for following along, and to those who have reached out with ideas and feedback.