We're back, and you didn't even notice the difference.

The old site was built from scratch on Rails during my heyday as a web developer. Then life got *really* busy and I stopped having the time to maintain the site or my skills. So I recently had it ported over to Wordpress, which is a much more stable (and secure) platform for keeping things up to date.

I hope this will help unleash the flow of writing that I've felt coming for a long time.

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This is based on a tweetstorm

Recently, @thisissethsblog posted about #NFTs, saying they are a "Dangerous Trap" His 4 arguments are reasonable-sounding surface concerns but they're all off the mark.

A point-by-point look at why we have to think more carefully about NFTs 👇

Argument #1: Art is silly too but it's ok for Real Art because we've done it that way for a long time.

This just doesn't make sense and seems backwardly purist.

Status conveyed by owning original editions of physical art doesn't just come from showing 3 ppl a week your wall, it's from making sure people know you own an original edition (art as flex, art as humble brag).

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This post is based on a Tweetstorm

It's pretty clear that NFTs have broken into the mainstream and, predictably, are causing all sorts of consternation because they're still in the "we're trying shit" phase. But, whether an artist, developer, or other creator, don't write them off yet. Here's why.

First of all, the initial phase of any market is a combination of "toys that look like crap" h/t @cdixon) and "us attempting to copy our mental models from the old world into this one... very awkwardly." (remember early mobile apps?) Welcome to NFTs 2021.

The first cryptokitties in 2017 first broke digital scarcity into the semi-mainstream and they were nearly impossible to acquire for average people because you needed to buy tokens just to try. "Kitties" are just collectibles and collectables make no sense to most people, particularly in the digital world where we're used to infinite replication.

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Everyone can agree that blockchain has a UX problem. The top apps right now have literally dozens of users. And no small effort has been undertaken to try and fix all the leaks in the onboarding funnel. But what if this effort is just mopping the decks of the Titanic on most of these systems… totally meaningless in the context of a looming disaster?

How do we begin to untangle which systems will stick around for the long haul and which are doomed to be disrupted from the start?

UX Equilibrium

In crypto economic and consensus discussions, we spend a lot of time cozying up to the work of mathematician John Nash, whose famous Nash Equilibriumis generally considered to be the ideal state for a game theoretic system to achieve.

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It started as a weekend project. A VC friend had put me in touch with one of his portfolio founders with a “you two should connect, I think you’ll really get along.” Suddenly I’m back in middle school and my mom is setting me up on play dates.

Wanting to have a better understanding of what they were working on, I took a look through their website and rolled my eyes when the word “Blockchain” came up. Not another one.

I was several months past the acquisition of my previous company and, during the scale-out phase, had gotten deep into artificial intelligence research. At the time, I was closing in on some interesting opportunities in the space and had a lot of momentum going. The tech is interesting, the problems are big and the space is highly active. I didn’t see any reason why I wouldn’t continue down that path.

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Despite being years deep into the new Age of AI, there is still surprisingly little clarity around how to systematically approach and model the space in order to find opportunities for investing and building new businesses.

My intention here is to create a simple top-down process for identifying opportunities by analyzing the core components that are critical in any viable AI-driven product or business.

As an entrepreneur, these are the questions I ask myself when deciding which areas look most fertile to pursue. As a VC, you might use this to find green fields for investment or as a tool to evaluate businesses that cross your radar. As a researcher, this may provide new areas to explore and as an industry expert this should help you think about triangulating in on a good opportunity within your domain space.

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The conversation about Artificial Intelligence has become so muddy lately that it is important to be very clear about what we mean when we say an organization is "using AI" or is "AI driven". This post will clear up those definitions and some of the implications of each classification.

There are three levels at which an organization can adopt artificial intelligence:

1. AI-Assisted: AI is a technical bolt-on to existing processes, usually through the use of AI-created tools, for example AI assisted sales or team management tools.
2. AI-Enabled / AI-Augmented: AI is applied to existing processes or product data to make the product or service better or more useful, for example the recommendation engines behind Netflix, Spotify, Amazon, etc. This is often in the form of one or more AI-driven features or products.
3. AI-Driven: AI is literally the lifesblood of the company or initiative, for example self-driving technologies and the companies providing tools to AI-Assisted companies. This is truly an AI-Driven company.

Each of these cases has different implications from the perspective of the implementing organization.

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Much of the hype around Artificial Intelligence centers on some vague sense that it continuously learns from the world around it so it gets ever-better at performing tasks. In reality, it's important to understand that the core truth underlying this is much simpler and more powerful: AI technologies allow us to make better predictions than we could before.

Despite the simplicity of that fact, it is an enormously powerful building block which enables automation on a scale we've never seen before by disrupting task loops across the information and physical worlds. Understanding it will allow you to better appreciate the kinds of change it can and will drive.

The semiconductor represents a useful analogy for this reduction. Semiconductor technology obviously changed the world but these empires were built on top of a single simple truth: it reduced the cost of arithmetic. That most fundamental use case forms the basis of computing and, at sufficient scale, allows us to do everything we can today.

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The Virtuous Cycle of AI Products, also called the "AI Flywheel Effect," is one of the most exciting ideas in Artificial Intelligence and it's also incredibly simple. Essentially, when AI technologies are integrated with a product properly, they create a feedback loop where the product continuously improves with use, generating more usage and a better competitive position relative to other products.

It looks like this:

1. Product gets used, generating data
2. Data from usage is fed into machine learning (or similar) models
3. Models improve the product, generating more usage

Context

Any product tends to improve with usage regardless of its underlying technology because a good team will use qualitative feedback and analytics data to bring it closer in line with user needs. This improvement, though, tends to reach an asymptote where additional usage and data no longer provide much marginal insight to the product.

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