The New AI Epoch

What more can be said about the AI boom that began its ascent less than a year ago? A lot! The potential of AI is immense and its influence on our lives is sure to be significant. And so I’ll continue. . .

In this essay I’ll focus more on Large Language Models (LLMs), but my thoughts apply to all other AI efforts as well.

An easy way to think of it is that LLMs will soon become the “autocomplete for everything”:

What’s common to all of these visions is something we call the “sandwich” workflow. This is a three-step process. First, a human has a creative impulse, and gives the AI a prompt. The AI then generates a menu of options. The human then chooses an option, edits it, and adds any touches they like.

. . . So that’s our prediction for the near-term future of generative AI – not something that replaces humans, but something that gives them superpowers. A proverbial bicycle for the mind. Adjusting to those new superpowers will be a long, difficult trial-and-error process for both workers and companies, but as with the advent of machine tools and robots and word processors, we suspect that the final outcome will be better for most human workers than what currently exists.

There seems to be an unlimited number of areas that language prompting + completion will enable. Some are obvious: a new iteration of Google, help with writing, content generation, help with marketing copy, etc. You see many startups and tools that have already sprung up to tackle these.

Some of the real interesting applications that are incubating now will have action models as a big component. Models will have the ability to take actions like: searching the web; ordering an item; making a reservation; using a calculator; or using any other digital tool that humans are capable of using. Imagine ChatGPT being able to confirm its answers with multiple sources, or having access to all your personal records it can use to assist you.

Prompt engineers are already discovering how much you can do with the existing models, without any new advancements or manipulation of the actual base model. Even if GPT-4 or an open-source LLM from Stability.ai take years to come out, the existing tools are enough for huge changes.

An idea maze for LLMs

The above is an idea maze I sketched out for products enabled by LLMs. The key question it starts with is “What kind of interface would the use have with the product?”

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Google Glass and the Segway Paradox

Google Glass

The customer rearely buys what the company thinks it is selling him. — Peter Drucker

Google Glass was finally announced to the public yesterday.

Glass is a solution looking for problems. It’s too hard to say what jobs-to-be-done Glass will be hired to do at this stage, or how widely used it will be. We’ll only know after it’s released.

The lean startup way of thinking heavily emphasizes the reverse sequence: find a problem (job), think of a way(s) to solve that problem, test your hypothesis using a minimum viable product, repeat. This method should work for most startups. It worked well for companies like Microsoft (Problem: I need an Operating System to put on the computers I sell so people can use them. Solution: Build/Buy/Copy Basic/DOS/Windows).

But there are some innovations where the solution=>problem sequence is necessary — anything that requires a lot of R&D and isn’t easily demo’ed on a large scale. Google Glass, Tesla cars, Segway, iPad, Lytro, etc. These are physical, more capital intensive examples, but the same still holds for some smaller software projects. Sometimes you just need to build the full version to see what it’s best used for.

One of the problems of this method is what I call the “Segway Paradox“: a new technology with huge initial interest and possibilities turns out to only be used in a few niche cases.

This may happen for a number of reasons (see Paul Graham’s The Trouble with the Segway). I think Google Glass may fall prey to this problem.

There are a few use cases I can think of that may make Glass worth the cost:

Hands-free sports — biking, skiing, football, climbing
Search & rescue, emergency — alerting the user to visual/audio anomalies
Jobs that require detailed visual instructions (“advanced checklists”)

But it seems from the videos that Google is focusing more on everyday consumer uses, competing more with smartphones.

Underestimating the Groupon Model

As widely reported, Groupon filed their first S-1 today in preparation for an IPO. They’re raising $750 million on top of the $160 million they have already raised from angel & venture capital investors so far. The likely valuation range will be $20-25 billion (or possibly more after what happened with the LinkedIn IPO).

The hefty valuation, along with the youth of the company (2.5 years) and the reported operating loss may lead observers and the media to cry “bubble.” While I think that $25 billion is a very rich valuation and wouldn’t pay that amount if it went public today, I think people in general underestimate the potential of Groupon’s business model. In other words, they were probably right to turn down Google’s offer of $6 billion (even if they don’t cash out during the offering).

Before going into Groupon’s business model and competitive advantages, here’s a quick run down of some of their customer statistics from the S-1:

In the above equation, those 5 metrics are multiplied to arrive at Groupon’s net revenue amount (the amount Groupon gets to keep after giving merchants their cut). So in the first quarter they made $270 million before expenses.

First the market, then the moat

Before Groupon and all the other deal sites began, local businesses had many lackluster options for advertising their product. They could send coupons in the mail; pay for ads in a local newspaper; pay for outdoor advertising; or pay for online advertising via Google, local news sites, etc. Most of these options (Google less so) are what Seth Godin calls interruption marketing. They are made to interrupt what you are normally trying to do. And because of that, people usually don’t like them, and they have a very low hit-rate in acquiring customers. Continue reading “Underestimating the Groupon Model” →

Why Google Continues to be the Best

Google As many have already seen, Google just posted some great third quarter figures. Both revenue and operating income were each up 23%, and Traffic Acquisition Costs (the revenue paid to AdSense partners) were at an all-time low of 25.7% of ad revenue. They also broke out some never-before-released sales figures: $2.5 billion a year for non-text display ads, and $1 billion for Google’s mobile search (driven mostly by use of their Android OS). But one part of the conference call caught my attention:

This is why we’re incredibly proud of Google Instant. Many of you guys speculated that we launched Instant to make more money. Well, let me tell you, that’s simply not the case. We launched Instant because it’s so much better for the user. In fact, from a revenue standpoint, its impact has been very minimal. And from a resource standpoint, it’s actually pretty expensive. So why did we do it? Well, we believe from a user standpoint, Instant is outstanding—and the data that we’re seeing actually bears this out.

The above was from Jonathan Rosenberg, Google’s SVP of Product Management. So, Google Instant was an expensive, non-revenue-producing upgrade to their lucrative search product. They did it, said Rosenberg, because it’s a huge improvement to the user experience. But how can that be measured? This got me thinking about what kind of metrics are truly important to Google in a broader economic sense. In Google’s financial reports they tout improvement in metrics like Traffic Acquisition Costs, Cost-Per-Click, and total number of Paid Clicks. All important to their business, but none that really capture Google’s overall business model. The most important metric to Google, I believe, is Revenue per Unit of User’s Time (or RUUT, for short).

Translating Time into Profit

Time is the ultimate scarce resource. Most businesses capture a portion of their customer’s wallets in exchange for a good or service. But businesses like Google (and TV networks, and most new media/web-based companies) capture a portion of customer’s time first, then translate that time into revenue.

Because time is scarce, when consumers choose to devote their time to a product or service, they are doing it at the exclusion of something else. So that company is literally capturing their customer’s time. Before Google and other search engines, when people wanted to “find” something, they went about it a multitude of ways: white & yellow pages, classifieds, a library or bookstore, or just plain leaving your house and searching (hard to believe, I know). These things took up a lot of people’s time. Continue reading “Why Google Continues to be the Best” →