Contents

Editorial: When is a Bubble not a Bubble?

Essays of the Week

The great rewiring: is social media really behind an epidemic of teenage mental illness?

The Day the Music Lied

Weapons of Mass Production

China’s future economy

This Message Will Self-Destruct in 33 Seconds

1 in 6 People Will Be Aged 65+ by 2050

Venture Investing This Week

Global Venture Funding In Q1 2024 Shows Startup Investors Remain Cautious

First Cut - State of Private Markets: Q4 2023

The Investments Where I’m Going to Lose All My Money

Quant VC and What it Means for Startup Investing

Video of the Week

New Apple Vision Pro Personas

AI of the Week

Meet the YC Winter 2024 Batch

The 18 most interesting startups from YC’s Demo Day show we’re in an AI bubble

YCombinator's AI boom is still going strong (W24)

Bubble Trouble

Big Tech companies form new consortium to allay fears of AI job takeovers

News Of the Week

Apple Vision Pro’s Persona feature gets collaborative

Jon Stewart Plunges the Knife into Apple

Startup of the Week

Rubrik’s IPO filing hints at thawing public markets for tech companies

X of the Week

Mike Maples on Y Combinator

Editorial

I’ve taken to writing this on Friday morning. I put the curated content together Thursday evening, which gives me overnight to reflect. Usually, the title comes first and is somehow correlated to the content below.

This week, there is a lot about AI. The Y Combinator story in AI of the week is the story that the “bubble” will be challenged due to a lack of training data. In contrast, the story is that AI will remove so many jobs that the larger companies have formed a consortium to allay fears.

I also created a new section separating out Venture Capital. This is the week of quarterly updates from Q1. They suggest there is no bubble at all. Only Amazon’s multi-billion dollar investment in Anthropic stands out.

But for me, the question posed in the title is - When is a Bubble not a Bubble? - is not triggered by the AI stories. The Economist’s Simon Cox writes about China and its future in a newsletter and the linked article. He frames it well:

In 2006, for example, China’s leaders declared the need to “rely more than ever on scientific and technological progress and innovation to drive a qualitative leap in productivity”. Science and technology, they added, are “the concentrated embodiment…of advanced productive forces”. That ambition, and indeed that diction, sound very similar to the slogans emanating from Beijing today. Xi Jinping, China’s leader, has, for example, urged provincial governments to cultivate “new productive forces”, based on science and technology. In this week’s issue I explore what those words might mean.

As Simon points out, “productive forces” is a formulation derived from Hegel and Marx. It combines technology and human beings into a duality that expresses how we produce things. Indeed, there is no pure “technology” separate from human beings and the division of labor. Productivity is the expression of both and the measurable thing.

In the Western enlightenment tradition, we use the word progress to mean the same thing.

All progress requires humans to invent time-saving methods to reduce the effort involved in making and doing things.

China’s discussion (especially if you remove the word China) is about building the future through innovation. It stands in contrast to the dominant discussions here in the US - Regulation, the dangers of Social Media, Immigration, Women’s Right to Choose, Guns, and even Climate. And a lot of pessimism around technology and science.

That is except for in the startup ecosystem. The dominant Silicon Valley belief system is similar to Simon Cox’s description of China’s goals.

Accelerated Innovation dominates the set of assumptions in the Bay Area. Why? Because AI, Nuclear Fusion, Decentralized Networks, Global Ambition, and the skills and money they require all live here. And their potential is real. And the timing of the potential is near-term (several years).

Strangely, the US Government seems to consider innovation, especially “Big Tech,” a problem. China and Silicon Valley seem to consider it a solution. And by “Silicon Valley,” I do not only mean geographically but also as a way of thinking.

That bifurcation of optimism and pessimism, enshrined in a Government that wants to restrict tech company power, has led many in the Valley to abandon traditional two-party politics and increasingly articulate agendas that are both optimistic and independent of Government. Government is perceived as a cost of doing business, not a benefit.

So, the innovation that comes out of Silicon Valley and the money it attracts are often scorned by those who are not part of it. The word “Bubble” is heavily laden and used to imply that there is nothing valid, real, or transformational. The money is simply irrational.

“Bubble” is a pessimists word for “fake”.

It goes alongside other narratives that cast doubt on innovation. In some ways, Tomasz Tunguz's piece on the shrinking attention span implies a problem caused by the abundance of content and limited time to read it. Although one might consider the ability to parse information and determine whether it is attention-worthy and do it quickly would be a good thing.

The idea that teens commit suicide and get depressed due to alienating social media comes to mind as another anti-technology narrative. The first ‘Essay of the Week’ from Nature magazine presents a strong case that this is bogus.

Rex Woodbury’s “Weapons of Mass Production” and Michael Spencer and Chris Dalla Riva’s “AI and the Future of Music Production and Creation” (The Day the Music Lied) point to the explosion of production and creative production that AI will trigger.

Rex:

Spotify reinvented music distribution. It put 100 million songs in your pocket. Generative AI will reinvent music production. There are a number of early-stage startups that let you toggle artist, genre, and ~vibe~ to create a wholly new work—e.g., “Create a Miley Cyrus breakup song with a sad, wistful feeling to it.” Of course, these companies will need to navigate the labyrinth of music rights, but some version of these tools feels inevitable.

This example embodies a broader shift we’re seeing from distribution ➡️ production.

Michael Spencer and Chris Dalla Riva:

In summary, the music industry will likely come to embrace much of this technology as long as AI firms properly license the music catalogs necessary to train their models. This still begs one final question: Is any of this good for music?

It’s important to unpack words like Bubble. They live in a context. As Simon Cox discusses, the future depends on progress, innovation, or “productive forces.” So, this “Bubble” is not a bubble.

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