1/15 I visited a physical bookstore after a long time yesterday, and had this revelation about knowledge distribution.
2/15: Knowledge on the internet is like a free market. Platforms serve you what they think you want (e.g. Netflix, Youtube, Instagram).
3/15: Knowledge before was a highly regulated market - you needed authority figures (publishers) to vouch for the quality of your work before it could be available on shelves. Still a market though, because more popular books would get more shelf space.
4/15: A complete free market has the obvious advantage that there's less gaps in the market - content is fresher, and appeals to very niche demographics...
But at the obvious cost of quality. Content that is vile, inappropriate, hateful, etc do exist.
5/15: Trying to establish partial regulation online is tricky because (1) regulation in many ways is not automatic enough to scale (besides porn filters, etc) (2) users have transparency so they complain when they disagree with regulation (YT takedowns, etc)
6/15: But even today, people are more likely to trust a book than something they read online. With deregulation, you lose that trust (fake news).
7/15: Doing regulation at scale is a tricky problem from a Machine Learning perspective. A high precision (high trust) solution impedes on free speech (low recall), creating a quality vs free speech tradeoff.
8/15: Another key aspect is about what content gets exposure (ranking) over just what content is allowed (regulation). Products often optimize on engagement vs expert regulation. An algorithm might put a listicle on the front page but an editor wouldn't.
9/15: A news editor cares about his expertise and his responsibility to the reader, regardless of what the user wants. He supplies what the user *needs*, not what they *want*.
10/15: However, in capitalism, that's fighting a losing battle. Capitalism rewards wants, not needs. More views is (usually) more money, not societal impact. How do we contend with that?
11/15: I think there are ways to tackle this problem with technology. Can we train on data only from an exclusive subset of the population whose opinions we trust?
Can we move to a subscription model to not have to rely on viewership numbers?
12/15: Non-tech heavy solutions that work already exist: Reddit and Wikipedia both have trusted "moderators" who regulate content quality cheaply and scalably very well. Maybe we can empower those moderators to be the editor and decide which posts/pages they recommend.
13/15: Another unintuitive idea: cap the amount of content like a newspaper. If users know they only get access to a select few high quality things, maybe they'll value quality over quantity, and retention will be higher (?)
14/15: The physical bookstore visit reminded me that there's so much fascinating content I'd never look up organically. But because of the way bookstores are organized, I got to delve into old South Indian folklore to graphic novels about capitalism.
15/15: That made me think - very rarely do I discover such niche timeless high quality content online, and maybe there are opportunities to change that!
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In the last 20yrs, "study CS and work in tech" became a "path" to wealth
Now:
—BigTech did layoffs, aren't hiring
—Tech job postings are ~40% of '21
—Startups often prefer tenured hires
—Huge pipeline of CS majors: 40% of MIT
Winter is coming for software engineering.
🧵
1/5
Beyond that, there seems to be less and less whitespace for software to create value in people's lives. Compare how much time we spent with tech in the year 2000 as a society vs 2024.
Companies are trending to being smaller and more efficient, not large and IBM-like.
2/5
It's not there yet, but AI is also slowly and steadily eating away at jobs humans used to do. LLMs will only write more code over time.
BigTech fueled a lot of the hiring with their consistent 20% YoY growth but they realize revenue doesn't come for free anymore.
3/5
My #1 tip for engineers navigating the rough hiring market is to uncover hot startups you may not know.
Reach out to the junior VCs with an email asking them who is doing well and whether they're hiring!
Some budding superstar startups compensate handsomely for talent.
1/5
Most engineers who are graduating college wrongly assume:
— Startups don't pay well
— Most startups can die suddenly (you usually have a healthy forewarning)
— Startups won't sponsor visas
— Lack of job security
These are usually false.
2/5
"Why would they help me?"
VCs are incentivized to help the startups they invest in (and are on the boards of), and hiring is one of the ways they can help.
Keep in mind, they'll likely only recommend companies in their firm's portfolio so you want to ask many
3/5