741 karma · joined September 29, 2021
Some questions don't have definite answers, it's the sophistication of the analysis that counts.
The big issue is a lot of people will believe what they want to believe. Most folks are not scientists - they start by assuming their conclusions and will choose the soothing moral and emotional rhetoric over evidence.
Trying to see the world objectively puts you in a category of outliers. The people you become friends with due to proximity in everyday life will not be outliers.
From the WSJ a few days ago:
For Russia’s Economy, Peace Poses a Threat
https://www.wsj.com/world/russia/russia-war-economy-ukraine-...
Perhaps the Cold War never really ended and there's been a secret conflict in the background ever since?
Storytelling, allegory, dramatic dialogue, compelling narratives, and 'myth making' can have different goals - sometimes it's to teach the glory of science. Other times it's about social relationships and how to be a functional human.
Bluey works so well because the combination of kids' entertainment with adult themes and storylines portraying positive role models feels unforced - some folks may not enjoy it, that's fine.
One incredible thing about Michael is behind every sentence you can hear the amount of pain he had to endure to succeed. He's not just giving you startup advice, he's a lone survivor who's come back to tell the tale. The whole startup game is about extreme outliers and he's always been very real about this fact.
Was re-reading his post on How to Pitch Your Company from 2016 (The Seven Questions), it's a great read:
https://www.michaelseibel.com/blog/how-to-pitch-your-company
Number of declared meetings with EU officials since 2014: Google (381), Airbus (318), the European Automobile Manufacturers Association (241), and Meta (235)
https://www.euronews.com/my-europe/2025/02/24/big-tech-banki...
The documents from Uber released a few years ago revealed a lobbying effort by some EU officials. In that context growing homegrown options will be difficult:
AI agents are software systems that use AI to pursue goals and complete tasks on behalf of users. They show reasoning, planning, and memory and have a level of autonomy to make decisions, learn, and adapt (Google)
Agents “can be defined in several ways” including both “fully autonomous systems that operate independently over extended periods” and “prescriptive implementations that follow predefined workflows” (Anthropic)
Agents are “automated systems that can independently accomplish tasks on behalf of users” and “LLMs equipped with instructions and tools” (OpenAI)
Agents are “a type of system that can understand and respond to customer inquiries without human intervention” in different categories, ranging from “simple reflex agents” to “utility-based agents” (Salesforce)
A few days ago in TechCrunch: No one knows what the hell an AI agent is
https://techcrunch.com/2025/03/14/no-one-knows-what-the-hell...
Agents are a coupling of perception, reasoning, and acting with preferences or goals. They prefer some states of the world to other states, and they act to try to achieve the states they prefer most (this book)
AI agents are rational agents. They make rational decisions based on their perceptions and data to produce optimal performance and results. An AI agent senses its environment with physical or software interfaces (AWS)
An artificial intelligence (AI) agent refers to a system or program that is capable of autonomously performing tasks on behalf of a user or another system by designing its workflow and utilizing available tools (IBM)
Agents are like layers on top of the language models that observe and collect information, provide input to the model and together generate an action plan and communicate that to the user — or even act on their own, if permitted (Microsoft)
Assumptions:
Focus on rationality vs. goal-seeking vs. autonomy
Whether tool use is emphasized
Architectural specificity
Relationship to users
Decision-making framework (optimality vs. preference satisfaction)
‘Axolotl’ is narrated by a lonely man who regularly visits the local zoo, where he becomes fascinated by the axolotls in the aquarium. In time, he states that he, too, is an axolotl, and feels he has become one of them.
https://ambystoma.uky.edu/teachers_materials/axolitbook/Axol...
Here's the letter sent by the CEO Assaf Rappaport to his team at the time (2024):
"Wizards,
I know the last week has been intense, with the buzz about a potential acquisition. While we are flattered by offers we have received, we have chosen to continue on our path to building Wiz.
Let me cut to the chase: our next milestones are $1 billion in ARR and an IPO.
Saying no to such humbling offers is tough, but with our exceptional team, I feel confident in making that choice."
https://techcrunch.com/2024/07/22/wiz-walks-away-from-google...
> The team has figured out how to compensate for potential line-of-sight interruptions like bird flights, rain, and wind. (Fog is the biggest impediment)
> “It’s fast and reliable but quite expensive.” He says he spent around $30,000 for the last light bridge setup he bought from Alphabet for testing.
Interesting that Meta was working on similar tech but abandoned the project:
Google’s Taara Hopes to Usher in a New Era of Internet Powered by Light
https://www.wired.com/story/plaintext-google-taara-chip-inte...
More on Meta's internet via lasers project:
https://www.wired.com/2016/01/facebook-zuckerberg-internet-o...
https://youtu.be/FskFXD-SQpI?si=UYapck6_51LcAi9y
The Simpsons did a famous rendition of The Raven read by James Earl Jones:
https://techcrunch.com/2025/03/13/openai-calls-deepseek-stat...
- Sarah Wynn-Williams (2025)
- Arturo Bejar (2023)
- Frances Haugen (2021)
- Sophie Zhang (2020)
- Chris Hughes, Co-Founder (2019)
- Roger McNamee, Investor (2019)
- Christopher Wylie (2018)
- Alex Stamos, ex-CSO (2018)
- Brian Acton, Co-Founder WhatsApp (2017)
- Sean Parker, ex-President (2017)
- Chamath Palihapitiya (2017)
- Justin Rosenstein (2017)
Ex-Meta executive: ‘People deserve to know what this company is really like’
https://www.cnn.com/2025/03/11/tech/meta-whistleblower-book-...
Meta’s Response to Explosive Tell-All Is Ripped From a Familiar PR Playbook
https://www.vanityfair.com/news/story/metas-response-explosi...
Sure all big successes are unique in time, and entrepreneurship & web tech are mainstream now. But there's still lots of industries to disrupt. What gets harder is that the level of expertise needed to disrupt an industry is now much higher. You didn't need to work in hotels to create Airbnb, or a bank to create Stripe, or deliveries to do Doordash, etc. A bunch of scrappy web founders that were hosts, or trying to sell on the internet was expert enough to get started. This may be changing in important ways, but huge successes will surely happen.
Steve Blank's quote about validating assumptions: "Lean was designed to inform the founders’ vision while they operated frugally at speed. It was not built as a focus group for consensus for those without deep convictions"
Is the Lean Startup Dead? (2018) https://medium.com/@sgblank/is-the-lean-startup-dead-71e0517...
Discussed on HN at the time: https://news.ycombinator.com/item?id=17917479
Sam Altman argued a startup's chance of success is “something like Idea times Product times Execution times Team times Luck, where Luck is a random number between zero and ten thousand”
A lot of the success in startups is not due to getting the initial idea right, but what founders do once they realize that their initial idea is wrong.
This is a great point. I've seem teams apply lean startup by testing -> changing something -> testing -> changing something -> testing ...
The problem is that the changes are so small that statistically you end up testing the same thing over and over and expecting different results. You need to make a significant change in your Persona, Problem, Promise, or Product to (hopefully) see promising results.
More details: Why a Chinese Gadget Company Can Make an Electric Car and Apple Can’t https://www.nytimes.com/2025/02/28/business/china-xiaomi-app...
AI makes devs more productive but the need for moats still applies and competition will be worse not better. Distribution and defensibility is key
The downside is there's a tendency to anthropomorphise AI, and you might not want to talk to your computer: it takes too long to explain all the details, can be clunky for certain tasks and as the author argues actually limiting if you don't already know what it can do.
There's a need to get past the "Turing test" phase and integrate AI into more workflows so that chat is one interface among many options depending on the job to be done.
It is in many ways a workaround to Google's SEO poisoning.
Doing very deep research requires a lot of context, cross-checking data, resourcefulness in sourcing and taste. Much of that context is industry specific and intuition plays a role in selecting different avenues to pursue and prioritisation. The error rates will go down but for the most difficult research it will be one tool among many rather than a replacement for the stack.