Entrepreneurship

Some U.S. tech firms are beginning to recruit for so-called “996” roles—an intense work schedule borrowed from China’s startup culture that runs from 9 a.m. to 9 p.m., six days a week, per Forbes.

Tailwind lays of 75% of their team. the reason is so ironic: > their css framework became extremely popular w AI coding agents, 75m downloads/mo > that meant nobody would visit their docs where they promoted paid offerings > resulting in 40% drop in traffic & 80% revenue loss

Europeans are shifting from Stripe to Polar I already have all the code ready for Stripe that can be reused. Is it worth switching to Polar just for tax handling?

The spread between how one-person dev teams are building software is fascinating: 1. Multiple agents, shipping at inference speed, not reading the code (but very involved designing it) - some 2. Heavy use of AI IDEs and a single AI agent - many 3. Mostly in the IDE - fewer

pro tip if you want to grow on here or grow anything anywhere online. when you create you should always craft content that is instantly shareable. this is because in today’s age group chats are the real distribution layer. feeds are just staging area. if something can’t survive being dropped into a chat with zero context, it’s not going to get you anywhere.

I want to start a community dedicated to Claude Code. It’s become the gateway drug to coding and experiencing the power of AI for tons of people. This will be a space for people to share killer use cases, agentic workflows, proven prompts, and connect with other CC obsessives. Comment “Claude” if you want to join.

human data will be a $1 trillion/year market This is not a short-term prediction. It is a structural claim about where the economy converges. To believe this, you need to accept two assumptions: • Digital and physical intelligence can eventually automate the tedious parts of the economy • Self-learning intelligence without human data is impossible at the frontier automation is the most useful & liberating thing humanity can do If AI systems can automate functions, then automating all functions is the highest-leverage task for humanity. Automation compresses time. It allows: • Aspirations to be fulfilled faster, by orders of magnitude • Humans to focus on the enjoyable, judgment-heavy parts of work while robots and agents to handle the rest As humans gain time, they create more. Net-new work is initially creative and high-value. Over time it becomes legible, repeatable, and ready for automation. Once automated, it continues delivering value while freeing humans to focus on new creative work. This loop is permanent. Automation does not eliminate human work. It pushes humans toward higher-value, more creative work. At a societal level, automation reshapes the economics of the world. As AI systems take on more production and coordination, the cost of producing goods and services collapses while availability explodes. At the same time, distribution becomes increasingly optimal. Digitally and physically intelligent systems coordinate supply and demand with less friction, less waste, and less delay, making access faster, cheaper, and more reliable every year AI models learn from humans forever Every artificially intelligent system learns from humans in some form: • Demonstrations • Supervised fine-tuning • Preference learning • Complex rubrics and evaluations • Continual corrections Even self-play and synthetic data depend on human grounding — humans define objectives, rewards, and what “good” looks like. As a result: • Every function in the economy contains useful learning signal • Every decision, exception, failure, and tradeoff creates data But raw activity is not enough. That data must be: • Recorded • Structured • Evaluated • Packaged into usable pipelines And importantly, functions must continue running while they are being automated. Automation is iterative, not instantaneous. this creates a universal obligation and opportunity To iteratively automate functions, every company, government agency, or institution running real operations must consume and produce structured data related to those functions. In most cases, it will not be optimal for them to create or structure that data themselves, due to scale inefficiencies, high fixed costs, and the operational difficulty of producing high-quality, reusable structured data in-house. We already see this dynamic today. For example, many lawyers produce more leverage per hour working on standardized, structured legal data through platforms like micro1 than they do performing unstructured work inside individual law firms. At micro1, over 1,000 lawyers work in structured data creation and earn on average ~20% more than in traditional firm roles. Law firms themselves are unlikely to become large-scale producers of structured training data, but they will increasingly be consumers of that data, either directly or by having it embedded in the tools they use. This creates a powerful incentive structure. Labs that are automating functions will pay for this data, because long term the value gained from incremental automation far exceeds the cost of acquiring the data. As a result: • Entities are incentivized to produce high-quality human data not just to automate themselves, but because that data has external market value • Every hour of work can simultaneously: • Run the organization • Train AI models • Generate additional revenue for the organization Human labor becomes not just labor to produce goods & services, but a revenue-generating asset on its own. the ultimate convergence: 5%+ of human time is spent on human data It’s reasonable to think that most functions in the economy will spend some amount of time trying to automate themselves. Not fully, and not all at once, but continuously pushing work out of the human loop as it becomes repeatable and scalable. Today, even knowledge workers spend the majority of their time on communication and coordination rather than on what we would consider actual productive work. As automation advances, tedious parts of knowledge work are progressively removed, and automation increasingly absorbs coordination, scheduling, routing, and routine communication. The result is a larger share of human time being spent on judgment heavy knowledge work. Even under conservative assumptions, it is reasonable to expect that in a more automated economy roughly 75% of work time is still spent on communication and coordination, while about 25% is spent doing actual work. Not all of that work needs to be structured. But a meaningful fraction does. Work that produces decisions, judgments, demonstrations, evaluations, and exceptions becomes far more valuable when captured in a structured, reusable form, both to complete the task and to enable future automation. If only one fifth of that actual work is performed in structured environments, that implies roughly 5% of total human labor time is spent generating structured human data. With global GDP at roughly $100T, and labor representing about 50% of that, total labor spend is around $50T annually. Five percent of that corresponds to roughly $2.5T per year of human time directed at enabling automation, creating demonstrations, feedback, evaluations, and learning signals for AI systems. Certainly not all of this will become explicit spend in the human data market. Much of it will remain implicit, fragmented, or unpriced. But even with aggressive discounting, you still arrive at something on the order of $1T per year. automation reshapes labor, it doesn’t shrink it This results in automation scaling, As automation scales, some amount of what was spent on human labor is redirected towards: • Energy • Compute • AI labor However, total human labor spend continues to increase. Why? Automation creates time. Time enables creativity. Creativity produces net-new functions within the economy. Those functions are initially done by humans. Over time, they follow the same automation cycle. human labor gets more expensive because: • Human time is finite at any moment • Creativity and judgment are scarce • Net-new ideas command premium value As automation expands, humans concentrate more of their time on higher-leverage work. While total human hours do grow over time, that growth cannot be rapidly accelerated in response to demand. The fastest and dominant way the labor market expands is by increasing the value created per human hour. As this continues: • Total human labor spend rises • A larger share of human time is spent generating learning signals and enabling automation we should never call it annotation again The importance of this work in shaping AI means calling it “data labeling” or “annotation” is completely inaccurate. These phrases describe mechanical tasks, when the real value comes from human judgment, expertise, and decision-making expressed in structured form. A more accurate description is expert human data creation or structured human judgment. This is how human expertise compounds in an automated economy. It explains why human data scales with automation rather than disappearing, and why it becomes a first-class economic input over time. human brilliance is needed more than ever This does not require extreme assumptions. It only requires that automation continues to work, and that intelligence continues to learn from humans. If that is true, then human data is not a phase or a temporary bottleneck. It is a structural input to the economy. Human judgment is captured, structured, and refined. That judgment becomes the training substrate of intelligence. That intelligence, in turn, produces more automation. As functions are automated, human time is freed. That time is spent creating new functions to automate, and the beautiful cycle continues.

small life update! i’ve joined Andreessen Horowitz’s @a16z venture scout program! they are essentially letting me manage a little bit of money to write checks into startups. super grateful for the opportunity and excited to begin my angel investing career with the institutional backing of a firm as storied as Andreessen Horowitz (early into facebook, twitter, airbnb, lyft, slack, coinbase and many more) big thank you to @dhaber for the connection and opportunity i’ve had a little success with investing in public equities, I have no idea if any of those skills could translate to the private markets. investing at pre-seed/seed requires a different paradigm as there are no numbers, financials, track-record, and quite frankly there may not even be a product – it really is a bet on the founder and their vision. having said that, if you are building something really cool and raising money, I’d love to hear about it. please email amit@akcomms.com. you can include anything you have whether that is a deck, one pager, etc. i’ve also built out a website that anyone can read to get a sense of how i think of investing in general and the 5 startups I’ve invested in over the past year (one has 20x’ed, one has 10x’ed, the other 3 just finished their seed round last month) no clue if I will be good at this but I appreciate a16z for the opportunity and hopefully we can try to find the next uber or robinhood being built in the early days – excited to see any pitches and please don’t hesitate to reach out! angel investing philosophy below!

It's time to build.

Inspired by Airbnb, I added a review system to TrustMRR 1. User leaves a review (KYC required) 2. Review is visible to other users 3. Increase trust

1 in every 5 founders I meet in the Bay Area is building an observability platform for agents. There are so many observability platforms, yet the least mature part of building an agent is still telemetry. Everyone is trying to solve the wrong problem with observability. Visibility is the easiest piece. The hard part is analyzing and understanding what you’re observing. I’ve spoken to teams recording 100k+ traces every single day. What are they doing with those traces? Literally nothing. Because it’s impossible to read and summarize 100,000 traces at any human scale. So stop vibecoding those stupid dashboards, and how about you re-center the problem from first principles?

AI agents will be a big part of how we shop in the not-so-distant future. To help lay the groundwork, we partnered with Shopify, Etsy, Wayfair, Target and Walmart to create the Universal Commerce Protocol, a new open standard for agents and systems to talk to each other across every step of the shopping journey. And coming soon, UCP will power native checkout so you can buy directly on AI Mode and the @Geminiapp.

I'm trying to put together a list of all the distribution "channels" that work in 2026: 1. Organic Short Form 2. Niche Communities (Reddit, Discord, FB Groups) 3. ASO (App Store & Platform) 4. Personal Brand 5. UGC 6. Influencers 7. Engineering As Marketing (Free Tools) 8. SEO & AIO (incl. programmatic) 9. X & LinkedIn 10. Viral Video Launches 11. Organic Long Form (YouTube) 12. Cold Email & Outreach 13. Salespeople 14. Paid Ads 15. Affiliate 16. Feedback/Customer Calls 17. Timing & Trends 18. Positioning 19. Open Source What am I missing?

JUST IN: X increases creator payouts.

DeepSeek founder Liang Wenfeng’s quantitative hedge fund generated returns of more than 50% last year

Anthropic launched Claude for Healthcare with HIPAA-ready products and expanded Claude for Life Sciences with new connectors ranging from clinical trial management to regulatory operations - Claude for Healthcare connects to Centers for Medicare and Medicaid Services Coverage Database, International Classification of Diseases 10th Revision codes, and National Provider Identifier Registry, with new Agent Skills for FHIR development and a sample prior authorization review skill that can be customized to organizations' policies - US Claude Pro and Max plan subscribers get beta access to HealthEx and Function connectors now, with Apple Health and Android Health Connect integrations rolling out in beta this week on iOS and Android apps for accessing lab results and health records - Claude for Life Sciences adds connectors to Medidata for trial data and site performance, ClinicalTrials[.]gov, ToolUniverse with 600+ vetted scientific tools, bioRxiv and medRxiv preprint servers, Open Targets, ChEMBL, and Owkin Pathology Explorer for tissue image analysis - New Agent Skills for scientific problem selection, converting instrument data to Allotrope, scVI-tools and Nextflow deployment for bioinformatics, and a sample skill for clinical trial protocol draft generation with endpoint recommendations accounting for regulatory pathways, competitive landscape, and FDA guidelines - Anthropic is hosting "The Briefing: Healthcare and Life Sciences", a free livestreamed virtual event on January 12 at 11:30 AM PST with Anthropic leadership and customer perspectives on AI in healthcare

> Tailwind lays of 75% of their team because of AI as they underwent a 80% revenue loss > 24 hours later sponsors arrive from big places internet is good

You guys realize that all software is about to be free, right? And software is presently the most valuable capital asset, right? Guys?

From an eng responsible for AI tooling at a mid-sized company (100+ devs): "Our execs read a blog post about Claude Code and ask: 'why are we not all using it?' Me: well, none of you would approve going from $40/mo on GitHub to $65/mo on Cursor... Claude Code is $150/mo."

OpenAI Residency 2026 applications are OPEN btw - 6-month full-time paid research gig in SF - ~$220K annualized ($18.3K/month) + relocation - NO prior ML/AI experience required, just strong technical fundamentals & fast learning - Work on frontier AI with top researchers Interviews starts in Jan 2026 Apply: https://openai.com/careers/residency-2026-san-francisco/…