Josh Klein

@firstadopter Cc: @pruuuie plz fix

Can someone ask the Gmail PMs to give me these two options? 1. I receive many long emails. Being forced to click to see the entire message every time is absurd: "[Message clipped] View entire message" Let me turn message clipping off. I would PAY FOR IT. 2. 99% of the emails put into my SPAM folder are not spam. Gmail spam AI is the dumbest in the world. Let me turn the Gmail spam filter off. Thanks!

It's going to be interesting to see how companies test candidates' ability to use AI efficiently and effectively. I feel like somebody has to have a startup working on this…

@a16z Cc: @MayorofSeattle

San Francisco is one of the clearest examples of what happens when a city commits to the full safety bundle: prosecution, modern technology, and political will. In 2024, the city deployed 400 license plate readers and 80 drones across the city. Major crimes fell 44% from 2023 to 2025, and car thefts dropped 54%. Evan Sernoffsky, a spokesperson for the SFPD, credited "tools like drones, ALPR, and public safety cameras." The decline shows up across nearly every crime category. Flock and the Future of Safety in American Cities: a16z.news/p/flock-and-th…

@AlexRoseJo @AndrewYNg What is unfair about recognizing outstanding with an A and median with a B? You might as well let combo of curriculum + performance track record mean something or else just get rid of grades all together

@AndrewYNg At Stanford we often see a Cauchy distribution in grading. Most students do really well, and a few fall off. Forcing a normal distribution might unfairly target the median student who still gets 90%.

Harvard University just voted to limit the number of A grades given in undergraduate classes to about 20% of the class. I’m not in favor of this. It deeply runs counter to how I believe education should be. We should hold a high bar, but also work mightily to support the success of 100% of learners, rather than a fraction. Harvard’s administration took this step — over the objections of a large fraction of the student body — to counter grade inflation. Grade inflation is real: Many universities have been awarding A and B grades to ever larger fractions of students, and this has caused grade point averages (GPAs) to become less useful as signals of student skill. At the same time, we want students to succeed. The heart of the question is the role of educational institutions. Should our goal be: - To help students succeed? - To judge students? Both of these have value. But my focus when working in education is almost entirely helping students succeed. To me, it is clear that many people want to learn, to be empowered, to build skills that let them do new things! This is what we focus on at DeepLearningAI. This philosophy is also why my online courses (going back to my early online Stanford courses on Coursera) permitted an unlimited number of retries for graded assignments. I believe in letting — and even encouraging — someone to redo something until they succeed. This is as opposed to standing in judgement of the fact they didn’t get it right the first time. Further, I want homework assignments to be designed primarily to help people practice and learn, rather than to judge their skill level. This is why I prefer to create “Practice Problems” and “Practice Labs” — questions that, when you think through them, help you to gain practice and reinforce what you know. As opposed to “Assessment Problems” designed primarily to judge skill. But won’t Harvard’s move make GPAs more meaningful and help prospective employers identify strong candidates? Having hired a large number of people from Harvard and other institutions, I can say confidently that GPA is not an important signal. We have screening and interviewing processes that give far more accurate ways to figure out if someone is truly skilled. I do not need a wider spread in applicant GPA scores to figure out who's really good! To be clear, there is also value in assessment. Even though standardized testing is much hated, high-quality tests like the SAT, ACT, GRE, TOEFL, etc. provide objective measures of ability in a domain. I find that most people want to learn and succeed. There are also people who want rigorous assessment (for example, to apply for school admissions), but this is a lesser need, and is not my focus when building educational products. Harvard is often described as an “elite” educational institution. There are two ways to be elite: One option involves limiting enrollments, and then even among admitted students, cap the number of people that do well at 20%. I would rather pursue a different path: Set a high bar and teach elite, cutting-edge skills, but strive relentlessly to help everyone succeed. This way, eliteness is defined not by excluding people but by helping as many people as possible to be excellent. [Original text: The Batch newsletter]

What’s happened is that we went from AI chat tools that were relatively cheap and had small context windows, to AI agents that have giant context windows, the ability to keep track of longer running work, and models that cost an order of magnitude more on inference because they’re that much better. This has compounded far faster than most realized (unless you were paying close attention at the middle or end of last year, which many here were), and the dollars flowing in now are much more real. What follows is a continued march of AI capability that will continue to be used by anyone with a frontier use-case (like coding, sciences, finance, consulting) and then a peeling off of tasks to lower cost models that are capable enough for the job. Whereas we thought the cost of AI might converge on a single low price per token before, it’s clear the stratification is only widening based on the task you need performed. This will be yet another component that has to be figured out for broad AI diffusion. Enterprises will need to put in programs, new finance teams, and technology solutions to manage this all. The labs and platforms that can ensure customers can price optimize for the task at hand will be in the best position.

If you asked 10 people what an "AI agent" is, I bet you'd get almost as many unique answers

@learncurvesai @AndrewYNg Maybe for technical roles and for the best resourced employers but so many positions, interviews start through who you know/a point of connection to someone at the company and end with whether or not the candidate relates well to the interviewer

@klezus @AndrewYNg The distribution collapse forces recruiters to build proprietary screening pipelines anyway. When a 4.0 becomes the baseline metric across elite institutions, the transcript gets bypassed entirely in favor of technical assessments that measure execution speed.

@a16z The barrier to build continues to fall!

Solopreneurship ticked up in Q1 Charts of the Week: a16z.news/p/charts-of-th…

@nickclement This is a real cost of meta layoffs… between the level of distraction and the morale issues caused by such a widely watched layoff

Name me a company you dislike more than Meta.

@GergelyOrosz This is completely wrong. Meta needs to free up cash to fun investments. Also they don't need all these employees. I wouldn't be surprised if this screen tracking initiative expose a lot of employees who have not been working even close to a 9-to-5

Meta laying off 10% of staff when revenue is at an all-time high, revenue growth is a beast (33% YoY!!), profits at an all-time high: just depressing These layoffs are not because Meta needs to lay off, but because Zuck wanted to lay off for whatever reason

@redtachyon It's called employment at will. I remember when hedge funds were going around parks in the middle of the day in Austin asking people which tech companies they worked for!

As an ex-Meta employee, I genuinely don't understand why people get butthurt about the constant layoffs. That's the game you signed up for. A deal with the devil. You get paid top of market, and in return every couple of months you have to do Hunger Games. It's not a secret.

@Incenzee @redtachyon There are lots of prestigious places that are explicitly up or out: everything from the Marines to consulting firms and investment banks

@redtachyon I don’t think anyone applies for jobs thinking they can get cut anytime (ie such rounds)

@Midnight_Captl Do you know if these companies are actively working on competitive memory solutions?

Do not be surprised if $NVDA (and/or hypers) announces a memory solution that competes with MU, SK, and Samsung The memory co’s are right at the razors edge of over monetizing if they haven’t already gone too far Pigs get fed, hogs get slaughtered

@dee_bosa @ReflectionAI_ The assumption is not that nobody can catch them but that OpenAI and Anthropic will figure out a way to create competitive moats. With LLMs commoditized everywhere but the frontier, vertical integration, application layer businesses, and deep tech innovation become the playbook

OpenAI and Anthropic are expected to go public at $800b+ valuations on the assumption that no one can catch them Chinese labs already have, at a fraction of the cost... and American players like Nvidia and @ReflectionAI_ are racing to build open weight alternatives OAI and Anthropic may get squeezed at both ends. New column with @jaswu_

Half of $NVDA rev comes from hyperscalers who are actively in-housing chip design in order to reduce dependence on NVDA and capture that ~70% gross margin themselves. Credible alternatives becoming available the largest leading-edge chip buyers will worsen NVDA’s leverage as a seller. This is the bear case but it’s what’s priced in

Jensen when asked why Nvidia’s valuation is lower than the market: “That’s one of the mysteries of the universe. I think all of this is going to get sorted out. In the end, they can’t hold back performance." $NVDA (via CNBC)

$NVDA's earning just now. Exceptional quarter and good signal for broader AI industry. Here are my initial top takeaways heading into the call: 1. Demand is very strong, evidenced by a comfortable beat on top line estimates ($81.6B vs. $79B consensus) and great outlook for next quarter 2. Progress diversifying its revenue base from hyperscalers - this is a master maneuver and a strong decision to segment out.ACIE (AI Clouds, Industrial & Enterprise) from hyperscalers so the market can track progress as the concentration risk eases. Look for commentary here on the earning call 3. Record guidance assumes zero Hopper shipments to China. With that market still locked and that baked into guidance, any movement on that front is pure upside 4. They're printing cash ($50.3B operating CF) and returning capital to shareholders via a dividend hike ($0.01 -> $0.25 per share) and an $80B buyback.

Just now per @WSJ: @OpenAI to IPO as soon as September. What's gonna happen to its rate of innovation and progress when early employees have life changing liquidity? Very interesting problem to solve as an organization.

@Geiger_Capital New York State spends 3x+ per student than Japan yet almost 50% of NY 8th graders are below basic proficiency on math whereas Japan ranks 5th globally

Jeff Bezos on NYC spending: "If we ran Amazon the way New York City runs their school system, packages would take 6 weeks to arrive, we would charge you a $100 delivery fee and when the package did finally arrive, it would have the wrong item in it."

Investing in OpenAI and Anthropic near ~$1 trillion is a bet they can figure out a durable moat (a real problem they haven't yet cracked). Today, frontier LLMs behave less like software and more like depreciating inventory, and switching costs are effectively zero