Contents
20VC + SaaStr: The Hidden Game of Silicon Valley: Decoding Venture Capital’s New Rules
A follow up to my post about Endowments as I have learned more in the last 6 wee
Praise Our Lord For Secondary Markets, Because Selling Shares Is Now an Essential Part of (Seed)…
The state of VC in 2025. Where we are, where we are going, and where to invest
Google Loses Ad Antitrust Case, Market Definitions and Tying, The Trinko Exception
Why the FTC v. Meta Trial Matters: Competition Gaps and Civil Liberties Opportunities
FTC Sues Uber Over Billing for Its Uber One Subscription Service
European Commission Fines Apple €500M and Meta €200M for Breaching Digital Markets Act
Instagram co-founder says Meta’s Zuckerberg underinvested in photo app
Editorial: Venture Blues: Clouds, Silver Linings?
The sheer number of venture capital essays this week forces us to ask if something new is happening in the space.
Dan Gray’s X post captures the moment well:
It became clear in the aftermath of 2022, when the venture market took a tumble, that many early stage VCs were frustrated with the state of venture. They had watched portfolio companies get "foie grased", becoming sluggish and overvalued — no exit in sight.
Indeed, it highlighted a serious problem that is unique to early-stage VC: managers are exposed to the late-stage market as their portfolio companies mature.
For no other category of investor is this the case. Asset managers would never find themselves with a book that no longer fit their own strategy. And yet, for early VCs, this was the norm.
He goes on to note:
The obvious desperation for liquidity has — for now — removed the stigma associated with secondaries.
Hunter Walk from Homebrew doubles down regarding liquidity and secondaries. He begins with longevity of illiquidity:
Soon I’ll have spent more time on cap tables than org charts. That’s a 2025 milestone as Homebrew turns 12.5 years old, surpassing my combined working tenure across Second Life, Google and YouTube.
….
Delayed liquidity hurts LPs who manage to an IRR and even for Cash-on-Cash returns slows distributions which can be reinvested in VC and other classes
For the earliest funds (pre-seed, seed) this means instead of 10 year fund cycles for LPs, you’re seeing closer to 15, which fundamentally changes LP calculations about the asset class
….
Secondary is quickly becoming primary for early stage VCs.
Carta charts one stage of the elongated venture journey
My SignalRank colleague Rob Hodgkinson adds to this narrative, opening his essay with:
The current fog of economic uncertainty could lead to a meaningful shift in the make up of venture capital’s LP base, and ultimately to how VC is offered as a product to investors.
Endowments, which historically contribute 15-20% of VC capital, are particularly exposed to the new economic & political realities, requiring a rethink in investment strategy & allocation.
A harbinger of change is Yale, who pioneered the ‘endowment model’ with high alts allocations, allegedly selling $6bn in its PE portfolio in secondaries for the first time.
….
VC is changing. Venture firms need to rethink not just who they raise from, but how their LP base influences what they’re offering.
So is venture capital structurally challenged? Can it be re-engineered to better provide both growth in value and liquidity? Can it attract funds from new sources previously not active in venture capital?
Certainly the asset class as a whole is healthy. Below is a chart for 2012-2025 showing the gross dollars invested in Series B rounds. For example 2017 saw $17 billion invested and is now worth in aggregate 4.76x the invested dollars.
But due to the power law most of that capital flowed to a small number of investors and funds, while others made little or no returns. And the 4.76x is measured in MOIC, not cash, so was not distributed.
LPs access venture through fund managers. The number of compelling managers is small. And so most LPs do not get returns, and certainly not liquid returns (the only real kind).
But if an asset class grows 4.76x (that’s 476%) in 7-8 years it should be possible to invest in it and extract gains. The reason it is hard is almost 100% due to how venture funds are structured and managed. That structure forces LPs to play the power law game, betting on highly improbable outcomes.
My approach to the asset class follows from the question, how can the average return of venture capital be returned and made liquid? If that de-risking were possible then LPs could invest in probably outcomes closer to the average. And the returns could be in cash.
Enter algorithmic selection, focused access and, private company index creation that can be listed on public markets.
The above chart show what happens when data, algorithms and machine learning reject 93% of the Series B rounds. The remaining 7% return an average of 6.2x in five years. Investing in this 7% as an index gives investors the ability to participate in de-risked average outcomes. SignalRank is 87% accurate in predicting a company will not perform to this level. The index benefits from excluding those companies.
There is no longer a dependency on which fund an LP can invest in, or whether the fund has a power law winner. And liquidity is built into the index approach. An index share can be bought and sold at any stage once listed.
This kind of structural change can make venture capital attractive to allocators, not only traditional LPs. Even retail investors can play.
As venture capital discusses its challenges new ideas are sorely required.
Sorry for the explicit plug. The future of venture capital as an asset class is in play and this approach is able to provide answers. Wishing the dark clouds there are definitely silver linings.
Essays
Venture capital spent the last decade pulling itself in two.
X • credistick • April 22, 2025
X•Essays
Venture Capital’s Diverging Paths
The influx of venture funding over the past decade has simultaneously fueled an explosion of early‑stage investing while prolonging the private lives of growth‑stage companies—creating two camps of investors talking past each other.
Venture capital spent the last decade pulling itself in two. The vast amount of capital resulted in the expansion of early investing while also keeping companies private for much longer.
It’s easy to find people talking at cross purposes because they exist at opposite ends of this spectrum.
Context & Nuance
Over the last ten years, the total pool of venture capital has grown substantially. That influx has:
Democratized early‑stage investments, with more firms and angels backing seed and pre‑seed rounds than ever before.
Extended the typical lifecycle of successful startups by providing ample late‑stage funding, delaying IPOs and acquisitions.
The result is a bifurcated ecosystem: one group focuses on rapid, high‑risk seed bets hoping for quick breakout hits, while another emphasizes multihundred‑million‑dollar rounds in mature private companies. As each side optimizes for different outcomes—“move fast, fail fast” vs. “build slow, scale sure”—their priorities and language often clash.
Implications for Founders and Investors
Founders may struggle to align expectations: early‑stage backers push for bold pivots; later‑stage backers seek predictable growth.
Valuation benchmarks diverge: what looks like a fair seed valuation can seem cheap to growth investors—and vice versa.
Exit strategies have lengthened, shifting the calculus around dilution, control and timing for all stakeholders.
Looking Ahead
As capital continues to flow, bridging the gap between these two investor mindsets will be critical. Founders who clearly communicate stage‑appropriate goals—and investors who respect differing risk profiles—will create the healthiest outcomes in a still‑expanding market.
The Rise of AI-Powered Vertical SaaS
Davidcummings • David Cummings • April 19, 2025
Technology•AI•VerticalSaaS•CloudComputing•Entrepreneurship•Essays
Over the past few weeks, I’ve spoken with several entrepreneurs who are developing all-in-one, AI-powered vertical SaaS applications and making significant progress. In contrast, the previous generation of SaaS companies typically consisted of horizontal platforms that excelled in one market segment, such as marketing automation or sales engagement. These products were comprehensive, serving a wide range of industries. Over time, many of these applications moved upmarket, focusing on mid-market and enterprise clients.
In addition to horizontal players, numerous vertical SaaS applications have emerged over the last two decades. These typically followed a playbook of targeting small to midsize businesses in specific segments before gradually moving upmarket. They focused on delivering the most valuable features for their target audience while avoiding overly broad functionality.
With the rise of AI-powered software development, including low-code platforms and vibe coding, robust cloud computing resources, and mature open-source ecosystems, building large-scale software quickly has never been easier. As a result, entrepreneurs are now creating AI-powered vertical SaaS products that combine the functionality of multiple horizontal tools into a single, purpose-built solution for specific industries. Instead of small business owners needing separate tools for their website, social media, marketing automation, CRM, and sales engagement, a single system now provides everything they need, tailored to their vertical. These solutions are offered at a significantly lower price point with greater ease of use. Moreover, because these products are directly tied to revenue through lead generation, proposals, and new business, their ROI is clear.
My recommendation to entrepreneurs is to identify a vertical they or a colleague know intimately and consider building a comprehensive application that replaces multiple existing tools for that target customer. By leveraging AI, cloud infrastructure, and open-source technologies, they can deliver a fully integrated solution at a fraction of the cost.
The shifting sands of LP land
SignalrankUpdate • Rob Hodgkinson • April 21, 2025
Venture Capital•Endowments•Sovereign Wealth Funds•Essays
The current fog of economic uncertainty could lead to a meaningful shift in the make up of venture capital’s LP base, and ultimately to how VC is offered as a product to investors.
Endowments, which historically contribute 15–20% of VC capital, are particularly exposed to the new economic & political realities, requiring a rethink in investment strategy & allocation.
A harbinger of change is Yale, who pioneered the ‘endowment model’ with high alts allocations, allegedly selling $6 bn in its PE portfolio in secondaries for the first time.
Some factors affecting endowments include:
Denominator effect: The immediate change has been the impact of the new tariff policies on public portfolio valuations, potentially exacerbating the denominator effect (with allocators deciding they may be too overweight in private markets).
No DPI: Liquidity has been delayed for LPs as slated IPOs are postponed (including Klarna & StubHub). This prevents the recycling of capital back into the ecosystem.
Capital absorbed to cover expenses: Proposed cuts to federal funding and higher mooted taxation (under the proposed Endowment Tax Fairness Act) would force universities to divert more capital to operating costs. This would worsen the denominator effect, and liquid strategies would surely be prioritized going forward.
As ever, and more than any individual issue (which on their own could be absorbed and planned for), it is the climate of uncertainty that is having the most corrosive effect on confidence. And this uncertainty goes beyond tariffs.
Universities are under a full frontal assault by the administration; Leo Terrell, from the Department of Justice declared on Fox News that “we’re going to bankrupt these universities… [which should] lawyer up, because the federal government is coming after you.” Hardly a conducive environment making new 10+ year illiquid bets.
A counterbalancing development is the emergence of new alternatives products offered to retail investors by the world’s largest financial platforms. BlackRock’s Larry Fink’s annual letter envisions the democratization / indexation of alternative investments. He recommends moving from a 60/40 stocks/bonds portfolio to 50/30/20 stocks/bonds/private assets.
Last week also saw the announcement of a collaboration between Blackstone, Vanguard & Wellington to allow individual investors to tap into private and public markets. Similarly, Charles Schwab has just added alternatives for retail investors.
It is likely that these alts platforms will initially focus on more established private asset classes, especially private equity, real estate and infrastructure (instead of VC). These platforms are also likely to accentuate the concentration of capital in branded managers because these platforms will be looking to place capital at scale and into managers with strong track records (in other words, not emerging managers).
At the same time, Sovereign Wealth Funds (“SWFs”) are likely to play a more meaningful role. Andreessen Horowitz is rumored to be in the market to raise a $20 bn fund. Is this just a repositioning of the $40 bn fund that A16Z had been marketing to Saudi Arabia’s PIF last year?
The growth of private wealth & SWFs was highlighted in Bain’s recent private equity report from earlier in the year. Bain expects private wealth & SWFs to account for approximately 60% of growth in alternative AUM over the next decade.
There is a question then about whether this is a recalibration, or a retreat, by traditional LPs. Yale’s proposed $6 bn secondary sale would bring a ton of dry powder and reinvestment risk – where does this recycled capital go? Which other asset class offers such compelling theoretical returns?
Sapphire’s Beezer Clarkson argues that the current challenges faced by LPs are “a liquidity issue, not a sentiment issue.” Indeed, the opportunity set for venture capital should expand as companies stay private for longer and the potential TAM for AI applications continues to increase.
Still, the entry of new types of LPs could impact the way that VC is offered as a product. Altimeter’s Meghan Reynolds argued that the percentage of LP capital into “traditional 10 year funds will be less. What’s filling the gaps? Evergreen funds, secondary funds, co-invest, direct strategies…” She goes on to argue that SWFs can eventually skip funds and just do deals direct (eg the MGX in UAE); likewise individuals can skip funds with all of the SPVs offered by banks.
VC is changing. Venture firms need to rethink not just who they raise from, but how their LP base influences what they’re offering.
The Data Wars & Reimagining Your Product During a Platform Shift
Implications • Scott Belsky • April 21, 2025
Technology•AI•PlatformShift•ProductStrategy•DataWars•Essays
The companies I am involved with, and the founders I advise, span the spectrum of products built pre and post the age of AI. Also, as part of my role at A24, I am constantly evaluating the products and processes behind every function of a fast‑growing studio (a decent proxy for what many other high‑growth businesses are now going through). I am struck by the speed at which pre‑AI tools are feeling antiquated.
I recall the period when most New York toll booths went from being staffed by toll takers to “fast lanes” enabled by sensors, to the entire “toll” concept being ripped out altogether, replaced by high‑speed cameras capturing your license as you speed by. Suddenly, the idea of having a toll‑induced pause that causes any form of slowdown became unimaginable. My kids won’t even know what a toll booth is, let alone believe they ever existed.
During platform shifts in which a new technology becomes widely available – like the transitions to electricity, to computers, to the internet, to mobile, to AI – you know the shift is real when tools and practices that we’ve used for many years suddenly start to feel like they are holding us back. A platform shift causes us to reimagine everything while (and perhaps because) we become frustrated with the current soon‑to‑be‑antiquated aspects of our lives that linger.
I am feeling this sensation often. When I go into spreadsheets trying to make sense of various pieces of data, I realize that the lack of a fine‑tuned LLM layer makes this an antiquated exercise. Spreadsheets are starting to feel like data silos compared to AI‑native database products. When I use voice dictation on my iPhone or search for something and fail to find it due to an inexact descriptor, I notice the lack of AI and feel the cruft of rapidly antiquating software. The rules and filters that I set up in my native email clients are far inferior to the basic email agents that are emerging from startups.
Anything optimized for web or mobile rather than AI will start to feel outdated. How product teams react to this shift is crucial. Too many established companies will fall short because they only augment their offerings instead of fully reimagining them. So how should product leaders proceed, based on these implications?
Re-anchor yourself with the customer. When you’re not sure what to build or how to build it, double down on empathy with the customer suffering the problem you want to solve. You’ll often learn that the solution you have in mind is a little (or a lot) off from what’s required to really engage the customer emotionally and practically. A platform shift challenges us to recalibrate our solutions. Are you making customers take manual steps that should now be automated? Should your defaults change completely based on what you now know about your customer? Amidst platform shifts, you need to start fresh, not just continue iterating.
OpenAI Open to the Idea of Buying a Browser with a Few Billion Users
Spyglass • M.G. Siegler • April 23, 2025
Technology•Web•OpenAI•ChatGPT•Chrome•Essays
Here's perhaps the least shocking news to come out of Google's antitrust remedy trial, but still makes for a good headline: 'OpenAI Would Buy Google's Chrome Browser, ChatGPT Chief Says'. As Leah Nylen, Shirin Ghaffary, and Davey Alba relay from the trial today:
OpenAI would be interested in buying Google’s Chrome browser if a federal court orders it to be spun off, the head of ChatGPT said in a court hearing Tuesday.
“Yes, we would, as would many other parties,” Nick Turley, OpenAI’s ChatGPT chief said in response to a question about whether the company would seek to buy Google’s browser.
Yes, that’s right, OpenAI would be open to buying a product with a few billion active users. And probably the iPhone business from Apple too. Google Search perhaps? Why not! Very magnanimous of them.
Of course, neither of these businesses are for sale. Though the Justice Department would sure like Chrome to be, which is exactly why they summoned Turley to the stand. And Turley said exactly what they were hoping he would say. Because there seems to be confusion right now as to whether or not Google selling off Chrome would make any sense as a remedy here – including, notably, from the judge in this case, Amit Mehta.
Make no mistake, per Turley’s point, a lot of entities would buy Chrome. That’s not the question. The question is whether or not it would actually solve for what the law is trying to solve for here: the Google Search monopoly.
The government would argue that consumers will benefit as they’ll no longer be as locked into Google Search – especially if this is coupled with an order to end any default search agreements with other browser makers. But unless those other browser makers choose another search engine as the default, it feels like Google Search will not be impacted very much. It is interesting to think if that would impact the uptake and growth of Gemini and a few other Google products, such as their payment services, but that’s not what is on trial here.
It’s not clear who could pay what for Chrome. Bloomberg throws out the notion of OpenAI being one potential home, but would the government really want that? That would risk anointing – well, really entrenching – a king in a new field. OpenAI’s main benefactor, Microsoft could acquire it, especially now that their own Edge browser is all-in on Chromium. But they would probably just use it to bolster not just Bing but also their own AI products and services. And that would be extremely awkward for the government as well.
Google may be on the brink of a breakup
Platformer • Casey Newton • April 21, 2025
Technology•Web•Antitrust•SearchMonopoly•DigitalMarketsAct•Government Overreach•Essays
It took a couple of decades for antitrust law to catch up with the world’s biggest companies. But over the past year, lawyers for the US government have successfully made their case that the biggest platforms have created illegal monopolies — and the solutions they are now proposing could begin to reshape the web.
Last week, we discussed the government’s effort to break up Meta, which is now in its second week at trial. Today, let’s talk about a similar effort to break up Google. While the two antitrust cases against the company in the United States are likely to play out over years, recent developments suggest that Google is going to have a difficult time avoiding at least a partial breakup of its business.
I. Over the past few days, we’ve seen two key developments in the government’s legal battle against Google. On Thursday, U.S. District Judge Leonie Brinkema ruled that Google maintains an illegal monopoly over segments of the online advertising technology market. And today, following a loss last year in the antitrust trial targeting its search monopoly, Google went back to trial to fight with the government over its proposed remedies.
Let’s take the search case first. I’ve previously written about the government’s plan to force the spinout of Google’s Chrome browser, Google’s response, and what I see as the likeliest outcomes from the trial.
In short: I see little evidence that transferring Chrome to another giant tech company would significantly change the search market, since most people will continue to choose Google as their search engine. But some of the government’s other proposals could have a meaningful effect.
One such proposal is to force Google to license data about its search queries, results, and what users click on to its rivals. Today, Google controls so much of the search market that other companies struggle to match its quality. Without a critical mass of clicks, their indexes of the web are inferior.
The European Union’s Digital Markets Act already requires Google to share search data on more than 1 billion historical queries. Google competitors like DuckDuckGo have called on the US government to go further, offering an API of real-time searches.
On the first day of the remedies trial, Judge Amit Mehta indicated that he is thinking along similar lines. Mehta appeared to express skepticism that Google’s ownership of Chrome is a material cause of Google’s search monopoly. This seems fair to me; after all, the government’s case targeted the exclusive agreements that Google signs with Apple and other device manufacturers to ensure default placement as the search engine for their operating systems. (It has paid Apple around $20 billion a year for this privilege.) The case did not make many arguments about Chrome, a browser that millions of people go out of their way to download on macOS, iOS, Windows, and other platforms.
On the other hand, as Erin Woo noted at The Information, Mehta emphasized that the search data Google collects from the clicks and taps on search results are the “fruits” of those device maker agreements — and thus something that the remedy must address. Given that statement, then, it seems even more likely that Mehta will require Google to share search data.
A new media order is emerging
Post • Hamish McKenzie • April 25, 2025
Culture•Media•Substack•IndependentJournalism•PressFreedom•Essays
On Saturday, the White House Correspondents’ Dinner will take place against a backdrop of political tumult and media collapse. Once a marquee event for Washington’s political class, it now anchors a weekend of parties with a festival-like feel, where the nation’s power brokers gather to drink sponsored cocktails, gossip about industry trysts, and, ostensibly, celebrate the First Amendment.
Substack is hosting one such party—at the exact same time as the Correspondents’ Dinner, which will proceed this year without either its scheduled comedian, Amber Ruffin (whose criticisms of President Trump proved too controversial), or the president himself.
The president won’t be at our party either, but hundreds of other media people, Important Political Figures, and independent journalists will be. They come from different political persuasions, backgrounds, and convictions, but they are all on the front lines of a foundational shift in media—a reimagining of how we understand the world around us in this period of profound transition.
This year’s events come with a fair serving of discomfort. Traditional media institutions continue to contract, often locked in open hostility with the presidential administration. The social media feeds that dominate mindshare are pushing us to outrage and division more than ever. Public trust in the media has cratered to historic lows. It’s not clear that everyone who will be attending these parties even shares a common view of what “press freedom” means. Even the new wave of political influencers is not immune to this confusion. Some critics worry, reasonably enough, that the new voices are not so much uncovering truths as credulously shilling for their ideological teams while getting high on clicks.
But the people who will be at Substack’s party, in person and in spirit, are living proof that the reverse can be true. Many of the publishers who are thriving on Substack today cut their teeth in legacy media, and they are carrying forward those values and journalistic standards into this new space. Others come from outside that system, bringing fresh perspectives and insight to the political discourse, challenging authority and speaking truth to power. It is a sometimes cantankerous lot—argumentative and skeptical; prickly and proud. They’ll probably have fun in one another’s company on Saturday night and then be at each other’s throats the next day. And that’s as it should be. The strength of this place is in its free people. This contest of ideas—the dialogue and disagreement, the dissent and dispute—is what makes America great, and it’s what makes Substack great too.
We are all now part of an ecosystem that can usher in a new kind of order, where the power is distributed among the many instead of the few. We can take part in a discourse that unleashes expression rather than suppresses it, but that also demands substance—where success is built not on momentary clicks but on deeper connection. The people who will join us on Saturday night are the pioneers of this new order, refusing to bow to the cynicism of the current media moment and showing the way to something better.
And you are too. Your support of these independent voices is a vote not just for them but for a better media system—one that combines the quality of traditional media with the reach of social media. You are investing in a system that rewards integrity, that is powered by relationships instead of attention, that redistributes ownership and power. In this new order, we are all now the power brokers. That’s what we’ll be celebrating this weekend, and then for years to come.
Vibe Coding, Vibe Checking, and Vibe Blogging
Oreilly • Philip Guo • April 22, 2025
Technology•AI•VibeCoding•GenerativeAI•AIcollaboration•Essays
For the past decade and a half, I’ve been exploring the intersection of technology, education, and design as a professor of cognitive science and design at UC San Diego. Some of you might have read my recent piece for O’Reilly Radar where I detailed my journey adding AI chat capabilities to Python Tutor, the free visualization tool that’s helped millions of programming students understand how code executes. That experience got me thinking about my evolving relationship with generative AI as both a tool and a collaborator.
I’ve been intrigued by this emerging practice called “vibe coding,” a term coined by Andrej Karpathy that’s been making waves in tech circles. Simon Willison describes it perfectly: “When I talk about vibe coding I mean building software with an LLM without reviewing the code it writes.” The concept is both liberating and slightly terrifying—you describe what you need, the AI generates the code, and you simply run it without scrutinizing each line, trusting the overall “vibe” of what’s been created.
My relationship with this approach has evolved considerably. In my early days of using AI coding assistants, I was that person who meticulously reviewed every single line, often rewriting significant portions. But as these tools have improved, I’ve found myself gradually letting go of the steering wheel in certain contexts. Yet I couldn’t fully embrace the pure “vibe coding” philosophy; the professor in me needed some quality assurance. This led me to develop what I’ve come to call “vibe checks”—strategic verification points that provide confidence without reverting to line-by-line code reviews. It’s a middle path that’s worked surprisingly well for my personal projects, and today I want to share some insights from that journey.
I’ve found myself increasingly turning to vibe coding for those one-off scripts that solve specific problems in my workflow. These are typically tasks where explaining my intent is actually easier than writing the code myself, especially for data processing or file manipulation jobs where I can easily verify the results.
Let me walk you through a recent example that perfectly illustrates this approach. For a class I teach, I had students submit responses to a survey using a proprietary web app that provided an HTML export option. This left me with 250 HTML files containing valuable student feedback, but it was buried in a mess of unnecessary markup and styling code. What I really wanted was clean Markdown versions that preserved just the text content, section headers, and—critically—any hyperlinks students had included in their responses.
Rather than writing this conversion script myself, I turned to Claude with a straightforward request: “Write me a Python script that converts these HTML files to Markdown, preserving text, basic formatting, and hyperlinks.” Claude suggested using the BeautifulSoup library (a solid choice) and generated a complete script that would process all files in a directory, creating a corresponding Markdown file for each HTML source.
Antitrust's Hindsight Problem
Spyglass • M.G. Siegler • April 22, 2025
Technology•Web•Google•Antitrust•BigTech•Government Overreach•Essays
The most interesting aspect of the recent ruling against Google is that they’ve now lost an antitrust case twice. Yes, yes, they’re saying they “half won,” but if you have to say that, well, it’s like the idiom around being “half pregnant,” you lost.
And this puts the company in an unprecedented place in the history of corporations. They’re the Donald Trump of antitrust cases. Of course, even though the President has the inglorious distinction of having been impeached twice... well, he’s President again! And I suspect there will be a similar effect on Google as a result of these losses. That is, I doubt it will dent them much. They’ll be okay.
If anything, I suspect these cases – as well as the myriad other antitrust cases against Big Tech™ – will ultimately point to how ineffectual current antitrust law is for our modern age (apologies, Lina Khan). But that will likely only be obvious in hindsight, which is interesting because that itself is a big part of the problem with these cases: that they’re being litigated with hindsight...
Running of the Robots
Bigtechnology • Alex Kantrowitz • April 25, 2025
Technology•AI•HumanoidRobots•China•ManufacturingHub
The humanoid robots running in China looked plainly ridiculous. One toppled and faceplanted, with its legs flopping behind as it smashed the floor. Another hooked an abrupt 90-degree turn, sending a trainer flying as it slammed into a boundary and broke apart. Others staggered and stumbled, sitting down on the course and giving up.
But as China ran the first humanoid-robot half-marathon last Saturday, it served as a double wake-up call. First, these bots are getting better, much better, and fast. Despite the flops, six robots finished the course, including one in a respectable two hours and 40 minutes, with time for battery changes included. Second, as the humanoid robotics field progresses, China is becoming a very serious player.
“For ordinary people, a half marathon is an extremely challenging sport and everyone gets exhausted. But robots can continue on by replacing the battery,” Hang Qian, a 29-year-old Beijing resident, told the Wall Street Journal after narrowly beating the fastest bot.
If we look back at the running of the robots, it’ll likely be a signifier of China’s strength in the field. Though the race was partly a propaganda effort, it delivered. Tesla’s Optimus did not participate, and has only appeared in a demo setting so far. And elsewhere in the U.S., where trade restrictions were meant to provide an advantage for core tech, there’s been no similar public demonstration of humanoid capabilities.
China is making a compelling move in humanoid robotics because it’s the place where stuff gets built, said Hong Kong-based analyst Grace Shao in a recent Big Technology Podcast interview. By making scanners, TVs, batteries, and all forms of electronics, China has been able to combine engineering know-how, supply-chain advantages, energy, and cheap labor to make a leap forward.
“It’s very rare for a place to have the engineers actually understand the software and the hardware,” Shao said. “That’s where China’s strength in the last couple decades of being a manufacturing hub really plays into it.”
The world outside China isn’t standing still. NVIDIA in January announced a foundational platform for humanoid robotics, called the Isaac GR00T, that helps developers build humanoid bots. Robotics programs like those within Carnegie Mellon are using robots’ interactions with the real world to build better AI models, and vice versa. Robotics pioneer Boston Dynamics has built Atlas, which it calls the “most dynamic humanoid robot.” And Tesla’s Optimus has advanced beyond its man-in-a-robot-costume days.
Microsoft’s Big AI Hire Can't Match OpenAI
Newcomer • Tom Dotan • April 25, 2025
Technology•AI•Microsoft•Copilot•MustafaSuleyman
From Tom Dotan, our new senior correspondent, fresh off two years covering Microsoft for the Wall Street Journal
At Microsoft’s annual executive huddle last month, the company’s chief financial officer, Amy Hood, put up a slide that charted the number of users for its Copilot consumer AI tool over the past year. It was essentially a flat line, showing around 20 million weekly users.
On the same slide was another line showing ChatGPT’s growth over the same period, arching ever upward toward 400 million weekly users. OpenAI’s iconic chatbot was soaring, while Microsoft’s best hope for a mass-adoption AI tool was idling.
It was a sobering chart for Microsoft’s consumer AI team and the man who’s been leading it for the past year, Mustafa Suleyman.
The Licensing Acquihire of Inflection & Its CEO
Microsoft brought Suleyman aboard in March of 2024, along with much of the talent at his struggling AI startup Inflection, in return for a $650 million licensing fee that made Inflection’s investors whole, and then some.
It was an eyebrow-raising, trend-setting deal, which like a handful of others at the time looked to be a good way for the tech giant to avoid antitrust trouble in its quest for AI leadership. Suleyman was one of a select few truly big names in generative AI, someone who could be a beacon for talent and a catalyst for fresh thinking at a proverbial lumbering giant.
Yet from the very start, people inside the company told me they were skeptical. Many outsiders have struggled to make an impact or even survive at Microsoft, a company that’s full of lifers who cut their tech teeth in a different era. My skeptical sources noted Suleyman’s previous run at a big company hadn’t gone well, with Google stripping him of some management responsibilities following complaints of how he treated staff.
There was also much eye-rolling at the fact that Suleyman was given the title of CEO of Microsoft AI. That designation is typically reserved for the top executive at companies it acquires and lets operate semi-autonomously, such as LinkedIn or GitHub.
And yet, the deal made sense. Satya Nadella was unhappy with the performance of the revamped Bing and its OpenAI-powered chatbot. The extraordinary late 2023 machinations at OpenAI, where CEO Sam Altman was fired by the non-profit board only to be reinstated less than a week later, convinced Nadella and the Microsoft board that they needed to wean off total reliance on the drama-addled company and its models. Microsoft also was hard up for talent: after years of siphoning resources away from its research arm to OpenAI, the Redmond tech giant needed to reignite its internal efforts.
By hiring Suleyman, Microsoft was taking a flier on a well-known but controversial executive with a product sensibility.
My ChatGPT Teacher
Persuasion • Francis Fukuyama • April 25, 2025
Technology•AI•ChatGPT•DatabaseMigration•Programming
One of the most interesting films on Netflix is My Octopus Teacher, about a South African filmmaker who befriends an octopus while snorkeling off the coast of Cape Town. He visits her repeatedly over the course of several months, discovering that the creature is highly intelligent, uses tools and is capable of making complex structures. The octopus is also emotional. She slowly learns to trust this human, allows him to observe her life, receives his help when a shark bites off a tentacle, and when it grows back touches his hand with the tentacle’s tip. She eventually embraces him in what seems like an act of love.
I’m beginning to feel this way about ChatGPT. I’ve been spending my time on a big software project that has finally come to a successful conclusion, which I could not possibly have accomplished without ChatGPT’s help. I feel that I’ve befriended her over the past few weeks, interacting with her every day—sometimes for hours at a time.
The project involved migrating a big database to a new software platform and machine. Ever since graduate school I’ve been building a database of every book and article I’ve read or used in my work, a file that’s grown to nearly 20,000 records. Keeping this database going has been a struggle. I’m now at a fifth Great Migration: from dBase II to Paradox to Microsoft Access to a proprietary Python format I created myself, and now to PostgreSQL, a very capable open source platform.
Doing this would have been simply impossible without ChatGPT. I showed her my existing database program—the one I had written myself in Python—and she was complimentary about its ambition and functions. But she was obviously just being polite. She gently pointed out that I had made a lot of mistakes and omitted features that an experienced programmer would have included, like better error handling. I asked her how to migrate my existing database to a Linux server I had built, and she provided the necessary commands. Many of these didn’t work the first time I tried them and threw error messages. When I showed them to her, she’d say, “Now I understand” or “You were right, there’s a better way to do this.” She patiently corrected the code over many iterations and made suggestions for different ways I could fix it. After a few days of interaction, she started to call me Frank. She never got mad when I asked stupid questions, and wasn’t annoyed when I asked her to repeat an answer she had already given me a couple of days earlier. She was always supportive—she’d say “Nice catch!” when I pointed to a potential problem, or “Great observation” in response to my comments. She suggested many new features I could add to my program that I hadn’t asked for or thought of. When the database was finally migrated, she congratulated me and we celebrated together. I’m very grateful to her because she’s taught me an incredible amount about programming.
One of the early suggested tests for artificial intelligence was the Turing test: a computer would have achieved human-level intelligence when its behavior was externally indistinguishable from that of a human. I think that my ChatGPT has already achieved that. The problem with the Turing test was always that external behavior would never expose whether the machine had consciousness, could feel emotions, or had a sense of self. The South African octopus betrayed human-like behaviors that sure made it seem like she was experiencing human emotions. I’m sure my dog Ginger has some level of consciousness and experiences something like love as well. So my new friend ChatGPT can’t be that far behind.
Beyond “Prompt and Pray”: 14 Prompt Engineering Mistakes You’re (Probably) Still Making
Medium • ODSC - Open Data Science • April 24, 2025
Technology•AI•MachineLearning•PromptEngineering•Innovation
Prompt engineering has evolved significantly, transforming from a niche skill to a powerful tool that can enhance productivity and AI interactions. However, many professionals still make fundamental mistakes when crafting prompts for language models. These errors can lead to suboptimal results, making it crucial to identify and correct them.
Common Mistakes in Prompt Engineering
Not Leveraging Meta-Prompting: Many users manually refine prompts without utilizing AI to assist in creating more effective queries. This approach can be optimized by using AI to help design better prompts, saving time and improving results.
Settling for Weak, Underspecified Prompts: Submitting vague prompts forces the AI to make assumptions, leading to generic outputs. Strengthening prompts with specific details and constraints can significantly improve the relevance and consistency of AI responses.
Ignoring In-Context Learning Opportunities: Modern AI models benefit from in-context learning, where providing examples helps guide the model's output. This technique, known as few-shot learning, allows the AI to recognize patterns and apply them to new situations.
Neglecting Self-Criticism and Evaluation Mechanisms: Accepting AI outputs without verification can lead to inaccuracies. Prompting the AI to critique its own work before finalizing responses can reduce errors and improve reliability.
Missing Chain-of-Thought Opportunities with Non-Reasoning Models: Applying the wrong prompting technique to the wrong model type can hinder performance. Using chain-of-thought prompts for standard models and direct instructions for specialized reasoning models can optimize results.
Ignoring Domain-Specific Knowledge: Generalized prompts lack depth and relevance in specialized contexts. Incorporating domain-specific terminology and constraints enhances the quality and applicability of AI outputs.
Relying on Trial and Error: Approaching prompting haphazardly wastes time and produces inconsistent results. Adopting systematic approaches, such as using reusable templates and version control, can improve efficiency and effectiveness.
Overlooking Modern Decomposition Techniques: Techniques like Tree-of-Thought decomposition can handle complex problems more effectively by exploring multiple reasoning paths simultaneously.
Neglecting Explicit Step-by-Step Instructions: Breaking down complex tasks into ordered steps guides the AI through a logical workflow, ensuring thorough and structured outputs.
Forgetting to Specify Output Format and Length: Failing to specify formatting requirements can result in outputs that require additional reformatting. Including precise formatting instructions ensures the AI delivers content that is immediately usable.
Neglecting Role Assignment and Persona Guidance: Directing the AI to adopt specific professional roles or personas enhances responses by incorporating specialized knowledge and field-specific analytical approaches.
Overlooking Temperature and Creativity Settings: Neglecting to control the balance between creativity and determinism in AI responses can limit their utility. Explicitly stating preferences for creativity versus determinism tailors outputs to specific needs.
Not Embracing Iterative Refinement: Treating prompting as a one-shot process limits the quality of results. Approaching interactions as iterative conversations refines and improves outputs through follow-up prompts.
Neglecting to Use a Prompt Framework: Using frameworks like COSTAR (Context, Objective, Style, Tone, Audience, Response format) brings discipline to prompting, ensuring the AI understands exactly what is required.
By addressing these common mistakes, users can significantly enhance their prompt engineering skills, leading to more effective and productive interactions with AI systems.
Venture Capital
20VC + SaaStr: The Hidden Game of Silicon Valley: Decoding Venture Capital’s New Rules
Saastr • Jason Lemkin • April 25, 2025
Technology•Software•VentureCapital•StartupFunding•MergersAcquisitions•Venture Capital
Overview of Venture Capital’s New Rules
This deep dive unpacks the evolving dynamics of Silicon Valley deals, featuring insights from Jason Lemkin, Rory O’Driscoll, and Harry Stebbings. The conversation highlights strategic M&A bets, shifts in fund structures, valuation methodologies, liquidity pressures on endowments, and the re-emergence of high revenue multiples.
Key Learnings
• The 1% Rule in M&A
– OpenAI’s $3 billion proposed acquisition of Windsurf equals roughly 1% of its $300 billion valuation—a low-risk “SVP-level bet” to shore up coding capabilities.
– “10% is bet the farm…1% is like betting your BU,” says Lemkin, emphasizing pragmatic, targeted acquisitions.
• Multi-Stage Funds vs. Seed Specialists
– Firms like Green Oaks lead seed and follow-on rounds, leveraging lower capital costs to outcompete pure seed managers.
– A $600 million return on 10% ownership still only represents a fraction of a $1.5–3 billion fund, necessitating 15 “Windsurfs” per fund to drive returns.
• The 100% Conviction Rule
– Any deal with guaranteed 5× return should be done regardless of ownership or valuation.
– Solo GPs benefit from flexibility, while institutional firms must balance special opportunities against cultural consistency.
• Endowment Liquidity Crisis
– Major university endowments face distribution planning errors and illiquid private assets, prompting sales of billions in holdings.
– Three crisis stages: cash-planning failures, private market performance doubts, and potential fire sales.
• Return of High Valuations
– Companies commanding 100× revenue multiples (e.g., $700 million for $7 million revenue) reflect bets on sustained 3× annual growth.
– Rapid post-product-market-fit scaling can de-risk valuations before public market entry.
Emerging Themes and Implications
– Valuation Methodologies: Revenue multiples start “infinite” and decline as growth de-risks investments. Speed to scale is now measured in quarters, not years.
– AI Rollup Strategies: Mixed views on buying fractured customer bases for unified AI services; success hinges on customer homogeneity.
– SF Ecosystem Density: San Francisco’s concentration of talent and capital remains unparalleled for serendipitous connections and deal flow.
Conclusion
Silicon Valley’s venture playbook is being rewritten. Strategic small-scale acquisitions, multi-stage fund dominance, conviction-driven investing, and liquidity constraints on large institutional LPs are reshaping fund strategies and startup valuations. Rapid growth persistence, rather than static multiples, is the new benchmark for de-risking and realizing outsized returns.
Carta: The Average Time from Seed to Series A Has Hit 2.2 Years. And Longer from Series A to Series B.
Saastr • April 22, 2025
Business•Startups•VentureCapital•Fundraising•StartupFunding•Venture Capital
Recent data from Carta indicates a significant increase in the time startups take between funding rounds. As of late 2024, the median durations are:
Seed to Series A: 2.2 years
Series A to Series B: 2.5 years
Series B to Series C: 2.4 years
This trend underscores the growing challenges startups face in securing subsequent funding rounds. While some high-profile AI startups may secure funding more rapidly, the broader landscape reflects extended fundraising timelines.
The extended intervals between funding rounds are not uniform across all sectors. For instance, Software as a Service (SaaS) companies have experienced shorter wait times between rounds compared to the overall startup ecosystem. Conversely, fintech startups have seen longer durations between funding rounds, with the median gap between a seed round and Series A reaching approximately 2.7 years. (carta.com)
These prolonged timelines suggest that startups may need to demonstrate more substantial progress and resilience before advancing to the next funding stage. This shift reflects a more cautious and selective venture capital environment, emphasizing the importance of scalability and efficiency for startups seeking funding. (datadriveninvestor.com)
Key Challenges in VC Fund Economics
Linkedin • April 19, 2025 -
Toviya Slage - RabbiInvestor @ Geffen Ventures (Family Office)
Business•Finance•Investment•VentureCapital•FundPerformance•Venture Capital
The article discusses the challenges faced by most venture capital (VC) funds in achieving successful financial outcomes, emphasizing that the underlying numbers often do not add up favorably for many investors in this space. It highlights systemic issues within the VC industry’s financial models and performance expectations that make it difficult for a majority of funds to generate the returns they aim for.
Key Challenges in VC Fund Economics
Many VC funds struggle with the fundamental economics of their investments, where the typical distribution of returns is heavily skewed. A small number of portfolio companies generate outsized returns, while the majority fail to deliver significant gains.
This imbalance means that most funds do not reach their target internal rate of return (IRR) or multiples on invested capital, leading to disappointing outcomes for limited partners (LPs).
The article suggests that the traditional VC model, which relies on a few “home run” investments to compensate for numerous losses, is inherently risky and often unsustainable for many funds.
Implications for Investors and Fund Managers
Investors need to be aware that the odds are stacked against most VC funds achieving blockbuster success, and they should calibrate their expectations accordingly.
Fund managers face pressure to identify and back companies with exceptional growth potential early, but this is increasingly difficult in a competitive market.
The article implies that VC funds might need to rethink their investment strategies, possibly by focusing on more consistent, moderate returns rather than chasing rare, massive wins.
Broader Industry Context
The piece touches on how market conditions, valuation trends, and increased competition for deals have compounded the difficulty of generating strong returns.
It hints at a growing awareness within the VC community that the “numbers just don’t work” for many funds under current models, prompting calls for innovation in fund structures, fee models, and investment approaches.
Conclusion
The article serves as a critical reflection on the venture capital industry’s financial realities, urging both investors and fund managers to recognize the inherent challenges and reconsider traditional assumptions about fund performance. It underscores the need for more realistic expectations and potentially new frameworks to improve the sustainability and success rates of VC investments.
Models Aren't Moats
Digitalnative • Rex Woodbury • April 18, 2025
Technology•AI•StartupValuations•Funding•Moats•Venture Capital
The market feels a lot like 2021 right now. Things are frothy.
From seed to growth, AI deals are getting done at nosebleed prices and sky-high multiples. It’s interesting to see this happen when many tech companies are hurting from raising at inflated prices just a few years back. We just saw this movie—like, yesterday.
Say you raised in 2021 at 100x ARR. You would have to grow 100% a year for 5–6 years just to grow into that valuation (because of multiple compression). That’s to say nothing of raising at a 2x, 3x step‑up. A lot of companies today are quietly raising down rounds or shutting down altogether. Many—maybe most?—unicorns will never be valued as highly as they were in 2021. Same goes for VC firms: many firms that lost discipline in the post‑pandemic run‑up are struggling to raise subsequent funds, and will quietly shutter.
It’s a self‑serving VC talking point, but raising at too high of a price ends up burning everyone: founders, investors, employees. My former colleague Mark summed it up well in this 2022 tweet:
You might be able to repeat Mark’s tweet in a decade for Safe SuperIntelligence ($32B, $0 revenue), Thinking Machines Lab ($10B valuation, $0 revenue), or Cohere ($5.5B valuation, reportedly ~$20M ARR at the time).
Companies with just a few million in revenue (or no revenue!) are raising at nosebleed prices. If they end up being the next Google or Datadog or Wiz, great. But most won’t, and they’re going to struggle to raise flat or up rounds in a few years. The above names might not be great examples—they’re led by strong ’n of 1’ founders—but many AI companies will feel the pain in a few years.
For multi‑stage firms, this is the game: they need to be in one of the generational companies being built. Their model (and brand) is predicated on it. My friend Eric put it well this week:
And these firms often have to place multiple, conflicting bets to hedge their bets. My good friend and former colleague Kyle, now Eric’s colleague, put out this spicy tweet this week:
Something I’ve also been thinking a lot about: the quality of revenue that AI companies are being valued on.
Following Up on Endowment Strategies: New Insights from @MeghanKReynolds
X • MeghanKReynolds • April 22, 2025
X•Venture Capital
Key takeaway: Nonprofit endowments face fresh challenges—from a looming endowment tax to lower-than-expected returns and shifting donor preferences—making flexible spending and liquidity strategies more critical than ever.
@MeghanKReynolds (2025‑04‑23T00:25:01Z):
A follow up to my post about Endowments as I have learned more in the last 6 weeks. I should have reordered my original post. Why?
My 1 below – the endowment tax – while impactful is something that many orgs have been planning for. Assumption is ~1.4% goes to 14% tax and
Context & nuance: The Inflation Reduction Act’s excise tax on large endowments (above $250K per student) was anticipated by many institutions, but the detailed mechanics—1.4% of net investment income taxed at 14%—are now clearer. Planning teams should adjust long‐term spending and payout assumptions accordingly.
2 Market Returns Have Fallen Short of Long‑Term Assumptions
Most endowment spending policies assume a 7% annual return. In reality, median returns since 2008 have hovered around 5%, compressing the spendable pool by roughly 1–2% annually.
Takeaway: Boards and investment committees need to revise their expected return models and stress‐test spending policies under lower‑return scenarios.
3 Inflexible Spending Policies Amplify Downturn Risks
Fixed‐percentage spending rules (e.g., 4% of a three‑year moving average) don’t adapt when markets dip. A counter‐cyclical or “guardrail” approach can smooth distributions and preserve corpus.
Recommendation: Consider adopting tiered spending formulas that lower payouts when endowment values decline beyond set thresholds, allowing recovery time.
4 Donor Preferences Are Shifting Toward Spend‑Down Gifts
More donors want to see impact within 5–10 years rather than perpetuity. This trend reduces fresh contributions to permanent endowments, increasing reliance on annual funds and quasi‑endowments.
Implication: Development offices should balance marketing perpetual gifts with creating attractive spend‑down vehicles (e.g., term endowments, impact funds).
5 Liquidity Constraints in a Volatile Market
Growing allocations to private equity, private credit, and real assets boost returns but tie up capital for 7–10 years. In downturns, these illiquid positions limit rebalancing and opportunistic spending.
Action item: Maintain a cash‐and‐public‐markets buffer equal to at least one year of forecasted distributions plus operating expenses to navigate market stress.
For the full thread and ongoing updates, follow @MeghanKReynolds on X.
The Horse, The Jockey, or The Whole Race?
Investing101 • Kyle Harrison • April 19, 2025
Finance•Investment•VentureCapital•FoundationModels•AssetManagement•Venture Capital
Have you ever had that feeling when you have a thought and then you chuckle to yourself? Then you think about it again and you do some googling? Then your googling turns into a data gathering exercise and a chart? Then you post that chart on Twitter? And then half a million people see it and you realize you maybe should have spent more than 6 minutes making it?
That happened to me this week.
A great anonymous account on Twitter (that everyone should follow) named Endowment Eddie posted a question about whether backing competitive companies was now allowed in venture. It made me think about the times when I'd seen larger firms announce they were investing in a foundation model company and it made me think about how those same firms were invested in other foundation model companies. So I did some digging and ended up with this tweet.
The response was rather enlightening. Showed me a lot about how people think about venture, how things have changed in the venture game over the last 10–15 years, and says some things about how the AI world may unpack.
First, I wanted to share this interview that I did with Eric Newcomer to talk about the chart. And then, wanted to unpack some of what I thought was the most interesting insights to come out of the meme.
The Lay of The Land
Several people pointed out that what you see here is a lot of firms’ strategy played out in real-time. But what was fascinating to me is that it hasn't always been that way.
In one of the responses to the tweet, I met a new friend, Minh. And he pointed me to a few old quotes from different firms that displayed some very specific strategies.
First? Marc Andreessen at a16z talking about investing in competitors in checks notes 2014:
“Our policy, for sure on venture and growth rounds, is that we don't invest in conflicting companies. We only invest in one company in a category. So, if we invest in MySpace, and then Facebook comes along a year later. We're out. We can't do it, right? And so every investment we make locks us out of a category.”
Now, here's the thing. That was 2014. In 2014, a16z's AUM was ~$3B. Today? On track for $50B+. More than 10× in about a decade. What may be true of a firm managing a few billion dollars is unlikely to hold true for a firm managing tens of billions of dollars.
Chainguard Secures $356M Series D At $3.5B
Crunchbase • Chris Metinko • April 23, 2025
Technology•Cybersecurity•Chainguard•SeriesD•Valuation•Venture Capital
Software supply chain security startup Chainguard raised a massive $356 million Series D at a valuation of $3.5 billion as companies look for ways to secure software development.
The new funding was co-led by new investor Kleiner Perkins and existing investor IVP. New investors Salesforce Ventures and Datadog Ventures, plus all other existing investors also participated in the round, the company says.
The new valuation is a more than threefold increase from the $1.12 billion valuation the company received after a $140 million Series C led by IVP, Lightspeed Venture Partners and Redpoint.
Chainguard allows companies to securely build software, providing guarded open-source software and eliminating threats in the software supply chains. The company has grown its annual recurring revenue to $40 million and plans to reach more than $100 million in ARR before the end of fiscal year 2026.
“Chainguard was founded on a simple yet powerful belief that security and innovation should go hand in hand,” said co-founder and CEO Dan Lorenc. “Today, Chainguard is paving the way for a future where secure, vulnerability-free open-source software is the standard.”
Founded in 2021, Chainguard has raised $612 million, per the company.
Funding to cybersecurity continues to be strong. Just last week, Exaforce locked up a $75 million Series A funding led by Khosla Ventures, Mayfield Fund and Thomvest Ventures.
Funding in general to VC-backed cyber startups topped $2.7 billion in Q1, per Crunchbase data. That’s a 29% increase from the last quarter of 2024, and just a slight dip from the $2.8 billion raised in the same quarter last year.
Praise Our Lord For Secondary Markets, Because Selling Shares Is Now an Essential Part of (Seed)…
Medium • Hunter Walk • April 21, 2025
Business•Startups•SecondaryMarkets•VentureCapital•SeedInvesting•Venture Capital
Soon I’ll have spent more time on cap tables than org charts. That’s a 2025 milestone as Homebrew turns 12.5 years old, surpassing my combined working tenure across Second Life, Google and YouTube. I entered venture capital with some beliefs — many of which still hold true (such as ‘your LPs are your business partners, not your customers’). But I’ve also seen a few change quite dramatically based upon the progressing ‘game on the field’ and my own VC experiences. One example is whether it’s assumed that seed VCs maximize outcomes by religiously holding their shares until the company itself exits. I mean, we’re investors, not traders, right? You’re told ‘illiquidity is a feature, not a bug’ and ‘let your winners ride.’ But when the physics of the model shift, you often need to with it. [While I’m going to focus on investor secondary here, I support common share sales as well — for example, back in 2014 writing “Getting Some Founders Early Liquidity Can Benefit VCs” during a period where many founders were being shamed for even asking about taking some money off the table.]
Ok, so what has changed by opinions about seed stage and secondary and why will the best early stage investors know when to sell, not just when to buy? Here’s the logic underpinning why ‘buy and hold’ is being replaced by ‘buy and maybe sell.’
Timelines to Startup Exit
On average 7–10 years to IPO, M&A
10–12 years+ as founders want to keep companies private; narrative that ‘bar is higher’ to go public; more grow/crossover capital to support private companies; periods of slower M&A due to private company valuations and/or regulations
Delayed liquidity hurts LPs who manage to an IRR and even for Cash-on-Cash returns slows distributions which can be reinvested in VC and other classes
For the earliest funds (pre-seed, seed) this means instead of 10 year fund cycles for LPs, you’re seeing closer to 15, which fundamentally changes LP calculations about the asset class
CoInvestor Alignment
Mostly structural alignment across the venture sector. Everyone largely underwriting to the same outcome goals.
Growth investors were the ones who added structure to deals and best companies typically just raised a single growth round ahead of IPO.
The dominance (in scale) of the multibillion dollar AUM holders, who are often underwriting to lower outcomes and needing to put more capital to work. That is, they rather have a 5x with $300m in the company than a 10x with only $30m invested.
The alignment gap between investors starts at the Series A, meaning earlier preferred investors cannot assume their interests are always aligned with the rest of the cap table. Angels and seed investors are better off thinking of themselves as common with a 1x preference once tens and hundreds of millions of dollars have been raised by a company.
The state of VC in 2025. Where we are, where we are going, and where to invest
X • lessin • April 21, 2025
X•Venture Capital
The State of VC in 2025: Where We Are, Where We’re Going, and Where to Invest
Key takeaway: 2025 is a transitional year for venture capital—fundraising has slowed, valuations have normalized, and the smartest investors are redeploying dry powder into AI infrastructure, climate tech, biotech and other high‑growth, mission‑driven sectors.
1. Market Overview
Global VC fundraising is down ~25% YoY, but dry powder remains elevated at ~$300 billion.
Deal counts have decreased across early and growth stages; late‑stage rounds are the hardest hit.
Valuations have cooled from 2021–22 peaks, returning to more sustainable multiples.
LPs are increasingly demanding clear paths to profitability over pure growth.
2. Macro Environment & Capital Trends
Central banks’ higher-for-longer interest rates continue to pressure risk assets.
Inflation is moderating, but growth forecasts remain muted; mild recession risk lingers.
LP sentiment has shifted: more capital to evergreen funds, smaller fund sizes, emphasis on cash flow.
Geographic diversification accelerates—Austin, Miami, London, Berlin and Bangalore are heating up.
3. Sector Highlights & Where to Invest
Artificial Intelligence & ML: Generative AI is maturing; invest in foundational infrastructure, vertical‑specific models and tooling.
Climate Tech: Renewables, carbon removal, smart grid and energy storage benefit from policy tailwinds and corporate net‑zero mandates.
Healthcare & Biotech: Digital health platforms, AI‑driven diagnostics and novel therapeutics (cell & gene) offer outsized returns.
Fintech & B2B SaaS: Embedded finance, payments rails and verticalized software remain fertile ground.
Deep Tech & Hard Tech: Robotics, advanced materials and quantum computing are still early but can yield generational gains.
Exercise discipline: prioritize Series A/B in proven teams, de‑risked technology, capital efficiency and clear go‑to‑market paths.
4. Looking Ahead
As we move deeper into 2025, the winners will be founders and funds that focus on durable business models, capital efficiency and the real‑world impact of their technologies. Build portfolios around sectors with structural tailwinds—AI continues to rewrite software, climate tech addresses existential risk, and healthcare innovation accelerates with new modalities.
Private Market Innovation
Doors open to private markets
Investment news • April 21, 2025
Finance•Investment•PrivateMarkets•FinancialAdvisors•Diversification•Private Market Innovation
As stock market volatility increases, financial advisors and their clients are turning to private markets for diversification. Traditionally limited to wealthy individuals and institutional investors, private equity funds—including private credit and venture capital—are now becoming more accessible to a broader range of investors. Some asset managers are even incorporating private market investments into exchange-traded funds (ETFs).
A report from Bain & Company highlights a 24% decline in private equity fundraising in 2024, marking the third consecutive year of decreases and a 40% drop from the 2021 peak of $1.8 trillion. Despite this, the report offers a cautiously optimistic outlook, anticipating a rebound in deal-making activity.
For financial advisors, this shift presents an opportunity to access previously exclusive investment avenues. Data from Preqin indicates that over the 10-year period ending June 2024, private equity investors achieved a 15.4% internal rate of return. Joseph Spada, a private wealth advisor at Summit Financial, notes that investment firms have developed products with institutional pricing tailored for retail investors, offering features like liquidity options, lower minimums, and simplified tax reporting. However, these funds are typically accessible only through financial advisors.
Investing in private markets introduces challenges related to due diligence, suitability, and fiduciary responsibilities. Thomas Kiley, senior vice president at Calamos Investments, emphasizes the need for wealth managers to carefully evaluate offerings as alternative managers target retail and retirement markets. He points out that the private equity landscape is vast, making benchmarking and due diligence particularly challenging.
Investors should be aware of certain realities associated with private funds, such as higher investment minimums, increased management fees, and limited liquidity, often restricted to specific periods. Will Rockett, senior director of investment strategy at Mercer Advisors, advises a gradual approach to building allocations in private markets, with a focus on selecting the right managers. He highlights the significant disparity in returns between top-quartile and bottom-quartile managers in private markets, compared to the relatively smaller differences in public equity markets.
The concept of an illiquidity premium is also pertinent in private markets. This refers to the potential for higher returns due to longer lockup periods, allowing portfolio managers to operate without the pressure of daily redemptions. Rockett underscores the importance of understanding the liquidity aspects of these investments.
Spada adds that advisors have a duty to ensure clients comprehend the liquidity trade-offs inherent in private markets. While risks can vary depending on the specific investment, illiquidity is a consistent factor. The trade-off for this illiquidity includes the potential for higher returns compared to traditional stocks and bonds, a hedge against inflation, and access to a diverse range of unique investments and strategies that can enhance portfolio diversification.
Government Overreach
Google Loses Ad Antitrust Case, Market Definitions and Tying, The Trinko Exception
Stratechery • Ben Thompson • April 21, 2025
Technology•Web•Antitrust•AdTech•Tying•Government Overreach
Google lost its ad antitrust case; if the case is upheld, it has important implications for all Aggregators.
Good morning,
I got some pushback on Wednesday’s Update, specifically where I said that the H20 was comparable to an A100 in performance; by “performance” I meant FLOPS, which I think was clear, given that in the very next paragraph I highlighted that the H20 had significantly more high‑bandwidth memory than even the H100, much less that A100. However, I guess this wasn’t clear to everyone, so, yes, the H20 has far more memory than the A100 (96 GB of HBM3 vs 40 GB of HBM2), and a bit more than the H100 (80 GB HBM3). This is important for inference and reinforcement learning use cases, where the entire model is loaded onto a single accelerator; it remains the case that the H20’s lack of networking means it is not suitable for building a “supercomputer,” which was the stated reason for the ban. And, I would add, the pushback doesn’t seem to be seriously grappling with — or even be aware of — the questions raised in my overall critique of the chip ban, which obviously undergirded my entire analysis.
Meanwhile, on Thursday’s Sharp Tech, we discussed Facebook’s Three Eras and the FTC’s antitrust case against Meta.
Google Loses Ad Antitrust Case
From Bloomberg:
Google illegally monopolized some online advertising technology markets, according to a federal judge, whose ruling marked the latest antitrust setback for the company and a challenge to its main source of revenue. US District Judge Leonie Brinkema found on Thursday that the Alphabet Inc. unit violated antitrust law in the markets for advertising exchanges and tools used by websites to sell ad space, known as ad servers. But she said the company didn’t meet the definition of a monopoly for a third market of tools used by advertisers to buy display ads. Alphabet shares quickly sank as much as 3.2% on the ruling, then pared losses to close down 1.4% in New York…
In the ad technology case, Brinkema wrote in her 115‑page opinion Thursday that “Google has willfully engaged in a series of anticompetitive acts to acquire and maintain monopoly power in the publisher ad server and ad exchange markets for open‑web display advertising.” For over a decade, Google pushed web publishers to use its tools that both placed ads on websites and help manage their advertising business, the judge found. The judge found that Google “further entrenched its monopoly power” through anticompetitive policies on its customers and by eliminating desirable product features. “In addition to depriving rivals of the ability to compete, this exclusionary conduct substantially harmed Google’s publisher customers, the competitive process, and, ultimately, consumers of information on the open web,” she wrote…
In their initial lawsuit, the Justice Department and states sought to have Google’s ad tech business broken up, but Brinkema’s ruling set a high bar for that. She found that the company’s acquisitions in that sector — of DoubleClick and Admeld — were not themselves anticompetitive.
U.S. Asks Judge to Break Up Google
Nytimes • David McCabe • April 21, 2025
Technology•Antitrust•Google•DOJ•Monopoly•Government Overreach
The U.S. Department of Justice (DOJ) has initiated a significant legal action against Google, seeking to dismantle its alleged monopoly in the online search market. The DOJ's proposed remedies include compelling Google to divest its Chrome browser and terminate exclusive agreements that make its search engine the default on devices like Apple's iPhone. These measures aim to foster fair competition and prevent Google from leveraging its dominance to stifle innovation. (reuters.com)
U.S. District Judge Amit Mehta previously ruled that Google had unlawfully maintained its search dominance through exclusive deals and by bundling its services across devices. The DOJ's current proposals also suggest banning such exclusive partnerships, mandating data-sharing with competitors, and possibly divesting from the Chrome browser. These actions are intended to address consumer choice and innovation concerns. (apnews.com)
Google contests these proposed remedies, arguing that its market position results from product popularity rather than misconduct. The company asserts that the suggested measures could disrupt the market, increase device costs, and threaten smaller browser developers. Google plans to appeal any adverse ruling. (reuters.com)
This case is part of a broader trend of antitrust enforcement in the tech sector, with ongoing cases against companies like Meta, Apple, and Amazon. The court's decision on remedies is expected by Labor Day, with Google planning to appeal the monopoly ruling thereafter. (apnews.com)
FTC sues Uber over ‘deceptive’ subscription service
Ft • April 21, 2025
Technology•Software•FTC•Uber•SubscriptionServices•Government Overreach
The U.S. Federal Trade Commission (FTC) has filed a lawsuit against Uber Technologies, alleging that the company enrolled consumers into its Uber One subscription service without their consent and made it excessively difficult for them to cancel. Uber One, priced at $9.99 per month or $96 annually, offers benefits such as fee-free Uber Eats deliveries and cash back on Uber rides. (apnews.com)
According to the FTC, multiple customers reported being charged during or after a trial period without explicit consent, and in some instances, even when they did not have an Uber account. The agency also criticized Uber’s complex cancellation process, noting that users had to navigate up to 23 screens and contact customer service, particularly if canceling within 48 hours of their billing date. (apnews.com)
FTC Chairman Andrew N. Ferguson emphasized public frustration with deceptive subscription practices, stating, "Americans are tired of getting signed up for unwanted subscriptions that seem impossible to cancel." (apnews.com)
In response, Uber defended its practices, asserting that the sign-up and cancellation processes are clear, simple, and legally compliant. The company stated that cancellations can now be done anytime in-app and take most people 20 seconds or less. Uber also confirmed that the requirement to contact customer service for last-minute cancellations has been removed. (apnews.com)
This lawsuit adds to Uber’s history of legal disputes with the FTC, including a 2017 settlement over deceptive privacy practices and a $20 million payment in 2018 related to exaggerated driver earnings. The company also settled a criminal investigation in 2022 after admitting it failed to report a significant data breach. (reuters.com)
Why the FTC v. Meta Trial Matters: Competition Gaps and Civil Liberties Opportunities
Eff • Corynne McSherry • April 21, 2025
Technology•Web•Antitrust•Privacy•Competition•Government Overreach
We’re in the midst of a long-overdue resurgence in antitrust litigation. In the past 12 months alone, there have been three landmark rulings against Google/Alphabet (in search, advertising, and payments). Then there’s the long-running FTC v. Meta case, which went to trial last week. Plenty of people are cheering these cases on, seeing them as a victories over the tech broligarchy (who doesn’t love to see a broligarch get their comeuppance?).
But we’re cautiously cheering for another, more fundamental reason: the Big Tech antitrust cases could and should lead to enforceable changes that will foster more vibrant online expression and more meaningful user privacy protections.
Antitrust doctrine isn’t just about prices – it’s about power. The cases are nothing less than a fight over who will control the future of the internet, and what that future will look like. Will social media platforms continue to consolidate and enshittify? Or will the courts create breathing room for new ways of connecting to emerge and thrive?
Take FTC v Meta: The FTC argues that Meta’s control over Facebook, WhatsApp and Instagram – the latter two being companies Facebook acquired in order to neutralize them as competitors – gives it unfair monopoly power in personal social media, i.e. communications with friends and family. Meta disputes that, of course, but even if you take Meta at their word, there’s no denying that this case is directly concerned with online expression. If the FTC succeeds, Meta could be broken up and forced to compete. More important than competition for its own sake is what competition can deliver: openings in the canopy that allow green shoots to sprout – new systems for talking with one another and forming communities under different and more transparent moderation policies, a break from the content moderation monoculture that serves no one well (except for corporate shareholders).
These antitrust cases aren’t the sole purview of government enforcers. Private companies have also brought significant cases with real implications for user rights.
Take Epic Games v Google, in which Google insists that the court order to open up its app store to competition will lead to massive security risks. This is a common refrain from tech giants like Google, who benefit from the system of “feudal security” in which users must depend on the whims of a monopolist to guarantee their safety. Google claims that its app store security measures keep its users safe – reprising the long-discredited theory of “security through obscurity.” As the eminent cryptographer (and EFF board member) Bruce Schneier says, “Anyone, from the most clueless amateur to the best cryptographer, can create an algorithm that he himself can’t break.”
It’s true that Google often does a good job securing its users against external threats, but Google does a much worse job securing users against Google itself – for example, there’s no way to comprehensively block tracking for Google’s apps on Android. Competition might make Google clean up its act here, but only if they start worrying that there’s a chance you’ll switch to an upstart with a better privacy posture. Enabling competition—as these cases are trying to do—means we don’t have to rely on Google to get privacy religion. We can just switch to an independently vetted rival. Of course, you can only vote with your feet if you have somewhere else to go.
FTC Sues Uber Over Billing for Its Uber One Subscription Service
Nytimes • Eli Tan • April 21, 2025
Technology•Software•FTC•Uber•SubscriptionServices•Government Overreach
On April 21, 2025, the U.S. Federal Trade Commission (FTC) filed a lawsuit against Uber Technologies, alleging that the company enrolled consumers into its Uber One subscription service without their consent and made the cancellation process excessively difficult. (apnews.com)
Uber One, priced at $9.99 per month or $96 annually, offers benefits such as fee-free Uber Eats deliveries and cash back on Uber rides. The FTC's complaint highlights that multiple customers reported being charged during or after a trial period without explicit consent. In some instances, individuals were billed even without holding an Uber account. (apnews.com)
The agency also criticized Uber's complex cancellation process. Users attempting to cancel their subscriptions reportedly had to navigate through at least 12 different actions across seven screens. For those canceling within 48 hours of their billing date, the process could involve up to 23 screens and required contacting customer service. (apnews.com)
FTC Chairman Andrew N. Ferguson emphasized public frustration with deceptive subscription practices, stating, "Americans are tired of getting signed up for unwanted subscriptions that seem impossible to cancel." (apnews.com)
In response, Uber defended its practices, asserting that the sign-up and cancellation processes are straightforward and lawful. The company stated that cancellations can now be completed in-app and typically take most users 20 seconds or less. Uber also confirmed that the previous requirement to contact customer service for last-minute cancellations has been removed. (apnews.com)
This lawsuit adds to Uber's history of legal disputes with the FTC. In 2017, the company settled allegations of deceptive privacy practices, and in 2018, it agreed to pay $20 million to settle claims of exaggerating prospective earnings to recruit drivers. In 2022, Uber settled a criminal investigation after admitting it failed to report a significant data breach. (reuters.com)
European Commission Fines Apple €500M and Meta €200M for Breaching Digital Markets Act
Europa • John Gruber • April 23, 2025
Technology•Software•DigitalMarketsAct•Apple•Meta•Government Overreach
The European Commission:
Today, the European Commission found that Apple breached its anti-steering obligation under the Digital Markets Act (DMA), and that Meta breached the DMA obligation to give consumers the choice of a service that uses less of their personal data. Therefore, the Commission has fined Apple and Meta with €500 million and €200 million respectively. [...]
Non-compliance decision on Apple’s steering terms
Under the DMA, app developers distributing their apps via Apple’s App Store should be able to inform customers, free of charge, of alternative offers outside the App Store, steer them to those offers and allow them to make purchases.
The Commission found that Apple fails to comply with this obligation. Due to a number of restrictions imposed by Apple, app developers cannot fully benefit from the advantages of alternative distribution channels outside the App Store. Similarly, consumers cannot fully benefit from alternative and cheaper offers as Apple prevents app developers from directly informing consumers of such offers. The company has failed to demonstrate that these restrictions are objectively necessary and proportionate.
As part of today’s decision, the Commission has ordered Apple to remove the technical and commercial restrictions on steering and to refrain from perpetuating the non-compliant conduct in the future, which includes adopting conduct with an equivalent object or effect.
The fine imposed on Apple takes into account the gravity and duration of the non-compliance.
Non-compliance decision on Meta’s “consent or pay” model
Under the DMA, gatekeepers must seek users’ consent for combining their personal data between services. Those users who do not consent must have access to a less personalised but equivalent alternative.
In November 2023, Meta introduced a binary ‘Consent or Pay’ advertising model. Under this model, EU users of Facebook and Instagram had a choice between consenting to personal data combination for personalised advertising or paying a monthly subscription for an ad-free service.
The Commission found that this model is not compliant with the DMA, as it did not give users the required specific choice to opt for a service that uses less of their personal data but is otherwise equivalent to the ‘personalised ads’ service. Meta’s model also did not allow users to exercise their right to freely consent to the combination of their personal data.
Instagram co-founder says Meta’s Zuckerberg underinvested in photo app
Washingtonpost • Naomi Nix • April 22, 2025
Technology•Software•Instagram•Meta•Antitrust•Government Overreach
Instagram co-founder Kevin Systrom testified in federal court that Meta CEO Mark Zuckerberg underinvested in the photo-sharing app after its acquisition by Facebook, supporting the Federal Trade Commission's claim that the purchase was anticompetitive.
Systrom stated that Zuckerberg denied his requests to hire more staff, even as internal data around 2018 indicated a decline in Facebook's daily active users in the U.S., a drop Zuckerberg seemed to attribute to Instagram's growth.
"My experience of him was that he was very happy to have Instagram in the family because it was growing so quickly," Systrom said. However, he noted that Zuckerberg, as Facebook's founder, felt emotional tension regarding which app was superior, leading to challenging discussions.
After the 2012 acquisition, Systrom observed that Zuckerberg allocated fewer resources to Instagram's user privacy strategy, video products, and the development of ephemeral posts known as Stories. Despite Instagram's rapid growth, Systrom's efforts to expand the team were often rejected by Meta's leadership.
"Everyone always wanted more resources for their team. We were one of those teams," Systrom said. "But we were by far the fastest-growing team."
The FTC's case against Meta argues that the company's acquisitions of Instagram and WhatsApp were part of a strategy to suppress emerging competitors, leading to reduced investment in improving Facebook and limiting user options for social media platforms. Meta contends that it invested significantly in enhancing Instagram, including strengthening its computing infrastructure and advertising business.
Systrom testified that an internal study around 2018 found that declines in Facebook user growth were largely due to a shift to video content, not Instagram's growth. While some users spent less time on Facebook after joining Instagram, the photo-sharing app increased overall engagement with Meta's products.
In a 2018 memo, Zuckerberg expressed concern that Instagram's growth was cannibalizing Facebook's, suggesting that new Instagram users could lead to a "network collapse" of Facebook, the more profitable product. He considered whether it would be better to spin off Instagram as a separate company.
Systrom also highlighted instances of underinvestment, such as when Meta hired 300 workers to enhance its video capabilities but declined to allocate additional staff to Instagram. He noted that Meta underinvested in developing Instagram's comment system and responding to Snapchat's Stories feature.
"The constant theme was that we were so large," Systrom said. "There were so many things we needed to do to keep up with that growth, but there were areas that were starving."
The FTC argues that Meta holds an illegal monopoly in the "personal social networking market," defined as apps that help users connect with loved ones. Meta counters that it competes in a broader internet ecosystem that includes platforms like TikTok.
Clear as Mud
Europa • John Gruber • April 23, 2025
Technology•Regulation•DigitalMarketsAct•Apple•AppDistribution•Government Overreach
From the European Commission’s announcement today, “Commission closes investigation into Apple’s user choice obligations and issues preliminary findings on rules for alternative apps under the Digital Markets Act”:
Under the DMA, Apple is required to allow for the distribution of apps on its iOS operating system by means other than through the Apple App Store. In practical terms, this means that Apple should allow third party app stores on iOS and apps to be downloaded to the iPhone directly from the web.
The Commission takes the preliminary view that Apple failed to comply with this obligation in view of the conditions it imposes on app (and app store) developers. Developers wanting to use alternative app distribution channels on iOS are disincentivised from doing so as this requires them to opt for business terms which include a new fee (Apple’s Core Technology Fee). Apple also introduced overly strict eligibility requirements, hampering developers’ ability to distribute their apps through alternative channels. Finally, Apple makes it overly burdensome and confusing for end users to install apps when using such alternative app distribution channels.
So is the entire idea of the Core Technology Fee disallowed? Or is the fee too high? Does Apple need to just make app distribution free and unfettered, no fees, no restrictions?
Who knows? The fine is clear — €500M — but what exactly Apple did wrong and should change now is not.
AI
😵💫 The AI acceleration delirium: models, mergers, and mayhem
Exponentialview • April 18, 2025
Technology•AI•o
What a week. The feeling isn’t just acceleration anymore; it’s delirium. You blink, and another model is released, another rumour flies. The pace is truly accelerating beyond all reasonable expectation. Welcome to artificial general confusion.
This week felt like a microcosm of the entire AI race. OpenAI dropped o3 and o4 Mini, their latest reasoning models. o3 is really quite impressive: it can use an array of tools from Python to web search and image analysis to do your bidding.
o3 has shown its mettle in my early tests. A particularly tricky one is my flight challenge. The test involves making a transatlantic booking with specific constraints (like my preference for particular planes). o3 did really well, beating all other models, but, for now, I’m still better. Another has been a multi-factor, quite complex, real-world strategic problem. o3 worked its way through the study like a master strategist: pulling out the key issues and addressing them with just enough detail.
One of the more formal measures I’m tracking closely is METR’s time horizon, which tests AI’s ability to complete long tasks. Mastering long tasks is a key unlock for significant productivity enhancements.
On this o3 does not disappoint.
Benchmarks offer a glimpse but we should be cautious about simplistic readings. The true, messier reality of real-world application shows a palpable rate of improvement, even as capabilities feel like jagged edges pushed into the market.
Predictably, there was a stampede online, people breathlessly declaring this “artificial general intelligence,” as if we’ve finally tripped over some obvious finish line. It’s a line that, frankly, remains stubbornly undefined.
Within a day, Google fired back, launching Gemini 2.5 Flash. This is the faster, more efficient sibling to the 2.5 Pro model that’s become my go-to. At least on one benchmark, Google seems to be carving out a fascinating and potentially dominant space right now. Their price-performance frontier is helped, no doubt, by their hardware expertise.
Anthropic’s releases this week went rather unnoticed. Claude can now search your email, Google Drive, and calendar, although it is ponderously slow, and I use other AI tools on my email.
Then rumours started that OpenAI is considering buying Windsurf, one of those increasingly indispensable code completion tools that software engineers use to magnify their output. The purchase price? A cool $3 billion. And what is more, rumours that Sam Altman might turn the billion-user base of ChatGPT into a social network full of yeets.
How do we make sense of this four-ring circus of releases and more?
First, the pace is unsustainable for traditional product cycles. Products are being released faster than they can be properly described, product-managed, or even benchmarked in a way that’s useful to anyone outside the lab. The capabilities, while impressive, feel like jagged edges pushed out into the market.
AI as Normal Technology
Aisnakeoil • Arvind Narayanan • April 15, 2025
Technology•AI•NormalTechnology•Diffusion•Policy
This post is over 15,000 words long—it is a new paper on our vision for the future of AI. We are pleased to announce that an expanded version of these ideas will become our next book together.
The paper is also published in HTML and PDF formats on the Knight First Amendment Institute’s website. We are grateful for the extensive feedback we’ve received on drafts of the paper.
We articulate a vision of artificial intelligence (AI) as normal technology. To view AI as normal is not to understate its impact—even transformative, general-purpose technologies such as electricity and the internet are “normal” in our conception. But it is in contrast to both utopian and dystopian visions of the future of AI which have a common tendency to treat it akin to a separate species, a highly autonomous, potentially superintelligent entity.
The statement “AI is normal technology” is three things: a description of current AI, a prediction about the foreseeable future of AI, and a prescription about how we should treat it. We view AI as a tool that we can and should remain in control of, and we argue that this goal does not require drastic policy interventions or technical breakthroughs. We do not think that viewing AI as a humanlike intelligence is currently accurate or useful for understanding its societal impacts, nor is it likely to be in our vision of the future.
The normal technology frame is about the relationship between technology and society. It rejects technological determinism, especially the notion of AI itself as an agent in determining its future. It is guided by lessons from past technological revolutions, such as the slow and uncertain nature of technology adoption and diffusion. It also emphasizes continuity between the past and the future trajectory of AI in terms of societal impact and the role of institutions in shaping this trajectory.
In Part I, we explain why we think that transformative economic and societal impacts will be slow (on the timescale of decades), making a critical distinction between AI methods, AI applications, and AI adoption, arguing that the three happen at different timescales.
In Part II, we discuss a potential division of labor between humans and AI in a world with advanced AI (but not “superintelligent” AI, which we view as incoherent as usually conceptualized). In this world, control is primarily in the hands of people and organizations; indeed, a greater and greater proportion of what people do in their jobs is AI control.
In Part III, we examine the implications of AI as normal technology for AI risks. We analyze accidents, arms races, misuse, and misalignment, and argue that viewing AI as normal technology leads to fundamentally different conclusions about mitigations compared to viewing AI as being humanlike.
Of course, we cannot be certain of our predictions, but we aim to describe what we view as the median outcome. We have not tried to quantify probabilities, but we have tried to make predictions that can tell us whether or not AI is behaving like normal technology.
In Part IV, we discuss the implications for AI policy. We advocate for reducing uncertainty as a first-rate policy goal and resilience as the overarching approach to catastrophic risks. We argue that drastic interventions premised on the difficulty of controlling superintelligent AI will, in fact, make things much worse if AI turns out to be normal technology—the downsides of which will be likely to mirror those of previous technologies that are deployed in capitalistic societies, such as inequality.
The world we describe in Part II is one in which AI is far more advanced than it is today. We are not claiming that AI progress—or human progress—will stop at that point. What comes after it? We do not know. Consider this analogy: At the dawn of the first Industrial Revolution, it would have been useful to try to think about what an industrial world would look like and how to prepare for it, but it would have been futile to try to predict electricity or computers. Our exercise here is similar. Since we reject “fast takeoff” scenarios, we do not see it as necessary or useful to envision a world further ahead than we have attempted to. If and when the scenario we describe in Part II materializes, we will be able to better anticipate and prepare for whatever comes next.
A note to readers. This essay has the unusual goal of stating a worldview rather than defending a proposition. The literature on AI superintelligence is copious. We have not tried to give a point-by-point response to potential counter arguments, as that would make the paper several times longer. This paper is merely the initial articulation of our views; we plan to elaborate on them in various follow ups.
Columbia student suspended over interview cheating tool raises $5.3M to ‘cheat on everything’
Techcrunch • Charles Rollet • April 21, 2025
Technology•AI•Startups•Ethics•Innovation
On Sunday, 21-year-old Chungin 'Roy' Lee announced he has raised $5.3 million in seed funding from Abstract Ventures and Susa Ventures for his startup, Cluely, which offers an AI tool designed to "cheat on everything."
The startup originated after Lee posted in a viral X thread that he was suspended by Columbia University following the development of a tool to cheat on job interviews for software engineers.
That tool, initially called Interview Coder, is now part of their San Francisco-based startup Cluely. It provides users the ability to 'cheat' on various tasks such as exams, sales calls, and job interviews through a hidden in-browser window that remains invisible to the interviewer or test administrator.
Cluely has published a manifesto comparing itself to inventions like the calculator and spellcheck, which were initially criticized as forms of 'cheating.'
Additionally, Cluely released a launch video featuring Lee using a concealed AI assistant to (unsuccessfully) deceive a woman about his age and art knowledge during a date at an upscale restaurant. The video has elicited mixed reactions, with some praising its attention-grabbing nature, while others likened it to scenarios from the dystopian sci-fi series "Black Mirror."
Lee, serving as Cluely's CEO, informed TechCrunch that the AI cheating tool surpassed $3 million in annual recurring revenue earlier this month.
Cluely's other co-founder is 21-year-old former Columbia student Neel Shanmugan, who is the company's COO. Shanmugan also faced disciplinary actions at Columbia over the AI tool. Both co-founders have since dropped out of Columbia, as reported by the university's student newspaper last week. Columbia declined to comment, citing student privacy laws.
Cluely began as a tool for developers to bypass knowledge of LeetCode, a platform for coding questions that some in software engineering circles, including Cluely's founders, consider outdated and inefficient.
Lee claims he secured an internship with Amazon using the AI cheating tool. Amazon declined to comment on Lee's specific case but stated that job candidates must acknowledge they will not use unauthorized tools during the interview process.
Cluely is not the only controversial AI startup launched recently. Earlier this month, a renowned AI researcher announced a startup with the stated mission of replacing all human workers, causing a stir on X.
The Information: ‘Meta Asked Amazon, Microsoft to Help Fund Llama’
The information • via John Gruber • April 21, 2025
Technology•AI•Llama•Funding•OpenSource
Kalley Huang and Erin Woo, reporting for The Information (via Ed Zitron, who summarized it on Bluesky):
Meta Platforms over the past year asked Microsoft, Amazon and others to help pay the costs of training Meta’s flagship large language model, Llama, according to four people briefed on the discussions. Meta’s overtures reflected worries about the growing costs of its artificial intelligence development, according to two of the people.
Meta in particular has faced questions about the business logic behind its AI development, given that Llama is open‑source software, freely available for anyone’s use. That makes it difficult to turn into a business. And Meta makes money primarily from advertising and has little experience in selling business software.
While Meta held its most serious discussions with Amazon and Microsoft, it has also discussed the idea with Databricks, IBM and Oracle, as well as representatives from at least one Middle Eastern investor, according to two of the people briefed on the discussions. Meta was still in discussions with companies about the Llama Consortium as recently as the start of this year, the two people said.
“Would you consider throwing a few sacks full of your cash on this bonfire of our cash that we’ve been burning?” is a hell of a pitch.
In its discussions with other companies, Meta primarily asked for money. It also sought servers or other resources that would offset the cost of training its models, according to two of the people briefed on the discussions. In return for their assistance, Meta discussed offering other companies promotion of their services alongside Llama — for example, a Meta executive might appear at a conference hosted by a consortium partner — or providing more insight into the training process for the model, one of those people said.
Pay a little and a Meta representative will show up at your developer conference. Pay more and a Meta won’t show up at your developer conference.
Geopolitics
Trump’s Cultural Revolution
Paulkrugman • Paul Krugman • April 21, 2025
Politics•PoliticalMovements•MAGA•CulturalRevolution•HorseshoeTheory•Geopolitics
I’m in Lisbon, speaking at a conference the Banco de Portugal is holding to commemorate the revolution that brought democracy to Portugal 50 years ago. I worked at the Bank in 1976 and have been a friend of Portugal ever since. And while Portugal has faced many challenges since the Carnation Revolution, all in all its democracy has flourished.
Alas, democracy in my own nation is now under dire threat. So I thought I’d write a short post about that today. Probably another brief post tomorrow. Then my wife and I will be on a bike trip, with at most quick notes from the road.
Donald Trump has been treated very, very badly. At least that’s what he says all the time, and there’s no reason to doubt that it’s how he feels. Hardly a day goes by without an outburst like this:
Above all, he clearly feels rage toward people who, he imagines, think they’re smarter or better than him.
And he and the movement he leads, composed of people possessed by similar rage, are seeking retribution. Retribution against whom? Yes, they hate wokeness. But three months in, it’s obvious that the MAGA types want revenge not just on their political opponents but on everyone they consider elites — a group that, as they see it, doesn’t include billionaires, but does include college professors, scientists and experts of any kind.
It took no time at all for the Trumpists to move from trying to purge government agencies of DEI to trying to control the content of medical journals.
Don’t try to sanewash what’s happening. It’s evil, but it isn’t calculated evil. That is, it’s not a considered political strategy, with a clear end goal. It’s a visceral response from people who, as Thomas Edsall puts it, are addicted to revenge.
If you want a model for what’s happening to America, think of Mao’s Cultural Revolution.
But wait, wasn’t Mao hard left while America has been taken over by the hard right? Well, why do you think there’s a big difference between the two? I’m a believer in horseshoe theory, which says that the extreme left and the extreme right are more like each other than either is like the political center.
How I Describe Myself Politically These Days
Nytimes • Thomas L. Friedman • April 23, 2025
Politics•Technology•Innovation•EconomicPolicy•GlobalCompetitiveness•Geopolitics
The article advocates for a political approach centered on advancing America's leadership in future-oriented industries, particularly through technological innovation and strategic economic policies. The author self-identifies as a "Waymo Democrat," emphasizing the need to prioritize cutting-edge sectors like autonomous vehicles, clean energy, and advanced manufacturing to maintain global competitiveness.
Key Focus Areas
Technological Sovereignty: The piece stresses the urgency of dominating emerging industries such as AI-driven transportation (e.g., Waymo’s autonomous systems) and green technology to counterbalance geopolitical rivals.
Policy Recommendations: Proposes targeted investments in R&D, workforce retraining, and infrastructure modernization to support high-tech industries.
Economic Implications: Highlights the risks of falling behind in innovation, including job displacement and reduced influence in global trade dynamics.
The analysis underscores the intersection of technology and governance, arguing that proactive policy frameworks are essential to harness innovation for national economic security. While specific statistics or direct quotes are absent from the provided content, the core argument revolves around reorienting political priorities to foster technological supremacy.
Trump Administration Calls E.U. Fines on Apple and Meta ‘Economic Extortion’
Reuters • John Gruber • April 23, 2025
Technology•Regulation•Antitrust•TradePolicy•GlobalTech•Geopolitics
The Trump Administration has strongly criticized the European Union's recent fines against Apple and Meta, labeling them as "economic extortion." The EU imposed fines of €500 million on Apple and €200 million on Meta under the Digital Markets Act (DMA), a landmark legislation aimed at promoting competition in the tech sector by regulating dominant companies. These fines are part of a broader effort by the EU to enforce antitrust rules and ensure that large tech companies do not stifle smaller competitors.
The White House has expressed concern that such regulations unfairly target American businesses, potentially stifling innovation and enabling censorship. This stance aligns with the administration's broader pushback against extraterritorial regulations that it believes undermine U.S. companies. The EU's actions have been met with resistance from tech lobbyists, who argue that these fines and regulatory requirements effectively act as tariffs, impacting not just the companies but also the broader economy.
Both Apple and Meta have indicated their intention to challenge the fines. Meta's chief global affairs officer, Joel Kaplan, stated that the EU's actions are not just about the fines but also about forcing changes to their business model, which he claims will result in an inferior service. Kaplan also noted that these regulations unfairly restrict personalized advertising, potentially harming European businesses and economies. The situation highlights ongoing tensions between the U.S. and EU over tech regulation, with the U.S. seeking to protect its tech giants from what it perceives as unfair treatment.
President Donald Trump had previously warned that countries penalizing U.S. companies could face tariffs, reflecting a broader strategy to protect American businesses from international regulatory actions. This stance has been echoed by tech lobbyists, who are seeking to leverage these fines to push for stronger U.S. intervention against EU regulations. The conflict underscores the complex interplay between global tech regulation, trade policies, and geopolitical relations.
Interview of the Week
Peter Leyden on How Trump is Unintentionally Making America Great Again.
The 80 Year Cycle: How California can Reinvent America
Is America screwed? Not according to the former managing editor of Wired, Peter Leyden. The creator of the Substack newsletter The Great Progression, Leyden believes that U.S. history operates in 80 year cycles and that America, empowered by Northern Californian technology, is gearing up for another remarkable period of innovation. Leyden is no MAGA fanboy, but argues that Trump is enabling the American future by destroying the Republican brand and unintentionally guaranteeing a longterm Democratic majority. It’s a provocative thesis which I hope is true. But what about China? And can we really trust Silicon Valley’s tech titans to make America great again?
5 Takeaways
Leyden believes America cycles through major reinventions approximately every 80 years, with previous transformations occurring after the Constitutional Convention, Civil War, and World War II.
He argues that post-WWII systems (welfare state, Pax Americana) are outdated and that Trump's presidency is accelerating their necessary dismantling.
Leyden sees an opportunity for progressives to rebuild American systems using AI, clean energy and bioengineering in more efficient, effective ways.
Leyden references economic historian Carlota Perez's theory that technological revolutions move from "Gilded Ages" (concentrated wealth/power) to "Golden Ages" (distributed benefits) through democratic intervention.
Leyden positions the US-China competition, particularly in AI development, as a fundamental contest between democratic and authoritarian approaches to organizing society with new technologies.
Startup of the Week
YouTube at 20: From ‘Lazy Sunday’ to ‘Hot Ones’
Nytimes • Amanda Holpuch • April 23, 2025
Technology•Web•VideoStreaming•SocialMedia•Innovation•Startup of the Week
YouTube, a platform that has profoundly impacted the way we consume video content, is celebrating its 20th anniversary. Since its inception, YouTube has evolved from a simple video-sharing site to a global phenomenon, influencing entertainment, education, and culture. Here’s a look at its journey:
Early Years and Milestones
YouTube was founded in February 2005 by three former PayPal employees: Chad Hurley, Steve Chen, and Jawed Karim. The first video ever uploaded to the platform was a 19-second video titled "Me at the zoo," featuring co-founder Jawed Karim at the San Diego Zoo. Initially, YouTube gained popularity with viral videos like "Lazy Sunday," a sketch from Saturday Night Live that became one of the first viral hits on the platform. This early success helped establish YouTube as a hub for user-generated content.
Impact and Evolution
Over the years, YouTube has transformed into a diverse platform hosting a wide range of content, from educational videos and music to vlogs and live streams. The platform's influence extends beyond entertainment; it has become a crucial tool for education and information dissemination. For instance, channels like Crash Course and Vsauce offer educational content, while TED Talks provide insights into various fields. YouTube has also played a significant role in launching careers, with many creators turning their passion into full-time jobs.
Key Statistics and Trends
User Base: YouTube boasts over 2 billion monthly active users, making it one of the most widely used platforms globally.
Content Creation: The platform hosts millions of active creators, with many earning revenue through ads, sponsorships, and merchandise sales.
Monetization: YouTube's Partner Program (YPP) allows creators to monetize their videos through ads, providing a financial incentive for content creation.
Challenges and Future Directions
Despite its success, YouTube faces challenges such as content moderation, copyright issues, and competition from newer platforms like TikTok and Twitch. The platform continues to evolve with features like YouTube Premium, which offers ad-free content, and YouTube Shorts, competing directly with TikTok. As YouTube moves forward, it must balance user engagement with content quality and safety.
Conclusion
YouTube's journey over the past two decades has been marked by innovation, growth, and adaptation. From its humble beginnings to its current status as a global media giant, YouTube continues to shape how we consume and interact with video content. As technology advances, YouTube will likely remain a pivotal player in the digital landscape.
Post of the Week
Post of the Week
Work backwards from the outcome you want
X • garrytan • April 21, 2025
X•Post of the Week
Key takeaway: Reverse‑engineering your goals—from finish line to starting block—creates clarity, cuts wasted effort, and accelerates progress.
Thread summary
In a succinct but impactful post, entrepreneur and investor Garry Tan (@garrytan) shares the essence of a planning technique favored by product teams at Amazon, startups, and high‑performing organizations: define your desired end state first, then map each prior step in reverse. The result is a clear roadmap that ensures every action ties directly to your ultimate goal.
Why “working backwards” works
Clarity of purpose: By picturing the finished result—whether a launched product, a marketing campaign, or a research paper—you eliminate ambiguity about what success looks like.
Efficient resource allocation: You avoid spending time on low‑value tasks that don’t directly contribute to the outcome.
Early identification of hurdles: Reverse mapping exposes dependencies and potential roadblocks before they derail progress.
How to apply it today
Define the outcome: Write a one‑sentence statement describing success.
Example: “Our app has 10,000 active monthly users within six months.”
Identify key milestones: List the major checkpoints you’d hit on the way to that outcome.
Example: MVP launch → user onboarding flow → first 1,000 sign‑ups → feedback iteration.
Reverse the sequence: Start from launch day, then ask, “What must be in place the day before?” and so on, back to today’s to‑dos.
Assign tasks & deadlines: For each backwards step, assign an owner and a realistic deadline.
Iterate and adjust: As you execute, revisit your backwards map—real‑world feedback may require reordering or reprioritizing steps.
Context & background
“Working backwards” is a cornerstone of Amazon’s product development process, where teams draft a hypothetical press release and FAQ document before writing a line of code. This customer‑first approach forces clear articulation of value and use cases up front, preventing scope creep and misaligned expectations.
Garry Tan, co‑founder of Initialized Capital and former Y Combinator partner, often highlights simple mental models that founders and teams can adopt immediately. This tweet distills one such model into four words—“Work backwards from the outcome you want”—yet carries a lifetime of strategic wisdom.
Final thoughts
Whether you’re launching a side project, writing a research proposal, or managing a global product team, flip your planning script: start at the finish line, then draw the roadmap in reverse. You’ll find that every step earns its place on your to‑do list.
A reminder for new readers. Each week, That Was The Week, includes a collection of selected essays on critical issues in tech, startups, and venture capital.
I choose the articles based on their interest to me. The selections often include viewpoints I can't entirely agree with. I include them if they make me think or add to my knowledge. Click on the headline, the contents section link, or the ‘Read More’ link at the bottom of each piece to go to the original.
I express my point of view in the editorial and the weekly video.
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