Sandy Kory

@kylegiff This is great! I really like "Often due to not knowing a precise use of their technology. This is chalked up to a go-to-market problem, when it's far deeper." GTM often takes the fall for much deeper and less legible issues.

Observations from Frank Slootman's "Tape Sucks." Tells the story of Data Domain, a data deduplication startup which Frank took over as CEO in 2003 at $0 in revenue and exited at $2.4B seven years later. Introduction • Data Domain's slogan "tape sucks" based on replacing tape with de-duplicated storage. Creative destruction. • "You are not going to grow much without exacting a proportional decrease in business somewhere else. This is creative destruction. You must know what you are eliminating." • Dozens of CS PhDs employed. Talent was there not just for interesting problems, but difficult problems. • Went zero to $1 billion in sales, strategy changed hardly at all. Resisted the temptation to muck with it. Failures of tech startups • Often due to not knowing a precise use of their technology. This is chalked up to a go-to-market problem, when it's far deeper. Customer love • Data Domain was incrementally improving an existing process. This meant customer obsession paid off. Does not apply to all startups (e.g. some are supply-led). • Placed engineers, PMs, field engineers, & executives next to customers. Competing thoughts took a backseat to what customers were saying. This was reverted to as True North consistently. Drive your own revenue • Letting OEMs distribute for you is doing a deal with the devil. No one wants to sell your product like you do. • "Building and scaling an effective S&M engine is painstaking work that takes years," but it pays off. • You cannot invest intelligently in revenue generation if you do not understand how to ramp effectiveness & make underlying economics work. • If you want to go public, you cannot do so without a predictable model you control. • With great S&M, you get control over your destiny; you don't need anybody else to hit your numbers. • "Victory is breaking the enemy's will to fight." Get great salespeople to quit your competitors to join you. This crushes your incumbents' morale. Fundraising • Set clear proof points at each successive round to attract T-1 investors (product, customers, business model). Must be objective. • Pull back and double down on execution if you have not hit these proof points. In transition to scaling • Be tight, disciplined, cost-conscious in operations. • "You cannot ingest lots of people if there is no defined model underpinning how they fit into the organization." • Define organizational model & allow for continuous, rapid cell division as long as the math works out. • Trust strong executives who know what resources they need and when, and who will make that case with unyielding conviction. Trust your team, nothing else scales. Hiring • Look for people who have their best work in front of them, rather than behind them. • Energy, pedigree, passion, ambition, intelligence, intensity, & desire for the job. • Everybody has something to prove. Fail fast • Admit when you are wrong and course correct rapidly. • Own up to it and deliberate alternative action. Don't defend poor positions. • Do NOT allow a culture that penalizes mistakes and keeps score. Everybody making decisions makes mistakes. Deal with mistakes swiftly and move on. • You do not have to be perfect, but "we do require you to be intellectually honest about how your decisions pan out." • Be direct and up front. If you don't come to the right conclusion, the team will point it out for you. Selling • Sales machine is a single organism of information gathering and sharing. • Develop best practices and share them instantly. Constantly communicate, assess, and synthesize what you need to do to sell. • Everybody help everybody else. Respond in seconds or minutes. Winning • "Instilling a fighting spirit into your company is the key to winning in the marketplace." • You need to know what it's like to get your nose bloodied, otherwise your troops can't relate to you and you can't relate to them. • Put yourself in contentious sales conversations. Show the organization you have no fear in difficult settings. • Beat your competitors in their own backyard. • Be at the transfer point from technology to sales to understand your business and how it evolves. • If you don't, you won't be good at it, and you will become more removed from everything. As CEO • Never miss an opportunity to shower your organization with praise. They deserve it. • It's not about you. • Resist the urge to be 'promotional' at board meetings. Board wants transparency and intellectual honesty. • Board can help you think through things, but it's your decision and you live with it. • Do what you think is right. • You might as well spend all your time on winning. Nothing else matters. Going Public • IPO motivations: i) liquidity, ii) branding, iii) discipline. • If you lack revenue, growth, and profitability, you don't want to go public. • If you cannot forecast your quarterly results, you don't want to go public. • Employees get to monetize holdings after years of slaving away at below market pay. Getting acquired • Can be great. Data Domain's acquirer (EMC) dramatically accelerated our business. We grew in excess of 100% year-on-year following the acquisition. • "Everybody won." Post-acquisition • Sticking around to ensure integration is the high integrity move. • "I did not want to incite attrition or instability in the organization by virtue of my abrupt absence." • Achieved 18 months of executive retention & 100% YoY growth following acquisition. Culture — RECIPE "I'd go so far as to say that company culture is the only enduring, sustainable form of differentiation. The one thing that is unique to us is how we choose to come together as a group of people, day in and day out." • Respect. Eliminate favoritism. Let anyone talk to anyone. Exist in a glass house. Never put your agenda above the company's. • Excellence. Blue collar culture. No flash. Be humble, heads down, and hungry. • Customer. Take matters into your own hands to help customers, far beyond the call of duty. Purest relationship in business is trust with customers. • Integrity. "Integrity means we never knowingly speak anything but the truth to our stakeholders." If it is in the best interest of the customer, partner, or investor to know, we must share that information. This goes so far as telling a customer to use a competitive service if you cannot help them. If you cannot answer a question, just say so, do not fabricate an answer. • Performance. Every incentive and behavior must elevate performance. Be a driver, not a passenger. Make performance central to compensation. Give all people (execs included) sales-like comp plans, paid on the same metric: growth of the business. No dilution of focus. • Execution. No strategy is better than its execution. Better execution crystallizes strategy. Do not change strategy too often. Allow time and nurturing. What's it like? • A startup is business in its rawest form. There is nowhere to hide. • CEOs live 12+ hours a day behind the plow. It doesn't feel glamorous. But it's exhilarating. Direct feedback all the time, close to the metal. Like breathing pure oxygen. Many constraints but few limits. • "On weekends I competed in triathlons and marathons. I needed it to maintain perspective and clarity of thought." • "Enjoy the ride. Those 8 seconds can become the most meaningful time in your work life!"

@Hadley AI startups in SF will continue to grow massively. SF will lose market share in AI moving forward. Same as for SaaS startups in 2010s. I remember SF VCs passing on Canva seed talking about how there wasn't enough SaaS talent in Australia.

VCs: There’s no AI talent outside of SF Anthropic: we need a 16 story building full of people in NYC

@milesgr_ I think it's like early SaaS, but a lot more volatile. Vendors selling usage need to partner with customers to make sure ROI is there. I wouldn't bet on VCs valuing revenue quality any time soon, but that's a separate post.

@sandykory usage-based sure makes sense but spiky inference revenue can trash how youre valued as a startup. I know early saas builds would typically see waves too but Id argue usage-based is still less sticky by design.

I haven’t been buying the "SaaSpocalypse," but Q1’s nosediving SaaS valuations gave me pause. After a week in SF last month sampling the AI zeitgeist, I have a better feel for where the software sector is heading. It’s the SaaS-to-inference transition, and it’s good. My long-standing view has been that AI is a net positive for the software industry. It radically raises the ceiling for what software products can do. It should dramatically expand the market opportunity for software, just like the on-prem-to-cloud transition did back in the day. Yet many have been freaking out. After all, haven’t SaaS switching costs come down dramatically in SaaS, threatening one of the pillars of the business model? Yes, there’s no doubt that the “cement around the ankles” of legacy SaaS has weakened. At the same time, most legacy SaaS companies have barely scratched the surface of AI innovation while maintaining their historically high retention. This is how it played out in the last major transition: on-prem-to-cloud. Many legacy players (pathetically) ignored cloud innovation for 5-10 years (or longer) and still kept their customers. It turns out that technology is stickier than most in the tech industry believe. Take a look at Bending Spoons, which IPO’d off the back of buying crappy legacy products and jacking up prices because users didn’t want to give up their AOL email or Evernote notes. Tech industry people are not like this. They tend to be part of the very small minority of early adopters. Most people aren’t like this. Neither are most organizations. Legacy software isn’t going to disappear. But if pre-AI software companies don’t embrace AI innovation, their customers will be much less forgiving than on-prem customers 10-20 years ago. AI capabilities are too potent and obviously beneficial. What does embracing AI innovation look like? It means layering intelligent actions into all software. Historically, great software has helped users follow the right workflow. Now, great software must do the workflow by triggering agents to take actions. In other words, inference. The great news for everyone is that this opens the door to consumption-based pricing models that can scale exponentially. For legacy players and startups alike, delivering amazing AI-powered, agentic features is the way to get on the vertical-growth train. Remarkably, the door is still open for legacy players. Intercom’s 3.6b exit to Salesforce is a great example. Of course, new pricing models mean new margin structures. Just as SaaS had lower gross margins than legacy on-prem, expect consumption-priced inference to have lower gross margins. This is OK! We’ve already seen massive wins for inference-selling startups with negative gross margins, like Cursor. Legacy SaaS companies need to find religion on this. Dropping margins is never easy. Lock up the finance team if you have to. The priority is delivering AI-powered value for customers. Everything else is just details.

Last month, @baseten announced a $1.5B Series F at a $13B valuation. I've known co-founders @tuhinone and @saltyph for over a decade. I’ve learned a lot watching them build Baseten, but the biggest takeaway for me is the value of deep conviction–even if it means being dangerously early to a market. I met Tuhin and Phil at their previous startup, where I angel invested in their pre-seed round. A few years later, I spent a lot of time helping them navigate the sales process after they got an offer from a strategic buyer. Unlike the typical founder who dreads working for a buyer, Tuhin was excited to join their team. He realized it could be a great learning experience. This intense thirst for learning is one of those founder traits that’s hard to teach but incredibly valuable. In 2019, they started a new company. I angel invested in their first round. That was Baseten. Their thesis was built on a problem they'd lived firsthand: productionizing sophisticated ML models. The typical Series A-to-D startup then had a small data science team. These were smart, math-oriented people who could analyze data and build ML algorithms. But getting actual business value from said models required engineering resources. Tuhin, Phil, and their other co-founders, @amiruci and @defpan, identified this gap and aimed their company directly at it. While this was years before the launch of ChatGPT and interest in ML models morphed into the AI tsunami, they presciently saw a market opportunity. That said, they were very early. Over the next few years, SaaS mania ensued. Startups with very incremental SaaS products could fetch unicorn valuations off of a few million in ARR. Those products had little use for the sophisticated models Baseten’s product was anticipating. So the Baseten team had to bide their time. (Easier said than done for a startup!) Crucially, they were 10/10 in 2 key areas: 1. Fundraising: Tuhin is a brilliant storyteller and has a knack for building high-trust relationships with VCs. He kept the company perpetually well-funded by raising multiple pre-traction rounds from top-tier VCs. 2. Conviction: It would have been easy for them to pivot. They were great builders and could have quickly hacked together a SaaS product that would’ve whet the appetite of SaaS-crazy VCs in 2021/22. But they didn’t. They had deep personal experience suffering through productionizing models and could see that momentum was about to build for products with sophisticated (read: AI) models. After ChatGPT launched in late ‘22, the market rapidly coalesced. Startups began to see opportunities to build AI products that were essentially “productionizing sophisticated ML models,” just as the Baseten founders had originally envisioned. By 2024, the AI tsunami was emergent, and Baseten couldn’t have been better positioned. All told, Baseten is a great example of the dangers of being too early to a market. But also the incredible advantages. I’m very glad to have been along for the ride.

For my first 5 years of angel investing, I ranked everything. Founders, market, traction, team. But when I looked back on the data, it was all noise. So instead of fancy quantitative systems, I get much more personal mileage from a few simple heuristics today. One of my favorites is to ask myself: how am I going to feel when the competition arrives? I've tried other frameworks. Peter Thiel wants to invest in monopolies. Jason Lemkin from SaaStr says he invests in CEOs who are better than him. As the sort-of CEO of a little company for a few years, that one doesn't really work for me. A lot of what passes for wisdom here is moderately helpful at best, pseudoscientific cliché at worst. When something is called part art or science, I think they mean the science isn’t that good. Or they misunderstand the meaning of science. For me, I’d say my science of evaluating founders is a work in progress. But one thing I know for certain is that if a founder has a high-quality, earned insight into a market opportunity, there will be many other startups funded to target the same market in the next 6-12 months. Sequoia, Kleiner, and Andreessen are likely each going to back one, and some of them will attract great talent. In short, the competition is coming. And if I’m wrong about that, it just means the market wasn't there. Now, I've had angel investments in companies where someone came to the market, and I started to worry. I've been in that category more than I'd like. And then I've had others–like BillionToOne or SendCutSend or Baseten–where competitors start to flood the market, and I couldn’t wait to watch them take each one behind the woodshed. I wasn’t a Bulls fan in the 90s, but that’s probably what it felt like. The reason I don’t worry is always the same: they’re amazing at execution and they’re building great businesses that will compound for a long time. Oguzhan and @jimbelosic and @tuhinone are all maniacs in this department. And because they know what great looks like here, they’ve built teams full of people similarly gifted at executing on a mission. After years of rankings that were nothing more than noise, the difference between “Oh boy…” and “This is going to be fun” is what I've come to trust.

Founder trauma as an investment signal is a little overdone. In fact, I'm not so sure it was ever that useful. Bill Gates, Larry Page, and Mark Zuckerberg all had pretty good upbringings. I want to tread lightly here. We all have different experiences, and I'm not dismissing anyone's. But you arguably can’t compare one person’s trauma to another's. Take any person on the planet and ask what's the worst thing that ever happened to them. There are some people whose “worst thing” is unimaginably bad. There are others whose “worst thing,” comparatively speaking, is like a walk in the park. But it was still the worst thing that happened to them, and it was traumatic by their standards. It’s all relative, which means it’s very challenging to differentiate. What I do think you need to look for are absolute signs of outliers, and they can come in different ways. If you're a basketball scout and you see someone who's 7’5”, there’s no question. That’s about as legible as it gets. But in the context of early-stage investing, I'm more interested in less legible signals. Like extreme determination & unique technical breadth. One of our portfolio companies, @raven_computer, is a smart glasses company. The founder, @tomthecarrot, is a great example of what we look for. When he was 10, he programmed the viral whack-a-mole app “Bustin Jieber.” At 12, he delivered a TED talk on programming that has accumulated over 11m views on YouTube. But his life was completely changed when Google Glass came out a few years later. He became totally obsessed with smart glasses, AR, and the idea of spatial computing. He went on to Georgia Tech, where he became a Thiel fellow before dropping out to start an (you guessed it) AR company. He raised a bit of money but didn't have much success. Fortunately, it’s been a very different story with Raven. His personality is very different from that of some other founders. But to me, the signal is his utter determination to birth a revolutionary smart glasses product and a new computing paradigm. He's also a polymath and largely self-taught. He can write software backwards and forwards and talk about waveguides and optics or thermal management and quantum leakage. Constructing a truly breakthrough wearable device & surrounding ecosystem requires extreme depth across a lot of areas, and he has just that. Granted, I don't have depth in any of those areas. But I think I'm pretty good at picking up on someone who is really legit across them. And often I'll get reinforcement talking to other world-class experts. Ultimately, I don’t find trauma narratives particularly instructive. I'd rather find out what a founder was obsessed with before anyone told them it could be the seed of a business.

In 2016, I became @BillionToOneInc's first outside investor when I wrote a check into their pre-seed round. I invested (and followed in every round until the IPO) because I saw exceptional potential in the founders. But my conviction had little to do with their science. I'd been interested in the intersection of software and bio for a while. A couple years earlier, I'd looked at Benchling but took too long to pull the trigger. Big mistake. So when B2O showed up in TechCrunch’s write-up of YC’s inaugural YC Fellowship program, I was already primed. I met the founders, Oguzhan Atay and David Tsao, for coffee in SF. I knew almost nothing about biotech diagnostics, so I asked newbie questions and paid close attention to how they answered them. In healthcare, you need to be able to communicate with people across different levels of background knowledge. These 2 could do that. By the end of the meeting, I felt like I really understood what they were building and why it had the potential to win a big market. I wrote a very small check initially and helped them informally as they kept raising. I doubled my investment a month later after observing impressive intensity and coachability. I wrote another check a year after that as they raised a small pre-seed extension. It was bigger than before, but it still wasn’t a huge position at that point. I had a bit of an impostor complex investing as a biotech novice. But imposter syndrome be damned, I ended up writing 7 more checks into the business and made it my single biggest position. The biggest signal of their potential was their almost fanatical level of execution. Every quarter, they sent out detailed investor updates. They always hit their goals, and usually extra ones. When they hit setbacks–no startup is immune to these slings and arrows–they always confronted them directly and overcame them. I can speak for other early investors: none of us ever saw another company with more consistently stellar performance. I take most satisfaction in the number of follow-on checks I wrote. It’s one thing to write a small check backing impressive founders doing something you can’t understand. I also love that I was able to put together 2 partnerships and convince 16 friends & family members to invest in between the A and the B. Some investors will never look at a round extension or a bridge round. Not me. I should be honest about the temptation to be overly deterministic about all of this. I made many other investments in 2016, all of which I was excited about. A few more became unicorns, and several others built strong businesses. But this is the best one, bar none. It’s easy to over-learn from low-sample-size successes. So I don’t try to look for clones of B2O. But my understanding of the B2O founder’s absolutely remarkable technical and operational brilliance informs my approach today. It’s a great example of the extremely high bar that founders must clear if they want to build a truly generational company.

@aashaysanghvi_ Reminiscent of Palantir using performance-based pricing early on to get skeptical customers over the hump. I'll take the under on outcome-based pricing scaling broadly in AI, but as a "do things that don't scale" tool it's a genius way to harden an org to deliver ROI.

Fascinating

The SaaS-to-tokens transition has begun. The on-prem to SaaS transition took 10-20 years. This one will go much, much faster. It will also expand the TAM for the best companies. @usepylon is nailing it. Love the analysis & transparency from @marty_kausas.

@FundOpsDaily @sequoia Proximity might well beat being inside the building. My point is that I opted for neither in early 2008.

@sandykory @sequoia The Sequoia name is almost a distraction. Did proximity actually beat being inside the building, or just lower the cost of being wrong?

Early in my career, I canceled an interview with @sequoia legend Mike Moritz. I'm probably one of the few people in this Hall of Shame. But in hindsight, that wasn't even my biggest mistake. Sequoia was starting their growth fund in early 2008. Their playbook for Associates was inspired by my previous firm. In many ways, I was an ideal candidate. I did 2 interviews with partners and did quite well. I figured I was likely to get an offer. But I called it off, canceling my scheduled time with Sir Mike. What was I thinking? I had a few reasons. The first was that I'd just gotten an offer from another firm, Media Venture Partners (MVP), and I felt honor-bound not to string them along. I didn't want to f*ck with them. The second had nothing to do with honor. I'd been living in Palo Alto, working all the time. I wanted to move to San Francisco and live a little. Sequoia's office was on Sandhill Road. I hated the idea of a long commute from SF. Most people probably wouldn't have let that be a deciding factor. I wasn’t most people. In hindsight, I think MVP would have given me more time to make my decision. They wanted me. But I didn't think that was the right thing to do. Counterfactuals are pretty hard. If I had gone to Sequoia and succeeded, the expected value of my bank account would probably be higher. But there’s no guarantee that it would have worked out. But the (hypothetical) financial loss wasn't actually the biggest mistake. That was failing to appreciate the value of Sequoia's talent density. The caliber of the Sequoia team was (and is) remarkably high. Now, the people I worked for at MVP were good and talented people as well. But there was a difference. My general advice to people thinking about career planning, and especially people interested in tech and startups, is to try to land in a cluster of amazing talent. That’s what drives the Power Law outcomes that dominate tech. You don't even have to work for them full-time. I never worked full time for Palantir, but being proximate to that cluster still mattered. If I'd been at Sequoia, I would have been embedded in another hyper-dense talent cluster. At certain stages in our life, our priorities are just different. I have no regrets about any of my career decisions. But the value of talent density wasn’t so legible to me back in 2008. It is today.

Congrats to @tuhinone and the @baseten team! They've been obsessed with building dev-focused tech for inference since they started in 2019. They've showed an amazing level of craftsmanship in building the company since its earliest days. It's been remarkable to watch that translate into a growing leadership position in one of the biggest market opportunities ever seen. LFG!!!

The incredible @sandykory is a Venture Capitalist who is hyper focused on finding unique and out of distribution founders This has led him to back @BillionToOneInc at a $4M valuation (now $5B), @PalantirTech very early, @baseten, @sendcutsend and others Full Episode ⬇️

@Shaughnessy119 @BillionToOneInc @PalantirTech @baseten @sendcutsend Thanks Tommy!

There's a mind virus spreading through VC circles right now. Follow the logic far enough and you end up in a place of utter hopelessness. The idea is that AI takeoff is already in motion. The people who believe this implicitly think they can see the future, a la Nostradamus. The implication is that we’re powerless and should all just sit back and let the singularity envelope us in digital bliss. It’s funny as the advocates of this argument have a few personas. Some are the Effective Altruist types who are extremely pessimistic about where this leads. I appreciate that they have genuine concern, although I don’t share their pessimism. Then there’s the Young-AI-VC persona who’s made winning, early bets on AI. They tend to view liftoff primarily as a validation of their investment choices. These are the types who blame Covid for their career blemishes and their bright, shining genius for their AI success. The other persona, and my favorite of the group, is the Singularity-devoted futurist. Personally, I don’t think humanity is at such a radical point of inflection, but I’d say that I identify more with this persona than the others. And please shoot me if I ever come across as smug as the Young-AI-VCs. No matter if you view AI takeoff as positive or negative, the problem is that it’s so easy to rationally end up in a place of “there's nothing I can do.” There are some very smart, very credible people who have this view. Take Demis Hassabis, who is obviously amazing and a legend. He's one of the least bullshit, least doing-it-for-his-ego people in this whole space. Even he recently went on the record saying we're in the foothills of the singularity. Yet it seems outrageous to me that many in tech are ready to concede human agency and declare that nothing can be done. It reminds me of the Austin Powers scene where the guy is 100 meters away from the slow-moving steamroller. Folks, we can get out of the way. Even if Demis thinks we are at liftoff, he isn't sitting still. He's working his tail off. There's a difference between believing the singularity is coming and giving up your agency today. Maybe if you think liftoff is happening in 1.6 years or whatever, that actually motivates you for the next 1.6 years. But the framing I’m seeing too often is different: “I figured it out a long time ago. It's all been written. There's nothing to do.” That’s the virus. Because fundamentally, great founders need a deep conviction that they can make big changes to the world. Without that, climbing the endless series of mountains that is building a generational company becomes almost impossible.

@jasonlk @saastrfund Founders rarely regret selling in a frothy market. Just bc it's frothy doesn't mean they should sell, but if it's a close call and market is frothy, yeah probably do it.

A few years back, I had a portfolio company at @saastrfund that got an offer to buy it for well over $1B. Most of the investors were opposed. To be clear, they didn’t vote “No” per se. But they were opposed. Why? The company was doing well (hence the offer) and its competitor had raised at $2.5B+. Of course they were worth even more! In the end, after much debate, the founder took the deal. More importantly, today their larger competitor is probably worth close to $0. Certainly, their larger competitor is illiquid with no M&A offers and growth that has stalled. Who knows. Who knows if this was the right decision or not. It’s just one decision where it’s tough to get the best, unbiased advice around the table. Everyone has so many biases. Even if they are trying to be objective.

Puzzling why the median large SaaS company is not more aggressive in AI-driven M&A. The public market is blessing hyperscalers putting all of their FCF in AI investment. Software companies don’t have to be disrupted by AI. But they must update their priors.

@raven_computer is building something very special in AR. @tomthecarrot has been under the radar for a while. Not for much longer!

A few months ago, we were deep in the SaaSpocalypse. SaaS stocks got crushed. Why? Because AI. AI is only getting smarter, yet public company Q1 results show SaaS fundamentals remain strong. Hindsight’s 20/20. Now, we can look back at December or January and see Claude Code really taking off. Public SaaS stocks started to get slammed in January. The narrative of AI threatening legacy software made for an easy justification. But public markets these days are very momentum-driven. The SaaSpocalypse narrative didn't really need actual evidence of deteriorating SaaS fundamentals to smash SaaS stocks. But if the AI threat narrative is real, it will have to show up in data. The most likely canary in the coalmine? Elevated churn. If AI is going to kill legacy SaaS, legacy SaaS will start losing customers. Maybe due to customers rolling their own software, or maybe AI-native startups eating their lunch. But neither of those potentialities has shown up in the data. The earnings announcements in Q1 were strong by historical standards. Not coincidentally, SaaS stocks have rebounded. WCLD, my favorite cloud ETF to track, is now down just 3% on the year. Not great, sure. But much better than down 30% on the year, as it was in April. There’s still plenty of time for AI to run over SaaS, but I’m more interested in real-time data than scary narratives. The latest data for SaaS fundamentals, despite AI fears, is pretty good. My original view was that AI was net positive for the growth prospects of legacy SaaS. I continue to think that’s the case. AI will boost software production and enable leaner operations. That said, AI creates plenty of risks–Anthropic could run the table, customers could in-source, AI-native startups could disrupt legacy SaaS. But I don’t think it’s zero-sum. Many winners are possible. Granted, I have no idea which direction stock prices will go from here. Here’s an uncomfortable fact: like all technology, AI creates inequality. Everyone will be better off on average, but we’ll see increased dispersion of outcomes across just about every dimension. Applied to software, there's a mean-median issue where the mean software company might strongly benefit, but the median might benefit as much, and maybe even get hurt a little bit. I'm very open-minded because it is a really unusual time. I’m ready to update my priors. But so far, I don't see evidence to change my general views on SaaS vs AI.