Start-Up Founders Club

State of Venture Q2 2024 Report AI startups steal the show, grabbing nearly a third of all funding, while the rest of the venture ecosystem remains tepid in Q2’24. Based on CB Insights deep dive below, here are some details about the current venture climate: ▫ Venture funding climbs for a second straight quarter, reaching $65.7B, up 8% quarter-over-quarter (QoQ). However, while funding gained momentum, deals slid for the ninth quarter in a row to 6,230. Global deal volume is now less than half of what it was at its peak in Q1’22. ▫ At $14.4M, the average deal size is up 17% this year so far vs. 2023. Even in a more cautious investing environment, the deals that do happen have ballooned in size as investors put more behind select startups. ▫ AI startups are dominating global funding, capturing 28% in Q2’24. This is the highest quarterly share on record. AI startups drew $18.3B in Q2’24 — up 32% QoQ — driven by mammoth $1B+ deals to Elon Musk’s xAI as well as Scale, CoreWeave, and others. ▫ The US is attracting a greater portion of exit activity, with exit share rising 4 percentage points QoQ to 39%. This represents its highest share in 2 years. Top US-based exits in Q2’24 included IPOs from Tempus and Rubrik — both valued at over $5B — as well as Hyundai’s acquisition of Motional priced at $4.1B. ▫ SOSV the most active venture investor, backing 35 companies in Q2’24. It’s followed by Andreessen Horowitz (33 companies), General Catalyst (31 companies), and Lightspeed (28). ▫ Fintech funding rebounds 19% QoQ to hit $8.9B — a 5-quarter high — led by $600M+ rounds to Stripe and AlphaSense. But it was a different story for the retail tech and digital health sectors: retail tech funding was stagnant from Q1 to Q2, while digital health funding slipped by 26%. ▫ Quarterly funding to startups in Asia falls below $10B for the first time since 2014. The drop was especially severe in China, where some international investors have pulled back or retreated altogether amid rising geopolitical tensions. Meanwhile, the US and Europe — the two largest regions for venture investment — each saw funding grow by double-digit percentages in Q2’24. #venture #VC

MVP is dead Sachin Rekhi, a product expert, believes that the traditional MVP model - a quick start with basic features - is outdated. He agrees with Tuomas Artman, co-founder of Linear startup, ex-senior engineer at Uber. Here is how Sachin Rekhi explains this: The MVP operates in the Blue Ocean markets where even the basic principles of the business concept need to be tested. The reality is that the vast majority of us do not produce in the blue ocean. In the modern ecosystem, where large players occupy almost every sector, MVP are dead. Now you have to make a first impression that not only meets the needs of the market, but also strikes from the beginning. This is a «minimally delightful product» (Minimum Delightful Product, MDP). Here’s how to find it: 🟣Focus on a narrow, well-defined market 🟣Find out what causes "magical sensations" (Magical Feeling) 🟣Conduct in-depth interviews with customers 🟣Validate based on the most risky assumptions 🟣Improve product with closed beta tests 🟣Use the waiting lists not only to create a buzz, but also to bring the product in line with user expectations #startup #MVP #MDP

AI batteries work 25% longer than normal and store more energy At The Battery Show Europe 2024 in Stuttgart, Eatron Technologies and Syntiant presented their AI-based battery management system. It is embodied as the Syntiant NDP120 neural processor. Developers claim that the use of this system extends the life of batteries by 25%, plus increases their capacity by 10%. The idea is to have an artificial intelligence analyze the state of the batteries with an accuracy that is not available in existing systems. The more information AI has on the state of charge and the battery itself, the easier it will be to predict and the more accurate its results will be. So the system can control the battery as efficiently as possible, reducing the loss and load on the device. The authors of the development specifically gave it the shape of a chip that is compatible with almost all existing types of batteries. It is a universal module for the modernization of energy storage in gadgets, household appliances, electric vehicles, etc. Syntiant NDP120 does not need to understand the specifics of what it works with, it just optimizes the battery functionality based on the features of its operation. There’s an obvious commercial benefit to developing - for example, you can change the batteries in an electric car in 1,250 recharge cycles, not 1,000 as you are now. But more importantly, the AI can predict the critical battery situation, assess the risk of failure, and warn about it in advance. #AI #batteries

Start-ups are closing more often in the USA. Second wave of venture winters? It’s been a while since we talked about this winter, but it’s time we did. Carta have published recent statistics (https://lnkd.in/eddDJc_Z) about startup's closure. Obviously, their customers are not the whole market, but I think their data more or less reflect general trends. And their statistics says that in the first quarter of 2024, 58% more startups closed than a year earlier. In the first quarter of 2023, Carta closed 161 customer companies, while in the first quarter of this year it closed 254. Whether it’s the new wave or the dudes have been putting up with it for the last couple of years, trying to survive on their last legs, and now everyone. Interestingly, those startups that have already attracted rounds have started to close more often. Such growth is 86%. And those who haven’t attracted the money have a 34% increase in closures. If you go deeper into these start-ups with rounds, the biggest increase is in those who raised round B - 133%. It’s not like there’s a huge number of closed projects, but since 2020, there’s never been more than 10 closed projects in a quarter, and there’s 14. It looks like these are the ones who haven’t been able to raise the money for development, and they haven’t become profitable. #startups #shuttingdown

TECHNOLOGY NEWS: Android xBot-L conquered the Great Wall of China Despite the enormous achievements in the field of robotics, the capabilities of humanoid robots are still limited mainly to information and entertainment services. A striking example of this is the Sophia robot from Hanson Robotics. Roboticists from the Chinese company Robot Era went further, sending the android xBot-L on a journey along the Great Wall of China - about which they filmed a video for YouTube. According to experts, xBot-L is inferior in many respects to its predecessor and “compatriot” H1 from Unitree and the famous Atlas from Boston Dynamics. But this in no way detracts from its merits: it confidently moves along the legendary wall, stepping over potholes and uneven surfaces. He easily climbs stairs and even performs tai chi exercises in one of the wall towers. All this is made possible thanks to patented technology and perception algorithms. With their help, xBot-L “can navigate in difficult environments and maintain walking posture.” It is possible that one day humanoid robots will become one of the attractions of the Great Wall of China, which will be appreciated by millions of its visitors. #robotics https://lnkd.in/dpTBh4f4

How AI will change Pricing Metrics in SaaS Applications. An interesting article from Sam Lee on the topic of monetizing the added value of products in the age of AI. There are no numbers, but there are cool structural diagrams. 1/ In addition to the basic subscription, which generally does not depend on anything, today’s SaaS businesses grow revenue when they either sell more subscriptions to users, or add elements of turnover there - for example, per usage / per seat. 2/ However, judging by the place AI can occupy in the value chain, they can create value that does not scale depending on the frequency of use or depending on the number of users. 3/ On the contrary, perhaps they should incentivize having fewer users and performing fewer requests. Hence output, performance and other KPIs are important. And in the future, the outcome may also be important! 4/ Sam expects AI models to go through the following stages: ▪️AI Assistant (conditional Grammarly or Github Copilot): they help a specific user work better; the more users there are, the more they earn; ▪️Generative AI (conditional Synthesia, Axiom): they are used to create a specific product, such as an image or video. They replace people’s work, but they need people - the more people, the more requests, the more they earn; ▪️Agentic AI (“Agent-type AI”): they must perform a full-fledged auxiliary function and provide value precisely through outputs. And this is already a kind of replacement for human labor. 5/ In the non-AI world, for example, Shopify or Klaviyo are focused on output. Why shouldn't there be such stories in the AI world? 6/ The article ends with a general sign that hints at which pricing model is best to use for different types of AI products and WHAT exactly should be priced. ▪️For example, the Generative type for B2B clients is best to have: Pricing that charges either activity or output (but not outcome); ▪️Hybrid pricing subscription + usage or simply usage-based pricing. #saas #startup #AI

What does a VC-backed exit have to look like to make it more attractive than bootstrapping? We are increasingly hearing recommendations not to take venture money (really, there is no need) and to develop independently on our own (=bootstrap). Dirk Salmer (SaaS Group) took this topic and made some interesting discussions. 1/ He took the following data: ▪️Carta data on startup estimates by rounds and, as a result, the dilution of founders; ▪️Data from SaaS Napkin on ARR threshold levels that a startup must achieve at different stages; ▪️I assumed that current market estimates are 5x revenue, a good exit to a strategist occurs for 10x of revenue, and bootstrapped businesses are bought for 2.5x revenue. Actually, these are three scenarios for evaluating a startup at the exit; ▪️The difference is that venture money washes away founders, so we need to take into account how much is left for them. 2/ As a result, he calculated how much more or less the founders personally receive, subject to the implementation of one of three scenarios: 🔹Bootstrapped businesses outperform venture ones, provided the latter retain a 50% premium in the exit multiplier. Here is the advantage by stage: ▪️Seed: $1.5M: 🟢 +5%; ▪️Series A: $5M: 🟢 +60%; ▪️Series B: $12M: 🟢 +17%; ▪️Series C: $22M (Dirk's benchmark): 🟢 +66%; ▪️Series D: $60M (Dirk's benchmark): 🟢 +151%. However, in case if the sale is for 10x, a premium of 4 times, the situation is different - bootstrapped businesses almost always lose: ▪️Seed: $1.5M: 🔴 -69%; ▪️Series A: $5M: 🔴 -58%; ▪️Series B: $12M: 🔴 -42%; ▪️Series C: $22M (Dirk's benchmark): 🔴 -17%; ▪️Series D: $60M (Dirk's benchmark): 🟢 +26%; 🔹Only in one case, in the very late stage Bootstrapped wins, but there is the most guesswork presence. 3/ This all leads me to the next thought. If you look at the picture statically, it’s definitely no worse to do a bootstrapped business. However, we still need to ask ourselves the question - what will happen to the market, do you expect the multipliers to grow? If you are expecting, then it is likely that it will be more profitable for you to do a startup. #startup #VC #bootstrap

Where do investors find startups? 70% of VC deals come from their own search or from other investors. 15% comes from cold emails from startups. 15% conference + experts + accelerators. ________________________________________________________ Success = Build network with investors in your area, have a good reputation as a founder.

UNIT ECONOMICS CALCULATOR 2x Templates: Unit economics calculator ✖️ Valuation calculator based on growth rate and profit margin. I went through the sources a little to get to the actual links to these templates, it was a small quest. But I want to share interesting templates for founders. I found both of these templates in the mailing list from Pretiosum Ventures, and the authors of the templates are Fuel Finance. 1/ A good template for calculating unit economics - enter your parameters and calculate LTV/CAC. Definitely suitable for b2c businesses and b2b businesses without a focus on hardcore enterprise (where it’s difficult to translate everything into monthly data). 💾👉 You can download from their website, leave your contacts, then you will receive a link to a Google Doc by email: https://lnkd.in/emptB-2e 2/ The second template is a calculation of the company’s valuation, which is based on benchmarks from a16z (which got me a little hooked, I started looking at it initially). The score in the template depends not only on the size of the business, revenue, but also on how much more profitable your business is through Rule of 40 (this is % growth + % margin). Fuel proposes to actually add a coefficient - if your Rule of 40 is better than the industry as a whole, then divide your Rule of 40 by the industry and multiply your revenue multiplier by this, that is, a higher coefficient should be applied to you. This is something that a16z did not have in its pure form, but that BVP had (look at the channel where their research on Rule of 40 was published). So essentially what you're being asked to do is take market multiples and adjust for your margin advantage. The only thing is that the Rule of 40 data for your business is taken as forecast data - I would take historical data. 💾👉 The template using the same logic with Google Doc is available at the link: https://lnkd.in/enSAKiSd #startup #uniteconomy

The art of the pivot: part 1 & 2. A pivot is when a startup breaks with its core focus and changes direction in a fundamental way. Lenny Raczycki recently made a very interesting selection and analyzed cases when startups made pivots. A pivot is a change in product/business model along the way. For most startups, this is a normal story, this happens very often, but this issue in general is not very studied and popular. In addition, investors themselves say that pivoting is normal, but they invest in pivots with difficulty, if they invest at all. So, founders, you are almost one on one. But you are not alone. 1/ In the first part of the article, Lenny provides a list of 30+ startups that pivoted. I note that there are 26 of them in the free part, but that’s enough for me; I haven’t forced myself to subscribe yet. So there: ▪️26 startups; ▪️A short description of the reason for the pivot; ▪️How long after the launch did this pivot occur (usually 1-2 years, but YouTube pivoted after 1 week, for example); ▪️How they found a new idea; ▪️What was left from the previous idea or product. 👉 You can view and download the sign at the link: https://lnkd.in/eSVAApNB 2/ In the second part, Lenny does a little more in-depth analysis. Let's look at the attached picture with the timeline of pivots, it's sticky. It’s amazing that Notion and Twitch pivoted 4 years after launch, and Lyft and Framer even 5 years later! Wow! 🤯 The article also describes factors that should make you think about making a pivot. 👉 Read here: https://lnkd.in/eCGEMy9U 3/ Additionally, Lenny, in his post on Twitter, made an interesting conclusion about the reasons for the pivot - in most cases, this is not a request from the market, but some kind of parallel software or tool that was developed nearby, as a rule, to help the main product. The ranking in descending order from the most popular reason to the least popular one looks like this: ▪️Notified internal tech seeing pull: 9; ▪️Notified one feature seeing pull: 7; ▪️Notified bigger adjacent market: 6; ▪️Internal brainstorming: 5. #startup #pivot