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Startup Decoupling & Reckoning - by Elad Gil - Elad Blog
Startup Decoupling & Reckoning - by Elad Gil - Elad Blog
The coming reset in mid-to-late stage startups in 2023-2024 is at this point likely largely decoupled from interest rates and inflation. Implications are discussed.
These trends have resulted in an overhang of companies that either (1) lived without product market fit and survived well past their natural expiration point, or (2) hired way ahead of progress and burned large sums with high valuations and now are stuck with little progress per dollar and a large preference stack.
Many companies are likely about to meet a hard reckoning. This is likely to start end of 2023 and accelerate through end of 2024 or so. The likely timing is +/- 6 months. It is based on when companies last fundraised at scale, 2021, and how much runway they raised.
The macro economy and the startup valuation reset are now decoupling. The reset in private tech will happen roughly no matter what happens in the macro economy.
Think through your investors individual incentives as you consider what to do and hear (and filter) their opinion. A given company in a VC portfolio is part of a portfolio and may function as an option - while for you the company or how you otherwise spend your time is your entire livelihood. And never forget how precious your time is. Life is short, and productive years in which you can take a lot of risk are quite limited[2].
Investor incentives differ early vs late (and based on their role in VC firm)
This suggests if you plan to sell your company, you should pursue an exit sooner than later. End of 2023/early 2024 will become a crowded market for companies trying to sell.
It is possible we will see a rise in M&A solely for a company’s cash, which is a thing in biotech, and may also become a (smaller) thing in tech. In this scenario a cash rich company (whose product is not working) gets bought by a cash poor company whose product is working and who just wants the cash. This sort of M&A is effectively raising a round from another company’s founders and backers.
A number of companies such as Stripe, Instacart (rumored to have repriced 74% from $39B to $10B), and Klarna (valuation drop of 85% from $45B to $6.7B) have proactively written down their companies’ market caps in order to reset prices for their employees and/or fundraises (either private rounds or IPOs). These companies should be applauded for trying to do the wholistic right thing for their shareholders given that comparable public market companies may be down 70-90%.
A founders big idea and great team, at some point, will largely be converted into numbers around revenue, burn, growth, margin and related by the public markets weighing machine.
Some companies may be less impacted by a recession, such as security products or ones that clearly decrease costs.
However, the biggest lever for many companies to cut costs is not software - it might be to lay people off or shut down marginal products. This means even software that increase efficiency gains may see a strong slow down in the short term as their enterprise customers focus on more pressing matters in the short run. Gains may still be had for many enterprises through reasonably brute force actions.
·blog.eladgil.com·
Startup Decoupling & Reckoning - by Elad Gil - Elad Blog
How IBM Public Cloud struggled against AWS and Microsoft - Protocol
How IBM Public Cloud struggled against AWS and Microsoft - Protocol
Insiders say that marketing missteps and duplicated development processes meant IBM Cloud was doomed from the start, and eight years after it attempted to launch its own public cloud the future of its effort is in dire straits.
·protocol.com·
How IBM Public Cloud struggled against AWS and Microsoft - Protocol
AI Platforms, Markets, & Open Source - by Elad Gil
AI Platforms, Markets, & Open Source - by Elad Gil
What does the future market structure look like for AI foundation and API companies? How does OSS play a role in a world of ever scaling models?
The cloud wars ended up with AWS, Azure, and GCP as three large scale, fierce competitors. This is an oligopoly market with no single winner. Based on what we currently know about the world, this seems the most likely near term market structure for foundation language models, but it is early days and the future is uncertain.
This may take some time and is in the extreme a race of asymptote versus AGI, as well as further technological breakthroughs that could accelerate progress indefinitely. Moore’s “law”, which was more an observation than an actual law, lasted longer than anyone originally expected.
One potential future world would be OpenAI/MSFT versus Anthropic/Google versus Stability/Amazon vs Cohere/Meta (these are all made up pairings!!!!!). In other words, each incumbent would chose a startup partner to take on the brand and safety risk while buying major ownership in said startup. In exchange, the startup would get access to data, distribution, and other resources from the incumbent. To some extent, this results in roughly the same market structure as (2).
Crypto (BTC and ETH) are the obvious OS counterexamples to this, although one could argue given monetization being built directly into the crypto protocol equated in these groups self-funding. In some sense, crypto is its own corporate sponsor.
·blog.eladgil.com·
AI Platforms, Markets, & Open Source - by Elad Gil
Defensibility & Competition - by Elad Gil - Elad Blog
Defensibility & Competition - by Elad Gil - Elad Blog
Are early SaaS or AI companies ever defensible early? What is the basis for competition for a startup?
These reviews can take many months to complete, so it is often easier for enterprises to buy a less-good bundled product from an existing vendor, then a better stand alone product from a new supplier.
Creator advantage. Sometimes the teams that implemented or created an open source software project are best positioned to commercialize said product, given their ability to drive and control contributions to said project + brand and relationships in that community. See e.g. dbt Labs.
New business models. Sometimes a startup can innovate on business model to create a higher leverage business or different incentive structure. For example, Anduril in defensetech has a traditional tech margin-based business, while all the incumbents sell “cost plus” (ie they charge 5-8% on top of what is cost them to build the product for the Department of Defense). The cost plus model creates a lot of incentives to act badly - for example since labor is charged as part of the cost plus, delays and doing things slowly means more revenue for the incumbents. Similarly, the reason some items have $100 screws is so they can charge 5% on top of it (instead of just using a 10 cent screw).
1. It is sometimes hard to know what is actually working, versus hype.
2. Founders have a lot of pride in what they build, and may not want to just copy and out-execute someone. Often when a startup copies another’s idea, they put a unique spin on that approach or product versus default blankly copying it. All these tweaks and changes tend to make the product worse.
Most good startup ideas are definitionally non-obvious (otherwise everyone would be doing them). Often, good startup ideas seem small, niche, or toy like. Only desperate people will go and build in these areas as they may seem too small a market or use case initially to large incumbents.
Finally, there is user centric focus. User-centric companies tend to have a better understanding of their customer and their ongoing needs leading to superior product, sales, customer success, and pricing approaches. By aligning against the customer, more things tend to go right. While being customer-centric helps enormously in most cases, occasionally an incumbent is just too dominant for a superior customer experience and product to win.
The takeaway is that serving a customer need well is often more important (and harder) to think about than defensibility. In many cases defensibility emerges over time - particularly if you build out a proprietary data set or become an ingrained workflow, or create defensibility via sales or other moats.
The less building and expansion of the product you do after launch, the more vulnerable you will be to other startups or incumbents eventually coming after and commoditizing you. Pace of execution and ongoing shipping post v1 matters a lot to building one forms of defensibility above. Obviously, if your company is defensible up-front it is better then if it isn’t.
·blog.eladgil.com·
Defensibility & Competition - by Elad Gil - Elad Blog
Self hosting in 2023
Self hosting in 2023
Why I've decided to host my page on my own in home server and how community is building amazing tools like Coolify.
·grifel.dev·
Self hosting in 2023
Reverse Engineering A Mysterious UDP Stream in My Hotel
Reverse Engineering A Mysterious UDP Stream in My Hotel
Hey everyone, I have been staying at a hotel for a while. It’s one of those modern ones with smart TVs and other connected goodies. I got curious and opened Wireshark, as any tinkerer would do.
·gkbrk.com·
Reverse Engineering A Mysterious UDP Stream in My Hotel
The power of defaults « julian.digital
The power of defaults « julian.digital
The world’s most successful companies all exhibit some form of structural competitive advantage: A defensibility mechanism that protects their margins and profits from competitors over long periods of time. Business strategy books like to refer to these competitive advantages as “economic moats”.
·julian.digital·
The power of defaults « julian.digital
The simplest thing that could possibly work
The simplest thing that could possibly work
I'm a programming child of the agile software movement. Just as I was starting out, Kent Beck published Extreme Programming Explained in 2000. It was a revelation. I had just enough exposure to Big Upfront Design and waterfall methodologies to appreciate what a monumental shift this was. Beck's methodology x-rayed the ills of the tradi...
Two expressions of that was the simplest thing that could possibly work and you aren't gonna need it.
What I've later also come to appreciate is how well these mottos pair with a cultivated strain of ignorance, if you want to tackle existing problems from first principles. It took being an outsider to J2EE to pursue Ruby on Rails. And being an outsider to JavaScript to chase Hotwire. And now, again, being an outsider to Kubernetes to sail with MRSK. It's easier to break the paradigms if you're not enmeshed in them on the daily.
·world.hey.com·
The simplest thing that could possibly work
More Art than Science
More Art than Science
When I hear something referred to as "more art than science," I get excited. That means one of two things to me: 1. There's alpha in mastering it, or 2. There's even more alpha in formalizing it. First, there's alpha in mastering it. Mastery of a "more art than science"
·matt-rickard.ghost.io·
More Art than Science
Commoditization of Large Language Models: Part 3
Commoditization of Large Language Models: Part 3
Meta just open-sourced the weights of LLaMa, a foundational, 65-billion-parameter large language model. I wrote part one of "Commoditization of Large Language Models" (July 2022) when EleutherAI challenged GPT-3 with open-sourcing GPT-J. I noted that GPT-3 was likely trained with mostly public datasets. The LLaMa model by Meta is trained
·matt-rickard.ghost.io·
Commoditization of Large Language Models: Part 3
Taxonomy of Startup Risk
Taxonomy of Startup Risk
A few different looks at the dimensions of risk in early-stage startups. Not a sufficient framework for evaluating an idea (from the founder or venture capitalist perspective), but a good start in categorizing major risk vectors. Startups are high-risk and have a corresponding risk-return profile to show for it. But
·matt-rickard.ghost.io·
Taxonomy of Startup Risk
When prophesy fails
When prophesy fails
Remember back in November, when seemingly every pious public persona and their coteries announced final farewells on Twitter? All in the clear expectation that the service would sink any moment? Like they had seen the iceberg, and was sure – just sure! – that impact was imminent. Except, there was no iceberg, no impact, no sinking ship...
·world.hey.com·
When prophesy fails
Defensibility & Competition
Defensibility & Competition
Are early SaaS or AI companies ever defensible early? What is the basis for competition for a startup?
·blog.eladgil.com·
Defensibility & Competition
M&A as R&D in Technology
M&A as R&D in Technology
How tech companies have used acquisitions as a form of R&D
When doing these acquisitions, more so than really buying the existing “business”, these companies are buying a mix of:A talented team Experience, know-how, and passion in a specific areaA product that is unreleased or in its early stages but has not quite taken off as a business
While these days, it may seem that this form of acquisition is less valuable if they don’t come with networks of users since every product can be “replicated”, it still serves as a valuable way to acquire new technologies, knowledge, and personnel and short circuit the time to enter a new market and get up to speed in a new area.
·tanay.substack.com·
M&A as R&D in Technology
Which Customer Segments are Healthiest During the Downturn? by @ttunguz
Which Customer Segments are Healthiest During the Downturn? by @ttunguz
In CloudFlare’s latest earnings report, the management team highlighted the strength of enterprise buyers within their customer base. I wondered if this were broadly true. Do public software companies with largely enterprise customer bases benefit from superior growth to their peers with mid-market or SMB focuses? Enterprise & Mid-Market public companies have seen a relatively constant decline in growth rates through the last six years. SMB businesses benefited from a post-Covid surge when the US re-opened - a phenomenon that seems to abate with time.
·tomtunguz.com·
Which Customer Segments are Healthiest During the Downturn? by @ttunguz
We stand to save $7m over five years from our cloud exit
We stand to save $7m over five years from our cloud exit
Since declaring our intention to leave the cloud in October, we've been busy at work making it so. After a brief detour down a blind alley with an enterprise Kubernetes provider, we found our stride building our own tools, and successfully moved the first small application out of the cloud a few weeks ago. Now our sights are set on a t...
·world.hey.com·
We stand to save $7m over five years from our cloud exit
Amdahl's Law and Optimization
Amdahl's Law and Optimization
Amdahl's Law is a formula that helps to estimate the maximum speedup that can be achieved by parallelizing a program. It's intuitive and practical. The equation is fairly simple: Speedup = 1 / ((1 - P) + (P / N)) Where: * Speedup is the improvement in performance that can be achieved by parallelizing the
·matt-rickard.ghost.io·
Amdahl's Law and Optimization
Why Python Won't Be the Language of LLMs
Why Python Won't Be the Language of LLMs
Python has long had a monopoly on data workflows — everything from data analysis to data science to machine learning. Anything that can't be done in SQL is done in Python. But Python won't be the language for LLMs. Why did Python become the language for data workflows? * Cross-platform. Data analysts
·matt-rickard.ghost.io·
Why Python Won't Be the Language of LLMs
Nix Is Fighting The Last War
Nix Is Fighting The Last War
Nix solves the problem of hermetic Linux environments. Your tools and configuration are deterministically sealed and packaged – always giving the same result. This was a real issue in the time of golden image machines when Linux distributions were hand-crafted to perfection. But that was the last war. Just as Nix
·matt-rickard.ghost.io·
Nix Is Fighting The Last War
Interfaces for Uncertainty
Interfaces for Uncertainty
The last public data point for Google's "I'm Feeling Lucky" button was in 2007 (less than 1%). On the search engine results page (SERP), around 28.5% of users click the first result (source). Algorithms that aren't perfect, or problems where there isn't always a "right" answer need interfaces for
·matt-rickard.ghost.io·
Interfaces for Uncertainty
Carrying a Dozen Problems
Carrying a Dozen Problems
Richard Feynman was fond of giving the following advice on how to be a genius. You have to keep a dozen of your favorite problems constantly present in your mind, although by and large they will lay in a dormant state. Every time you hear or read a new trick
·matt-rickard.ghost.io·
Carrying a Dozen Problems