"While a handful of startups start off with defensibility intact, the vast majority do not and need to add defensibility over time."

"Network effects are one of the few forms of defensibility that can arise immediately upon launch of a company."

"Some SaaS or operating system companies benefit from a platform effect. For example, Salesforce is a sticky product because so many other companies have integrated against it."

"Bundling and cross selling products prevent other companies from finding a wedge to compete with you, or create better sales success via superior interoperability, pricing, or procurement processes."

"Early access to APIs, data or other unique assets may provide a competitive advantage. For example, OpenAI has provided some companies with early access to GPT-4."

"Sales as moat. Many SaaS or enterprise companies eventually develop sales driven moats."

"Having unique or proprietary data, or owning a customers data or having a long historical record of it can create defensibility. Similarly, being a “system of record”, for a user, entity, etc. can be a powerful position to be in."

"Speed of iteration and execution, responsiveness to customers request, and speed of hiring and closing candidates are all advantages. Oddly, speed also turns out to be an advantage versus most other startups - few teams can execute at high levels consistently."

"“Your margin is my opportunity” is a famous quote by Jeff Bezos on Amazon and its advantages entering new markets."

"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."

"For some reason, most bitter founder companies tend to fail."

"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."

"Unexpectedly, the prior wave of value from AI roughly all went to incumbents over startups, despite a lot of startup activity."

"In the first internet wave most of the value went to startups (Google, Amazon, Paypal, Ebay, Salesforce, Facebook, Netflix) while some was captured by incumbents (Microsoft, Apple, IBM, Oracle, Adobe) who extended their franchises onto the internet. Perhaps this was a 60:40 or 70:30 startup:incumbent split."

"For mobile, most of the value went to incumbents (Apple, Google, and then every mobile version of an incumbent’s app - e.g. “Mobile CRM” was not a stand alone startup but rather Salesforce on your iphone) while there will still significant capture by startups (Whatsapp, Uber, Doordash, Instagram, Instacart etc). Perhaps this was a 20:80 startup:incumbent split."

"Crypto in contrast has been roughly 100% startup capture (Bitcoin, Ethereum, Coinbase, Binance, FTX, etc) with very little participation in value creation by existing financial services or infrastructure companies."

"While there were many “AI first” companies over the last decade (prior to the current transformer and unsupervised learning revolution) the really big AI applications landed with Google, Facebook (newsfeed and ads), Tiktok (Bytedance), Netflix (recommendations), Amazon (Alexa) etc."

"To beat an incumbent as a startup you usually need to either build something so dramatically better that you overcome the distribution, capital, and pre-existing product moats of the incumbent, or you need to focus on a brand new customer segment or distribution moat the incumbent can not serve for some reason. In general you need a 10X better product."

"Perhaps incumbents won due to a data advantage that is now going away as companies use the broader internet as an initial training set + are switching to models that work more robustly against smaller data sets?"

"Many prior-wave AI companies either directly took on incumbents or worked in hard markets. Hard markets include things like education or healthcare, where technological innovation is often crushed by market structure, regulation, or a seeming indifference to actual end-user needs by people already in the field."

"While many of the prior innovations in AI were striking and exciting (AlexNet, CNNs, RNNs, GANs etc) this time does feel different for a few reasons. There is reason to believe while incumbents should capture a good amount of the value in this wave, startups will take a bigger share of AI generated value this time around."

"One of the remarkable things about this current technology wave is the speed of innovation across many areas."

"This time, the technology seems dramatically stronger, which means it is easier to create 10X better products to overcome incumbent advantages. The ""why now"" may simply be a technology sea change."

"GPT-3 seems to be useful but not ""breakthrough"" useful to the point where large numbers of startups are building big businesses on it yet. This could also just mean not enough time has passed since it launched recently. However, a 5-10X better model then GPT-3 should create a whole new startup ecosystem while also augmenting incumbent products."

"Unlike the prior wave of AI startups, there are a clear set of infrastructure-centric companies with broad adoption and rapidly growing usage - this includes OpenAI, Stability.AI, Hugging Face, Weights and Biases, and others."

"There are highly repetitive, highly paid tasks (code, marketing copy, images for websites etc)"

"Workflow tools do not exist or are weak for the use case, so the AI features become a core and useful part of a broader workflow tool"

"Summarization or generation of text or images is useful for the product application - this is enabled in a high fidelity way by new AI tech in a way that did not exist before."

"The key with all this exciting tech will be to avoid the hammer-looking-for-a-nail problem. It will be important to identify actual end user needs and unserved product/markets that will benefit from this wave of exciting technology."

"After having personally worked for 15 years on AI-related products directly, or investing in them, it feels like startups will finally start to get real value from AI. Exciting times lie ahead!"

"Media brands will continue to thrive, but we will also see a blossoming of individual brands and niche publications that are self sustaining."

"Substack is changing the nature of journalism, reporting, blogging, podcasting, and content creation. Strikingly, it is turning a generation of content creators and journalists into business owners."

"Newspapers used to have local monopolies or oligopolies. This was changed by the Internet, where it was suddenly easy to use any news source from any geography. Later, social networks like Twitter and Facebook built massive audiences and drove more news distribution and interaction."

"Newspapers & magazines had enormous costs associated with printing presses and the physical production and distribution of their content. Digital media emerged without these baked in costs"

"Convert Twitter or other followers into regular subscribers and readers by building a direct channel outside of Twitter or other social media gatekeepers. It also allows for targeted niche writing."

"A subset of journalists can make 2X-20X more on substack than at their old job."

"It also creates a direct financial incentive for writers on the platform to build their userbase and readership by giving them revenue tied directly to usage."

"Ads based models are less able to incentivize content providers directly, nor do they create an entirely new media business model like Substack does."

"One of the challenges of local news was its cost structure. Each paper needed to physically produce papers, sell inventory to advertisers, and distribute."

"While the old paradigm of journalism was “get on the front page”, Substack is an interesting move towards “get in the inbox”."

"Society is moving from public to private content and mainstream to new fragmented niches. Part of this shift is driven by a fear of cancel culture, which appears to be evident in the newsrooms for a subset of journalists who shifted to Substack."

"Given Twitter’s increasing censorship, erm I mean, ""content moderation"", of scientific and political content, it will be interesting to see the trade off between censorship and distribution reach."

"One of the most interesting societal transformations driven by Substack is converting a generation of journalists into small (and in some cases large) business owners. During the last two decades, journalists have suffered from the “creative destruction” aspects of capitalism - where the business model of their platforms were hurt and many journalists were laid off."

"It will be interesting to see how this adoption of direct business ownership shapes the writing and thinking of a generation of writers - and might end up being one of the biggest societal impacts of Substack."

"One of the bigger breakthroughs of recent times was the emergence of Transformer models in 2017 for natural language processing (NLP). Transformers were invented at Google, but quickly adopted and implemented at OpenAI to create GPT-1 and more recently GPT-3."

"Transformers and NLP more generally are still nascent in application today but will likely be a crucial wave over the next 5 years."

"Much of the world of an enterprise is effectively pushing around bits of language - legal contracts, code, invoices and payments, email, sales follow ups - these are all forms of language. The ability of a machine to robustly interpret and act on information in documents will be one of the most transformative shifts since mobile or the cloud."

"Applications of large language models (LLMs) today include things like GitHub Copilot for code, or sales and marketing tools like Jasper or Copy.AI."

"The challenge for many startups will be to determine what is a de-novo product/market versus one where an incumbent should “just add AI”. Sometimes the best way to figure this out is to simply try it. Startups are about iteration and “just doing” and many of these things can be overthought and misanalyzed."

"Consumer applications. Enhanced search. Interactive, language native chat-bots. Eventually one can imagine an intelligent agent as a replacement for Google search. Other areas like smart commerce are big applications."

"if you hit writers block the AI can suggest 5 different next paragraphs. At some point these language models should be good enough to write end-to-end novels, poems etc"

"Doctor & Lawyers Assistants. Eventually much of what health professionals do in terms of diagnosis may be replaceable by AI. Ditto for lawyers and a number of other white collar jobs."

"One big open question on large scale language models translating into new startups - is the degree to which challenges are science problems, versus engineering problems. There is a lot of room to make advancements from an algorithm and architecture perspective in machine learning. However, there also appears to be significant room for incremental engineering iteration and efficiency gains."

"Semiconductor innovation can increase performance of various systems dramatically. Each major technology wave tends to have an underlying major semiconductor company emerge that underlies it"

"Many core AI researchers I know at OpenAI, Google, and various startups, think true Artificial General Intelligence (AGI) is anywhere from 5 to 20 years away. This may end up like self driving cars (perpetually 5 years away until it is not), or it may happen much sooner."

"At some point it may make sense to either (1) give up on your original product and to sell the company, (2) shut down what you are doing and return money to investors, or (3) to pivot."

"1. Pivot inside your existing market, without clear new signal."

"In general founders worry too much about sunk cost and the industry knowledge they have built. So when they pivot, they pivot inside their market instead of considering new areas to work in. In general, startups tend to fail due to bad product/market fit"

"""When a great team meets a lousy market, market wins When a lousy team meets a great market, market wins. When a great team meets a great market, something special happens.""  - Andy Rachleff, founder of Benchmark Capital"

"2. Reposition or edit down your product. Find behavior in existing product/market and amplify or focus on that (Instagram, Twitch)."

"If your product is seeing enthusiastic adoption in a single userbase or use case, it might make sense to focus all your attention on that use case."

"The downside of keeping the original product alive is the time and attention the product and its customers demand from your team. It may also create a lack of clarity and confusion about your brand and the changes you are making."

"If your legacy business is providing sufficient cash flow to fund a new business it is probably worth keeping and launching a new brand."

"2b. Market pivot or product repositioning."

"3. Launch a tool that you used while building your own company (Yammer, Slack)."

"Building something for others, that you need for yourself, is often a successful way to identify a real product or market need."

"In the case of reconfiguring an entire company, the hard part of this sort of pivot is to rebuild the team to be able to build the product or sell into the new market."

"If you need to make this transition and do lay offs, do it quickly and be as fair as possible to your employees who supported you in the past."

"You may lose employees who no longer believe along the way, but that may be a good thing - you need a core set of true believers to weather the storm."

"if the co-founders of the company need to change. Or alternatively, investors no longer want to support the team and its new vision. In this case, it is better to restart the company than to fight how to allocate new equity or with investors who no longer believe."

"The two biggest ways early stage companies die is by running out of money and founder conflicts."

"During a pivot some employees may rally and do whatever they can to help the company make it through a rough transition. Other employees may become fearful or anxious or lose belief in the company."

"If they drag their feet and are truly unhappy with the new direction, you can propose a restart of the company (see above), a buyout of investors who do not want to be involved anymore, or try to sell the company."

"The key is to manage the various stakeholders (co-founders, employees, investors, customers) through the transition, let go of your legacy past, and focus on creating a bright new company and bright new future."

"In this post I argue most SaaS software starts off default non-defensible (hence all the HackerNews posts & Reddit threads saying as much), and tend to build a moat over time. In parallel this post delves into different forms of moats as well as competition more generally."

"There are of course counterexamples to this - either in terms of it taking longer to build a technically challenging more defensible product (see eg Snowflake), or there are considerations around IP (for biotech), deals (e.g. payments needing a backend), regulation (“we need to be a broker/dealer first”), talent (“there are 20 people on the planet who can train this sort of foundation model”) or other areas (for example, pre-existing open source software that is being commercialized by the team who created it)."

"Adding defensibility over time"

"Network effect. In network effects every user creates more value for other users, forming a positive feedback loop."

"Platform effect. Some SaaS or operating system companies benefit from a platform effect. For example, Salesforce is a sticky product because so many other companies have integrated against it."

"Services does integrations for the company against other vendors."

"The company integrates against many other APIs, code bases, etc."

"EMRs such as Epic are another example of integration-driven and long-term contract driven defensibility."

"Bundling. Bundling and cross selling products prevent other companies from finding a wedge to compete with you, or create better sales success via superior interoperability, pricing, or procurement processes."

"Big product footprint. For some products, the breadth and depth of the product footprint means it is hard for new entrants to compete, as it takes too much time to reach feature parity before one can sell to customers competitively."

"This is why many startups first launch to the low end of a market segment, and then build depth of product over time as they move up into enterprise."

"Early access. Early access to APIs, data or other unique assets may provide a competitive advantage"

"Exclusive provider or distribution. Sometimes locking in as an exclusive provider can get scale, brand, or distribution advantages for a company."

"Backend. Some markets require hard to get deals in place or ones that take a year to execute and implement."

"Multi year contracts. Long term contracts to lock in customers for multiple years."

"Sales process. Often when selling to a large enterprise you need to go through multiple reviews and functions at the buyer including security review and audit, procurement, IT, and other areas."

"Regulatory. Some companies receive regulatory approvals that provide a moat."

"Data or system of record effect. Having unique or proprietary data, or owning a customers data or having a long historical record of it can create defensibility."

"Similarly, being a “system of record”, for a user, entity, etc. can be a powerful position to be in."

"Capital scale. Having access to large sums of money sometimes creates the environment to execute quickly and build a network effect or defensible position - this happened with both Uber and TikTok."

"Business scale and negotiation. Sometimes pre-negotiating “scaled pricing” allows you to offer a service cheaper than anyone else, creating a self-fulfilling loop."

"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."

"Openness. Some dev teams only want to adopt open-source software for specific areas."

"Brand. Eventually a brand may be built that positions a company as the default choice for a specific use case."

"IP Moat. Intellectual property tends to be more capable of protecting hard tech or biotech companies than most consumer or SaaS products."

"Speed. One of the few advantage a startup has against an incumbent is its speed."

"Pricing. Occasionally startups may have a pricing advantage relative incumbents (but maybe not other startups) via a lower cost structure, a lack of an existing product to cannibalize, or due to a different business model."

"New business models. Sometimes a startup can innovate on business model to create a higher leverage business or different incentive structure."

"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."

"3. Perception that “the market is over” so no one copies a company even if it might be tractable to out execute them."

"4. It is harder to hire strong employees to work on what is initially a clone company. People assume more defensibility than tends to exist early, so are harder to convince to join your efforts until traction is clear."

"Often in the first 4-5 years of a successful startup’s life, its main competition is other startups."

"Usually this transition from startup competition to incumbent competition takes between 4-7 years."

"Fast market entry or expansion by an incumbent can at times be the death knell for a startup, especially if done early (i.e. the first 1-3 years of startup life)."

"As a proto-founder, it is worth thinking through how aggressive the incumbents are for your potential product area, how clear they are on the opportunity you are going after, and how likely they are to act."

"There are roughly three forms of focus for a company. Some large incumbents become so focused gazing at their own navels (or, relatedly their regulators’ navels) that they forget about users and start to create competitive openings for others to exploit."

"These large companies often have process after process and approval after approval on how to launch, how safe and “equitable” a product is, how to set goals and OKRs, performance reviews, executive reviews, legal reviews, marketing reviews, PR reviews, pre-review meeting reviews, how to map across every single geography at once, etc that they never launch anything that good anymore (or perhaps, launch at all)."

"Another form of company focus is competitor-centric focus. These companies continuously copy what their competitors are doing or have announced, versus what users are actually asking for or want."

"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."

"The takeaway is that serving a customer need well is often more important (and harder) to think about than defensibility."

"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."

"Perhaps the biggest outcomes in the first wave of AI to date are the self-driving car companies, many of which are subsidiaries of the incumbents (Google, GM and Tesla, respectively), or were SPACd during the financial mania of the COVID era."

"1. Technology created a 0.5-3X better, versus 10X better set of products (?)"

"One hypothesis is that for some products the prior wave of AI helped create better products, but not so remarkably better that they could beat incumbents or hard market structures."

"2. Data differentiation used to be more important (?)"

"3. Hard markets (?)"

"An incumbent can be 50% as good as something, but as long as they bundle it with a core pre-existing product with lots of customers they can still win (see e.g. Teams versus Slack)."

"Hard markets include things like education or healthcare, where technological innovation is often crushed by market structure, regulation, or a seeming indifference to actual end-user needs by people already in the field."

"1. Better tech is coming across many areas."

"Future GPT-like language models (GPT-4? GPT-N?) should increase the power, fidelity, and reach of natural language across consumer and B2B in deep ways and potential change everything from human interactions (dialogue based interactions?) to white collar work (co-pilot for anything that touches text, by vertical)"

"In parallel, advances in image generation, speech to text, text to speech, music, video, and other areas are happening."

"The pivot point for whether now is the moment where AI takes off for startups depends on whether GPT-4 (or some other API platform) is dramatically more performant than GPT-3/3.5. GPT-3 seems to be useful but not ""breakthrough"" useful to the point where large numbers of startups are building big businesses on it yet."

"2. New tech means there are startups providing valuable infrastructure to the rest of the industry."

"The failure of Google to capitalize on its many advantages specifically in AI has been striking."

"3. There are clear app use cases without strong incumbents."

"A number of the earliest use cases and startups - for example marketing copy (Copy.AI or Jasper), Image Gen (Midjourney, Stable Diffusion, etc.) and code gen (Github Copilot, Replit) are seeing nice adoption and growth in a way that did no exist in the prior AI wave."

"Imperfect fidelity is fine, as you have a human in the loop who wants to review the items (which creates a nice feedback loop or future training set)."

"Focus on end-used and markets"

"As the builders in the market shift from research scientists to product-centric builds (including, of course,  some product-minded research scientists) we should see a blossoming of new machine learning driven applications."

"Even if incumbents capture most of the value this time due to raw scale, startups should participate in a significant way in new market cap and impact to the world."

"Sequential inventions and discovery include CNNs, RNNs, various forms of Deep Learning, GANs, and other innovations."

"One of the bigger breakthroughs of recent times was the emergence of Transformer models in 2017 for natural language processing (NLP)."

"Transformers were invented at Google, but quickly adopted and implemented at OpenAI to create GPT-1 and more recently GPT-3."

"The 3 types of companies to expect: Platforms, AI-de novo, and Incumbent AI-enabled."

"1. Platforms & infrastructure. The mobile platforms were eventually iphone and Android."

"2. Stand alone (de-novo applications built on top of the platforms)."

"For the transformer companies this may include Jasper/Copy on the B2B and other exciting applications on the consumer side which would not be able to exist without advanced machine learning breakthroughs."

"3. Tech-enabled incumbents (products where the incumbent should “just add AI” where startups will lose to distribution)."

"For example, while many startups tried to build “mobile CRM”, the winners were existing CRM companies who added a mobile app."

"Platforms - Models/APIs. There is a bit of an arms race ongoing where a set of companies are trying to build ever larger scale models."

"Tooling. Hugging Face is a great example of a tooling company for the space - think of it as Github for transformers and other models."

"Code. Github Copilot is an example of a code-centric ML tool built on top of OpenAI."

"Any member of an enterprise should be able to query any analytics tool easily over time with natural language questions[1]."

"Sales & marketing tools. LLMs hold the promise of a variety of sales tools - from initiating inside sales emails algorithmically to creating marketing copy like Jasper and Copy.AI do today."

"In-enterprise verticals, RPA, data infra. Better tooling for finance, HR, and other teams. Adding NLP to RPA tools like UIPath should turbo charge them."

"ERP disruption. An understanding of what data and various fields actually mean could create the opportunity to augment or displace ERP systems."

"Consumer applications. Enhanced search. Interactive, language native chat-bots. Eventually one can imagine an intelligent agent as a replacement for Google search."

"Creator & visual tools. Writing and art augmented by AI. See e.g Dall-E, MidJourney, Disco Diffusion, Stable Diffusion, Imagen, or Artbreeder."

"Some of these companies will require technical breakthroughs, others can be built today on top of existing APIs."

"One big open question on large scale language models translating into new startups - is the degree to which challenges are science problems, versus engineering problems."

"“PaLM 540B is in the same league as some of the largest LLMs available regarding the number of parameters: OpenAI’s GPT-3 with 175 billion, DeepMind’s Gopher and Chinchilla with 280 billion and 70 billion, Google’s own GLaM and LaMDA with 1.2 trillion and 137 billion and Microsoft – Nvidia’s Megatron–Turing NLG with 530 billion”"

"Wozniak was famous for this in the early days of Apple - how to best utilize limited compute, create color output, etc."

"An increasing number of LLM platform startups are raising smaller financing rounds ($10-$50M versus hundreds of millions) under the assumption that the future may be as much about better engineering than sheer scalability."

"As an example, Stable Diffusion cost just $600K to train."

"As we move from the era of only big models to the era of more engineering and applications, the other shift in the market segment will be from PhDs and scientists to product, UI, sales, and app builders."

"Semiconductor innovation can increase performance of various systems dramatically."

"Google invented TPUs - tensor processing units - which are custom ASICs that perform much better than GPUs for many models. However, Google has not sold them as stand alone chips but does offer them in their cloud."

"In the case of current AI models, much of the work is in the form of matrix multiplication and chips that are custom for current AI models have a larger portion of their surface area devoted to this type of math."

"All of the startups have overinvested in raw performance and underinvested in a software stack that makes it easy to use. This includes everything from the kernel to tooling. NVIDA in contrast has CUDA."

"Interconnects to allow hundreds or thousands of chips to act in concert versus single chip performance."

"It is possible that for a startup to compete well in the silicon space for ML, an emphasis on software and interconnects will be key."

"Google recieved a lot of attention for it TPUs, but never sold them externally as stand alones (and they also had a difficult form factor for some to use). Perhaps this is a multi-hundred billion dollar opportunity they forewent for other strategic reasons?"

"At that moment in time, we will have a few big shifts in our conception of machine awareness and we will be dealing with bona fide digital lifeforms (DILIs)."

"Imagine if you could create 100,000,000 simultaneous clones of yourself and modify different aspects of yourself, and create your own utility function and selection criteria. DILIs should be able to do all this (assuming sufficient compute / power resources)."

"if you simulate pain in a DILI that is self aware, are you torturing a sentient being?)."

"Either way, it seems like one of the eventual potential existential threats to humankind is the potential to compete with its digital progeny."

"Therefore a future AI species will be part human-part machine, love us, and will not want to leave us behind once fully sentient and superintelligent."

"The highest probability event seems to be that humanity roughly acts as a boot-loader to AI as the dominant future species in our solar system."

"maybe all organic organisms are eventually displaced by their home-brewed AI and von Neumann probes"

"Rather, a potential un-nuanced description is the model wakes up, is given an input, provides an output and goes back to sleep."

"Imagine if your brain was frozen in a moment and time, and could process information and provide input, but would never learn anything new. Transformer models sort of work that way right now and their later more advanced form may represent a new form of consciousness if they ever become sentient."

"Similarly, people are building models on top of transformers that learn off of new inputs to provide the underlying transformer models with inputs and take outputs from transformers to train on."

"visual system (which tends to be tractable to lab study in ways some other systems are not, and recapitulation of function and modeling by ML systems in the short run in the lab)."

"This will be a multi-decade transformation and will require ongoing improvement in base models and engineering to reach its full potential."