Since OpenAI released ChatGPT in Nov 2022, the pace of innovation, adoption, investment and revenue for AI products and infra have grown at an awesome rate - but mostly for tech incumbents, and the best funded tech companies.

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The resources and momentum of the early leaders can make founders (and investors) feel like it’s too late, or too hard for new AI startups to win. There’s “OpenAI killed my startup” gestalt, and concern about AI startups being snuffed by a black hole of big tech gravity.

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Incumbents Apple, Amazon, Google, Meta, Microsoft and Nvidia are worth an unprecedented $14.5T in combined market cap. OpenAI, Anthropic, Mistral and Perplexity are worth a combined $110B+ (or $170B+ if OpenAI is valued at $150B) and they have raised $20B+. 

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Do startups have a shot given big tech’s current gravity and momentum?  

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Based on historical cycles - YES! But in due time. And if the AI era unfolds like prior platform shifts, the best time to start an AI company might be ahead. In historical platform shifts, tech incumbents and the best funded startups initially benefited. In the ensuing years, startups grew to match and surpass incumbents. Aka many of the early winners were just paving a road for later entrants to drive on.

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Lessons from the web, mobile and cloud: leadership played out over decades

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Every decade or two, the tech industry has been propelled by a major wave of tech innovation (parallels b/w dot.com and the most recent bull market here). This creates a BIG window of opportunity for new tech to gain adoption, change customer habits and how businesses operate. 

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The web: winners were founded 2-10 years after the browser. And many of the early leaders didn’t make it.

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In 1994, the launch of the Netscape browser unleashed a tech “gold rush” in hardware and software. Companies rushed to buy servers, storage, routers, rack space and more to build, serve, secure and analyze new websites. This boom primarily benefited Netscape and incumbent tech players like Sun, Oracle, Cisco, Juniper and EMC. Lots of parallels to what’s happening right now in the AI boom.

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In the following years, as new needs arose for web companies, a wave of VC-backed infra companies like Akamai, Cloudflare, Doubleclick, Infinera, Verisign and VMWare were founded to solve the new challenges - and they surpassed many incumbents in time.

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The first wave of consumer web startups also attracted millions in VC dollars and users, and were instrumental in changing user behavior. 

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Remember Altavista, AskJeeves, eToys, Geocities, Go!, Infoseek, Lycos, Pets.com, and Webvan? In addition to Netscape, they were early web leaders, but don’t exist anymore - most were overtaken by later entrants who learned from and surpassed them.

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Google was founded four years after the browser, after many search and discovery companies had become mainstream. They learned from the strengths and weaknesses of earlier products and benefitted from years of infrastructure investment and behavior change (i.e., a future $2T leader could be founded two years from now!)

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It was years after the browser that startups like Expedia, Facebook, Netflix, PayPal, Linkedin, Skype and Yelp out-innovated incumbents to shift how people connected, shopped, traveled and invested. Amazon is the rare still-thriving web company started in ‘94. Leadership, legendary customer obsession and focus (books) helped - they waited four years before adding music and video. 

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A raft of early web B2B startups also attracted lots of funding and attention - remember Ariba, Chemdex, CommerceOne, Freemarkets, i2, Neoforma and VerticalNet? It was five years (1999) before one of the biggest B2B winners was founded - Salesforce.

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Mobile: early breakouts also paved the way for today’s leaders 

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Massive investment in cellular networks and early success by companies you may not have heard of preceded today’s biggest mobile players.

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In 1996, Motorola was the dominant, global pioneer in mobile. Their landmark clamshell Startac phone catapulted them to $48B in revenue and $125B in market cap in just four years ($228B in today’s dollars). International mobile players Nokia and NTT Docomo saw similar success, propelling mobile adoption around the globe.

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A year later in 1997, a startup called Palm unveiled the Pilot: a “personal digital assistant” (PDA). It was a breakthrough mobile device synched to the desktop. Palm grew to > $500M revenue in just 2 years ($1B in today’s dollars!) and soon went public at a $53B market cap, with ~70% of the PDA market in 2000.

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Then a Canadian startup called RIM launched the Blackberry in 2003 - the first phone with a hardware keyboard and email for business customers. RIM skyrocketed to $11B in revenue in just six years, building a seeming lock on sticky, security-conscious enterprise customers.

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The first decade of mobile drove $Bs in revenue, R&D and market caps, and was dominated by companies whose success seemed a lock.

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Then in 2007, Apple launched the iPhone. They overtook Motorola, Nokia and Blackberry phone sales in a few years. In 2008, they launched the iOS developer platform; and Google released Android.

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Then came applications. The most valuable mobile companies started two to nine years after iOS and Android launched (Uber, Instagram, WhatsApp, Doordash, Tiktok…). The shift to mobile and its new capabilities created a huge window for new B2C leaders to be built. Mobile enterprise infra and apps trailed in terms of the number and scale of successful startups.   

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A similar lag happened in the shift to cloud infrastructure, noted by Maggie Basta at Scale Ventures. Today’s biggest cloud infra companies were founded two to 10 years after AWS released S3; and Wiz was founded fourteen years later.

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If history is a predictor, the coming years will be ripe for AI startups

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It’s been almost two years since chatGPT was released. This means the stage is set for long-term successful AI-native companies to rise over time.  

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In the web and mobile, many “first movers” who attracted early funding, revenue and adoption were surpassed by later entrants. Could there be 1st mover disadvantages in big platform shifts? And, will some of today’s frontrunners in retrospect become the Netscape, MySpace, Chemdex or Motorola of the AI boom?

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Notably, in web and mobile, B2C applications led the way before enterprise. We haven’t yet seen a wave of innovative AI-native B2C apps, and are excited to. Consumer tech has been tough the last decade - our compilation on building consumer startups offers some tips.

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That said - the enhanced ‘intelligence’ of AI offers a different ability to replace knowledge workers versus prior platform shifts. This might drive a different and earlier B2B adoption curve for AI applications vs B2C adoption. And, the current compute, cost and data considerations for AI might make iterating on ideas more challenging than developing and A/B testing ideas on the web or mobile.

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Some other notes learning from history: 

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• Long term winners purpose-built their product to leverage the inherent capabilities of the new platform, not just offering a replacement on a new platform. This likely means leveraging AI capabilities to offer new intelligent functionality - e.g., predictions, trend or anomaly spotting, summarization and sentiment analysis, automation of tasks, visual or audio inputs and outputs, content generation, personalization, dynamic adaptation…
• As accessibility of AI models and services increases, we expect future breakouts to leverage increasingly commoditized infrastructure to deliver innovation in key features and UX.
• Prior platform shifts did not require access to compute or data like AI currently does. This may increase moat for incumbents, and create bigger challenges for startups with less resources. 
• Given today’s economic environment, it’s key for most enterprise products to offer clear and rapid ROI. There’s a lot of AI interest and trial right now, driving ERR (experimental run rate revenue) vs ARR (h/t to Jamin Ball at Altimeter). ERR can be a way to get started - just make sure to turn it into ARR with clear ROI and time to value, ideally selling to a stakeholder with budget.

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Some considerations in challenging tech incumbents:

• Understand the customer’s switching cost from an incumbent product. Low switching cost opportunities can be a good target (but cuts both ways); and/or provide an easy migration path with quick time to value
• Startups will have big opportunities to solve new problems that emerge and grow due to the AI platform shift, that incumbent products don’t address well (Cloudflare and Wiz are examples of this)
• Incumbents are often hesitant to disrupt the pricing and business model of their existing product. For example, a per-seat pricing model might be embedded in an incumbent product - down to the code level. Challengers have a great shot against incumbents who have financial disincentive or an inherent inflexibility to disrupt themselves
• It will be important to maintain high product velocity, especially when platform shift leaders like Apple, Amazon and OpenAI may have the strongest product/eng orgs we’ve seen from incumbents. Launchnotes offers a great teardown on product velocity here.

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The table is set. Where we’re excited to meet AI-native software investments in the coming months/years: 

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Category creators that leverage the inherent capabilities of AI 

• In each platform shift, some of the biggest winners have been “category creators” - innovators without a clear TAM that build new user behavior. Succeeding often involves overcoming hairy technical, operational, user acquisition and/or regulatory challenges. Airbnb, eBay, Facebook, OpenAI, Roblox and Uber are examples of category creators (and in B2B, Cowboy portfolio co Guild offering education, skilling and mobility as an employer benefit)
• Platform shifts are ripe for category creation. The existing TAM may not be obvious (e.g., for AirBnB, Facebook, Slack or Uber) - but the potential TAM is what matters. Matt Heiman of Mercury Bank points out great examples of TAMs that were initially vague.

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AI-powered software to upgrade incumbent B2B and B2C software

• B2B: There will be lots of opportunities to upgrade existing applications serving an existing horizontal business process (examples: Ironclad in legal, Textio in performance reviews, Vic.ai for AP and bill pay) or a vertical industry-specific process (e.g.,  healthcare, shipping, construction) to make things more time-efficient, accurate, or cost-effective. Given tight budgets, deliver measurable ROI and time to value to a stakeholder with budget, influence and urgency. Given the accessibility of AI, be thoughtful about building in differentiation and stickiness, assuming lots of competition. Ideally, become a new system of record over time.
• B2C: focus on something people spend time or money on - e.g., learning, bargain hunting, making plans, communicating - and make it fundamentally more delightful, personalized, accessible or affordable. Leverage inherent AI capabilities, don’t just sprinkle AI on top. Personalized recs based on historical behavior or wisdom-of-crowds data could be a key element.
• Cowboy’s Jill Williams shares more of what we’re looking for in the AI-ification of “unsexy tech” 
• Many leading B2B and B2C apps are now decades old, as are their UX and functionality. There’s opportunity to disrupt with modern UX and AI-native functionality for increasingly distributed, collaborative and software-savvy users. For B2B applications, G2 can be a good place to learn which incumbents get customer criticism, and why.

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In summary - despite big tech’s momentum, the coming years should be incredible for startups to build enduring enterprise and consumer success.

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If you’ve been thinking it’s too late to start an AI company given the hype, or that it’s too late to join an AI-native company - it’s not. History indicates we’ve got decades of exciting innovation ahead. And, today’s leaders may just be paving the road for other companies to take the wheel. Long term success will play out in the coming decade(s).  

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If you might be working on a big idea and want to chat with long term-minded partners who start with founders early and are geeky about the history of the tech industry - find us at Cowboy Ventures, hello@cowboy.vc, or Cowboy on LinkedIn.

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