Contestants in the generative artificial intelligence model business are following a “winner takes all” approach to the market, investing at a pace that gets criticized by financial analysts (and rightly so, in some respects) for exceeding what seems immediate and tangible financial payback.
But the “winner takes all” strategy has worked often in the internet era, across many different segments of the market. So we already can predict some outcomes.
“Wild” levels of spending are going to pay off for at least one and perhaps two providers of GenAI models, in terms of leadership of the market (ecosystems built on them). Some of the contestants will eventually “break even” for their investors, neither gaining or losing equity value.
But most will fail, losing their investors most to all of the invested capital. That is simply what has happened in winner-take-all markets.
As we saw early on, even pre-internet, for operating systems, and later saws for search engines, mobile operating systems, e-commerce, social media, ridesharing, peer-to-peer lodging and other app segments, market leadership is highly concentrated with one or sometimes two providers dominating.
So the companies developing GenAI frontier models, such as OpenAI, Google DeepMind, and Anthropic, are assuming the market will eventually shake out with a “winner takes all” structure, with market leadership highly concentrated and creating a new ecosystem of value around the leading models, with the hundreds of other would-be contestants relegated to history.
Market/Product Category | Dominant Players |
Search Engines | Google |
Social Media | Facebook, Instagram, TikTok |
E-commerce | Amazon |
Mobile Operating Systems | Apple iOS, Google Android |
Desktop Operating Systems | Microsoft Windows, macOS |
Web Browsers | Google Chrome, Safari |
Streaming Video | Netflix, YouTube |
Cloud Computing | Amazon AWS, Microsoft Azure, Google Cloud |
Online Advertising | Google, Meta (Facebook) |
Smartphones | Apple (iPhone) |
Productivity Software | Microsoft Office, Google Workspace |
Mobile Payment Systems | Apple Pay, Google Pay, PayPal |
Digital Maps | Google Maps |
Food Delivery Apps | DoorDash, Uber Eats |
Ride-Hailing Apps | Uber, Lyft (US), Didi (China) |
Video Conferencing | Zoom, Microsoft Teams |
By way of contrast, end users of GenAI--especially enterprises--will have to answer for their choices, as they would for any other capital investments made to support their businesses. And that goes for suppliers fo hardware and software incorporating AI functionality.
GenAI model leadership will undoubtedly shake out into a “winner takes all” pattern. But that also means hardware or software suppliers might “bet on the wrong horse,” as might their customers. That generally means high danger for model suppliers and those firms that are direct parts of those ecosystems.
Hardware and software participants might face moderate to high impact if they are in the “wrong” ecosystem or platform. End users--whether consumers or businesses--might face low repercussions if their chosen GenAI supplier does not emerge as the eventual market leader (either provider one or two).
Basic or firm-specific functionality might not be the big problem, as niche solutions might be perfectly viable, especially if a model emerges as providing superior industry-specific functionality.
Value Chain Segment | Example Participants | Degree of Danger if Supplier/Partner is Not a Winner | Reason for Risk |
Foundation Model Providers | OpenAI, Google DeepMind, Anthropic, Meta | High | If the chosen provider loses, end-users face high costs in switching models due to integration dependencies. |
Model Fine-Tuners & Adaptation | Hugging Face, Scale AI, Stability AI | High | Fine-tuning investments and adaptations may not be transferrable to new, winning models. |
Hardware Providers | Nvidia, AMD, Intel, custom chip startups | Moderate to High | Dependence on specialized chips may limit flexibility; non-dominant players may face R&D cutbacks. |
AI Infrastructure Platforms | AWS, Microsoft Azure, Google Cloud | High | Non-winner platforms may lack interoperability, leading to high switching costs and stranded data assets. |
Middleware Providers | Databricks, Snowflake, API integration services | Moderate to High | Middleware built for a specific provider may need reconfiguration, reducing compatibility with other models. |
Application & Service Developers | Jasper, ChatGPT plugins, custom AI tools | Moderate to High | Apps tightly integrated with a non-winning model may struggle with compatibility and require costly rewrites. |
Consulting & Implementation | Deloitte, Accenture, AI-specific consultancies | Moderate | Models selected may lose support, limiting longevity of solutions delivered to clients. |
AI Tooling & DevOps | MLOps tools, AI pipeline solutions (e.g., Weights & Biases) | Moderate | DevOps tools tied to a specific model may lose relevance if clients pivot to dominant providers. |
Enterprise & Custom Solutions | SAP AI, Oracle AI, Salesforce Einstein | Moderate | Custom solutions based on non-winning models may have compatibility and feature limitations. |
Data Providers | Web-scraping firms, domain-specific datasets | Low to Moderate | Dependence on any one model is less critical, though dataset compatibilities may shift. |
Consumer-facing Applications | Grammarly, Canva, AI-powered image and video tools | Low to Moderate | User-facing tools may need updates for compatibility but can adapt faster than core infrastructure layers. |
End Users & Businesses | Large enterprises, SMEs, individual users | Low to Moderate | Users may face inconvenience, but model-agnostic interfaces could minimize direct switching costs. |
Still, longer term, the specific GenAI ecosystem should matter, as network effects should eventually emerge.
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