In the case of AI, BigTech is less about introducing wholly novel products than bolstering existing ones with generative techniques. Benedict Evans captures this well below; we should be asking the question “what new products are enabled by AI” instead of just “what existing products get better with AI?
Some of these companies are still focused on reaching profitability; figuring out how to balance existing products with new AI-based workflows and teams can be a tough juggling act in a scaling environment.
One of the key advantages of generative-native companies is their use of LLM architecture at their core. These startups can employ one or multiple LLMs, boasting features such as representation and embeddings, text generation and summarization, and co-pilot-like capabilities. This architecture allows for improved product recommendations, travel itineraries, and document retrieval, disrupting various industries and applications. In contrast, generative-enhanced companies were not natively built on LLMs at their core, and have to work backward to incorporate generative elements as indicated above
That is rapidly changing with generative AI, which is more a “system of action” than a “system of record”, and can create near-finished outputs or supercharged assistance vs. merely faster workflows.
LLMs have enabled new applications across fields such as biotech and healthcare.
generative-native startups can stitch together multiple best-in-class models and do not have the same limitation. Given how fast the pace of innovation is in LLMs, it is advantageous for startups to have a flexible architecture that allows them to leverage the best-performing models and embrace open-source models, something BigTech can’t do as easily.
Davids in the Land of (AI) Goliaths
Reimagining underserved verticals → In the prior cloud era, new vertical software giants were created to serve specific verticals; think Procore ($8B mkt cap) for construction, Veeva ($28B) for life sciences, and Toast ($9B) for restaurants. However, many large sectors remain underserved by technology, partly because digitized workflows don’t always result in 10x work improvements. That is rapidly changing with generative AI, which is more a “system of action” than a “system of record”, and can create near-finished outputs or supercharged assistance vs. merely faster workflows. We are seeing this play out in the legal industry, a $500B+ market fairly averse to traditional software but now embracing GenAI-Native tools like Harvey and EvenUp.
Novel use cases → LLMs have enabled new applications across fields such as biotech and healthcare. AI tools can now be leveraged to create personalized treatment plans, synthetic data, and even new drugs. AI is revolutionizing drug discovery, reimagining every type of drug from peptides to spatial biology to small molecule drug discovery. We believe that there are other novel use cases that will emerge around climate tech, biodiversity, sustainability, energy, logistics, and more that were previously not possible without LLMs. Some examples include A-Alpha Bio, Atomic AI, and Profluent.
Tooling → As more open-source and proprietary models are released, developers will need better tools to be able to stitch together, fine-tune, and optimize these models. A new crop of companies is emerging in the middleware space which can be complementary to the model providers, like Fixie, Langchain, and others. In virtually any new platform shift, selling the “picks and shovels” can be a lucrative bet.
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