chatgpt ai writer
We’re starting to see the very early stages of a tech stack emerge in generative artificial intelligence (AI). Hundreds of new startups are rushing into the market to develop foundation models, build AI-native apps, and stand up infrastructure/tooling.
Many hot technology trends get over-hyped far before the market catches up. But the generative AI boom has been accompanied by real gains in real markets, and real traction from real companies. Models like Stable Diffusion and ChatGPT are setting historical records for user growth, and several applications have reached $100 million of annualized revenue less than a year after launch. Side-by-side comparisons show AI models outperforming humans in some tasks by multiple orders of magnitude.
So, there is enough early data to suggest massive transformation is taking place. What we don’t know, and what has now become the critical question, is: Where in this market will value accrue?
Over the last year, we’ve met with dozens of startup founders and operators in large companies who deal directly with generative AI. We’ve observed that infrastructure vendors are likely the biggest winners in this market so far, capturing the majority of dollars flowing through the stack. Application companies are growing topline revenues very quickly but often struggle with retention, product differentiation, and gross margins. And most model providers, though responsible for the very existence of this market, haven’t yet achieved large commercial scale.
In other words, the companies creating the most value — i.e. training generative AI models and applying them in new apps — haven’t captured most of it. Predicting what will happen next is much harder. But we think the key thing to understand is which parts of the stack are truly differentiated and defensible. This will have a major impact on market structure (i.e. horizontal vs. vertical company development) and the drivers of long-term value (e.g. margins and retention). So far, we’ve had a hard time finding structural defensibility anywhere in the stack, outside of traditional moats for incumbents.
We are incredibly bullish on generative AI and believe it will have a massive impact in the software industry and beyond. The goal of this post is to map out the dynamics of the market and start to answer the broader questions about generative AI business models.
Mots-clés : cybersécurité, sécurité informatique, protection des données, menaces cybernétiques, veille cyber, analyse de vulnérabilités, sécurité des réseaux, cyberattaques, conformité RGPD, NIS2, DORA, PCIDSS, DEVSECOPS, eSANTE, intelligence artificielle, IA en cybersécurité, apprentissage automatique, deep learning, algorithmes de sécurité, détection des anomalies, systèmes intelligents, automatisation de la sécurité, IA pour la prévention des cyberattaques.
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