The Golden Age and Anxiety of AI

From the establishment of broadband to the ubiquitous presence of PCs, history has shown that technological maturity takes time. Yet, OpenAI seemingly shattered this norm as ChatGPT amassed 100 million users within just two months of its launch.
However, the majority of these users are fleeting engagements, failing to translate into long-term active usage, posing a genuine challenge in converting them into loyal users.

Similar dynamics are observed in the corporate landscape. Despite the heightened interest in generative AI, its actual deployment remains nascent. A notable phenomenon is the prevalent experimental application of Large Language Models (LLMs) within the industry, yet few instances exist where they have been seamlessly integrated into core business processes.
A recent CIO survey by Morgan Stanley reveals that 30% of large enterprises' CIOs anticipate no deployment before 2026 – a testament to the lengthy IT sales cycles that hinder the swift adoption of technologies like ChatGPT.

Concurrently, the practical value of ChatGPT remains largely unproven. Microsoft's attempt to leverage it in Bing, aiming to rival Google, fell short last year, underscoring the inadequate commercialization of AI.
Large models resemble a "database" disguised as a search engine, capable only of generating content based on input prompts, lacking the sophistication and niche-specificity to build upon.

Amidst the struggle to retain users, the rapid technological advancements necessitate colossal capital investments, with numerous companies pouring funds into AI experiments, pushing the limits of IT budgets.
The lofty expectations (paired with substantial capital infusions) surrounding AI drive businesses to hastily commercialize applications without considering the technology's nascent "experimental" stage.

Viewing AI large models as a technology rather than a product, this intense investment can be interpreted as a collective Silicon Valley wager, requiring traditional refinement processes to achieve product-market fit.