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AI Developments and Business Models

5 min read

From: allin

The AI industrial complex is rapidly evolving, driving significant changes in technology and business models [00:07:37]. This new era of AI technology and market trends is characterized by faster, cheaper, and more advanced models, leading to a re-evaluation of traditional product development and monetization strategies [00:22:42].

OpenAI’s GPT-4o Launch and Model Evolution

OpenAI launched GPT-4o (Omni) on a Monday, three days after Sam Altman’s appearance on the All-In Podcast [00:07:47]. The “O” in GPT-4o stands for Omni, indicating its multimodal capabilities, taking in audio, text, images, desktop input, and video [00:13:32].

Key innovations of GPT-4o include:

  • Multimodal Input: Processes text, audio, video, and images simultaneously [00:24:20].
  • Enhanced Conversational Abilities: Understands tone and sentiment, enabling smoother, more natural interactions [00:13:53]. Examples include real-time translation and adaptive learning through observation, like a personal math tutor watching a student’s drawing app [00:14:14][00:15:20].
  • Increased Speed: Described as feeling “10 times as fast” compared to previous versions, significantly reducing delays after prompts [00:24:50].

This marks an evolution in model architecture, moving away from large, costly, infrequent model releases toward a system of continuously tuned and updated individual models [00:17:16]. These smaller, specialized models can be linked together for specific tasks, like mathematics or image rendering [00:18:14]. Performance assessments by Stanford suggest GPT-4o outperforms GPT-4, countering initial criticisms of degradation [00:19:05].

Business Model Implications for OpenAI and the Consumer Market

The rapid advancement of AI advancements and the economic impact raises questions about sustainable business models [00:21:03]. ChatGPT’s web visits have plateaued, indicating that initial “lookie-loos” have left, and real use cases are now needed to drive growth [00:21:00].

There’s a strong belief that a B2C subscription model for AI is the least attractive long-term option due to low consumer willingness to pay and high churn rates [00:23:40]. Instead, a shift towards a B2B direction, monetizing applications built on top of the models through APIs and developer tools, seems more promising [00:25:57]. New models are becoming significantly more efficient and cheaper to operate, potentially enabling OpenAI to offer many basic services for free or near-free [00:22:42].

Impact on Startups and Existing Enterprises

The rapid pace of AI advancements and their impact on technology and society means that product roadmaps for some startups can become obsolete almost overnight [00:26:37]. Companies that invested heavily in making virtual customer support agents conversational, for example, may find their R&D rendered moot by advances in models like GPT-4o [00:26:57].

This rapid innovation creates a “deflationary tail” [01:20:34]:

  • Obsolescence of Work: App developers may find their work replaced every 18 months [01:20:01].
  • Open Source: The incentive to push operationally necessary, but not core, solutions to open source is high, as it offloads support and allows for easier model switching [00:28:27].
  • Reduced Opex: The cost of operations for companies will decrease significantly, leading to smaller company sizes and the ability to sell products cheaper [01:20:18].
  • Niche Businesses: The reduced cost of building products means many more niche ideas can become profitable [01:21:52]. This also means seed funding requests are becoming smaller [01:22:09].

The “automate, deprecate, delegate” framework is proposed for businesses:

Google’s AI Search and Business Model Shift

Google’s latest AI search and business model shift includes “AI Overviews,” which provide direct answers with citations, resembling Perplexity’s model [01:22:49]. This move addresses the concern that users might not click on ads if they get answers directly [01:25:50].

Potential outcomes for AI and its impact on Google’s business model:

  • Increased Search Volume: Providing direct answers might lead to more complex follow-up questions and more overall searches [01:25:55].
  • Higher Revenue Per Query: By keeping users on Google’s page and integrating more services (e.g., selling relevant products like steak knives or booking travel directly), Google can monetize queries more effectively [01:28:04].
  • Cannibalization of Long-Tail Content: The “snippet” or “one-box” experience may cannibalize traffic to third-party content sites that typically rely on Google for referrals [01:28:10].
  • Legal Challenges: There is a potential for class-action lawsuits from content creators whose data is used to generate AI answers without explicit permission or compensation [01:30:21]. This may lead to a new “rights marketplace” for AI and M&A in the tech industry [01:31:11].

Google, like Microsoft, benefits from a strong monopoly that allows it to recover from initial missteps in emerging technologies [01:34:03]. Despite potentially missing early AI advancements and their impact on productivity and economy, Google’s ability to copy innovators and leverage its existing platform allows it to remain competitive [01:32:36]. Large tech companies can afford a portfolio of bets, some of which will fail, but the successful ones keep the company going [01:36:04].

Investment Philosophy in the AI Era

In the context of rapidly evolving markets like AI, investing requires a focus on accuracy over precision [01:09:47]. Precision involves detailed analysis of every specific aspect, while accuracy means correctly identifying the right trend or sentiment [01:09:52]. While precise analysis can be inaccurate if it misses broader trends, accurate bets on trends, especially those with network effects, can yield significant returns, provided there is patience [01:11:00].

A prominent example of betting on a trend is Stanley Druckenmiller’s investment in Argentina after seeing Javier Milei’s speech [01:16:16]. He made a quick macro assessment and an “accurate bet” on the unique fiscal austerity policies being implemented [01:40:02].

Another investment trend is in services leveraging AI advancements and their impact on technology and society, such as virtual EA services. Athena, a company offering virtual executive assistants, exemplifies rapid growth by providing highly trained knowledge workers, often augmented by AI investment and technology tools, at a fraction of the cost of traditional hires [01:15:18]. This highlights how AI can drive efficiency and enable new business models by delegating repetitive tasks and freeing up high-value personnel for more complex work [01:17:10].

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