Microsofts investment in OpenAI and generative AI technologies

From: allin

Microsoft’s Investment and OpenAI’s Technology

Microsoft is reportedly close to investing $10billionin[[OpenAIandAIadvancements|OpenAI]]througha"convolutedtransaction"<aclass="yt-timestamp"data-t="00:30:03">[00:30:03]</a>.Thedealmightinvolve[[GooglesAIinvestmentandstrategy|Microsoft]]owning59$10 billion paid back over some period [00:30:03]. This investment includes Azure credits [00:30:25].

OpenAI is recognized for its ChatGPT technology, which is described as an “incredible demonstration of what AI can do in terms of text-based creation of content and answering queries” [00:30:35]. ChatGPT has “taken the net by storm” and inspired many [00:30:43]. The technology allows users to type questions and retains conversation history, functioning similarly to an iMessage thread [00:45:35].

The Hype vs. Reality of Generative AI

Generative AI is currently the “hottest thing in Silicon Valley” [00:31:31]. While it is a “VC hype cycle,” similar to mobile and cloud computing, it doesn’t mean the technology isn’t real [00:31:09]. However, unlike web3 or VR, the technological potential of AI is considered significant [00:31:33]. The core capabilities of AI, involving large datasets and learning techniques, are already transforming various industries beyond consumer applications [00:33:01].

The challenge for venture capitalists is identifying how startups can benefit, as OpenAI appears to be a Microsoft proxy, and large companies like Google (through DeepMind) and Facebook are also heavily investing [00:32:05]. Startups may find opportunities by leveraging AI APIs rather than building their own models [00:32:49].

OpenAI’s Evolution: From Non-Profit to For-Profit

OpenAI was initially founded as a non-profit AI research company in 2015, with a stated goal to “advance digital intelligence in the way that is most likely to benefit Humanity as a whole unconstrained by a need to generate Financial return” [00:39:56]. Early funders included Reid Hoffman, Peter Thiel, and Elon Musk [00:39:27].

However, the organization has since “diverged into this deeply profitable profit-seeking kind of Enterprise model” [00:41:13]. This shift raises questions, especially considering the initial philosophy that this technology was “too powerful for any company to own” [00:38:29]. This contrasts with Google which has open-sourced models like AlphaFold and TensorFlow [00:40:51].

The shift to a commercial alignment is seen as necessary to drive the technology forward [00:40:43], but also highlights the significant political and regulatory battles ahead over “who owns the AI, who owns the models, what can they do with it, and what are we legally going to be allowed to do with it” [00:42:03].

AI and the Future of Industries

The fundamental capabilities of machine learning systems are already transforming nearly every industry, making predictions and driving businesses in ways human knowledge alone cannot [00:33:50].

Specific examples of generative AI’s potential impact include:

• Legal Profession: ChatGPT could instantly summarize legal precedents [00:57:56]. This could help legal associates get answers to validate [00:59:52].
• Content Creation: Generative AI could lead to the first AI novel published by a major publisher, AI symphonies performed by orchestras, and AI-generated screenplays turned into 3D movies [01:07:07].
• Gaming: Users could instruct video game platforms to create their desired worlds, leading to new immersive environments [01:07:36].
• Business Operations: ChatGPT could assist with business research, finding venues, analyzing competitors, and tracking hiring trends [00:52:51].

The Conductor Economy and Prompt Engineering

The rise of AI is seen as moving society from a “knowledge economy” to a “narrator economy” or “conductor economy” [01:04:06]. This involves manipulating tools to achieve intentional outcomes [01:04:25].

A new skill, “prompt engineering,” is emerging, referring to the ability to interface with AI instances and maximize or refine results [01:03:00]. A “great prompt engineer” could be “10 or 20 times more valuable” and become the “proverbial 10x engineer” [01:03:22]. This could lead to companies running with significantly fewer people, but also foster new work and opportunities [01:03:51].

Challenges and Legal Questions

The integration of AI into existing business models presents significant challenges:

• Business Model Transformation: Existing business models, especially those dependent on historical “information retrieval,” will need to be fundamentally rewritten because AI systems synthesize and represent data rather than just retrieving and displaying it [00:53:30].
• Copyright and Data Usage: Questions arise regarding copyright infringement when AI models use data from sources like Yelp without direct links or explicit permission [00:48:18]. The “effect on the potential market” is a key factor in fair use [00:49:47].
  • It is suggested that AI services “must use citations of where they got the original work, they must link to them, and they must get permission” [00:50:23].
  • The concept of an “ai.txt” file is proposed, similar to robots.txt, to control whether a dataset can be used by AI and how data owners would be compensated [00:51:06].
• Accuracy and Trust: While AI can achieve high accuracy (e.g., 90-99%), some professions, like law, require “six nines accuracy” (99.9999%) [00:59:11].
• Proprietary Data: The true competitive advantage in AI may lie in proprietary datasets and the reinforcement learning derived from them [01:01:27]. Companies with unique data, like specific scientific screening results, can leverage AI to gain an advantage not available through publicly known libraries [00:55:39].
• Existential Threats: There is ongoing debate about the potential for AI to pose an “existential threat to humanity” if not properly controlled [00:41:47].# Microsoft’s Investment in OpenAI and Generative AI Technologies

Microsoft’s Investment and OpenAI’s Technology

Microsoft is reportedly close to investing $10billionin[[OpenAIandAIadvancements|OpenAI]]througha"convolutedtransaction"<aclass="yt-timestamp"data-t="00:30:03">[00:30:03]</a>.Thedealmightinvolve[[GooglesAIinvestmentandstrategy|Microsoft]]owning59$10 billion paid back over some period [00:30:03]. This investment includes Azure credits [00:30:25].

OpenAI is recognized for its ChatGPT technology, which is described as an “incredible demonstration of what AI can do in terms of text-based creation of content and answering queries” [00:30:35]. ChatGPT has “taken the net by storm” and inspired many [00:30:43]. The technology allows users to type questions and retains conversation history, functioning similarly to an iMessage thread [00:45:35].

The Hype vs. Reality of Generative AI

Generative AI is currently the “hottest thing in Silicon Valley” [00:31:31]. While it is a “VC hype cycle,” similar to mobile and cloud computing, it doesn’t mean the technology isn’t real [00:31:09]. However, unlike web3 or VR, the technological potential of AI is considered significant [00:31:33]. The core capabilities of AI, involving large datasets and learning techniques, are already transforming various industries beyond consumer applications [00:33:01].

The challenge for venture capitalists is identifying how startups can benefit, as OpenAI appears to be a Microsoft proxy, and large companies like Google (through DeepMind) and Facebook are also heavily investing [00:32:05]. Startups may find opportunities by leveraging AI APIs rather than building their own models [00:32:49].

OpenAI’s Evolution: From Non-Profit to For-Profit

OpenAI was initially founded as a non-profit AI research company in 2015, with a stated goal to “advance digital intelligence in the way that is most likely to benefit Humanity as a whole unconstrained by a need to generate Financial return” [00:39:56]. Early funders included Reid Hoffman, Peter Thiel, and Elon Musk [00:39:27].

However, the organization has since “diverged into this deeply profitable profit-seeking kind of Enterprise model” [00:41:13]. This shift raises questions, especially considering the initial philosophy that this technology was “too powerful for any company to own” [00:38:29]. This contrasts with Google which has open-sourced models like AlphaFold and TensorFlow [00:40:51].

The shift to a commercial alignment is seen as necessary to drive the technology forward [00:40:43], but also highlights the significant political and regulatory battles ahead over “who owns the AI, who owns the models, what can they do with it, and what are we legally going to be allowed to do with it” [00:42:03].

AI and the Future of Industries

The fundamental capabilities of machine learning systems are already transforming nearly every industry, making predictions and driving businesses in ways human knowledge alone cannot [00:33:50].

Specific examples of generative AI’s potential impact include:

• Legal Profession: ChatGPT could instantly summarize legal precedents [00:57:56]. This could help legal associates get answers to validate [00:59:52].
• Content Creation: Generative AI could lead to the first AI novel published by a major publisher, AI symphonies performed by orchestras, and AI-generated screenplays turned into 3D movies [01:07:07].
• Gaming: Users could instruct video game platforms to create their desired worlds, leading to new immersive environments [01:07:36].
• Business Operations: ChatGPT could assist with business research, finding venues, analyzing competitors, and tracking hiring trends [00:52:51].

The Conductor Economy and Prompt Engineering

The rise of AI is seen as moving society from a “knowledge economy” to a “narrator economy” or “conductor economy” [01:04:06]. This involves manipulating tools to achieve intentional outcomes [01:04:25].

A new skill, “prompt engineering,” is emerging, referring to the ability to interface with AI instances and maximize or refine results [01:03:00]. A “great prompt engineer” could be “10 or 20 times more valuable” and become the “proverbial 10x engineer” [01:03:22]. This could lead to companies running with significantly fewer people, but also foster new work and opportunities [01:03:51].

Challenges and Legal Questions

The integration of AI into existing business models presents significant challenges:

• Business Model Transformation: Existing business models, especially those dependent on historical “information retrieval,” will need to be fundamentally rewritten because AI systems synthesize and represent data rather than just retrieving and displaying it [00:53:30].
• Copyright and Data Usage: Questions arise regarding copyright infringement when AI models use data from sources like Yelp without direct links or explicit permission [00:48:18]. The “effect on the potential market” is a key factor in fair use [00:49:47].
  • It is suggested that AI services “must use citations of where they got the original work, they must link to them, and they must get permission” [00:50:23].
  • The concept of an “ai.txt” file is proposed, similar to robots.txt, to control whether a dataset can be used by AI and how data owners would be compensated [00:51:06].
• Accuracy and Trust: While AI can achieve high accuracy (e.g., 90-99%), some professions, like law, require “six nines accuracy” (99.9999%) [00:59:11].
• Proprietary Data: The true competitive advantage in AI may lie in proprietary datasets and the reinforcement learning derived from them [01:01:27]. Companies with unique data, like specific scientific screening results, can leverage AI to gain an advantage not available through publicly known libraries [00:55:39].
• Existential Threats: There is ongoing debate about the potential for AI to pose an “existential threat to humanity” if not properly controlled [00:41:47].