Generative AI as a New Innovation Platform

Summary

In this video, Michael Cusumano from MIT Sloan School of Management discusses generative AI as a potential game changer and emerging innovation platform. He outlines the technology's foundation in large language models, its rapid evolution, and the growing ecosystem of applications and infrastructure. Cusumano highlights the market's potential to reach over one trillion dollars and the three main layers of the generative AI ecosystem. He also addresses the risks, including market power concentration, content ownership, privacy, information accuracy, and environmental impact, emphasizing the need for responsible development and collaboration among industry, government, and experts.

Takeaways

• 📚 Michael Cusumano introduces the concept of generative AI as a new innovation platform, similar to operating systems and system platforms like PCs and smartphones.
• 🌐 Innovation platforms leverage network effects where more applications attract more users, and vice versa, increasing the platform's value.
• 🚀 Generative AI is an emerging technology that, while not yet fully mature, supports a growing ecosystem of applications and infrastructure providers.
• 📈 The generative AI market is currently valued at $40 billion and is projected to grow to over one trillion dollars in the next decade.
• 🛠️ The generative AI ecosystem consists of foundational models, specialized infrastructure, and applications for users and developers.
• 🤖 Major players in the foundational models space include OpenAI, Microsoft, Google, and Meta, each contributing to the development of generative AI.
• 💻 Infrastructure for generative AI systems requires GPUs and data centers, with NVIDIA leading in GPUs and cloud providers offering necessary infrastructure.
• 🔍 Applications of generative AI are being integrated into search engines by Microsoft and Google, making the technology accessible to billions.
• 🛑 There are significant risks associated with generative AI, including market power concentration and potential privacy issues related to data scraping.
• 🤖 Concerns over information accuracy and authenticity arise from biases in AI algorithms and the potential for incorrect responses from LLMs.
• 🌳 The environmental impact of generative AI is significant, with high energy consumption in data centers being a growing concern.
• 👷‍♂️ Generative AI has the potential to disrupt jobs and occupations, necessitating careful management and ethical considerations in its deployment.

Q & A

Who is Michael Cusumano and what is the context of the video?

Michael Cusumano is a professor and currently Deputy Dean for Faculty at the MIT Sloan School of Management. The video is based on a column he wrote for Communications of the ACM in October 2023 and explores the emergence of generative AI as a new innovation platform.

What is an innovation platform?

An innovation platform is a foundational technology with products and services built on top of it. Examples include operating systems like Microsoft Windows, Apple iOS, and Google Android, as well as broader system platforms like personal computers and smartphones.

How do innovation platforms benefit from network effects?

With innovation platforms, third parties create their own products and services accessed through the platform. More applications attract more users, which leads to more applications and even more users. This creates a valuable network of users and applications, enabling continual improvements in the foundational technology.

What is generative AI and why is it considered a potential game changer?

Generative AI is a class of systems that can generate text, graphics, audio, and video using learning algorithms based on large language models (LLMs). It is considered a potential game changer because it supports a growing ecosystem of applications, infrastructure providers, and specialized use cases, showing signs of becoming a powerful enabling technology and innovation platform.

What key events contributed to the development of generative AI?

Key events include a 2017 paper by Google researchers showing how neural networks could analyze and predict language patterns, the introduction of GPT-3 by OpenAI in 2020, and the release of ChatGPT in 2022. These developments brought generative AI into the mainstream.

What are the three main layers of the generative AI ecosystem?

The three main layers are: (1) Foundational models and related development tools, (2) Specialized infrastructure needed to run generative AI systems, and (3) Applications for users and developers.

Who are the major players in the foundational models layer of generative AI?

Major players include OpenAI and Microsoft with ChatGPT and Bing, Google with DeepMind, Bard, and AlphaFold, and Meta with Llama 2.

What infrastructure is needed to run generative AI systems, and who are the leading providers?

Generative AI systems require GPU chips optimized for parallel processing, with NVIDIA leading in the GPU space. Cloud giants like AWS, Microsoft Azure, and Google provide the data center infrastructure.

What are some potential risks associated with the widespread deployment of generative AI?

Potential risks include concentration of market power, content ownership and privacy issues, concerns over information accuracy and authenticity, the need for regulation, environmental impact, and unintended consequences such as job displacement.

What are some potential applications of generative AI?

Potential applications of generative AI include enhancements in search engines, and targeted use cases across various industries such as manufacturing, healthcare, finance, media, construction, and agriculture.

Outlines

00:00

🚀 Generative AI: The New Innovation Platform

Michael Cusumano introduces generative AI as a potential game changer in the digital world, exploring its emergence as a new innovation platform. He explains that innovation platforms are foundational technologies with products and services built on top, like operating systems and personal computers. Generative AI, with its ability to generate text, graphics, audio, and video, is seen as a powerful enabling technology. Cusumano discusses the growth of the generative AI ecosystem, including foundational models, specialized infrastructure, and applications. He also highlights the rapid evolution of the technology and its potential to transform various industries, while noting that there is no single dominant player yet.

05:02

🔍 Risks and Considerations of Generative AI

This paragraph delves into the risks associated with the widespread deployment of generative AI. It discusses the inevitable concentration of market power due to platform dynamics and the financial resources required for development. Content ownership and privacy are highlighted as significant concerns, with generative AI models training on public web data raising questions about fair use. The paragraph also addresses issues of information accuracy and authenticity, given the biases in AI algorithms and the potential for incorrect responses, referred to as 'hallucinations.' The need for a collaborative approach between industry leaders, policymakers, and experts to establish ethical norms is emphasized. Additionally, the environmental impact of these technologies and their potential to alter human jobs and occupations are considered. The paragraph concludes by stressing the importance of managing this technology carefully to ensure its positive impact.

Keywords

💡Innovation Platform

An innovation platform is a foundational technology upon which various products and services are developed. It is central to the video's theme as it sets the stage for discussing generative AI's potential to be such a platform. In the script, examples like Microsoft Windows, Apple iOS, and Google Android are given to illustrate this concept, highlighting how these platforms support a wide array of applications and services, fostering network effects.

💡Generative AI

Generative AI refers to artificial intelligence systems capable of creating new content, such as text, graphics, audio, and video. The video discusses its emergence as a new platform technology, emphasizing its ability to learn from large datasets and generate novel outputs. The script mentions GPT-3 and ChatGPT as examples of generative AI tools that have brought this technology into the mainstream.

💡Network Effects

Network effects describe a phenomenon where a product or service becomes more valuable as more people use it. In the context of the video, network effects are associated with innovation platforms, where an increase in applications attracts more users, which in turn attracts even more applications, creating a virtuous cycle of growth. This concept is integral to understanding the potential impact of generative AI as an innovation platform.

💡Foundational Models

Foundational models in the video refer to the underlying algorithms and systems that form the basis for generative AI. These models are rapidly evolving and are the core of the technology's development. The script discusses the importance of these models in supporting a growing ecosystem of applications and specialized use cases, indicating their central role in the generative AI platform.

💡Large Language Models (LLMs)

Large Language Models (LLMs) are AI systems trained on vast amounts of data to understand and generate human-like text. The video emphasizes their role in enabling generative AI to produce various types of content. The script mentions that these models are trained on 'almost whole languages,' illustrating their complexity and capability in the context of generative AI.

💡Ecosystem

The term 'ecosystem' in the video refers to the community of applications, infrastructure providers, and specialized use cases that are built around generative AI. It is a key concept as it represents the interconnected components that contribute to the growth and success of generative AI as an innovation platform. The script outlines the three main layers of this ecosystem, including foundational models, specialized infrastructure, and applications.

💡Market Power

Market power in the context of the video relates to the control or influence that a company has over a market, often due to its size or resources. The script discusses the concentration of market power as an inevitable risk posed by the widespread deployment of generative AI, suggesting that due to platform dynamics and network effects, a few large tech giants may come to dominate the field.

💡Content Ownership and Privacy

Content ownership and privacy are critical issues raised in the video concerning generative AI models that train on data from the public web. The script points out the legal and ethical questions surrounding the fair use of training data, highlighting the need for clarity in regulations to protect both the creators of the data and the developers of generative AI systems.

💡Information Accuracy and Authenticity

Information accuracy and authenticity refer to the reliability and truthfulness of the content generated by AI systems. The video script addresses concerns about biases in AI algorithms and the potential for 'hallucinations'—incorrect responses generated when the model cannot find an exact answer. This concept is crucial as it speaks to the trustworthiness of generative AI outputs.

💡Self-Regulation vs. Government Oversight

The video discusses the balance between self-regulation by industry leaders and government oversight in the development of generative AI. It suggests that a collaborative approach is essential to establish ethical norms and guide responsible innovation. This concept is significant as it touches on the governance and ethical considerations of AI deployment.

💡Environmental Impact

The environmental impact of generative AI refers to the energy consumption and carbon footprint associated with the computational resources required for these technologies. The script notes the exponential increase in resource usage, indicating that this is a growing concern as the technology advances and becomes more widespread.

💡Unintended Consequences

Unintended consequences in the video refer to the potential negative effects that generative AI may have on jobs and occupations. The script suggests that while the technology could enhance or alter human work, there is also the risk of job displacement. This concept is important as it raises questions about the societal implications of AI advancements.

Highlights

Michael Cusumano discusses the emergence of generative AI as a new innovation platform in the digital world.

An innovation platform is a foundational technology with products and services built on top of it, like Microsoft Windows or Google Android.

Network effects are common with innovation platforms, where more applications attract more users, leading to continual improvements.

Generative AI is evolving as a powerful enabling technology but is not yet a fully established innovation platform.

The technology supports a growing ecosystem of applications, infrastructure providers, and specialized use cases.

AI and machine learning have been developing for decades, with significant advancements in recent years.

Google researchers demonstrated neural networks' ability to analyze and predict language patterns in 2017.

OpenAI introduced GPT-3 in 2020 and ChatGPT in 2022, bringing generative AI into the mainstream.

Generative AI systems can generate text, graphics, audio, and video using large language models trained on vast datasets.

Hundreds of startups are already targeting the generative AI space, with a market currently valued at $40 billion.

Bloomberg projects the generative AI market could grow to over one trillion dollars in the next decade.

The generative AI ecosystem consists of foundational models, specialized infrastructure, and applications for users and developers.

Major players in the foundational models space include OpenAI, Microsoft, Google, and Meta.

Specialized infrastructure for generative AI requires GPU chips and data center support from companies like NVIDIA and cloud providers.

Applications of generative AI are being integrated into search engines and targeting various industries.

The widespread deployment of generative AI poses risks such as market power concentration and content ownership issues.

Generative AI models raise questions about information accuracy, authenticity, and the potential for 'hallucinations'.

There is a need for collaborative approaches between industry, policymakers, and experts to establish ethical norms for AI development.

The environmental impact of generative AI is significant, with its computing resource usage increasing exponentially.

Generative AI technologies may have unintended consequences on human jobs and occupations.

Generative AI holds many positives but requires careful management and collaboration to establish guardrails for responsible innovation.

The future of generative AI is emerging, and it is crucial to shape its development responsibly from the outset.

Transcripts

Michael Cusumano: Hello, I'm Michael Cusumano,

a professor and currently Deputy Dean for Faculty

at the MIT Sloan School of Management.

In this video, which is based on a column I wrote for

Communications of the ACM in October 2023,

we'll explore a potential game changer

in the digital world:

the emergence of generative AI

as a new innovation platform.

Now, what's an innovation platform?

It's a foundational technology with

products and services built on top of it.

Examples include operating systems like

Microsoft Windows, Apple iOS,

and Google Android, as well as

broader system platforms like

personal computers and smartphones.

With innovation platforms, you'll

often see network effects.

To start, third parties create

their own products and services

accessed through the platform.

More applications attract more users,

which leads to more applications and even more users.

The platform becomes increasingly

valuable with these networks of users and applications.

Growth in usage then enables

continual improvements in the foundational technology.

Now, generative AI shows signs of becoming

a powerful enabling technology and innovation platform,

but it isn't fully there yet.

The foundation models are rapidly evolving,

but there's still no one dominant player.

However, the technology has advanced enough to

support a growing ecosystem of applications,

infrastructure providers, and specialized use cases.

Let's look at how generative AI

emerged as a new platform technology.

Artificial intelligence and machine learning

has taken decades to develop,

but some recent events have been especially important.

In 2017, researchers at

Google published a key paper showing how

neural networks could analyze and then predict

language patterns rather than just individual words.

Those researchers went on to firms like OpenAI.

Then, OpenAI introduced GPT-3 in

2020 and ChatGPT in 2022.

These new tools brought

generative AI into the mainstream.

This whole class of systems can

"generate" text, graphics, audio,

and video using learning

algorithms based on large language models, or

LLMs, that train on huge datasets,

almost whole languages.

The potential applications are vast.

Now, a huge class of generative AI systems has emerged.

Several hundred start-ups already target the space.

Generative AI hardware and software

currently comprise a $40 billion market,

and Bloomberg projects that it could grow to

over one trillion dollars over the next decade.

Now, the generative AI ecosystem has three main layers.

First, the foundational models

and related development or programming tools.

Users can access generative AI chatbots

through Internet browsers,

but the underlying computing environment

is the LLM software.

The major players in this space include OpenAI

and Microsoft with ChatGPT and Bing,

Google with DeepMind, Bard,

and AlphaFold, and Meta with Llama 2.

Second, there's the specialized infrastructure

needed to run generative AI systems.

These models require GPU chips,

optimized for parallel processing.

NVIDIA leads in the GPU space,

while cloud giants like AWS,

Microsoft Azure, and Google

provide the data center infrastructure.

Third, we have applications for users and developers.

For example, Microsoft and

Google added LLMs to their search engines,

providing easy access to

this technology for billions of people.

You'll also find vertical start-ups targeting

use cases across products and

services for manufacturing,

healthcare, finance, media, construction,

agriculture, and more.

In summary, foundational models

and development tools set the stage,

infrastructure powers them,

and applications realize their potential.

However, this brings us to our next question:

what are the risks posed by

widespread deployment of generative AI?

For one, concentration of

market power is almost inevitable.

Given platform dynamics with network effects,

and the enormous financial resources

required to develop and run these systems,

we're likely to see consolidation

down to just the biggest tech giants.

Second is content ownership and privacy.

Generative AI models train

on data scraped from the public web.

Some scraping has been allowed for search engines,

but courts and legislators will have

to answer the new trillion-dollar question:

What is fair use of

training data for generative AI systems?

Then there are rising concerns over

information accuracy and authenticity.

We know there is a lot of bias built into

AI algorithms based on the data

they train on and the people who build them.

Also, when LLMs cannot find an answer to a query,

they use predictive analytics to

make up reasonable responses,

but sometimes they are incorrect.

These "hallucinations" currently

limit potential applications.

There are questions around

self-regulation versus government oversight, as well.

A collaborative approach between

industry leaders, policymakers,

and experts to establish

ethical norms is essential

to help guide responsible development.

The environmental impact of

these technologies is rising fast.

By some estimates, generative

AI's use of computing resources has been

increasing exponentially for years and already has

become an enormous use of energy in data centers.

Then there are unintended consequences.

For example, these technologies could replace,

enhance, or greatly alter

many human jobs and occupations for better or for worse.

In closing, generative AI should bring many positives,

but we have to manage this increasingly

powerful technology very carefully.

With models advancing so rapidly,

this new platform can potentially transform

everything from scientific discovery

to education and business.

Industry, government, and

company leaders will need to collaborate

closely to establish

guardrails for responsible innovation.

The future of this technology is just emerging,

and so this is

the moment to shape what comes next.