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Generative AI Charts & Research

Technology can replace some tasks, but it can also make us more productive performing other tasks, and create new tasks — and new jobs


From Goldman Sachs

As more generative AI tools are developed and layered into existing software packages and technology platforms, the team sees businesses across the economy benefiting, from enhancing office productivity and sales efforts, to the design of buildings and manufactured parts, to improving patient diagnosis in healthcare settings, to detecting cyber fraud.

While much is unknown about how generative AI will influence the world economy and society, and it will take time to play out, there are clear signs that the effects could be profound.

60% of today’s workers are employed in occupations that didn’t exist in 1940. This implies that more than 85% of employment growth over the last 80 years is explained by the technology-driven creation of new positions
Jobs displaced by automation have historically been offset by the creation of new jobs, and the emergence of new occupations following technological innovations accounts for the vast majority of long-run employment growth
A new wave of AI systems may also have a major impact on employment markets around the world. Shifts in workflows triggered by these advances could expose the equivalent of 300 million full-time jobs to automation


From McKinsey

Generative AI and other foundation models are changing the AI game, taking assistive technology to a new level, reducing application development time, and bringing powerful capabilities to nontechnical users.

The organizational requirements for generative AI range from low to high, depending on the use case.
Our research found that 90 percent of commercial leaders expect to utilize gen AI solutions “often” over the next two years
We found cautious optimism across the board: respondents anticipated at least moderate impact from each use case we suggested. In particular, these players are most enthusiastic about use cases in the early stages of the customer journey lead identification, marketing optimization, and personalized outreach
Leaders can start thinking strategically about how to invest in AI commercial excellence for the long term. It will be important to identify which use cases are table stakes, and which can help you differentiate your position in the market. Then prioritize based on impact and feasibility.
Engagement is accelerating at record pace the three powerful flywheels that drive platform success—scale, learning, and network. The success of the platform is also likely to drive the success of companies on the platform.


From Boston Consulting Group

The new wave of generative AI systems, such as ChatGPT, have the potential to transform entire industries. To be an industry leader in five years, you need a clear and compelling generative AI strategy today.

Once leaders identify their golden use cases, they will need to work with their technology teams to make strategic choices about whether to fine-tune existing LLMs or to train a custom model.
As AI initiatives roll out, regular pulse checks should be conducted to track employee sentiment; CEOs will also need to develop a transparent change-management initiative that will both help employees embrace their new AI coworkers and ensure employees retain autonomy. The message should be that humans aren’t going anywhere—and in fact are needed to deploy AI effectively and ethically.


From Gartner

The costs for generative AI will range from negligible to many millions depending on the use case, scale and requirements of the company. Small and midsize enterprises may derive significant business value from the free versions of public, openly hosted applications, such as ChatGPT, or by paying low subscription fees. For example, OpenAI is currently $20 per user per month. However, free and low-cost options come with minimal protection of enterprise data and associated output risks.

The Impact Radar portrays the maturity, market momentum and influence of technologies, making it a handy tool for product leaders to identify and track the technologies and trends that will help them improve and differentiate their products, remain competitive and capitalize on market opportunities.
Gartner analysts described 34 types of AI technologies in the report and also noted that the AI hype cycle is more fast-paced, with an above-average number of innovations reaching mainstream adoption within two to five years.
In a recent Gartner webinar poll of more than 2,500 executives, 38% indicated that customer experience and retention is the primary purpose of their generative AI investments. This was followed by revenue growth (26%), cost optimization (17%) and business continuity (7%).
All digital technologies go through phases as we learn to use them responsibly, and generative AI is no exception. Organizations will need to develop best practices and governance to manage risks and adhere to regulations likely to emerge.
To date, generative AI applications have overwhelmingly focused on the divergence of information. That is, they create new content based on a set of instructions. In Wave 2, we believe we will see more applications of AI to converge information. That is, they will show us less content by synthesizing the information available. Aptly, we refer to Wave 2 as synthesis AI (“SynthAI”) to contrast with Wave 1. While Wave 1 has created some value at the application layer, we believe Wave 2 will bring a step function change.


From Andreesen Horowitz

The potential size of this market is hard to grasp — somewhere between all software and all human endeavors — so we expect many, many players and healthy competition at all levels of the stack. We also expect both horizontal and vertical companies to succeed, with the best approach dictated by end-markets and end-users

Use cases that will benefit most from synthesis AI: A high volume of information, such that it’s not pragmatic for a human to manually sift through all the information. A high signal-to-noise ratio, such that the themes or insights are obvious and consistent. In the name of accuracy, you don’t want to task an AI model with deciphering nuance.
Preliminary generative AI Tech Stack: applications, models, and infrastructure.
Examples to bring the comparisons of GenaAI and SynthAI to life.