As ChatGPT turns two, AI innovation is booming

ChatGPT turns two years old. Here’s a look at the state of the generative AI union.

OpenAI’s ChatGPT celebrates its second year of general availability on November 30, which means it’s time to take stock of generative AI’s progress in 2024. And what a whirlwind it’s been.

Two years ago, few people knew GenAI was possible, but today about 40% of Americans have used the technology. To put this adoption trajectory into perspective, this growth doubles the 20% of Americans who used the Internet within two years of its launch, according to National Bureau of Economic Research.

You read that right: The Friggin’ Internet.

The pace of GenAI innovation has been unprecedented. Alone in 2024, OpenAI broke new ground in LLM Reasoning, while Meta introduced the first open boundary class model. Google in the meantime conjured a breakthrough in GenAI-powered podcasting.

And Anthropic launched tools to help users create and modify content in a separate window, as well as the option of computers to use computers. (You have to see it to understand it).

AI agents shine bright

The excitement over AI agents is palpable as organizations seek to enhance not just employee productivity, but operational efficiency. At a high level, AI agents are pieces of software code that perform tasks to achieve a predetermined goal. Most AI agents can “think” or reason, plan and learn from feedback.

However, agents also take many forms. First, AI agents can include digital assistants that help consumers. Think software bots that can book trips and handle other transactions etc. Then there are enterprise agents, which can work individually or as part of teams (multi-agent architectures) to automate entire workflows or entire business processes. Finally these agents will be able to “self-heal”, identify errors and correct course.

And while it is too early to claim that AI agents will automate an entire business, organizations are certainly interested in their potential. 82 percent of managers surveyed by Capgemini said they expect to use agents to automate the generation of emails, software code and data analysis.

Small language models can do big things

Some people are wary of money hyperscaler pumping in GenAI infrastructure, software and talent. But to understand motivation is critical; these companies are investing in super intelligent systems – a big leap beyond the day-to-day content creation applications most organizations pursue.

The reality is that organizations don’t need to spend millions of dollars building or licensing large language models (LLMs). Rather, small language models (SLMs) running in hybrid IT environments provide more than enough AI firepower to satisfy most targeted business applications.

“You’re going to see a set of use cases that emerge where a small, less accurate model will be much better than what you had and probably good enough,” Mindy Cancila, vice president of enterprise strategy at Dell Technologies, said in a latest webinar.

Furthermore, SLMs’ smaller footprint means they can run on everything from servers to laptops to smartphones, fed by data located anywhere from corporate data centers to public cloud services and out to the edgewhere breakthroughs in model compression and performance will enable high quality inference at low latency.

Progress will bring productivity windfalls

Lots of research suggests that GenAI has increased productivity across organizations. In truth, actual results are hard to quantify, according to academics Ethan Mollickan expert on GenAI adoption within organizations, who noted that business leaders report little AI use and few productivity gains outside of niche uses.

Mollick further argues that organizations need to conduct research and development to understand organizational AI usage measures productivity and other progress metrics. And these R&D analyzes have yet to be codified – even by the consultancies that are paid to do the work.

“No one has specific information on how to best use AI in your business, or a handbook on how to integrate it into your organization,” Mollick said.

Still, consultancies continue to find positive metrics from GenAI investment and adoption.

For example, Ernst & Young LLP found that senior executives whose current budgets for AI investments are 5% or more of their total budgets experienced higher positive returns in several key areas compared to those spending less than 5%.

Those who allocated 5% or more of their budgets outperformed their lower spenders, 76% to 62% for employee productivity, 71% to 55% for product innovation and 73% to 47% for creating competitive advantage, according to EY.

This suggests that organizations can ill afford not to increase investment in GenAI. Their the competitors certainly will.

Of course, it is never easy to balance business strategy with IT investments to achieve the desired business results. But you don’t have to go it alone; trusted advisors like Dell are here to help.

Learn more about Dell AI factory.