Big Tech Taps AI Agents to Drive Revenue Growth

Big tech companies are reportedly in a race to unveil software to fuel AI spending.

For example, Microsoft is at work trying to automate tasks such as invoicing or the ability to rewrite code for applications in a different language and then make sure it works as intended, the Information Reported Thursday (April 18), citing company employees.

This software is being powered by OpenAI’s technology and will improve on Microsoft’s existing Copilot, the report said. Sources said Microsoft could debut the new features at its Build developer conference next month.

As for Google, that company’s DeepMind arm is developing AI “agents” to carry out tasks, conceivably doing things like taking over a user’s computer and working on multiple apps simultaneously, the report said.

While these high-profile artificial intelligence (AI) firms are developing new technologies, small and mid-sized businesses (SMBs) are struggling to keep up, PYMNTS wrote earlier this week following the release of the 2024 AI Index report from Stanford University.

Speaking with PYMNTS, that study’s editor-in-chief Nestor Maslej underlined the report findings on the growing AI divide between large and small firms. While mammoth tech companies invest billions into AI research and development, smaller firms don’t have the resources and talent to directly compete. 

“A small or even medium-sized business will not be able to train a frontier foundation model that can compete with the likes of GPT-4, Gemini or Claude,” Maslej said. 

“However, there are some fairly competent open-source models, such as Llama 2 and Mistral, that are freely accessible. A lot can be done with these kinds of open-source models, and they are likely to continue improving over time. In a few years, there may be an open, relatively low-parameter model that works as well as GPT-4 does today.”

Research last year by PYMNTS Intelligence found that generative AI technologies such as OpenAI’s ChatGPT could substantially improve productivity, yet they also risk disrupting employment patterns.

The Stanford report also found that while private investment in AI generally fell last year, funding for generative AI saw a dramatic leap, increasing nearly eightfold from 2022 to $25.2 billion, with companies like OpenAI, Anthropic, Hugging Face and Inflection reporting significant increases in their fundraising efforts.

Maslej said that while the costs of adopting AI are steep, they are overshadowed by the expenses that come with training the systems.

“Adoption is less of a cost problem because the real cost lies in training the systems. Most companies do not need to worry about training their own models and can instead adopt existing models, which are available either freely through open source or through relatively cost-accessible APIs,” he explained.