GenAI will be transformative – given time
Generative AI is poised to extend revenue and profitability opportunities for some software companies. The impact won’t stop at tech, but has the potential to disrupt the entire ecosystem, according to Matt Hedberg, who leads the TIMT research team.
“We think it will significantly change the way businesses, communities, and people interact with one another and machines,” he says, comparing its potential to that of innovations such as the PC, internet, and mobile phone.
While the team are bullish about GenAI’s potential, they are realistic about the time it will take to deliver value. “It will take several years to play out,” says Hedberg.
Vertical players have a unique advantage
Projections by Bloomberg suggest GenAI revenue will grow by 42% CAGR to top $1.3 trillion by 2032, including $280 billion from software. Hosting sessions at RBC’s recent Private Technology Conference, Hedberg got a sense of the companies who might be among the first to benefit.
These are players in fields such as transport, education, insurance and healthcare, who are now considering how they can offer their customers valuable new insights. “The opportunity here is for these vertical software companies to leverage unique sets of data that are very different from horizonal providers like Amazon or Google,” Hedberg explains. “They have the power to train their large language models to offer very specific insight.”
“The opportunity here is for vertical software companies to leverage unique sets of data that are very different from horizonal providers like Amazon or Google.”
- Matt Hedberg, Software Analyst, RBC Capital Markets
Dredging the data lakes will yield results
GenAI’s capabilities to analyze data that doesn’t come in traditional spreadsheet form will also be transformative. Enabling customers to utilize all aspects of their ‘data lakes’ – including unstructured data such as social media posts or audio files – opens the potential for unique insights. “It’s understanding that not all answers come out of rows and columns,” says Hedberg.
For instance, calls can be analyzed to gauge customer sentiment and to identify keywords that could enable sales conversions. Again, the potential is not limited to the tech sector, but is ripe for deployment across financial, consumer, utilities and other fields.
Security is a brake – and an opportunity
As data volumes grow, so does the need for data security. The imperative to protect sensitive data is one of the brakes on GenAI deployment, Hedberg says, as companies pause to reconsider the security fabric required when developing large language models.
In the same way, GenAI’s code suggestion capabilities – while providing a breakthrough by enabling non-coders to write code – can also provide openings for malicious actors. These drawbacks are also an opportunity, Hedberg adds: “There’s a whole offensive angle to generative AI that can help companies become more agile in stopping some of these cyber threats,” he says.
Limitations hand power to the giants
Besides cybersecurity, other current limiting factors on GenAI are a scarcity of talent and lack of access to graphic processing units (GPUs), as well as the high cost of training large language models. This potentially hands an advantage to the biggest tech companies, says Hedberg. “As time progresses, I think a lot of regulatory bodies will think long and hard about where this power is accruing,” he suggests.
As the current limitations are resolved, he says, companies will gradually be able to unleash the full power of GenAI. “We could start to see some green shoots of monetization for a select number of companies this year, but more likely 2025 and into 2026,” he predicts.
“We could start to see some green shoots of monetization for a select number of companies this year, but more likely 2025 and into 2026.”
- Matt Hedberg, Software Analyst, RBC Capital Markets
