“We’re about three years into the current one, which is AI ... we expect this to be a multi-decade phenomenon,” he adds.
The fund, part of Franklin Templeton Investment Funds, has grown into a US$12.84 billion ($16.67 billion) strategy with a net asset value of US$61.09 for its A (acc) USD share class as at Sept 30. It has lived through four major tech waves: the internet build-out, mobile, cloud computing and now AI.
Cioppa believes the fund’s longevity is tied to both structure and people. “The fund celebrated its 25th anniversary this year,” he says. “We have a lot of continuity in our team ... several people on the team have seen a lot of these cycles in tech.”
That experience, he adds, “helps inform how we manage risk and volatility on a go-forward basis”.
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Rather than making narrow bets, the fund is run as a multi-thematic strategy. “We don’t try to concentrate on any one particular theme,” Cioppa says. “There are always a lot of different secular growth drivers driving the technology industry.”
Five secular themes, one multi-thematic AI strategy
The portfolio of Franklin Technology Fund is currently organised around five long-term themes: AI proliferation, intelligent platforms, digital commerce, fintech and digital media transformation.
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The first is what the team calls AI proliferation. “We had the introduction of these AI tools to us two to three years ago,” Cioppa says. “We think over the next 10 years we’re going to see AI proliferate across knowledge workers, but also the physical world.” That includes categories such as autonomous driving and robotics, which sit within this theme.
The second theme is intelligent platforms, which covers the foundational infrastructure for modern computing. “There are businesses that are building the foundational layer for modernisation,” Cioppa adds. “Cybersecurity is a component of intelligent platforms, something that we call data centre 2.0, these new, massive, modern data centres that are being built, and the companies that are exposed to that: data platforms and data modernisation.”
Digital commerce is the third leg. Cioppa argues that e-commerce still has considerable runway. He describes it as “a US$6 trillion market that continues to grow double digits and should for some time”, boosted by companies that are “driving innovation around how we all transact online”.
Fintech is the fourth theme, with the fund focused not only on modern payment rails and digital payments but also on what Cioppa calls programmable money. “For the next five to 10 years, we should be at a point when we can start to see technologies like stablecoin, for example, proliferate a bit more,” he says.
The fifth theme is digital media transformation, which targets companies “changing the way that we consume media, but also changing the way that we create content.”
There is no hard allocation to any of these five buckets. “We don’t have any fixed allocation across those five,” Cioppa says. “We are allowed to dynamically invest between them. There’s sometimes a lot of overlap between them as well, but they are all five distinct categories of secular growth that we target.”
Despite the thematic framing, the investment process is primarily stock-specific. “While we’re multi-thematic and we have a focus on those five core themes, we are bottom-up as well,” says Cioppa. “Each company in the fund has to make its way in on its own merit, not just because it’s attached to one of those themes.”
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That bottom-up lens also shapes how Franklin Templeton defines technology exposure. On one level, there is the “strict” Global Industry Classification Standard (GICS) definition of technology companies: semiconductors, hardware and software. On another level, businesses in other sectors are powered by technology. “There are the companies that fall outside of that but are what you and I would consider to be technology companies,” he says, citing Amazon and MercadoLibre as examples. “The foundation of their business was using technology to change how a market operates.”
The fund describes such firms as tech-enabled. “We invest across both those strictly defined technology companies and tech-enabled businesses as well,” Cioppa says. Sometimes that leads to obvious adjacencies, sometimes to less obvious ones.
Tempus, a healthcare name, is one of these. “Their business is building a data and AI platform that aggregates molecular and clinical data for oncology patients, and that data and AI model are used to help oncologists better diagnose their patients,” he adds. “Tempus is to us an example of a company that is using deep technology expertise to transform an industry ... and that fits the mandate that we’re looking to follow.”
For AI adoption, he sees several layers. The first involves “heavy professional services, or knowledge work-intensive industries”, such as financial services, law and real estate management. In these fields, tools are making experts “dramatically more efficient and cut out a lot of the mundane work that they have to do every day”, whether preparing a legal case or consolidating documents.
The next layer is sectors that require deeper domain knowledge and data, particularly healthcare. “It’s really tough to crack the big problems like drug discovery,” he says. “But I think the upside is very high, where if you do eventually get to the point where AI models can truly understand all the data that comes with a patient’s background, it could be really impactful.”
Beyond that lies the physical world, including the manufacturing and automotive industries. “The clearest example of that today, where AI is having an impact, is in autos and autonomous driving,” he says. Over time, he expects robotics to become “more impactful to areas like manufacturing, where you have intelligent robots in a manufacturing facility doing more work on behalf of humans”.
Physical AI is directly aligned with the fund’s view on industrials. “It’s a big deal because it touches on two vast markets, autonomous driving and robotics,” Cioppa says. The value chain runs from semiconductor firms to large industrial companies that “for years have participated in the robotics market” and now “have an opportunity to benefit from much higher proliferation of robotics in a manufacturing facility”. Beneficiaries also include “all the companies that will actually be deploying this technology to become more efficient” across industrials, manufacturing, autos and natural resources.
Is there a bubble?
With AI so central to recent market gains, the obvious question is whether the current cycle resembles the dotcom bubble. Cioppa argues that several key metrics point in the opposite direction.
In terms of valuations, he notes that “during the dotcom bubble, the tech sector, at the March 2000 peak, traded at like 70 times forward earnings.” Today, he says, “tech trades something closer to 30 times,” less than half of the earlier peak. On a stock level, he contrasts Cisco at the height of the internet build-out, which “traded at something like 130 times earnings at the peak” after its multiple “expanded almost 300% in the two years leading up to it”, with Nvidia today. Nvidia, now central to AI infrastructure, “trades at a little over 30 times and hasn’t seen multiple expansions over the last couple of years. Their performance has been driven by earnings growth.”
Another difference that Cioppa sees is the financial health of tech companies today. In the dotcom era, telecom operators were investing ahead of demand. “Global Crossing was one of these telecom operators that was investing 200% of their revenue to build out fibre capacity,” he says. “All of these companies that were making these capital investments were using debt to do so, so they were leveraging their balance sheets.”
Today’s AI infrastructure buyers look quite different. Looking at customers that drive data centre capacity, for instance, free cash flow is at a healthy level even after the massive capex spending. “In aggregate, they have hundreds of billions of dollars of cash on their balance sheets. Most of them don’t have to raise debt to support this level of investment,” he adds.
There are also real-world bottlenecks in this cycle. It was “fairly straightforward to go out and raise capital and build and deploy telecom equipment and fibre” in the late 1990s, with “no bottlenecks or constraints to that build out”. By contrast, several bottlenecks in AI prevent the build-out of these data centres, including power.
At the company level, Franklin Templeton assesses whether AI investments are genuinely value-accretive. For software firms, that means understanding how customers of a given company are deploying their technology and the level of efficiency achieved. The team then examines how that software company or enabler can capture some of that value, since investors care about the monetisation opportunity and growth durability.
Cioppa rejects the idea that this is still simply a “capex first, return on investment later” phase. “We wouldn’t see all of this consumption of AI that’s happening right now across consumers, across businesses, if there weren’t a payback,” he says. “Businesses don’t have the resources or budget just to go spend as much as they can on these things, hoping to achieve some outcome.”
On where the most attractive long-term returns will come from, Cioppa avoids choosing between hardware, software and AI services. “On balance, we really want to have exposure to all three,” he says. “I think there are higher returns to be had across all three of those categories.”
Overall, the strategy remains majority US (88.98%), reflecting the country’s role at the centre of AI innovation, but it is run as a global fund. While the top 10 holdings are concentrated in the US, with names like Nvidia, Broadcom, Microsoft and Apple, the fund also holds Taiwan Semiconductor Manufacturing Co (TSMC), Tencent and Grab.
