At this year’s Web Summit conference (May 11-14), Fusion Fund founder and managing partner Lu Zhang said she sees a very different AI conversation taking shape.
“Last time when I was in the Vancouver event, the discussion was more about better AI performance, better benchmark,” she told the Investing News Network as the event kicked off. “But this year it’s very practical about the deployment of AI.”
According to Zhang, the shift is broad and unmistakable. Security, compliance, governance and cost, topics that once sat in the background, now dominate discussions, especially in highly regulated sectors such as healthcare, financial services and insurance.
“All these practical problems become the center of the discussion,” she noted.
For Zhang, the most important sign that AI is maturing is that industry participants are finally talking about cost.
She frames today’s AI race as a competition over total cost. “When we talk about cost… compute, energy consumption, data consumption, everything all together, inference optimization, architecture design — how to make AI solution better and cheaper,” she adds. “I like this direction… I’m actually very happy.”
One example she highlighted is Sensenet, a Vancouver-based company that has built a dedicated AI solution that integrates multiple data sources, including satellite imagery and proprietary gas sensor data, to detect wildfires before they are visible.
“They’re not only working with government, they’re working with utility companies, carriers, insurance businesses, because they also suffer a lot, and have to pay a lot of penalty if they trigger the wildfire.”
Zhang first met the founder at last year’s event when the company was pre-revenue. Less than a year later, she says, Sensenet has scaled to “almost double-digit million” revenue. For her, Sensenet is a proof point that when AI is tied to a clear, high-stakes problem and when deployment economics work, growth can be extremely rapid.
A shift to physical data
Zhang said she has observed a fundamental shift in the AI landscape. “The narrative is changing, shifting from language model to world model to physical AI, and from the chat to agentic,” she remarked.
While standardized digital language data may be plentiful, the specialized information required for physical AI, encompassing robotics, sensing and real-world interactions, remains scarce. “We have an infrastructure of around 50 percent and compute 50 percent. The data is not even 10 percent.”
This deficit represents what she considers one of the premier innovation prospects in the current AI sector: the rise of firms focused on tactile sensors, novel data platforms and uniform pipelines designed for 3D real-world data.
Major corporations are beginning to appreciate the importance of this data. Zhang reports that among the 45 CTOs in her professional circle spanning the logistics, semiconductor, and manufacturing industries, there is a growing demand for startups that can assist in harvesting and organizing their industrial data assets.
On the energy side, Zhang argues that the biggest burden in AI systems isn’t necessarily computation itself, but moving data around. “That part [of] energy consumption actually [is] 100x more than compute itself,” she said.
This becomes particularly acute for three-dimensional physical data, which is far larger than text. In Zhang’s opinion, as physical AI and robotics scale, edge computing will become more essential. Without that shift, she doesn’t believe widespread deployment will be sustainable.
Where’s the ROI?
The same practical lens applies when Zhang looks at AI in healthcare. While most of the public discussion has focused on AI for drug discovery, she says the real budget, and therefore the real return on investment, sits in optimizing clinical trial results.
Her firm has already invested in a founder using AI to improve clinical trials, and in several companies building vertical AI models for specific therapeutic areas.
She cited a company developing a vertical AI model for cell therapy, effectively building a digital twin of the human cell, as one example.
“Just think about different indications — they can potentially just mimic the whole process of evolution of the human cell [under] different disease conditions.”
She said the company recently announced a major partnership with a large European pharmaceutical firm, focused on Parkinson’s disease, involving assets “a couple of hundred million dollars” in size.
Zhang also pointed to other portfolio companies, including one using microglial cells and vertical AI to treat Parkinson’s and dementia, and another using high-density ultrasound with AI to treat depression via a non-invasive, highly targeted approach.
For Zhang, the key development in healthcare is that the value chain is finally becoming complete. Early diagnostics, once under-rewarded by the healthcare system, are increasingly being linked to targeted treatment plans.
That, she argues, is why “it’s a great time for AI healthcare.”
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Securities Disclosure: I, Meagen Seatter, hold no direct investment interest in any company mentioned in this article.
