At Rise of AI 2019 in Berlin, Bart Schneider, Senior Director Sales EMEA at Nvidia, argues that AI has already moved from a research topic into everyday business and consumer life, and walks through dozens of production use cases his team works on. He opens with personal examples (voice assistants, a Nike shoe-fitting app, early autonomous driving, a back-diagnosis prediction) before framing AI as a new industrial revolution after steam, electricity and compute. He cites market figures including a projected 20 percent boost to global GDP by 2030, an EU investment push, Nvidia's roughly 3,000-startup Inception program, and a data center AI market he says grew from about 339 million to 2 billion dollars in two years within a roughly 50 billion dollar total addressable market. He then runs through concrete deployments across healthcare, infrastructure, defense, retail, agriculture, customer service, smart cities, space, entertainment and drug discovery, each tied to a named customer or partner. He closes by noting, per Accenture, that only about 5 percent of large companies were using AI in production at the time.
Key takeaways
- Schneider projects AI will boost global GDP by 20 percent by 2030, citing an EU Commission investment push of about 20 billion euros.
- Nvidia's Inception program supports roughly 3,000 AI startups, and the data center AI market grew from about 339 million to 2 billion dollars in two years.
- Named healthcare deployments include Ohio State (80 percent diagnostic accuracy), early lung-cancer detection from CT scans, and 52-70 percent faster drug discovery.
- Swiss Federal Railways simulates a full day of train physics in 0.3 seconds to proactively catch problems before they occur.
- According to Accenture data cited by Schneider, only about 5 percent of large companies were using AI in production at the time.
Detailed notes
AI already in everyday life
Schneider opens with personal examples: his children talking to Google Home, a Nike app that scans feet to recommend shoes, early autonomous and semi-autonomous cars, and an AI tool his doctor used to predict how his spinal condition (degeneration of L4-L5-L6) will progress. He frames AI as the current stage of a sequence of industrial revolutions, following steam, electricity and compute, applied mainly to speed up and improve workloads across healthcare, media, security, defense, financial services and education.
Market momentum: GDP, EU investment, startups
Schneider cites a projected 20 percent boost to global GDP from AI by 2030, an EU Commission investment push of about 20 billion euros (noting Germany's own contribution was only 500,000 euros), and 28 countries building AI research platforms. He points to Nvidia's Inception program, which supports around 3,000 startups building on GPUs, and to the data center AI market, which he says grew from about 339 million dollars two years earlier to a projected 2 billion dollars, within a total addressable AI market he puts at around 50 billion dollars.
Healthcare and drug discovery
In healthcare, Schneider cites Ohio State reaching 80 percent diagnostic accuracy with immediate alerts to radiologists, a partnership using CT scans to detect early signs of lung cancer before they are visible to humans, and the Martinos Center using prior patient data to predict infant blindness earlier. He says AI-assisted drug discovery can cut development time by 52 to 70 percent.
Infrastructure, defense and smart cities
He cites Kansas City reducing emergency route-repair costs by 50 percent, GE saving about 6 million dollars through reduced machine outage risk, and Swiss Federal Railways simulating a full day of train physics in 0.3 seconds to spot problems proactively. On security, he cites a cooperation with Accenture on cyber defense and national security, and a smart-city video surveillance project. NASA, he says, uses AI to help predict potential asteroid impacts.
Consumer, retail and automotive applications
Schneider gives further examples: Bing narrowing image search results (for example, to only the jeans a user is looking for), KLM's AI-assisted customer service across social channels, SAP's brand-impact tool measuring ad ROI for Audi during ski events, a song-identification app matching listeners to tracks among 11 million songs, Olay's facial-scan skincare tool, and an agricultural machine that checks every plant in real time before applying pesticide. On automotive, he notes over 1 billion drivers logging around 1 trillion miles a year worldwide, positioning autonomous driving data as a massive future market, and mentions a joint venture (which he calls "Cinuity") building toward fully autonomous vehicles.
Q&A highlights
Asked where Nvidia will be in ten to fifteen years, Schneider says the company was already building toward today's position a decade ago under CEO Jensen Huang, and expects rapid, tangible progress in automotive within two to three years, though he declines to give specifics. Asked about media and entertainment, he clarifies his talk covered the enterprise side, while gaming and entertainment remain Nvidia's largest and still-growing business under a separate division. Asked how many businesses use AI in production, he cites Accenture data showing only about 5 percent of large companies do so, with healthcare, higher education, research, and oil and gas catching up fastest, and manufacturing and retail lagging behind.
Quotes
There are over 1 billion car drivers in the world who are driving around 1 trillion miles a year.
Bart Schneider
Questions answered in this talk
What share of large companies were using AI in production as of 2019?
According to Bart Schneider at Rise of AI 2019, citing Accenture data, only about 5 percent of large companies were using AI in production, with healthcare, higher education, research, and oil and gas catching up fastest.
How much could AI add to global GDP by 2030?
According to Bart Schneider at Rise of AI 2019, AI was projected to increase global GDP by about 20 percent by 2030, alongside an EU Commission investment push of roughly 20 billion euros.
What examples did Nvidia give of AI already used in healthcare in 2019?
Bart Schneider at Rise of AI 2019 cited Ohio State reaching 80 percent diagnostic accuracy with immediate radiologist alerts, early lung-cancer detection from CT scans, infant-blindness prediction at the Martinos Center, and AI-assisted drug discovery cutting development time by 52 to 70 percent.