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Alex Balazs spent more than 20 years indoors. intuitionstarted as an engineer working on early versions of QuickBooks Online when moving financial workflows to the Internet still felt experimental. Now, as CTO, he is helping lead a more fundamental transformation of financial software into systems that can think and act on behalf of users. “This combines speed and scale. A.I. with human judgment and responsibility,” he says. fast company.
For decades, financial software has acted as a ledger, categorizing transactions and generating reports about what has already happened. That model is starting to break down. The progress of AI is push categories Achieve real-time interpretation and action with software that allows you to perform tasks and manage workflows, rather than simply recording them.
The shift creates core tension. Financial systems require accuracy, accountability, and auditability. AI systems operate probabilistically, producing output based on probabilities rather than certainties. As risk increases, so does the challenge of delegating financial decisions to machines.
Intuit is aggressively moving into that gap. The company, which controls more than 60% of the small business accounting software market, is working to transform finance into what it calls a “system of intelligence,” a continuously operating layer that understands and responds to financial conditions in real time. Its platform processes approximately 60 billion machine learning predictions per day across 180 petabytes of data infrastructure, serving nearly 100 million consumers and 10 million small businesses.
This strategy is already leading to growth. In its most recent quarter, Intuit reported revenue of $4.7 billion, up 17% year-over-year, and operating income increased 44% on a GAAP basis. The company says its platform facilitates nearly $890 billion in fund transfers and $336 billion in payroll payments annually.
Under Balazs, Intuit built what the company calls a generative AI operating system (GenOS), designed to orchestrate models, data, and workflows to create task-specific agents that can perform complex financial operations. Through partnerships with OpenAI and Anthropic, the company is building these capabilities into external AI ecosystems while maintaining control over customer data.
Still, the central question remains. If AI begins to act like an autonomous CFO, who will be held accountable if something goes wrong?
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Scientists have designed a way to save our brains from fake AI videos
Visual truth is going down in flames, thanks New generative AI model Produce synthetic media that appears indistinguishable from reality. But a team of university researchers has figured out a hardware fix that might save us. Engineers at ETH Zurich have designed a working prototype of a camera that physically stamps every photo or video with a cryptographic seal of authenticity at the location of the image sensor (electronic chip) that captures each photon in the real world.
“Trust in digital content is eroding. We wanted to create a technology that gives people a way to check whether something is real or not,” explained co-developer Felix Franke. in a press release. This new hardware architecture fundamentally changes the way media is authenticated.
Currently, the technology industry relies on a standard called C2PA.Coalition on Content Origin and Authenticity-In other words Already available on some devicesLeica, Nikon, Fuji, and Sony Alpha line high-end cameras. It also recently entered the mobile market natively with Google Pixel 10.
The standard relies on a device’s main processor to stamp videos and photos with a cryptographic seal proving their authenticity. When you view a photo or video on a C2PA-enabled player or TV, the software indicates it’s real. For example, if Meta allows Instagram to read these C2PA labels, a video in your feed could indicate that it’s trustworthy, just as a small lock icon appears in your browser to indicate that you have a verified and secure connection with your bank.
Here’s how the current solution works: The camera lens captures the scene, converts the light into digital information, and sends it down internal wires to reach the main computer chip. The processor cryptographically signs the file only after the data has been exchanged.
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