AI is setting off a great scramble for data
artificial intelligence (AI) would be the death of Adobe, a maker of software for creative types. New tools like DALL-E 2 and Midjourney, which conjure up pictures from text, seemed set to render Adobe’s image-editing offerings redundant. As recently as April, Seeking Alpha, a financial news site, published an article headlined “Is AI the Adobe killer?" Far from it.
Adobe has used its database of hundreds of millions of stock photos to build its own suite of AI tools, dubbed Firefly. Since its release in March the software has been used to create over 1bn images, says Dana Rao, an executive at the company. By avoiding mining the internet for images, as rivals did, Adobe has skirted the deepening dispute over copyright that now dogs the industry.
The firm’s share price has risen by 36% since Firefly was launched. Adobe’s triumph over the doomsters illustrates a wider point about the contest for dominance in the fast-developing market for AI tools. The supersized models powering the latest wave of so-called “generative" AI rely on gargantuan amounts of data.
Having already helped themselves to much of the internet—often without permission—model builders are now seeking out new data sources to sustain the feeding frenzy. Meanwhile, companies with vast troves of the stuff are weighing up how best to profit from it. A data land grab is under way.
The two essential ingredients for an AI model are datasets, on which the system is trained, and processing power, through which the model detects relationships within and among those datasets. Those two ingredients are, to an extent, substitutes: a model can be improved either by ingesting more data or adding more processing power. The latter, however, is becoming difficult amid a
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