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Wall Street Says “Show Me” on AI Technology Earnings

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the staff of the Ridgewood blog

Ridgewood NJ, once upon a time, merely mentioning AI on a Wall Street earnings call was enough to send investors into a frenzy. But as the dust settles, a more discerning reality is emerging.

The grand promises of AI technologies come at an immense cost — from the staggering demands on natural resources to the colossal investments in hardware. Despite Big Tech’s lofty valuations, doubts linger as the improbable state of AI development clashes with financial expectations.

As we navigate through what some dub as the “show me” year of 2024, skepticism looms large. Earnings season approaches, with AI once again at the forefront of discussions, but the latest wave of skepticism hints that the much-hyped returns may remain elusive.

The treasure trove of content that fuels advanced AI models — whether generating synthetic images or crafting convincing LinkedIn posts — is not infinite. Even the boundless expanse of the internet has its limits.

This scarcity has sparked a frenzied pursuit among AI companies to acquire more content: from appropriating copyrighted material to transforming videos into text, or even resorting to AI-generated data to train AI systems. However, relying on synthetic data compromises the quality and reliability of AI models, as research has revealed.

Researchers at Rice University have drawn a chilling parallel, likening the perils of training generative models on synthetic material to the spread of mad cow disease through cannibalistic feeding practices in livestock. The proliferation of synthetic content on the web only exacerbates the problem, cluttering search engine results with authorless, synthetic articles that offer little substance or reliability.

As existing AI systems ingest their own results, the cycle perpetuates, leading to a concerning degradation of quality and integrity.

But the challenges don’t end there. AI models are voracious consumers of electricity, with predictions suggesting that AI data centers could account for up to 20% to 25% of US power requirements by the end of the decade. This unsustainable trajectory underscores the urgent need for more energy-efficient solutions.

Furthermore, tech companies are doubling down on efforts to reduce their reliance on external suppliers for AI chips, pouring billions into developing their own hardware. Google and Meta recently unveiled their proprietary chips, signaling their costly commitment to the AI-led future.

While these investments hold the promise of prosperity, they also bring the industry closer to the inevitable reckoning: the need to deliver on the lofty promises of AI amidst mounting concerns over data integrity, resource depletion, and environmental sustainability.

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