When generative AI (GenAI) broke onto the scene in late 2022, a floodgate of market opportunity and innovation was unleashed with it. In recent months, we’ve seen substantial technical and affordability progress by many measures: AI now beats out human performance on many benchmarks of simple tasks, OpenAI’s release of its high-performing GPT-4o “mini” small AI model is 60% cheaper than its predecessor, Meta’s open-source Llama 3.1 release gifted developers worldwide with a very capable large language model, and Google’s Gemini 1.5 Pro release can process significantly more information, running up to 1 million tokens (about 1,500 pages of information).
And yet, the frenzy that greeted GenAI’s china rcs data rapid developments is tempered by slower-moving regulatory constraints as well as financially limited or risk-averse businesses. According to Gartner, GenAI is creeping toward the deflating but predictable “Trough of Disillusionment,” which signals a more measured, grounded approach to AI innovation that favors stricter testing and evaluation cycles that illuminate only the most attractive investments.
You don’t have to look far to see executives reconciling their prior lofty and sometimes untethered optimism with practical realities. According to a PwC survey, a sobering 46% of CEOs worldwide agree that GenAI will increase their company’s legal liabilities and reputational risks. In comparison, 64% agree that it will do the same for their cybersecurity risks, and 86% foresee that it will increase competitiveness in their industry.