In his presentation, Blumofe used Akamai\u2019s AI evolution as an example of how business leaders can break the hype cycle and build AI fluency across their organizations.\u00a0<\/p>\n
Employee engagement should be at the center of this strategy, he said. Earlier this year, a survey conducted by Pew Research found that about 1 in 6 U.S. workers use AI to do their job in some way<\/a>, compared with some 81% who do not.\u00a0<\/p>\n Blumofe, who holds a PhD in computer science from MIT, sees low AI adoption as a missed opportunity. \u201cMost jobs at this point can benefit from AI,\u201d he said. \u201cIt\u2019s a matter of which tasks can most benefit, and how, [using] which form of AI.\u201d<\/p>\n He offered four tips for business leaders striving to help their organization attain AI fluency.<\/p>\n Don\u2019t overinvest in large language models<\/p>\n Too many people think of AI solely in terms of large language models, which are trained on trillions of parameters, when in fact purpose-built AI models are better suited for addressing specific tasks. Akamai, for example, has deployed many custom models for identifying and analyzing potential cybersecurity threats.\u00a0<\/p>\n \u201cIn many ways, an LLM is a ridiculously expensive way to solve certain problems. It\u2019s rare in an enterprise that you need a trillion-parameter model covering \u2026 every event in history,\u201d Blumofe said. \u201cOne of the lessons from DeepSeek is that you can do a lot more with less,\u201d he said, referring to the Chinese startup whose AI models are less expensive and less compute-intensive than U.S. competitors.<\/p>\n Don\u2019t let LLM success cloud your judgment<\/p>\n An LLM is well suited for something like classifying email, but that is \u201csuccess theater,\u201d Blumofe said. Most enterprise problems require more complex solutions beyond writing clever prompts to organize basic forms of structured data.\u00a0<\/p>\n In most cases, LLMs are one part of an \u201censemble of technologies\u201d that come together for a purpose-built solution. Along with its models for cybersecurity threat hunting, for example, Akamai has developed a chatbot that answers employees\u2019 questions about migrating customers to a new platform, and a tool that writes responses to vendor and customer requests for proposals.<\/p>\n Explore the world of AI beyond LLMs<\/p>\n Maturing beyond the one-size-fits-all approach of using LLMs means looking at technology more holistically. That means knowing when approaches like deep learning (to recognize patterns) or symbolic AI (to craft logical responses) are a better bet. \u201cThere\u2019s a whole world of AI beyond LLMs,\u201d Blumofe said. \u201cI would argue these models are in many ways more likely to offer enterprise value over the longer term.\u201d<\/p>\n Let employees experiment<\/p>\n Akamai has built an internal sandbox to \u201callow everybody to do their own thing and play with AI,\u201d Blumofe said. That approach contrasts with that of enterprises that select a handful of AI pilots from a list of dozens of proposals. Akamai\u2019s IT team may cry uncle at some point, given bandwidth use and computing costs, Blumofe conceded \u2014 but until that happens, he said, he feels no need to evaluate each possible AI use case.<\/p>\n At the end of his talk, Blumofe fielded a question from an audience member about companies like\u00a0Shopify<\/a> and\u00a0Duolingo<\/a> that are requiring hiring managers to prove that AI cannot do a job before they hire a human for the role.\u00a0<\/p>\n Such companies are \u201cgetting the tail before the dog,\u201d Blumofe said. \u201cThe burden of proof should go the other way. What\u2019s the problem you\u2019re trying to solve? What\u2019s the right technology? If it\u2019s AI, that\u2019s great \u2014 but I don’t think the burden of proof should be on \u2018Why not AI?\u2019\u201d<\/p>\n
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