Elias Baltassis is a Partner and Senior Director at the Boston Consulting Group. He has deep expertise in AI- and data-enabled strategy, data operating models, data governance, responsible artificial intelligence and ethics, and new data-driven business development. Elias is passionate about data and analytics and the transformative impact of artificial Intelligence on business and society. He shares his insights about generative AI and LLMs, their potential effect on business, productivity, and relationships, including our necessary attention to ethics and far-reaching implications of AI in the workplace and on the Future of Work.     KEY TAKEAWAYS   [03:42] Elias starts trading bonds after studying math, econometrics, and computer science.   [04:17] From notation calculators to basic spreadsheets to nascent AI, Elias sees patterns in tool evolution.   [05:17] Elias moves to consulting, always involved in quantitative fields.   [06:20] The significant AI break throughs since 2016-17.   [07:12[ Why self-supervised learning was one critical advance.   [07:50] New architectures--enabling much larger models—were a second step, leading to generative artificial (GenAI) models.   [08:55] What the “language” of Large Language Models (LLMs) covers.   [10:00] After training ChatGPT by absorbing the internet, “hallucinations” need to be eliminated.   [11:06] “Red teaming” to eliminate hallucinations.   [12:11] The next refinement step is “reinforcement learning from human feedback”.   [13:00] The issue of “jail-breaking” models to circumvent “blocked” answers.   [14:32] Data embedding or fine-tuning: using private data to train GPT.   [16:02] Why did ChatGPT stop data accretion in 2021?   [16:30] The considerable cost of topology, training, and refining AI models.   [17:43] User input in ChatGPT serves to refine the model more so than to teach it.   [19:37] The Future of Jobs: Will generative AI lead to mass job losses? If so, when?   [21:37] Why the impact of GenAI will be delayed in some areas.   [23:00] GenAI is impacting certain areas faster—such as coding and customer service—generally enabling significant productivity gains.   [24:35] Career progression must adjust as corporate pyramids’ bases shrink.   [26:00] Knowledge management will change appreciably, with new jobs created and new tools and processes invented.   [29:14] Different professions and companies try to codify their “secret sauce”—what can GenAI take care of?   [30:30] What will remain? How people show empathy, interact, and give emotional support.   [32:05] Many existing articles about GenAI contain factual inaccuracies.   [33:19] Training to understand applied technologies is becoming much more important.   [34:40] In a time of exponential curves, doom predictions are imprudent and never verified.   [35:18] What Elias is most excited about—especially leveling up the playing field.   [36:30] Likely effects: huge productivity improvements depending on the country’s social contract and a reduction in work time.   [37:40] Elias explains why timelines relating to GenAI are difficult to circumscribe and more than five years is now considered “long-term”.   [38:50] How Elias anticipates the dynamics of change over time due to GenAI.   [39:39] Why the “truth function” matters.   [40:26] AI may be capable of a kind of informed creativity, as humans do.   [40:44] The beneficial mix of technology, regulation, and internal company rules and the emerging need for a Chief AI Ethics Officer role.   [44:01] Misinformation is a major concern for Elias.   [45:22] The possible negative impact of generative AI on kids.   [47:02] We need a definition of what it means to be “human” and “intelligent”—remembering the movie “Her”.   [48:06] Comments on the open letter written by Musk, Wozniak, Harari, and others.   [49:47] What Geoff Hinton has achieved and what he has to say about GenAI.   [51:33] Fellow