We revisit the findings in Babina et al. (2024) by extending the measures of firm-level investments in artificial intelligence (AI) from 2010–2018 through 2024. These AI measures are intended to proxy for firms’ internal AI development efforts through AI-skilled hiring observed in employee resumes. We update our AI measurement methodology to capture recent advances in AI technologies. We find that firms continue to grow their investments in AI, although the rate of growth has slowed down in the past few years. By 2024, more than 2% of employees of US public firms have been working on internal AI implementation and integration. While machine learning is still the most common AI-skilled group, we find a sharp rise in the number of employees working on generative or agentic AI implementation since 2020. As in the earlier period, firms that invest in AI continue to grow sales. In contrast to the earlier period where we find no changes in productivity, we find that the productivity of AI-investing firms has increased from 2018 through 2024. Consistent with this increase in productivity, we find that employment grew less than sales during this recent period. AI-investing firms increase hiring of more senior and highly educated workers, suggesting that AI might be favoring more the increased productivity of high-skilled workers. AI investing firms also increase operating costs but do not increase capital expenditures, consistent with intangible reorganization rather than physical-capital deepening. Overall, our results support the productivity J-curve hypothesis, where productivity gains from AI take a long time to materialize.