The $776M Slack Bot and the Model That Won’t Ship
📰 Medium · AI
Learn how a Slack bot achieved $776M in labor-equivalent work in 30 days, while a capable AI model refuses to open its weights, and what this means for the future of AI development
Action Steps
- Analyze the performance of Perplexity's Computer agent in Slack
- Evaluate the potential of AI models in automating labor-intensive tasks
- Consider the challenges of developing and deploying AI models, including weight sharing and interpretability
- Explore the possibilities of integrating AI agents into existing workflows and platforms
- Develop strategies for addressing the limitations and risks associated with advanced AI models
Who Needs to Know This
AI researchers, developers, and product managers can benefit from understanding the implications of these two models on the future of AI development and its potential applications
Key Insight
💡 The success of Perplexity's Computer agent in Slack demonstrates the potential of AI models in automating labor-intensive tasks, but also highlights the challenges of developing and deploying these models
Share This
💡 A Slack bot just did $776M of human labor in 30 days, while a top AI model won't share its weights. What does this mean for AI development? #AI #MachineLearning
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