The article highlights the distinction between generative and reasoning AI models, noting their different goals, architectures, and applications. While generative models are beneficial for creative tasks, reasoning models are suited for analytical tasks requiring logical inference and error mitigation. A survey of 60 CFOs from large American companies revealed that 90% reported positive returns on generative AI investments, underscoring the importance of selecting the appropriate AI model for specific business needs.
Key takeaways:
- Actively AI has raised $22.5 million to develop AI-powered sales reps using custom reasoning models to identify high-value prospects.
- Reasoning models in AI mimic human logical thinking, decision-making, and problem-solving, differing from generative models that focus on pattern recognition.
- Actively AI's approach, termed "GTM Superintelligence," combines in-house and popular reasoning models to make optimal decisions for growth.
- Understanding the distinction between reasoning and generative AI models is crucial for selecting the right tool for specific tasks, as their goals and applications differ.