The future of AI research is proactive discovery. The goal is a system that not only monitors a portfolio but also recognizes what it doesn't know, then autonomously tasks its AI interviewer to conduct expert calls to generate the missing insights and deliver the new analysis to the user.

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The ultimate vision for AI in product isn't just generating specs. It's creating a dynamic knowledge base where shipping a product feeds new data back into the system, continuously updating the company's strategic context and improving all future decisions.

As platforms like AlphaSense automate the grunt work of research, the advantage is no longer in finding information. The new "alpha" for investors comes from asking better, more creative questions, identifying cross-industry trends, and being more adept at prompting the AI to uncover non-obvious connections.

The popular conception of AGI as a pre-trained system that knows everything is flawed. A more realistic and powerful goal is an AI with a human-like ability for continual learning. This system wouldn't be deployed as a finished product, but as a 'super-intelligent 15-year-old' that learns and adapts to specific roles.

The most powerful use of AI for business owners isn't task automation, but leveraging it as an infinitely patient strategic advisor. The most advanced technique is asking AI what questions you should be asking about your business, turning it from a simple tool into a discovery engine for growth.

OpenAI announced goals for an AI research intern by 2026 and a fully autonomous researcher by 2028. This isn't just a scientific pursuit; it's a core business strategy to exponentially accelerate AI discovery by automating innovation itself, which they plan to sell as a high-priced agent.

The company developed an AI that conducts highly technical expert network interviews, automating a high-friction manual process. This enables new, scalable content creation like monthly channel checks across dozens of industries—a task too repetitive for human analysts to perform consistently at scale.

A key strategy for labs like Anthropic is automating AI research itself. By building models that can perform the tasks of AI researchers, they aim to create a feedback loop that dramatically accelerates the pace of innovation.

Unlike consumer chatbots, AlphaSense's AI is designed for verification in high-stakes environments. The UI makes it easy to see the source documents for every claim in a generated summary. This focus on traceable citations is crucial for building the user confidence required for multi-billion dollar decisions.

Go beyond using AI for simple efficiency gains. Engage with advanced reasoning models as if they were expert business consultants. Ask them deep, strategic questions to fundamentally innovate and reimagine your business, not just incrementally optimize current operations.

The company wasn't built to solve a minor inconvenience. It was born from founder Jack Kokko's intense fear as an analyst of missing critical information in high-stakes M&A meetings. This deep-seated professional anxiety, not just a need for efficiency, fueled the creation of a market intelligence platform.