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Discusses how AI progress is rate-limited by real-world feedback loops, unlike coding, and emphasizes the need for applied AI to integrate into industry workflows.
The article argues that as AI models commoditize intelligence, businesses must focus on uniquely leveraging their corporate IP through workflows, evals, and routing to create value, presenting opportunities in the applied AI layer.
The article discusses how AI transformation in the enterprise requires changing underlying workflows and deploying agents against business processes, rather than just rolling out tools to end users. It emphasizes deep domain expertise, data organization, and comprehensive evaluations for ROI.
A tweet highlighting the importance of becoming an applied AI engineer to capitalize on the next big tech wave.
The post discusses the dynamic between frontier AI models and specialized tuned models, emphasizing that both will continue to grow due to the applied AI layer that allows enterprises to evaluate and mix models for their specific use cases.
AI Engineering Academy is an open-source learning platform that provides structured learning paths for applied AI topics like prompt engineering, RAG, LLM fine-tuning, deployment, and agents.
A tweet thread discusses best practices for AI token cost optimization, arguing that a deep understanding of workflows and architecture is needed for enterprises to maximize ROI, and that this represents a major opportunity for applied AI companies.
An analysis of the emerging applied AI layer in enterprises, outlining key components such as building workflow-specific features, intelligent model routing, change management via FDEs, and domain-specific go-to-market strategies. Argues that this layer will create sustainable moats and value despite some critiques.
Meta's Applied AI unit faces record-low morale and a multi-billion dollar cost crisis as employees artificially inflate AI token usage ('tokenmaxxing') in response to performance metrics tied to AI consumption, leading to internal rebellion and strict token budgets.
Ramp launches Applied AI Solutions, a service that embeds AI agents into finance teams to improve operational efficiency and deliver measurable ROI, leveraging a model-agnostic approach.
This article discusses how AI deployments in businesses often fail not due to model quality but because of the lack of ownership for keeping the model's knowledge current as the world changes, highlighting the challenge of 'silent drift' and the need for ongoing operational maintenance.
A comprehensive guide to Forward Deployed Engineering (FDE) in AI companies, explaining why Anthropic, OpenAI, Google, and others need FDEs, and how to excel in the role through auditing, evals, and deployment.
TLDR is hiring a Senior Software Engineer for its Applied AI team, offering $250k-$350k and fully remote work, focusing on making processes legible to code and composable into workflows.
An in-depth guide explaining the role of Forward Deployed Engineers (FDEs) in AI companies, covering why they are in high demand, what the job entails, and how to succeed in it.