Introducing Real-World AI Benchmarks for AGI Progress

• Intern-to-Expert Coding Tasks: Next-generation coding challenges ranging from tasks a junior developer could handle to problems requiring superhuman-level code generation and architecture design skills.
• Authentic Engineering Workflows: Scenarios to simulate real development practices at various seniority levels—writing functions, debugging legacy code, performing code reviews, and designing systems—just as an engineer would in the field.

• End-to-End Data Pipelines: Benchmarks covering the full lifecycle of a data science project, from raw data ingestion and cleaning to feature engineering, model training, and deployment.
• Daily Data Science Tasks: Challenges mirroring a data scientist’s daily responsibilities: data wrangling of unstructured datasets, exploratory analysis, iterative model refinement, and delivering insights or predictions in a production-like environment.

• Complex Numeric & Symbolic Reasoning: Numeric and symbolic reasoning challenges that focus on open-ended problem-solving and interdisciplinary applications of Math.
• Beyond Textbook Problems: These benchmarks will go beyond standard academic math tests, capturing multi-step, context-rich problem solving—more akin to how math is used to tackle complex, real-world engineering or research problems.

• Integrated Modalities: Tasks that integrate text, images, audio, video, computer use and more.
• Diverse Input Challenges: Each scenario reflects how AI must process and integrate diverse real-world inputs. For example, a benchmark might involve reading an email, looking at an attached spreadsheet or image, listening to a voice memo, and then synthesizing a coherent answer or action plan.

• Multiple Industries: Vertical benchmarks tailored to Banking and Financial Services, Retail, and more added over time.
• Grounded in Reality: Each use case will target an industry-specific workflow (e.g., underwriting in insurance).
• Built with Industry Partners: We are rapidly developing these specialized benchmarks in collaboration with industry domain experts to ensure each test is grounded in the actual challenges and standards of that field.

Beyond providing high-quality training data to leading AI labs, Turing’s goal is to build AI systems that solve essential problems for people, enterprises, and governments. We need to measure what today’s AI models can do, identify gaps, and chart the path forward.

We’re creating these benchmarks to:

• Reflect Real-World Needs: Move beyond purely academic tests to tasks that matter in everyday workflows. While many academic benchmarks have saturated, there exists a substantial gap in creating large knowledge-work productivity improvements using AI. Our benchmarks will more closely approximate the capabilities needed to produce measurable productivity improvements in real-life knowledge work. 
• Learn from AGI Deployment: Leverage Turing Intelligence’s AI deployment know-how to ground benchmarks in what companies and governments need to be successful.
• Shape the Future AI Research: Mark areas for improvement, guiding the AI community’s next steps in 2025, 2026, and beyond.

As models get smarter, we’ll keep updating these benchmarks so we can all see what’s new, what’s solved, and what still needs solving. We view this new set of benchmarks as  complementary to the existing AGI metrics, bringing sharper focus to practical, high-impact applications.  

We’re collaborating with AI labs, academia, and the industry as a whole to refine and expand these benchmarks. If you’re working on evaluation methodologies or have real-world tasks you’d like to see tested, please reach out to us at research@turing.com. We’d love to work together on shaping a new standard for real-world AI performance. We think AGI is a journey not a destination. Let’s make it a journey that delivers tangible value to humanity at every step of the way. 

Thank you for reading, and stay tuned for more on how we’re bridging the gap between cutting-edge AI research and meaningful, tangible outcomes.