Cassie Kozyrkov

Kozyr

Speaking and advisory services. Request form: makecassietalk.com

Kozyr was renamed from Data Scientific.

Chief Executive Officer

Federal Reserve Bank of New York

Served a full two year term as Innovation Advisor and Councillor at the Federal Reserve Bank of New York, with a focus on the impact of AI on the financial markets and global policy.

Innovation Advisory Council Member

Google

Decision Intelligence is an interdisciplinary field concerned with all aspects of decision-making.  It combines Data Science (statistics, machine learning, AI, analytics) with the Behavioral Sciences (psychology, neuroscience, economics, and the managerial sciences).

I serve as a thought leader in this new discipline, guiding Google and the world in making safe, effective use of AI and data.

Chief Decision Scientist

My mission is to democratize artificial intelligence and guide business leaders in harnessing the transformative power of machine learning.

Chief Data Scientist, Office of the CTO

Youngest ever member of Google's Office of the CTO, specializing in AI and data science.

I'm also a coauthor of Google's Technical Director training program to teach others how to succeed in the role.

Technical Director, AI and Data

My roles are: statistician, machine learning expert, mentor, data scientist, evangelist, decision scientist, and consultant.  I bridge the gap between leaders and quantitative analysts / researchers.

As Google's statistics and machine learning sensei:
* I've advised 200+ teams at Google on their analytics processes.
* My ideas have sparked 400+ new projects.
* I've trained 20,000+ analysts/leaders/engineers in rigorous data-driven decision-making and/or using machine learning to transform their work.

Largest projects in: Search Advertising, Google Maps, Machine Intelligence, Infrastructure, YouTube

Statistician

Self-Employed

Data science consulting and analytics services for clients from government organizations, private sector firms, and academia.

Data Science Consultant

North Carolina State University

Statistics Lecturer

Statistical Computing, Advanced Topics in Statistics: Big Data, Special Topics in Statistics: Data Mining, Applied Time Series Analysis, Applied Multivariate Data Analysis, Advanced Probability and Measure Theory, SAMSI Computational Advertising, Statistical Methods II, Statistical Consulting, Statistical Theory I, Statistical Theory II, Linear Models and Variance Components, Stochastic Processes, Doctoral Research, Supervised Doctoral Teaching, R Programming

Mathematical Statistics PhD Student

Duke University

Psychology & neuroscience with specialization in transcranial magnetic stimulation (TMS) methods and the science of decision-making.

Neuroeconomics PhD Candidate

Center for Cognitive and Social Neuroscience at the University of Chicago

Coordinated lab research projects and team effort. Designed and ran experiments.

Project Manager

Charitable contributions and decision-making research with Professors Steve Levitt and John List.

Research Assistant - Prof. John List

Cassie is Google’s former Chief Decision Scientist and a sought-after AI advisor, working with senior leaders at places like Lenovo, NASA, and Gucci. She is currently CEO of Data Scientific and is known for founding the field of Decision Intelligence.

Cassie is a beloved personality in the data and AI community, followed by over half a million tech professionals. She is also a renowned keynote speaker and writer. ( If you'd like her to speak at your event, here's her booking form: makecassietalk.com )

In almost 10 years at Google, Cassie personally trained over 20,000 Googlers in data-driven decision-making and AI and has helped over 500 projects implement decision intelligence best practices. Before that, she served in Google's Office of the CTO as Chief Data Scientist. The rest of her 20 years of experience was split between data science, consulting, lecturing, and academia.

If you've had a good chuckle while learning about AI, statistics, or decision-making, chances are you've encountered her writing, which has reached millions of readers. She has been honored as a LinkedIn Top Voice for 5 years and as a #1 Artificial Intelligence writer on Medium for 7 years.

Speaking and advisory services. Request form: makecassietalk.com

86% of enterprises plan to invest more in agentic AI.
6% trust agents to run core processes.

Let's talk about that gap!

Harvard Business Review just released some findings about tech decision-makers (n=603):
🤖 86% expect agentic AI investment to grow in 2 years
🤖 6% fully trust agents with end-to-end processes
🤖 ~25% say they're "ready" on infrastructure, data, security, and governance

That lack of trust is a good thing, in my opinion. Trusting complex probabilitistic systems without re-archtiecting complex processes is a recipe for kablooies. If anything it should give you hope. The trouble is that the redesign is difficult except in narrow use cases.

The model isn't the bottleneck anymore.
The plumbing and the people are.

Fragmented apps and data → agents can't get reliable context 
Legacy processes → never designed for autonomous execution 
Security and compliance → no playbook for "agents at the core" 
Employees → unclear if agents are tools or replacements

The report points to enterprise orchestration as the missing layer—connecting systems, standardizing actions, enforcing governance so agents behave predictably instead of becoming shadow IT.

Old question: "Which LLM should we use?"
New questions:
Which processes are we willing to redesign around agents?
How do we orchestrate data, tools, and guardrails so we can trust them with real work?

In 3 years, the gap won't be "who has agents."
It'll be who rebuilt their processes so agents can actually run them.

Where is your team right now?
A) Still experimenting at the edges (help desks, copilots) 
B) Re-architecting workflows so agents own real outcomes 
C) Somewhere in between, trying to figure out where to start
D) Waiting and watching others stub their toes

Drop your letter below. Your comments make my (digital) world 'round.

P.S. On a completely different note,  my Decision-Making with ChatGPT course is running on demand right now with a live Q&A with me next week. Come ask me anything! decisiongptcourse.com

CEO, Google's first Chief Decision Scientist, AI Adviser, Decision Strategist, Keynote Speaker (makecassietalk.com), LinkedIn Top Voice

Agentic HBR

Been wondering when my next cohort will be? Surprise! The time is NOW. If you missed the previous run or have been waiting to join, enrollment is now live:

 👉 https://lnkd.in/eG7EBtTv

The last cohorts made something clear: leaders aren’t looking for more information. They’re looking for clearer judgment. ChatGPT helps - but only when you use it in the right parts of the thinking process.

In this cohort, we’ll continue the work of turning AI into a genuine thinking partner instead of a shortcut. You’ll learn how to:

• Use ChatGPT to reduce effort without losing rigor
• Avoid the trap that turns “AI-assisted” into lazy thinking
 • Spot cognitive biases before they distort outcomes
 • Separate luck from decision quality
 • Build a decision scribe so you finally learn from your choices
 • Operate with confident judgment instead of drowning in advice

You’ll get 30+ on-demand videos (available as soon as you enroll), a 2-hour live Q&A with me (at 1 PM ET Dec 18), prerecorded Q&A from earlier cohorts, templates, frameworks, and a global community.

If you're working on sharpening your thinking in an AI world, Cohort 3 is now open.
 👉 https://lnkd.in/e-RaxVJJ

If this resonates, a repost or subscribing to decision.substack.com helps the course reach the leaders who need it.

#DecisionIntelligence #AILeadership #StrategicThinking #DecisionMaking #AI

Anthony Alcaraz

Amazon Web Services (AWS)

Driving growth in the startup ecosystem by leveraging AWS's cutting-edge AI/ML technologies. My team helps startups accelerate their go-to-market strategies and technological development.

Key focus areas:
- Developing AI/ML startups
- Guiding high-potential startups through AWS's AI/ML offerings
- Fostering partnerships within the EMEA tech community
- Aligning AWS's AI/ML solutions with startup needs

Passionate about empowering the next wave of AI-driven innovation. 

Let's connect and explore how AWS can supercharge your startup's AI/ML journey!

Senior AI/ML Strategist Startups & VC EMEA Strategic Pursuits

Fribl

Advisor

GenAI x HR 
Designed AI strategy
Lead engineering team 

With recruiting topping the list of concerns for many CEOs and companies spending upwards of $4,000 on average per hire, the need for innovation is clear. 

All too often, the arduous recruiting process leaves both employers and applicants frustrated after 42 days of effort with uncertainty if the right match was made...

At Fribl , we are leading the charge to transform this status quo, starting with reinventing the screening process using our proprietary GenAI technology enhanced by symbolic AI. 

This powerful combination doesn't just accelerate hiring, but actually personalizes the experience to precisely match candidates with roles that align to the company's unique culture - a monumental undertaking previously thought impossible.

As innovations like video interviews, CV parsers and AI recommendations shake up recruitment, we remain laser focused on four seismic shifts: Diversity, Advanced Interviewing, Data Analytics and AI Adoption. 

Our mission? To disrupt outdated screening tactics by simplifying the complex, multidimensional analysis of applicant credentials at scale to surface the ideal talents that personify an organization's ethos.

Chief AI/ML Product Officer & Partner

University of Oxford

Guest lecturer to Ajit Joakar Course : Artificial Intelligence: Generative AI, Cloud and MLOps (online)
Subject : the intersection of LLMs and graphs

AI/ML Guest Lecturer

Aldecis

My client portfolio was L’Oreal Finance Transformation 
AI for BI 

Management in AI Projects and Engineering Teams:
In my managerial role, I oversee AI projects and lead teams of engineers in crafting cutting-edge solutions. My focus is on driving strategic initiatives, ensuring projects are aligned with organizational goals, and fostering a culture of innovation and excellence. I take a hands-on approach to problem-solving and mentorship, ensuring that both projects and people reach their fullest potential.

Development in LLM/ML Ops:
My development expertise lies in the specialized domains of Large Language Models Operations (LLM Ops) and Machine Learning Operations (ML Ops). I am responsible for streamlining machine learning workflows from development to deployment. My work enables organizations to not only adopt but excel in leveraging machine learning technologies, facilitating continuous learning and adaptability across the board.

Marketing and Strategic Partnerships:
In the realm of marketing, I focus on building and nurturing strategic partnerships. My efforts are geared towards creating synergies with like-minded organizations and industry leaders. By forming valuable partnerships, I amplify brand visibility, foster innovation, and open doors to new business opportunities. The result is a win-win situation for all stakeholders involved, enhancing both market reach and impact.

Chief AI/ML Product Officer

CNAF

AI for performance management and customer support

I served as a Senior Executive after a competitive exam (EN3S). 
In this multifaceted role, I spearheaded a range of initiatives across key sectors such as Finance, Fraud Detection, Quality Control, Accounting, and Data & Performance Management.

Signature Project in ML Ops:
Facing the pervasive challenge of managing recurring incoming flows, I took the lead in conceptualizing and executing an avant-garde Proof of Concept (POC). This POC employed Azure ML-based ML Ops solutions, setting a precedent by being the organization's inaugural foray into Machine Learning Operations. The result was a transformative improvement in operational efficiency and service delivery.

Influence on Pluriannual Strategy 2023-2027 :
My pioneering project had a profound impact on the organization's multi-year strategic plan, specifically in the following domains:

Predictive Analytics: My efforts catalyzed the adoption of predictive analytics, enabling the organization to preemptively identify and mitigate challenges in benefits delivery. This proactive methodology revolutionized our service approach.

Sentiment Analysis: The launch of the "Lab Parentalité" was a critical milestone. Through the use of sentiment analysis, we gained unprecedented insights into parent feedback. This initiative not only enriched our data but also helped us better comprehend and meet the needs of parents.

Chatbots & Virtual Assistants: I played a key role in overhauling our customer service paradigm. By integrating cutting-edge chatbots and virtual assistants, we were able to enhance customer engagement across multiple platforms, including phone support, online portals, and mobile apps.

Personalization Algorithms: To elevate the user experience, we incorporated algorithms designed to customize services and recommendations. This bespoke approach significantly increased user satisfaction by tailoring the experience to individual preferences and needs.

Writes on ML/LLM Ops 🎨 | Follow me (4,8 K+) 🔔

As AWS's Senior AI/ML Strategist for EMEA Startups, I specialize in helping early-stage AI companies accelerate their development and go-to-market strategies. My expertise spans:

Technical Domains:
- Large Language Models (LLM) and GenAI architectures
- Knowledge Graphs and Vector Spaces
- MLOps and production AI systems
- Multi-agent and agentic AI systems

Strategic Value-Add:
- Technical due diligence for  AI investments
- Architecture optimization for AWS AI/ML services
- GTM strategy development for AI startups
- Deep expertise in evaluating GenAI applications

Recent Achievements:
- Guest lecturer at Oxford on LLMs and graph-based systems
- Led AI strategy for multiple venture-backed startups
- Published thought leadership on agentic AI systems
- Built and scaled ML platforms for enterprise clients

I'm passionate about helping VCs and founders navigate the technical and strategic challenges of building transformative AI companies. Happy to provide technical evaluation, architectural guidance, and strategic insights to help portfolio companies succeed.

Chief AI Officer & Decision scientist

Management in AI Projects and Engineering Teams:
In my managerial role, I oversee AI projects and lead teams of engineers in crafting cutting-edge solutions. My focus is on driving strategic initiatives, ensuring projects are aligned with organizational goals, and fostering a culture of innovation and excellence. I take a hands-on approach to problem-solving and mentorship, ensuring that both projects and people reach their fullest potential.

Development in LLM/ML Ops:
My development expertise lies in the specialized domains of Large Language Models Operations (LLM Ops) and Machine Learning Operations (ML Ops). I am responsible for streamlining machine learning workflows from development to deployment. My work enables organizations to not only adopt but excel in leveraging machine learning technologies, facilitating continuous learning and adaptability across the board.

Marketing and Strategic Partnerships:
In the realm of marketing, I focus on building and nurturing strategic partnerships. My efforts are geared towards creating synergies with like-minded organizations and industry leaders. By forming valuable partnerships, I amplify brand visibility, foster innovation, and open doors to new business opportunities. The result is a win-win situation for all stakeholders involved, enhancing both market reach and impact.

Your AI agents can't reason about what they can't structurally represent. ⭕ 

The gap between demos and deployable agents comes down to one thing: knowledge representation strategy.

Here's when knowledge graphs become non-negotiable for agentic systems:

🔗 Multi-hop inference is core to your reasoning tasks

Vector embeddings excel at semantic similarity but collapse on relationship queries. When your agent needs to traverse "Service → DEPENDS_ON → Database → PROTECTED_BY → SecurityGroup" to assess blast radius, you need graph topology. Property graphs give you O(1) relationship traversal. Document stores force you to reconstruct connections at query time.

⚡ You're hitting the relational complexity threshold

The 30-table heuristic is real. Beyond this, JOIN complexity explodes exponentially. More critically: when understanding connections requires traversing 3+ relationship types simultaneously, relational schema optimization becomes the bottleneck. Graph databases are optimized for exactly this access pattern.

🧠 Your agents must handle temporal reasoning and contradictory perspectives

Event-centric schema patterns capture causality. Multi-perspective patterns represent conflicting data sources with confidence scores and provenance. Try modeling "Terraform says X, AWS API says Y, confidence scores differ" in a relational schema without creating a maintenance nightmare.

🔄 Entity resolution across fragmented systems is blocking coherence

When the same entity appears as "Bob Jones" in CRM, "Robert Smith Jr" in fraud detection, and "R.J. Smith" in compliance, your agent needs evidence-based resolution using relationship patterns and contextual validation. Graph neighborhoods provide the evidence structure for identity inference.

⚙️ The three-graph architecture matters for production

Separate domain graph (trusted structured data), lexical graph (unstructured source text), and subject graph (LLM extractions). Entity resolution creates CORRESPONDS_TO edges between them. This prevents extraction artifacts from polluting canonical knowledge while maintaining full provenance.

❌ Skip graphs when:

Simple retrieval suffices, no multi-hop reasoning required, or relationship density is low. RAG with vector search handles these cases at 1/10th the infrastructure complexity.

The technical reality: RDF gives you formal reasoning semantics but slower performance. Property graphs give you traversal speed but require explicit inference logic. Hypergraphs handle n-ary relationships elegantly but lack mature tooling.

Choose based on your reasoning requirements, not architectural trends.

What's your biggest challenge in getting LLMs to reason over complex, interconnected enterprise data?

Senior AI/ML Strategist Startups @AWS | O’Reilly Author | Business Angel

AI agents are economically broken at scale without caching ❌ 

A single user goal triggers 5-10 LLM calls in loops. At 10K daily conversations, you're burning $547K/year on a single workflow pattern.

The obvious fix? Semantic caching with vector embeddings.

The problem? It fails catastrophically for agents.

🔴 Why Semantic Caching Breaks:

Agents execute context-dependent reasoning chains. Each step builds on previous steps.

"Analyze Tesla's Q3 performance" vs "Analyze Tesla's stock price"

Cosine distance: 0.28 → Cache hit 

Actual equivalence: False

Your agent retrieves a financial analysis plan when the user wanted technical specs. 

The entire workflow executes the wrong reasoning path.

Tune your threshold tighter → 5% hit rate (still burning $520K) Tune it looser → 65% hit rate but 40% false positives

The F1 optimization doesn't solve context drift.

🔧 The Architecture That Works:

Hybrid Cache = Vector Speed + Graph Intelligence

Layer 1: Vector DB for fuzzy matching (~30% of redundant queries) 

Layer 2: Knowledge Graph storing reasoning structures, not text
Explicit entity disambiguation (Apple fruit ≠ Apple stock)
Multi-hop traversal for validating context alignment 

Layer 3: Validation gate before serving cached plans

🎯 The Critical Shift: Granularity

Don't cache full responses. Cache atomic sub-queries.
"Compare platforms" decomposes into:
FetchFeatures(Platform A)
FetchPricing(Platform A)
FetchFeatures(Platform B)
CompareAttributes(A, B)

These canonical sub-tasks repeat at 40-70% rates vs 5-15% for full workflows.

The graph stores execution plan templates. 
Vector search finds intent. 

Graph traversal validates the reasoning path applies to current context.

📊 Production Results:

Hit rate: 65% (vs 8% with semantic-only)
Precision: 95% (vs 40% with loose thresholds)
Cost: $547K → $191K annually (65% reduction)
Latency: 60% faster (cold → hot state)

Graph adds 20ms per query, but prevents false positives that would cost 2000ms+ in LLM calls or failed workflows

The Mental Model:

Traditional cache: "Have I seen this text?" 
Semantic cache: "Have I seen something similar?" 
Agentic cache: "Do I know how to solve this class of problem?"

You're not storing what agents said. 
You're storing how agents think.

The cache evolves from a lookup table to a reasoning substrate, the agent's long-term memory of validated strategies.

Agentic needs graphs. 

At scale, the hybrid architecture : the difference between agents that burn capital and agents that generate ROI.

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The Battle

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