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Sunday, January 11, 2026

Intelligent Evolution of Individuals and Organizations: How Harvey Is Bringing AI Productivity to Ground in the Legal Industry

January 11, 2026

Over the past two years, discussions around generative AI have often focused on model capability improvements. Yet the real force reshaping individuals and organizations comes from products that embed AI deeply into professional workflows. Harvey is one of the most representative examples of this trend.

As an AI startup dedicated to legal workflows, Harvey reached a valuation of 8 billion USD in 2025. Behind this figure lies not only capital market enthusiasm, but also a profound shift in how AI is reshaping individual career development, professional division of labor, and organizational modes of production.

This article takes Harvey as a case study to distill the underlying lessons of intelligent productivity, offering practical reference to individuals and organizations seeking to leverage AI to enhance capabilities and drive organizational transformation.

The Rise of Vertical AI: From “Tool” to “Operating System”

Harvey’s rapid growth sends a very clear signal.

Total financing in the year: 760 million USD
Latest round: 160 million USD, led by a16z
Annual recurring revenue (ARR): 150 million USD, doubling year-on-year
User adoption: used by around 50% of Am Law 100 firms in the United States

These numbers are more than just signs of investor enthusiasm; they indicate that vertical AI is beginning to create structural value in real industries.

The evolution of generative AI roughly经历了三个阶段：

Phase 1: Public demonstrations of general-purpose model capabilities
Phase 2: AI-driven workflow redesign for specific professional scenarios
Phase 3 (where Harvey now operates): becoming an industry operating system for work

In other words, Harvey is not simply a “legal GPT”. It is a complete production system that combines:

Model capabilities + compliance and governance + workflow orchestration + secure data environments

For individual careers and organizational structures, this marks a fundamentally new kind of signal:

AI is no longer just an assistive tool; it is a powerful engine for restructuring professional division of labor.

How AI Elevates Professionals: From “Tool Users” to “Designers of Automated Workchains”

Harvey’s stance is explicit: “AI will not replace lawyers; it replaces the heavy lifting in their work.”
The point here is not comfort messaging, but a genuine shift in the logic of work division.

A lawyer’s workchain is highly structured:
Research → Reading → Reasoning → Drafting → Reviewing → Delivering → Client communication

With AI in the loop, 60–80% of this chain can be standardized, automated, and reused at scale.

How It Enhances Individual Professional Capability

Task Completion Speed Increases Dramatically
Time-consuming tasks such as drafting documents, compliance reviews, and case law research are handled by AI, freeing lawyers to focus on strategy, litigation preparation, and client relations.
Cognitive Boundaries Are Expanded
AI functions like an “infinitely extendable external brain”, enabling professionals to construct deeper and broader understanding frameworks in far less time.
Capability Becomes More Transferable Across Domains
Unlike traditional division of labor, where experience is locked in specific roles or firms, AI-driven workflows help individuals codify methods and patterns, making it easier to transfer and scale their expertise across domains and scenarios.

In this sense, the most valuable professionals of the future are not just those who “possess knowledge”, but those who master AI-powered workflows.

Organizational Intelligent Evolution: From Process Optimization to Production Model Transformation

Harvey’s emergence marks the first production-model-level transformation in the legal sector in roughly three decades.
The lessons here extend far beyond law and are highly relevant for all types of organizations.

1. AI Is Not Just About Efficiency — It Redesigns How People Collaborate

Harvey’s new product — a shared virtual legal workspace — enables in-house teams and law firms to collaborate securely, with encrypted isolation preventing leakage of sensitive data.

At its core, this represents a new kind of organizational design:

Work is no longer constrained by physical location
Information flows are no longer dependent on manual handoffs
Legal opinions, contracts, and case law become reusable, orchestratable building blocks
Collaboration becomes a real-time, cross-team, cross-organization network

These shifts imply a redefinition of organizational boundaries and collaboration patterns.

2. AI Is Turning “Unstructured Problems” in Complex Industries Into Structured Ones

The legal profession has long been seen as highly dependent on expertise and judgment, and therefore difficult to standardize. Harvey demonstrates that:

Data can be structured
Reasoning chains can be modeled
Documents can be generated and validated automatically
Risk and compliance can be monitored in real time by systems

Complex industries are not “immune” to AI transformation — they simply require AI product teams that truly understand the domain.

The same pattern will quickly replicate in consulting, investment research, healthcare, insurance, audit, tax, and beyond.

3. Organizations Will Shift From “Labor-Intensive” to “Intelligence-Intensive”

In an AI-driven environment, the ceiling of organizational capability will depend less on how many people are hired, and more on:

How many workflows are genuinely AI-automated
Whether data can be understood by models and turned into executable outputs
Whether each person can leverage AI to take on more decision-making and creative tasks

In short, organizational competitiveness will increasingly hinge on the depth and breadth of intelligentization, rather than headcount.

The True Value of Vertical AI SaaS: From Wrapping Models to Encapsulating Industry Knowledge

Harvey’s moat does not come from having “a better model”. Its defensibility rests on three dimensions:

1. Deep Workflow Integration

From case research to contract review, Harvey is embedded end-to-end in legal workflows.
This is not “automating isolated tasks”, but connecting the entire chain.

2. Compliance by Design

Security isolation, access control, compliance logs, and full traceability are built into the product.
In legal work, these are not optional extras — they are core features.

3. Accumulation and Transfer of Structured Industry Knowledge

Harvey is not merely a frontend wrapper around GPT. It has built:

A legal knowledge graph
Large-scale embeddings of case law
Structured document templates
A domain-specific workflow orchestration engine

This means its competitive moat lies in long-term accumulation of structured industry assets, not in any single model.

Such a product cannot be easily replaced by simply swapping in another foundation model. This is precisely why top-tier investors are willing to back Harvey at such a scale.

Lessons for Individuals, Organizations, and Industries: AI as a New Platform for Capability

Harvey’s story offers three key takeaways for broader industries and for individual growth.

Insight 1: The Core Competency of Professionals Is Shifting From “Owning Knowledge” to “Owning Intelligent Productivity”

In the next 3–5 years, the rarest and most valuable talent across industries will be those who can:

Harness AI, design AI-powered workflows, and use AI to amplify their impact.

Every professional should be asking:

Can I let AI participate in 50–70% of my daily work?
Can I structure my experience and methods, then extend them via AI?
Can I become a compounding node for AI adoption in my organization?

Mastering AI is no longer a mere technical skill; it is a career leverage point.

Insight 2: Organizational Intelligentization Depends Less on the Model, and More on Whether Core Workflows Can Be Rebuilt

The central question every organization must confront is:

Do our core workflows already provide the structural space needed for AI to create value?

To reach that point, organizations need to build:

Data structures that can be understood and acted upon by models
Business processes that can be orchestrated rather than hard-coded
Decision chains where AI can participate as an agent rather than as a passive tool
Automated systems for risk and compliance monitoring

The organizations that ultimately win will be those that can design robust human–AI collaboration chains.

Insight 3: The Vertical AI Era Has Begun — Winners Will Be Those Who Understand Their Industry in Depth

Harvey’s success is not primarily about technology. It is about:

Deep understanding of the legal domain
Deep integration into real legal workflows
Structural reengineering of processes
Gradual evolution into industry infrastructure

This is likely to be the dominant entrepreneurial pattern over the next decade.

Whether the arena is law, climate, ESG, finance, audit, supply chain, or manufacturing, new “operating systems for industries” will continue to emerge.

Conclusion: AI Is Not Replacement, but Extension; Not Assistance, but Reinvention

Harvey points to a clear trajectory:

AI does not primarily eliminate roles; it upgrades them.
It does not merely improve efficiency; it reshapes production models.
It does not only optimize processes; it rebuilds organizational capabilities.

For individuals, AI is a new amplifier of personal capability.
For organizations, AI is a new operating system for work.
For industries, AI is becoming new infrastructure.

The era of vertical AI has genuinely begun.
The real opportunities belong to those willing to redefine how work is done and to actively build intelligent organizational capabilities around AI.

Google Colab Data Science Agent with Gemini: From Introduction to Practice

April 05, 2025

Google Colab has recently introduced a built-in data science agent, powered by Gemini 2.0. This AI assistant can automatically generate complete data analysis notebooks based on simple descriptions, significantly reducing manual setup tasks and enabling data scientists and analysts to focus more on insights and modeling.

This article provides a detailed overview of the Colab data science agent’s features, usage process, and best practices, helping you leverage this tool efficiently for data analysis, modeling, and optimization.

Core Features of the Colab Data Science Agent

Leveraging Gemini 2.0, the Colab data science agent can intelligently understand user needs and generate code. Its key features include:

1. Automated Data Processing

Automatically load, clean, and preprocess data based on user descriptions.
Identify missing values and anomalies, providing corresponding handling strategies.

2. Automated Modeling

Generate code for data visualization, feature engineering, and model training.
Support various modeling techniques, including linear regression, random forests, and neural networks.
Applicable to classification, regression, clustering, and time-series analysis tasks.

3. Smart Code Optimization

Optimize parameters and select the best algorithms using the AI agent, reducing manual debugging.
Perform cross-validation automatically, evaluate model performance, and provide optimization suggestions.

4. End-to-End Notebook Generation

Simply provide a description of the analysis goal, and the system generates a fully executable Python notebook, including library imports, data processing, modeling, and visualization.

How to Use the Colab Data Science Agent

1. Start Colab and Enable Gemini Agent

🔹 Step 1: Open Colab

Visit Google Colab and create a new notebook.
Ensure you are logged into your Google account.

🔹 Step 2: Upload Your Data

Upload CSV, JSON, or TXT files (maximum 1GB).
Use pd.read_csv() or json.load() to read the data.

🔹 Step 3: Enable Gemini

In the Colab interface, click "Analyze file with Gemini" or open the Gemini agent from the sidebar.

Describe Your Task and Generate Code

Key Steps:

Enter your analysis goal in the Gemini sidebar (see examples below).
Review and approve the generated Colab notebook.
Execute the code, review the results, and make necessary adjustments.

🔹 Example 1: Time-Series Data Analysis

User Input Description:

"Visualize the trend of my sales data over time."

Gemini-Generated Code Example:

import pandas as pd
import matplotlib.pyplot as plt

# Load data
df = pd.read_csv('sales_data.csv', parse_dates=['date'])
df.set_index('date', inplace=True)

# Plot the trend
plt.figure(figsize=(10, 5))
plt.plot(df['sales'], marker='o', linestyle='-')
plt.title('Sales Trend Over Time')
plt.xlabel('Date')
plt.ylabel('Sales')
plt.grid(True)
plt.show()

Example 2: Building a Regularized Linear Regression Model

User Input Description:

"Train a regularized linear regression model with feature selection to predict house prices."

Gemini-Generated Code Example:

from sklearn.linear_model import LassoCV
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error

# Load data
df = pd.read_csv('house_prices.csv')
X = df.drop(columns=['price'])
y = df['price']

# Data preprocessing
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Split dataset
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)

# Train Lasso model
lasso = LassoCV(cv=5)
lasso.fit(X_train, y_train)

# Predictions
y_pred = lasso.predict(X_test)
print("MSE:", mean_squared_error(y_test, y_pred))

Gemini automatically:

Selects Lasso regression for regularization.
Performs data standardization.
Splits the dataset into training and test sets.
Computes model performance metrics.

Example 3: Handling Imbalanced Classification Data

User Input Description:

"Generate synthetic data for an imbalanced classification dataset."

Gemini-Generated Code Example:

from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split

# Load data
df = pd.read_csv('imbalanced_data.csv')
X = df.drop(columns=['target'])
y = df['target']

# Handle imbalanced data
smote = SMOTE(sampling_strategy='auto', random_state=42)
X_resampled, y_resampled = smote.fit_resample(X, y)

# Split into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42)

print("Original dataset shape:", df['target'].value_counts())
print("Resampled dataset shape:", pd.Series(y_resampled).value_counts())

Gemini automatically:

Detects dataset imbalance.
Uses SMOTE to generate synthetic data and balance class distribution.
Resplits the dataset.

Best Practices

1. Clearly Define Analysis Goals

Provide specific objectives, such as "Analyze feature importance using Random Forest", instead of vague requests like "Train a model".

2. Review and Adjust the Generated Code

AI-generated code may require manual refinements, such as hyperparameter tuning and adjustments to improve accuracy.

3. Combine AI Assistance with Manual Coding

While Gemini automates most tasks, customizing visualizations, feature engineering, and parameter tuning can improve results.

4. Adapt to Different Use Cases

For small datasets: Ideal for quick exploratory data analysis.
For large datasets: Combine with BigQuery or Spark for scalable processing.

The Google Colab Data Science Agent, powered by Gemini 2.0, significantly simplifies data analysis and modeling workflows, boosting efficiency for both beginners and experienced professionals.

Key Advantages:

Fully automated code generation, eliminating the need for boilerplate scripting.
One-click execution for end-to-end data analysis and model training.
Versatile applications, including visualization, regression, classification, and time-series analysis.

Who Should Use It?

Data scientists, machine learning engineers, business analysts, and beginners looking to accelerate their workflows.

The MEDIC Framework: A Comprehensive Evaluation of LLMs' Potential in Healthcare Applications

November 28, 2024

In recent years, the rapid development of artificial intelligence (AI) and large language models (LLMs) has introduced transformative changes to the healthcare sector. However, a critical challenge in current research is how to effectively evaluate these models’ performance in clinical applications. The MEDIC framework, titled "MEDIC: Towards a Comprehensive Framework for Evaluating LLMs in Clinical Applications," provides a comprehensive methodology to address this issue.

Core Concepts and Value of the MEDIC Framework

The MEDIC framework aims to thoroughly evaluate the performance of LLMs in the healthcare domain, particularly their potential for real-world clinical scenarios. Unlike traditional model evaluation standards, MEDIC offers a multidimensional analysis across five key dimensions: medical reasoning, ethics and bias concerns, data understanding, in-context learning, and clinical safety and risk assessment. This multifaceted evaluation system not only helps reveal the performance differences of LLMs across various tasks but also provides clear directions for their optimization and improvement.

Medical Reasoning: How AI Supports Clinical Decision-Making

In terms of medical reasoning, the core task of LLMs is to assist physicians in making complex clinical decisions. By analyzing patients' symptoms, lab results, and other medical information, the models can provide differential diagnoses and evidence-based treatment recommendations. This dimension evaluates not only the model's mastery of medical knowledge but also its ability to process multimodal data, including the integration of lab reports and imaging data.

Ethics and Bias: Achieving Fairness and Transparency in AI

As LLMs become increasingly prevalent in healthcare, issues surrounding ethics and bias are of paramount importance. The MEDIC framework evaluates how well models perform across diverse patient populations, assessing for potential biases related to gender, race, and socioeconomic status. Additionally, the framework examines the transparency of the model's decision-making process and its ability to safeguard patient privacy, ensuring that AI does not exacerbate healthcare inequalities but rather provides reliable advice grounded in medical ethics.

Data Understanding and Language Processing: Managing Vast Medical Data Efficiently

Medical data is both complex and varied, requiring LLMs to understand and process information in diverse formats. The data understanding dimension in the MEDIC framework focuses on evaluating the model's performance in handling unstructured data such as electronic health records, physician notes, and lab reports. Effective information extraction and semantic comprehension are critical for the role of LLMs in supporting clinical decision-making systems.

In-Context Learning: How AI Adapts to Dynamic Clinical Changes

The in-context learning dimension assesses a model's adaptability, particularly how it adjusts its reasoning based on the latest medical guidelines, research findings, and the unique needs of individual patients. LLMs must not only be capable of extracting information from static data but also dynamically learn and apply new knowledge to navigate complex clinical situations. This evaluation emphasizes how models perform in the face of uncertainty, including their ability to identify when additional information is needed.

Clinical Safety and Risk Assessment: Ensuring Patient Safety

The ultimate goal of applying LLMs in healthcare is to ensure patient safety. The clinical safety and risk assessment dimension examines whether models can effectively identify potential medical errors, drug interactions, and other risks, providing necessary warnings. The model's decisions must not only be accurate but also equipped with risk recognition capabilities to avoid misjudgments, especially in handling emergency medical situations.

Prospects and Potential of the MEDIC Framework

Through multidimensional evaluation, the MEDIC framework not only helps researchers gain deeper insights into the performance of models in different tasks but also provides valuable guidance for the optimization and real-world deployment of LLMs. It reveals differences in the models’ capabilities in medical reasoning, ethics, safety, and other areas, offering healthcare institutions a more comprehensive standard when selecting appropriate AI tools for various applications.

Conclusion

The MEDIC framework sets a new benchmark for evaluating LLMs in the healthcare sector. Its multidimensional design not only allows for a thorough analysis of models' performance in clinical tasks but also drives the development of AI technologies in healthcare in a safe, effective, and equitable manner. As AI technology continues to advance, the MEDIC framework will become an indispensable tool for evaluating future AI systems in healthcare, paving the way for more precise and safer medical AI applications.

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Sunday, January 11, 2026

The Rise of Vertical AI: From “Tool” to “Operating System”

How AI Elevates Professionals: From “Tool Users” to “Designers of Automated Workchains”

How It Enhances Individual Professional Capability

Organizational Intelligent Evolution: From Process Optimization to Production Model Transformation

1. AI Is Not Just About Efficiency — It Redesigns How People Collaborate

2. AI Is Turning “Unstructured Problems” in Complex Industries Into Structured Ones

3. Organizations Will Shift From “Labor-Intensive” to “Intelligence-Intensive”

The True Value of Vertical AI SaaS: From Wrapping Models to Encapsulating Industry Knowledge

1. Deep Workflow Integration

2. Compliance by Design

3. Accumulation and Transfer of Structured Industry Knowledge

Lessons for Individuals, Organizations, and Industries: AI as a New Platform for Capability

Insight 1: The Core Competency of Professionals Is Shifting From “Owning Knowledge” to “Owning Intelligent Productivity”

Insight 2: Organizational Intelligentization Depends Less on the Model, and More on Whether Core Workflows Can Be Rebuilt

Insight 3: The Vertical AI Era Has Begun — Winners Will Be Those Who Understand Their Industry in Depth

Conclusion: AI Is Not Replacement, but Extension; Not Assistance, but Reinvention

Related Topic

Saturday, April 5, 2025

Core Features of the Colab Data Science Agent

1. Automated Data Processing

2. Automated Modeling

3. Smart Code Optimization

4. End-to-End Notebook Generation

How to Use the Colab Data Science Agent

1. Start Colab and Enable Gemini Agent

🔹 Step 1: Open Colab

🔹 Step 2: Upload Your Data

🔹 Step 3: Enable Gemini

Describe Your Task and Generate Code

🔹 Example 1: Time-Series Data Analysis

Example 2: Building a Regularized Linear Regression Model

Example 3: Handling Imbalanced Classification Data

Best Practices

Related Topic

Thursday, November 28, 2024

Core Concepts and Value of the MEDIC Framework

Medical Reasoning: How AI Supports Clinical Decision-Making

Ethics and Bias: Achieving Fairness and Transparency in AI

Data Understanding and Language Processing: Managing Vast Medical Data Efficiently

In-Context Learning: How AI Adapts to Dynamic Clinical Changes

Clinical Safety and Risk Assessment: Ensuring Patient Safety

Prospects and Potential of the MEDIC Framework

Conclusion

Related Topic

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