3 posts tagged with "AI"

Anecdotes are great but what does the data say? Let us see some interesting insights that come out of analyzing 20M job postings over 16 months.

Key Findings​

1. Overall software engineering job postings have remained stable
2. There is a significant shift in required skills
3. AI/ML skills are increasingly in demand

Detailed Analysis​

Job Market Trends​

• AI and ML Engineers: Showing strongest growth in demand, leading the market
• Front-end Engineers and Data Engineers: Experiencing significant decline in demand
• Data Scientists: Demonstrating resilience with stable demand levels

Salary Insights​

• Salary ranges remain relatively flat when adjusted for inflation
• Current market supply suggests limited potential for significant salary increases in the near term

Most In-Demand Skills​

1. NLP and LLM Technologies

   • Natural Language Processing emerges as the most desired skillset
   • LLM-related skills, particularly chatbot development, showing exponential growth

2. Programming Languages

   • Rust: Gaining significant momentum in the market
   • React: Taking substantial market share from Angular in front-end development
   • Python: Maintains its position as the de facto language for ML development

Tech Company Hiring Patterns​

• Large tech companies that previously conducted layoffs are now actively hiring again
• Hiring patterns show balanced recruitment across all roles, not just AI positions
• Evidence suggests focus on talent upgrade and correction of previous hiring practices from 2021

Key Takeaway​

The data presents a clear message for both new graduates and experienced software engineers: incorporating AI skills into your toolkit is becoming increasingly important for career growth and marketability.

Source: Analysis based on 20M job postings over a 16-month period. Original data

The enterprise data stack is going through an inflection point and the battle lines are being redrawn - which happens every decade or so. This time, to no surprise, the AI agenda in Enterprise is going to determine the winners and losers.

The Changing Landscape​

The battleground for data platforms has shifted dramatically, now centering on enabling AI applications. Success in this space requires excellence in four critical areas:

1. Semantic Layer: Understanding the nuances of business data across the enterprise
2. Application Development Tools: Providing low-code/no-code toolsets for building applications
3. Orchestration Simplicity: Delivering workflows and interoperability that simplifies ecosystem management
4. AI Capabilities: Offering powerful AI frameworks that deliver results

The Major Players​

Four tech giants are aggressively positioning themselves in this space, each making significant moves in AI capabilities over the last twelve months:

Databricks​

• Strong position with Unity Catalog for data context understanding
• Introduced AI Playground and DBRX
• Application toolset strategy remains unclear

Microsoft​

• Leverages strong OpenAI partnership
• Microsoft Fabric provides excellent orchestration
• Strong application development tools
• Semantic layer remains a weakness

Salesforce​

• Comprehensive solution for customer applications with Einstein AI
• Challenge: Limited to Salesforce data ecosystem
• Faces hurdles with external data integration

Snowflake​

• Under competitive pressure but has comprehensive offerings
• Cortex AI shows promise
• Benefits from walled garden approach for orchestration
• Needs to balance user experience with economics
• Working to expand app building capabilities for business users

Enterprise Challenges​

A significant organizational challenge exists: data teams and application teams typically operate under separate leadership with limited collaboration. As AI applications become the central battleground, this could drive:

1. New winners and losers in the Data Platform space
2. Fundamental changes to enterprise IT operating models

Arvind Narayanan is the AI critic you may want to follow for two reasons:

1. He is not trying to be sensational by taking extreme positions
2. He is a first rate academic

His work tries to define the boundaries of what can be useful and where you have to be careful in a pragmatic way.

He has published a solid paper outlining these boundaries in legal space:

1. Information processing tasks have high clarity and high observability and are best use cases to start (categorizing requests for legal help, e-discovery)
2. Creativity, reasoning tasks are a range (spotting errors in legal filings are easier, preparing legal filings harder)
3. Predictive tasks are fraught with challenges (legal judgment predictions)

Lots of legal departments are evaluating Generative AI - and this is a good paper for them. In fact, we are doing some solid value accretive work in contract extraction space - that helps turn unstructured data into structured information for downstream applications. And our experience has been consistent with these observations.

Current State of AI in Legal Industry​

1. Document Review and Analysis
2. Legal Research
3. Contract Management
4. Predictive Analytics

Challenges and Opportunities​

1. Accuracy and Reliability
2. Ethics and Bias
3. Data Privacy
4. Integration with Existing Systems