Month: April 2025

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Many systems rely on precise and consistent timekeeping for coordination, logging, security, and distributed operations. Even a one-second discrepancy can cause failures in time-sensitive processes such as financial transactions, database replication, and scheduled tasks. For systems that require strict synchronization—like distributed databases, telemetry pipelines, or event-driven architectures—handling leap seconds incorrectly can lead to data loss, duplication, or inconsistencies. As such, managing leap seconds accurately ensures system reliability and consistency across environments that depend on high-precision time.

For those unfamiliar with the concept leap seconds are periodic adjustments added to Coordinated Universal Time (UTC) to account for irregularities in Earth’s rotation, ensuring that atomic time remains synchronized with astronomical time. While necessary for precise timekeeping, these adjustments can pose challenges for systems requiring high-precision synchronization, such as those utilizing the Precision Time Protocol (PTP). PTP is designed to synchronize clocks within a network to sub-microsecond accuracy, making the handling of leap seconds particularly critical.​

Traditional methods of handling leap seconds, such as smearing—where the extra second is spread over a period to minimize disruption—are often employed in Network Time Protocol (NTP) systems. However, applying similar techniques in PTP systems is problematic due to their higher precision requirements. Even minimal adjustments can lead to synchronization errors, violating the stringent accuracy standards PTP aims to maintain.

To address this, Meta has developed an algorithmic approach that is integrated into their PTP service. This method involves a self-smearing technique implemented through the fbclock library, which provides a “Window of Uncertainty” (WOU) by returning a tuple of time values representing the earliest and latest possible nanosecond timestamps. During a leap-second event, the library adjusts these values by shifting time by one nanosecond every 62.5 microseconds. This stateless and reproducible approach allows systems to handle leap seconds automatically without manual intervention. ​

This self-smearing strategy offers several benefits, including seamless handling of leap seconds and maintaining the high precision required by PTP systems. However, it also introduces trade-offs. For instance, discrepancies can arise when integrating with systems that use different smearing methods, such as NTP’s quadratic smearing, potentially leading to synchronization issues during the smearing period.

Managing leap seconds in high-precision environments like those utilizing PTP requires innovative solutions to maintain synchronization accuracy. Meta’s algorithmic approach exemplifies how tailored strategies can effectively address the challenges posed by leap seconds, ensuring the reliability and precision of time-sensitive systems.

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Stifel has adjusted its price target for MongoDB (MDB, Financial), lowering it from $340 to $275 while maintaining a Buy rating for the stock. This decision follows an assessment involving 25 customers of MongoDB, aimed at evaluating growth in usage, the likelihood of customers switching to PostgreSQL, and the comparative operational expenses between the two database systems.

The survey findings suggest that customers continue to support MongoDB’s Atlas platform, indicating no significant shifts in market share. However, Stifel’s revision of the price target reflects broader concerns related to recent compression in group multiples and a less predictable economic environment.

Wall Street Analysts Forecast

Based on the one-year price targets offered by 34 analysts, the average target price for MongoDB Inc (MDB, Financial) is $297.36 with a high estimate of $520.00 and a low estimate of $180.00. The average target implies an
upside of 84.36%
from the current price of $161.29. More detailed estimate data can be found on the MongoDB Inc (MDB) Forecast page.

Based on the consensus recommendation from 38 brokerage firms, MongoDB Inc’s (MDB, Financial) average brokerage recommendation is currently 2.0, indicating “Outperform” status. The rating scale ranges from 1 to 5, where 1 signifies Strong Buy, and 5 denotes Sell.

Based on GuruFocus estimates, the estimated GF Value for MongoDB Inc (MDB, Financial) in one year is $433.07, suggesting a
upside
of 168.5% from the current price of $161.29. GF Value is GuruFocus’ estimate of the fair value that the stock should be traded at. It is calculated based on the historical multiples the stock has traded at previously, as well as past business growth and the future estimates of the business’ performance. More detailed data can be found on the MongoDB Inc (MDB) Summary page.

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From this page you also have access to our recorded show notes. They all have clickable links that will take you directly to that part of the audio.

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Google Cloud unveiled a major round of database enhancements at its Next 2025 conference, including a host of new AI features in AlloyDB, a MongoDB-compliant API for Firestore, continuous materialized views in BigTable, MCP connections galore, new database migration services, and the introduction of Oracle Exadata in its cloud.

When it comes to AI, Google Cloud is seeing a considerable amount of momentum in AlloyDB, its Postgres-flavored relational database service. It adopted the open source pgvector extension for Postgres in mid 2023, allowing AlloyDB customers to store vector embeddings directly in their database and query them using the extension’s approximate nearest neighbor (ANN) algorithm.

In April 2024, Google Cloud added the internally developed Scalable Nearest Neighbor (ScaNN) algorithm to AlloyDB, giving its database an immediate 8x performance boost in creating vector indexes, a 4x boost in serving vector queries, and a 10x boost in write throughput, according to its April 2024 white paper.

All that AI-focused data processing horsepower has translated directly into a 7x increase in vector searches on AlloyDB over the past year, according to Andi Gutmans, the vice president and general manager of databases at Google Cloud.

“We’re seeing thousands and thousands of customers doing vector processing,” Gutmans said. “Target went into production with AlloyDB for their online retail search and they have 20% better hit rate on recommendations. That’s real revenue.”

Now the company is preparing AlloyDB database for the next round of AI innovation: agentic AI. That work takes several forms, which the company outlined at its Next 2025 conference at Mandalay Bay in Las Vegas.

For starters, the company is enabling its new Google Agentspace offering, which uses Google’s Gemini AI model to power autonomous AI agents, to conduct structured data searches in AlloyDB. Now GenAI developers can get access to all of the data stored in AlloyDB–structured, unstructured, and real-time–to build AI agents.

In 2024, Google Cloud introduced a natural language interface for AlloyDB, enabling customers to query data using natural language. Now it’s bolstering that natural language query capability by supporting parameterized secure views, which provide an extra layer of security for agents and GenAI apps, Gutmans said.

The company is also working to optimize SQL functionality that spans vector search and structured filters and joins in AlloyDB, which also helps the GenAI developer.

“Vector search is great, but it can be really slow to index vectors. It can be expensive to search them,” Gutmans told BigDATAwire in an interview at Next 2025. “For example, AlloyDB can index 10 times faster than open source Postgres just getting those vectors indexed. Search we do up to 10 times faster if you combine your vector search with database filters and joins. We have optimized the query processor.”

Google Cloud is also introducing a new AlloyDB AI query engine, which allows developers to use natural language expressions and constructs directly within SQL queries. That enables developers to use free text questions, such as “find family-friendly hotels in Orlando,” and directly embed them in their SQL queries.

“We’ve got to win the minds and hearts of the GenAI developers, right?” Gutmans said. “And so a lot of this is around how do you make the AI easier for developers? How do you give them the right APIs? How do you make it more efficient? By bringing things lower down into the database. So that’s a lot of the work we’re doing.”

Google Cloud has also added support for Model Context Protocol (MCP) to a range of its databases via the Gen AI Toolbox for Databases, which the company first unveiled in February. Gen AI Toolbox originally was focused on providing LangChain data integration capabilities to AlloyDB, Spanner, Cloud SQL for Postgres, Cloud SQL for MySQL, and Cloud SQL for SQL Server. Now it’s supporting MCP, the protocol Anthropic unveiled late last year to connect a range of AI models with data and databases.

MCP fits nicely into Google Cloud’s plans for enabling developers to build the next generation of GenAI applications, Gutman said.

“We’re definitely seeing interest in in MCP,” he said. “MCP is really a very simple but very effective way to expose data services, or any service to foundation models. It’s JSON-RPC based. It’s got a way to do discovery. It’s got a way to have natural language descriptions for services. So you can actually have agents and models reason around which APIs should I be calling. So it’s a very simple but I think very effective protocol.”

Other DB Announcements at Next 2025

It’s not all the GenAI and AlloyDB show at Google Cloud, which sports half-a-dozen or so distinct database offerings. One of those other databases is Firestore, the company’s NoSQL document store.

At Next 2025, the company announced the addition of a MongoDB-compatible wire protocol to Firestore, which will essentially enable customers to plug in Firestore as the backend to applications that are currently backed by MongoDB, the JSON data store that’s immensely popular with developers.

“Firestore is a serverless, virtually unlimited-scale document database, with up to five 9s availability,” Gutmans said. “But it had a bespoke API. Customers love it. We have 600,000 developers on it. But they all said ‘Hey can you give me a MongoDB-compatible API so I can use my tools and frameworks that I’m already using?’”

Bigtable, the company’s other NoSQL database (of the wide-column variety, ala Cassandra) is also getting some new capabilities at Next 2025. Specifically, Google Cloud is giving Bigtable continuous materialized views, which will provide an easy way to build counters for real-time analytics.

Vector search is now generally available on Google Cloud Spanner, the company’s planet-scale operational data store. The vector search functions spans Spanner’s SQL, graph, key-value, and full-text search modalities.

Google Cloud also extended its partnership with database giant Oracle in several ways. First, it launched a new offering called Oracle Base Database Service, which expands on the Oracle Database@Google Cloud offering it launched in four global regions last year. It also announced the GA of Oracle Exadata X11M, which brings the latest generation of Oracle’s Exadata platform to Google Cloud. The Oracle databases are available in 20 Google Cloud locations, according to Google Cloud (Oracle says it’s available in 11 regions due to the way it measures infrastructure).

Microsoft SQL Server is now supported in Google Cloud’s Database Migration Service (DMS), giving customers the ability to migrate their SQL Server installations to Postgres running either on AlloyDB or Cloud SQL. Google Cloud is welcoming SQL Server customers with

“Microsoft has made it very difficult for customers to use their licenses wherever they want to,” Gutmans said. “It’s not a technical problem. It’s a commercial problem. And customers just are getting more and more frustrated with the fact that they’re being like into the Azure environment as opposed to being able to have choice.”

Related Items:

Google Cloud Preps for Agentic AI Era with ‘Ironwood’ TPU, New Models and Software

Google Cloud Bolsters GenAI with ScaNN Index, Valkey Updates

Google Revs Cloud Databases, Adds More GenAI to the Mix

Editor’s note: This article was updated to clarify the number of Google Cloud locations Oracle database offerings are in.

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Amazon Bedrock Knowledge Bases now extends support for hybrid search to knowledge bases created using Amazon Aurora PostgreSQL and MongoDB Atlas vector stores. This capability, which can improve relevance of the results, previously only worked with Opensearch Serverless and Opensearch Managed Clusters in Bedrock Knowledge Bases.

Retrieval augmented generation (RAG) applications use semantic search, based on vectors, to search unstructured text. These vectors are created using foundation models to capture contextual and linguistic meaning within data to answer human-like questions. Hybrid search merges semantic and full-text search methods, executing dual queries and combining results. This approach improves results relevance by retrieving documents that match conceptually from semantic search or that contain specific keywords found in full-text search. The wider search scope enhances result quality, particularly for keyword-based queries.

You can enable hybrid search through the Knowledge Base APIs or through the Bedrock console. In the console, you can select hybrid search as your preferred search option within Knowledge Bases, or choose the default search option to use semantic search only. Hybrid search with Aurora PostgreSQL is available in all AWS Regions where Bedrock Knowledge Bases is available, excluding Europe (Zurich) and GovCloud (US) Regions. Hybrid search with Mongo DB Atlas is available in the US West (Oregon) and US East (N. Virginia) AWS Regions. To learn more, refer to Bedrock Knowledge Bases documentation. To get started, visit the Amazon Bedrock console.

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Microsoft has partnered with Anthropic to develop an official C# SDK for the Model Context Protocol (MCP), an open protocol designed to connect large language models (LLMs) with external tools and data sources. The SDK is open-source and available under the modelcontextprotocol GitHub organization.

The C# SDK is based on an existing community project called mcpdotnet, originally started by Peder Holdgaard Pederson. Microsoft acknowledged the groundwork laid by Pederson and other contributors, which helped shape the foundation for this release. David Fowler, a distinguished engineer at Microsoft, noted:

Most of the credit goes to Peder Holdgaard Pedersen who we teamed up with after he built a great implementation!

Initially introduced by Anthropic in late 2024, MCP has seen increasing adoption across AI platforms. It defines a set of messages and behaviors that allow applications to communicate with tool- and resource-hosting servers in a standardized way. Several Microsoft products, including Copilot Studio, Semantic Kernel, and GitHub Copilot agent mode in VS Code, have already adopted MCP internally.

Robert Recalde, a principal engineer and cloud solution architect at Cigna Healthcare, highlighted the significance of the release:

As a software architect and engineer deeply rooted in .NET, I am excited about Microsoft’s collaboration with Anthropic to deliver the official C# SDK for the Model Context Protocol (MCP). This partnership significantly expands our ability to integrate advanced AI capabilities seamlessly into our .NET applications, empowering our organization and business partners to innovate rapidly and deliver tangible, real-world value.

The protocol supports a variety of standard messages for tool execution and resource access, such as ListToolsRequest, CallToolRequest, and ReadResourceRequest. This allows agentic applications to reason across a wider context and consistently call out to external services.

Some developers have raised questions about authentication support, including OAuth and OpenID Connect. Responding to these concerns, Microsoft engineer Mike Kistler confirmed:

We plan to support all the authentication protocols described in the MCP spec. We do not have a definite target date for this but it is very high on our priority list.

The SDK aims to take advantage of modern .NET runtime improvements and is intended to build clients and servers in the MCP ecosystem. Applications built using it can expose functionality via custom MCP servers or connect to others to extend LLM capabilities.

The SDK is available now via NuGet. Documentation, samples, and source code can be found in the official GitHub repository.

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USA, New Jersey- According to Market Research Intellect, the global NoSQL market in the Internet, Communication and Technology category is projected to witness significant growth from 2025 to 2032. Market dynamics, technological advancements, and evolving consumer demand are expected to drive expansion during this period.

The NoSQL Market is expected to grow significantly at a CAGR of 15.6% from 2025 to 2032. This surge is propelled by the explosive growth of unstructured and semi-structured data across various industries. Unlike traditional relational databases, NoSQL solutions offer scalability, flexibility, and performance advantages that are ideal for big data, cloud computing, and real-time analytics. The rising adoption of IoT devices, mobile applications, and social media platforms generates massive datasets that require efficient, schema-less data management. As enterprises aim to improve data processing speed and accessibility, the adoption of NoSQL databases is accelerating, creating robust growth opportunities in the forecast period.

The main drivers behind the NoSQL Market include the growing demand for high-performance and scalable database solutions that can handle large volumes of diverse data types. Businesses are increasingly dealing with dynamic, unstructured data from sources such as social media, web logs, IoT devices, and mobile apps-data types that traditional relational databases struggle to manage efficiently. NoSQL databases address this gap by offering flexible data models, horizontal scalability, and real-time processing capabilities. Cloud-native architecture and the surge in big data analytics have made NoSQL a critical technology for modern enterprises. Additionally, the microservices and DevOps movements in software development are boosting demand for NoSQL databases that can adapt quickly to changing application requirements. Startups and large enterprises alike are leveraging these databases for personalized customer e

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Market Growth Drivers-NoSQL Market:

The growth of the NoSQL market is driven by several key factors, including technological advancements, increasing consumer demand, and supportive regulatory policies. Innovations in product development and manufacturing processes are enhancing efficiency, improving performance, and reducing costs, making NoSQL more accessible to a wider range of industries. Rising awareness about the benefits of NoSQL, coupled with expanding applications across sectors such as healthcare, automotive, and electronics, is further accelerating market expansion. Additionally, the integration of digital technologies, such as AI and IoT, is optimizing operational workflows and enhancing product capabilities. Government initiatives promoting sustainable solutions and industry-standard regulations are also playing a crucial role in market growth. The increasing investment in research and development by key market players is fostering new product innovations and expanding market opportunities. Overall, these factors collectively contribute to the steady rise of the NoSQL market, making it a lucrative industry for future investments.

Challenges and Restraints-NoSQL Market:

The NoSQL market faces several challenges and restraints that could impact its growth trajectory. High initial investment costs pose a significant barrier, particularly for small and medium-sized enterprises looking to enter the industry. Regulatory complexities and stringent compliance requirements add another layer of difficulty, as companies must navigate evolving policies and standards. Additionally, supply chain disruptions, including raw material shortages and logistical constraints, can hinder market expansion and lead to increased operational costs.

Market saturation in developed regions also presents a challenge, forcing businesses to explore emerging markets where infrastructure and consumer awareness may be lacking. Intense competition among key players further pressures profit margins, making it crucial for companies to differentiate through innovation and strategic partnerships. Economic fluctuations, geopolitical instability, and changing consumer preferences add to the uncertainty, requiring businesses to adopt agile strategies to sustain long-term growth in the evolving NoSQL market.

Emerging Trends-NoSQL Market:

The NoSQL market is evolving rapidly, driven by emerging trends that are reshaping industry dynamics. One key trend is the integration of advanced digital technologies such as artificial intelligence, automation, and IoT, which enhance efficiency, performance, and user experience. Sustainability is another major focus, with companies shifting toward eco-friendly materials and processes to meet growing environmental regulations and consumer demand for greener solutions. Additionally, the rise of personalized and customized offerings is gaining momentum, as businesses strive to cater to specific consumer preferences and industry requirements. Investments in research and development are accelerating, leading to continuous innovation and the introduction of high-performance products. The market is also witnessing a surge in strategic collaborations, partnerships, and acquisitions, as companies aim to expand their geographical footprint and technological capabilities. As these trends continue to evolve, they are expected to drive the market’s long-term growth and competitiveness in a dynamic global landscape.

Competitive Landscape-NoSQL Market:

The competitive landscape of the NoSQL market is characterized by intense rivalry among key players striving for market dominance. Leading companies focus on product innovation, strategic partnerships, and mergers and acquisitions to strengthen their market position. Continuous research and development investments are driving technological advancements, allowing businesses to enhance their offerings and gain a competitive edge.

Regional expansion strategies are also prominent, with companies targeting emerging markets to capitalize on growing demand. Additionally, sustainability and regulatory compliance have become crucial factors influencing competition, as businesses aim to align with evolving industry standards.

Startups and new entrants are introducing disruptive solutions, intensifying competition and prompting established players to adopt agile strategies. Digital transformation, AI-driven analytics, and automation are further reshaping the competitive dynamics, enabling companies to streamline operations and improve efficiency. As the market continues to evolve, businesses must adapt to changing consumer demands and technological advancements to maintain their market position.

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The following Key Segments Are Covered in Our Report
Global NoSQL Market by Type
Key-Value Store
Document Databases
Column Based Stores
Graph Database
Global NoSQL Market by Application
Data Storage
Metadata Store
Cache Memory
Distributed Data Depository
e-Commerce
Mobile Apps
Web Applications
Data Analytics
Social Networking
Major companies in NoSQL Market are:
Microsoft SQL Server, MySQL, MongoDB, PostgreSQL, Oracle Database, MongoLab, MarkLogic, Couchbase, CloudDB, DynamoDB, Basho Technologies, Aerospike, IBM, Neo, Hypertable, Cisco, Objectivity

NoSQL Market -Regional Analysis
The NoSQL market exhibits significant regional variations, driven by economic conditions, technological advancements, and industry-specific demand. North America remains a dominant force, supported by strong investments in research and development, a well-established industrial base, and increasing adoption of advanced solutions. The presence of key market players further enhances regional growth.

Europe follows closely, benefiting from stringent regulations, sustainability initiatives, and a focus on innovation. Countries such as Germany, France, and the UK are major contributors due to their robust industrial frameworks and technological expertise.

Asia-Pacific is witnessing the fastest growth, fueled by rapid industrialization, urbanization, and increasing consumer demand. China, Japan, and India play a crucial role in market expansion, with government initiatives and foreign investments accelerating development.

Latin America and the Middle East and Africa are emerging markets with growing potential, driven by infrastructure development and expanding industrial sectors. However, challenges such as economic instability and regulatory barriers may impact growth trajectories.

Frequently Asked Questions (FAQ) – NoSQL Market (2025-2032)
1. What is the projected growth rate of the NoSQL market from 2025 to 2032?
The NoSQL market is expected to experience steady growth from 2025 to 2032, driven by technological advancements, increasing consumer demand, and expanding industry applications. The market is projected to witness a robust compound annual growth rate (CAGR), supported by rising investments in research and development. Additionally, factors such as digital transformation, automation, and regulatory support will further boost market expansion across various regions.

2. What are the key drivers fueling the growth of the NoSQL market?
Several factors are contributing to the growth of the NoSQL market. The increasing adoption of advanced technologies, a rise in industry-specific applications, and growing consumer awareness are some of the primary drivers. Additionally, government initiatives and favorable regulations are encouraging market expansion. Sustainability trends, digitalization, and the integration of artificial intelligence (AI) and Internet of Things (IoT) solutions are also playing a vital role in accelerating market development.

3. Which region is expected to dominate the NoSQL market by 2032?
The NoSQL market is witnessing regional variations in growth, with North America and Asia-Pacific emerging as dominant regions. North America benefits from a well-established industrial infrastructure, extensive research and development activities, and the presence of leading market players. Meanwhile, Asia-Pacific, particularly China, Japan, and India, is experiencing rapid industrialization and urbanization, driving increased adoption of NoSQL solutions. Europe also holds a significant market share, particularly in sectors focused on sustainability and regulatory compliance. Emerging markets in Latin America and the Middle East & Africa are showing potential but may face challenges such as economic instability and regulatory constraints.

4. What challenges are currently impacting the NoSQL market?
Despite promising growth, the NoSQL market faces several challenges. High initial investments, regulatory hurdles, and supply chain disruptions are some of the primary obstacles. Additionally, market saturation in certain regions and intense competition among key players may lead to pricing pressures. Companies must focus on innovation, cost efficiency, and strategic partnerships to navigate these challenges successfully. Geopolitical factors, economic fluctuations, and trade restrictions can also impact market stability and growth prospects.

5. Who are the key players in the NoSQL market?
The NoSQL market is highly competitive, with several leading global and regional players striving for market dominance. Major companies are investing in research and development to introduce innovative solutions and expand their market presence. Key players are also engaging in mergers, acquisitions, and strategic collaborations to strengthen their positions. Emerging startups are bringing disruptive innovations, further intensifying market competition. Companies that prioritize sustainability, digital transformation, and customer-centric solutions are expected to gain a competitive edge in the industry.

6. How is technology shaping the future of the NoSQL market?
Technology plays a pivotal role in the evolution of the NoSQL market. The adoption of artificial intelligence (AI), big data analytics, automation, and IoT is transforming industry operations, improving efficiency, and enhancing product offerings. Digitalization is streamlining supply chains, optimizing resource utilization, and enabling predictive maintenance strategies. Companies investing in cutting-edge technologies are likely to gain a competitive advantage, improve customer experience, and drive market expansion.

7. What impact does sustainability have on the NoSQL market?
Sustainability is becoming a key focus area for companies operating in the NoSQL market. With increasing environmental concerns and stringent regulatory policies, businesses are prioritizing eco-friendly solutions, energy efficiency, and sustainable manufacturing processes. The shift toward circular economy models, renewable energy sources, and waste reduction strategies is influencing market trends. Companies that adopt sustainable practices are likely to enhance their brand reputation, attract environmentally conscious consumers, and comply with global regulatory standards.

8. What are the emerging trends in the NoSQL market from 2025 to 2032?
Several emerging trends are expected to shape the NoSQL market during the forecast period. The rise of personalization, customization, and user-centric innovations is driving product development. Additionally, advancements in 5G technology, cloud computing, and blockchain are influencing market dynamics. The growing emphasis on remote operations, automation, and smart solutions is reshaping industry landscapes. Furthermore, increased investments in biotechnology, nanotechnology, and advanced materials are opening new opportunities for market growth.

9. How will economic conditions affect the NoSQL market?
Economic fluctuations, inflation rates, and geopolitical tensions can impact the NoSQL market’s growth trajectory. The availability of raw materials, supply chain stability, and changes in consumer spending patterns may influence market demand. However, industries that prioritize innovation, agility, and strategic planning are better positioned to withstand economic uncertainties. Diversification of revenue streams, expansion into emerging markets, and adaptation to changing economic conditions will be key strategies for market sustainability.

10. Why should businesses invest in the NoSQL market from 2025 to 2032?
Investing in the NoSQL market presents numerous opportunities for businesses. The industry is poised for substantial growth, with advancements in technology, evolving consumer preferences, and increasing regulatory support driving demand. Companies that embrace innovation, digital transformation, and sustainability can gain a competitive advantage. Additionally, expanding into emerging markets, forming strategic alliances, and focusing on customer-centric solutions will be crucial for long-term success. As the market evolves, businesses that stay ahead of industry trends and invest in R&D will benefit from sustained growth and profitability.

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Europe Collagen Peptide Effervescent Tablets Market Segmentation by Applications with 16.27% CAGR Data https://www.linkedin.com/pulse/europe-collagen-peptide-effervescent-tablets-market-segmentation-7za1f/

Europe Sweet Corn Seed Market Segmentation by Applications with 14.99% CAGR Data https://www.linkedin.com/pulse/europe-sweet-corn-seed-market-segmentation-applications-lan0f/

Europe Dental Local Anesthesia Drugs Market Forecast by Applications | CAGR 15.83% Value Trends https://www.linkedin.com/pulse/europe-dental-local-anesthesia-drugs-market-forecast-80ouf/

Europe Termite Control Market Size & Share by Applications – 14.31% CAGR Outlook https://www.linkedin.com/pulse/europe-termite-control-market-size-share-applications-auqxf/

Europe Compound Nystatin Market Size & Share by Applications – 11.47% CAGR Outlook https://www.linkedin.com/pulse/europe-compound-nystatin-market-size-share-dr5tf/

Europe Low Voltage Ac Motor Market Size & Share by Applications – 9.42% CAGR Outlook https://www.linkedin.com/pulse/europe-low-voltage-ac-motor-market-size-share-tjmlf/

Europe Skid Plates, Market Size & Share by Applications – 15.4% CAGR Outlook https://www.linkedin.com/pulse/europe-skid-plates-market-size-share-applications-biorf/

Europe Pacs And Emr Market Size & Share by Applications – 12.94% CAGR Outlook https://www.linkedin.com/pulse/europe-pacs-emr-market-size-share-applications-hv4vf/

Europe Chemical Surface Treatment Market Size by Application Segment With CAGR 8.62% Forecast https://www.linkedin.com/pulse/europe-chemical-surface-treatment-market-size-i1i8f/

Europe Driver Drowsiness Detection System Market Size by Application Segment With CAGR 10.19% Forecast https://www.linkedin.com/pulse/europe-driver-drowsiness-detection-system-market-undif/

Europe Meta Phenylene Diamine-4-Sulfonic Acid Market Size by Application Segment With CAGR 10.8% Forecast https://www.linkedin.com/pulse/europe-meta-phenylene-diamine-4-sulfonic-acid-tbkzf/

Europe Pen Testing Market Size by Application Segment With CAGR 9.96% Forecast https://www.linkedin.com/pulse/europe-pen-testing-market-size-application-segment-khhwf/

Europe Hybrid SSD Market Size by Application Segment With CAGR 16.71% Forecast https://www.linkedin.com/pulse/europe-hybrid-ssd-market-size-application-segment-fu3mf/

Europe Lithium-Ion Battery CNT (Carbon Nano Tube) Conductive Agent Market Size by End-Use Applications – 14.28% CAGR Report https://www.linkedin.com/pulse/europe-lithium-ion-battery-cnt-carbon-nano-tube-vxj3f/

Europe De-SOx Scrubber Systems Market Size by End-Use Applications – 7.35% CAGR Report https://www.linkedin.com/pulse/europe-de-sox-scrubber-systems-market-size-end-use-hmbzf/

Europe Thermostats For Electrical Underfloor Heating Market Size by End-Use Applications – 7.64% CAGR Report https://www.linkedin.com/pulse/europe-thermostats-electrical-underfloor-heating-mhcdf/

Europe Refurbished DNA Sequencing Platforms Market Size by End-Use Applications – 9.88% CAGR Report https://www.linkedin.com/pulse/europe-refurbished-dna-sequencing-platforms-market-rvvhf/

Europe Sodium Laurate Market Size by Application Segment With CAGR 16.24% Forecast https://www.linkedin.com/pulse/europe-sodium-laurate-market-size-application-t9ycf/

7.55% CAGR Growth for Europe Ophthalmic Knives Market by Application Type https://www.linkedin.com/pulse/755-cagr-growth-europe-ophthalmic-knives-market-rb7yf/

10.69% CAGR Growth for Europe Automotive Mid Range Radar (MRR) Market by Application Type https://www.linkedin.com/pulse/1069-cagr-growth-europe-automotive-mid-range-sqtpf/

Europe Polymerdispersed Liquid Crystals Pdlcs Market Size by Application Segment With CAGR 9.81% Forecast https://www.linkedin.com/pulse/europe-polymerdispersed-liquid-crystals-pdlcs-czycf/

Europe High-end Necktie Market Size by Application Segment With CAGR 8.96% Forecast https://www.linkedin.com/pulse/europe-high-end-necktie-market-size-application-tzjsf/

Europe Elevating Work Platform (EWP) Market Size by End-Use Applications – 10.4% CAGR Report https://www.linkedin.com/pulse/europe-elevating-work-platform-ewp-market-size-3qeif/

16.65% CAGR Growth for Europe Precipitated Barium Sulfate Market by Application Type https://www.linkedin.com/pulse/1665-cagr-growth-europe-precipitated-barium-sulfate-1e6mf/?lipi=urn%3Ali%3Apage%3Ad_flagship3_publishing_published%3B5WCkRXTTSyCamAeVLm%2Fgjg%3D%3D

9.04% CAGR Growth for Europe Carboxylic Styrene Butadiene Latex Market by Application Type https://www.linkedin.com/pulse/904-cagr-growth-europe-carboxylic-styrene-butadiene-7mijf/

11.12% CAGR Growth for Europe Football Camp Market by Application Type https://www.linkedin.com/pulse/1112-cagr-growth-europe-football-camp-market-application-54shf/

13.9% CAGR Growth for Europe Garbage Collection Trucks Market by Application Type https://www.linkedin.com/pulse/139-cagr-growth-europe-garbage-collection-trucks-fljtf/

14.9% CAGR Growth for Europe Clad Steel Plate Market by Application Type https://www.linkedin.com/pulse/149-cagr-growth-europe-clad-steel-plate-market-application-m1sqf/

Europe Smoke Exhaust Fans Market Size by End-Use Applications – 9.29% CAGR Report https://www.linkedin.com/pulse/europe-smoke-exhaust-fans-market-size-end-use-applications-liz9f/

Application-Based Europe Mlss Analyzer Market Size with CAGR 12.29% Insights https://www.linkedin.com/pulse/application-based-europe-mlss-analyzer-market-size-aw8nf/

Europe Digital Wall Murals Market Size by End-Use Applications – 13.75% CAGR Report https://www.linkedin.com/pulse/europe-digital-wall-murals-market-size-end-use-applications-km48f/

Europe Sugar Reducing Ingredients Market Size by End-Use Applications – 10.39% CAGR Report https://www.linkedin.com/pulse/europe-sugar-reducing-ingredients-market-size-end-use-pguif/

Application-Based Europe Arsenic Trioxide Injection Market Size with CAGR 10.09% Insights https://www.linkedin.com/pulse/application-based-europe-arsenic-trioxide-injection-ogbwf/?lipi=urn%3Ali%3Apage%3Ad_flagship3_publishing_published%3B5ltu8uT8Q4y4EmBfwB7k9Q%3D%3D

Application-Based Europe Container Lift Trucks Market Size with CAGR 7.32% Insights https://www.linkedin.com/pulse/application-based-europe-container-lift-trucks-market-qp2xf/

Application-Based Europe Silicon Carbide Ceramic Roller Market Size with CAGR 14.8% Insights https://www.linkedin.com/pulse/application-based-europe-silicon-carbide-ceramic-8mqrf/

Application-Based Europe Sedan & Hatchback ACC ECU Market Size with CAGR 15.53% Insights https://www.linkedin.com/pulse/application-based-europe-sedan-hatchback-acc-ecu-pdd5f/

Application-Based Europe Metallurgical Grade Chrome Oxide Green Competitive Market Size with CAGR 9.93% Insights https://www.linkedin.com/pulse/application-based-europe-metallurgical-grade-chrome-yzouf/

Application-Based Europe Automotive Retainer Market Size with CAGR 9.62% Insights https://www.linkedin.com/pulse/application-based-europe-automotive-retainer-market-z8cxf/

Application-Based Europe Terpinyl Acetate Market Size with CAGR 16.47% Insights https://www.linkedin.com/pulse/application-based-europe-terpinyl-acetate-market-0xvvf/

Application-Based Europe Sodium Metatitanate Market Size with CAGR 8.21% Insights https://www.linkedin.com/pulse/application-based-europe-sodium-metatitanate-market-3nhdf/

Application-Wise Growth of Europe Automotive ADAS Sensors Professional Market | CAGR 14.46% Analysis https://www.linkedin.com/pulse/application-wise-growth-europe-automotive-adas-sensors-9dz8f/

Application-Wise Growth of Europe Nylon 66 Resin Market | CAGR 9.61% Analysis https://www.linkedin.com/pulse/application-wise-growth-europe-nylon-66-resin-market-dok4f/?lipi=urn%3Ali%3Apage%3Ad_flagship3_publishing_published%3BtroDrQQgROmFeKuV%2BpUpWQ%3D%3D

Application-Wise Growth of Europe Meat Stabilizers Market | CAGR 15.73% Analysis https://www.linkedin.com/pulse/application-wise-growth-europe-meat-stabilizers-market-g5jgf/

Application-Wise Growth of Europe Oxygen Ventilator Market | CAGR 8.51% Analysis https://www.linkedin.com/pulse/application-wise-growth-europe-oxygen-ventilator-market-pjfrf/

Application-Wise Growth of Europe UV-Cured Powder Coatings Market | CAGR 13.19% Analysis https://www.linkedin.com/pulse/application-wise-growth-europe-uv-cured-powder-coatings-dhpxf/

Application-Wise Growth of Europe Cable Tie Mount Market | CAGR 10.51% Analysis https://www.linkedin.com/pulse/application-wise-growth-europe-cable-tie-mount-market-8eyff/

Application-Wise Growth of Europe Geomembrane Welding Machines Market | CAGR 9.6% Analysis https://www.linkedin.com/pulse/application-wise-growth-europe-geomembrane-welding-fnhcf/

Application-Wise Growth of Europe EPTFE Products Market | CAGR 7.76% Analysis https://www.linkedin.com/pulse/application-wise-growth-europe-eptfe-products-market-xbg0f/

Application-Wise Growth of Europe Premium Paper Market | CAGR 7.37% Analysis https://www.linkedin.com/pulse/application-wise-growth-europe-premium-paper-market-yywif/

Europe Electronic Skin Application Segment Growth Rate and Market Size Overview https://www.linkedin.com/pulse/europe-electronic-skin-application-segment-growth-50wnf/

Europe Biofunctional Peptide Application Segment Growth Rate and Market Size Overview https://www.linkedin.com/pulse/europe-biofunctional-peptide-application-segment-growth-ikwgf/?lipi=urn%3Ali%3Apage%3Ad_flagship3_publishing_published%3BtuEZjjdzRYSvLqGcTj74Gw%3D%3D

Europe Glass Wool Panels Application Segment Growth Rate and Market Size Overview https://www.linkedin.com/pulse/europe-glass-wool-panels-application-segment-growth-rate-jf1tf/

Europe Semiconductor Fabrication And Packaging Materials Application Segment Growth Rate and Market Size Overview https://www.linkedin.com/pulse/europe-semiconductor-fabrication-packaging-materials-gvd9f/

Europe Polyether Based Polyurethane Foams Application Segment Growth Rate and Market Size Overview https://www.linkedin.com/pulse/europe-polyether-based-polyurethane-foams-application-kopif/

Europe Cranes Rental Application Segment Growth Rate and Market Size Overview https://www.linkedin.com/pulse/europe-cranes-rental-application-segment-growth-rate-imtff/

Europe Performance Enhancing Drugs Application Segment Growth Rate and Market Size Overview https://www.linkedin.com/pulse/europe-performance-enhancing-drugs-application-segment-olxqf/

Europe Systemic Aspergillosis And Systemic Candidasis Application Segment Growth Rate and Market Size Overview https://www.linkedin.com/pulse/europe-systemic-aspergillosis-candidasis-application-p3ddf/

Europe Integrated Facility Management (IFM) Application Segment Growth Rate and Market Size Overview https://www.linkedin.com/pulse/europe-integrated-facility-management-ifm-application-ap00f/

Application-Wise Growth of Europe UV Fluorescent Inks Market | CAGR 7.37% Analysis https://www.linkedin.com/pulse/application-wise-growth-europe-uv-fluorescent-inks-market-ir9ff/

Europe Waterborne Polyurethane Adhesives Market by Key Applications with 14.51% CAGR and Forecast Value https://www.linkedin.com/pulse/europe-waterborne-polyurethane-adhesives-market-key-qd8xf/?lipi=urn%3Ali%3Apage%3Ad_flagship3_publishing_published%3BoDn1jaFrQ1mgbPcIWS8s%2FA%3D%3D

Europe Ethylene Vinyl Acetate(EVA) Film Market by Key Applications with 9.84% CAGR and Forecast Value https://www.linkedin.com/pulse/europe-ethylene-vinyl-acetateeva-film-market-key-applications-rtwof/

Europe Communication And Energy Wire And Cable Market by Key Applications with 10.57% CAGR and Forecast Value https://www.linkedin.com/pulse/europe-communication-energy-wire-cable-market-key-applications-vra2f/

Europe Steering Wheels, And United States Market by Key Applications with 14.02% CAGR and Forecast Value https://www.linkedin.com/pulse/europe-steering-wheels-united-states-market-key-applications-t6m0f/

Europe Synthetic Diamond Micro Powder Market by Key Applications with 11.15% CAGR and Forecast Value https://www.linkedin.com/pulse/europe-synthetic-diamond-micro-powder-market-key-applications-0zlff/

Europe Ultra Fine Grinder Market by Key Applications with 13.56% CAGR and Forecast Value https://www.linkedin.com/pulse/europe-ultra-fine-grinder-market-key-applications-1356-9sjvf/

Europe Genetic Virus Vector Market by Key Applications with 14.63% CAGR and Forecast Value https://www.linkedin.com/pulse/europe-genetic-virus-vector-market-key-applications-czakf/

Europe Automotive Radiator Cap Market by Key Applications with 15.51% CAGR and Forecast Value https://www.linkedin.com/pulse/europe-automotive-radiator-cap-market-key-applications-3zalf/

Europe Mpp Magnetics Powder Core Application Segment Growth Rate and Market Size Overview https://www.linkedin.com/pulse/europe-mpp-magnetics-powder-core-application-segment-ufxdf/

Europe Medical Waste Services Market Forecast by Applications | CAGR 14.67% Value Trends https://www.linkedin.com/pulse/europe-medical-waste-services-market-forecast-applications-kherf/?lipi=urn%3Ali%3Apage%3Ad_flagship3_publishing_published%3BQ8XU214LQ1Kpll0sK73FpA%3D%3D

Europe High Purity Tantalum Sputtering Targets Market Forecast by Applications | CAGR 16.95% Value Trends https://www.linkedin.com/pulse/europe-high-purity-tantalum-sputtering-targets-rratf/

Europe Automatic Crystal Growing Furnace Market Forecast by Applications | CAGR 10.12% Value Trends https://www.linkedin.com/pulse/europe-automatic-crystal-growing-furnace-market-pehrf/

Europe Product LifeCycle Management And Engineering Software Market Forecast by Applications | CAGR 8.68% Value Trends https://www.linkedin.com/pulse/europe-product-lifecycle-management-engineering-btrgf/

Europe Rfid Lock Market Forecast by Applications | CAGR 10.44% Value Trends https://www.linkedin.com/pulse/europe-rfid-lock-market-forecast-applications-cagr-djs2f/

Europe Automotive Smart Driving Information Display Market Forecast by Applications | CAGR 14.75% Value Trends https://www.linkedin.com/pulse/europe-automotive-smart-driving-information-display-2yjff/

Europe Organic Elemental Analyzer Oea Market Forecast by Applications | CAGR 14.77% Value Trends https://www.linkedin.com/pulse/europe-organic-elemental-analyzer-oea-market-forecast-ejhif/

Europe Nasal Rinse Salt Market Forecast by Applications | CAGR 10.46% Value Trends https://www.linkedin.com/pulse/europe-nasal-rinse-salt-market-forecast-applications-h0n0f/

Europe Egg Substitutes Market Segmentation by Applications with 16.6% CAGR Data https://www.linkedin.com/pulse/europe-egg-substitutes-market-segmentation-applications-e7mkf/

Europe Internal Combustion Engines Market Segmentation by Applications with 14.65% CAGR Data https://www.linkedin.com/pulse/europe-internal-combustion-engines-market-segmentation-tkrif/?lipi=urn%3Ali%3Apage%3Ad_flagship3_publishing_published%3BCUix3RS3SVyg9hHt1ftTTA%3D%3D

Europe Snowboard Travel Bags Market Segmentation by Applications with 7.5% CAGR Data https://www.linkedin.com/pulse/europe-snowboard-travel-bags-market-segmentation-dep1f/

Europe Civil Air Traffic Control (ATC) Systems Market Segmentation by Applications with 15.22% CAGR Data https://www.linkedin.com/pulse/europe-civil-air-traffic-control-atc-systems-market-tzwaf/

Europe Packaged Tacos Market Segmentation by Applications with 8.61% CAGR Data https://www.linkedin.com/pulse/europe-packaged-tacos-market-segmentation-applications-ntexf/

Europe Geopolymers Market Segmentation by Applications with 15.72% CAGR Data https://www.linkedin.com/pulse/europe-geopolymers-market-segmentation-applications-nlkof/

Europe Coloring Foodstuff Market Segmentation by Applications with 12.53% CAGR Data https://www.linkedin.com/pulse/europe-coloring-foodstuff-market-segmentation-applications-8yjgf/

Europe Chemotherapy Induced Acral Erythema Treatment Market Segmentation by Applications with 13.84% CAGR Data https://www.linkedin.com/pulse/europe-chemotherapy-induced-acral-erythema-treatment-9ndrf/

Europe Bench-top UV Transilluminator Market Segmentation by Applications with 10.21% CAGR Data https://www.linkedin.com/pulse/europe-bench-top-uv-transilluminator-market-segmentation-74dcf/

Europe Pegaspargase Market Forecast by Applications | CAGR 9.63% Value Trends https://www.linkedin.com/pulse/europe-pegaspargase-market-forecast-applications-svytf/

Europe Electrical Sheet Molding Compound (SMC) Market Size & Share by Applications – 11.52% CAGR Outlook https://www.linkedin.com/pulse/europe-electrical-sheet-molding-compound-smc-market-yfk5f/?lipi=urn%3Ali%3Apage%3Ad_flagship3_publishing_published%3BnpPTLs0VQZG1ab967Rp3DQ%3D%3D

Europe Silicon Dioxide Powder Market Size & Share by Applications – 13.28% CAGR Outlook https://www.linkedin.com/pulse/europe-silicon-dioxide-powder-market-size-share-applications-qnxlf/

Europe Clouding Agents Market Size & Share by Applications – 14.79% CAGR Outlook https://www.linkedin.com/pulse/europe-clouding-agents-market-size-share-applications-e0dxf/

Europe Multi-Cable Transit System Market Size & Share by Applications – 12.32% CAGR Outlook https://www.linkedin.com/pulse/europe-multi-cable-transit-system-market-size-share-3ztff/

Europe Single Beds Market Size & Share by Applications – 8.44% CAGR Outlook https://www.linkedin.com/pulse/europe-single-beds-market-size-share-applications-xdq2f/

Europe Natural And Synthetic Vanillin Market Size & Share by Applications – 11.22% CAGR Outlook https://www.linkedin.com/pulse/europe-natural-synthetic-vanillin-market-size-share-2ikdf/

Europe Healthcare Technology Management Market Size & Share by Applications – 7.34% CAGR Outlook https://www.linkedin.com/pulse/europe-healthcare-technology-management-market-size-wtqmf/

Europe High Performance Optocoupler Market Size & Share by Applications – 8.74% CAGR Outlook https://www.linkedin.com/pulse/europe-high-performance-optocoupler-market-size-share-syvzf/

Europe Processed Chicken Feet Market Segmentation by Applications with 14.44% CAGR Data https://www.linkedin.com/pulse/europe-processed-chicken-feet-market-segmentation-bwcqf/

Europe Tetraethoxysilane (TEOS) Market Size by Application Segment With CAGR 9.41% Forecast https://www.linkedin.com/pulse/europe-tetraethoxysilane-teos-market-size-application-jswjf/?lipi=urn%3Ali%3Apage%3Ad_flagship3_publishing_published%3Bextzq1mZQ8yDXhQ5C84zgw%3D%3D

Europe Conducting Polymers Market Size by Application Segment With CAGR 11.87% Forecast https://www.linkedin.com/pulse/europe-conducting-polymers-market-size-application-segment-okxrf/

Europe Medical Metal Tubing Market Size by Application Segment With CAGR 7.7% Forecast https://www.linkedin.com/pulse/europe-medical-metal-tubing-market-size-application-uecwf/

Europe Cell Cultured Meat Market Size by Application Segment With CAGR 12.54% Forecast https://www.linkedin.com/pulse/europe-cell-cultured-meat-market-size-application-segment-luiaf/

Europe Laser Line Powell Lenses Market Size by Application Segment With CAGR 16.83% Forecast https://www.linkedin.com/pulse/europe-laser-line-powell-lenses-market-size-application-yzlcf/

Europe Covid-19 Impact On Residential Ornamental Fish Market Size by Application Segment With CAGR 14.12% Forecast https://www.linkedin.com/pulse/europe-covid-19-impact-residential-ornamental-fish-market-hlwpf/

Europe Electroless Nickel Immersion Gold (ENIG) Market Size by Application Segment With CAGR 8.03% Forecast https://www.linkedin.com/pulse/europe-electroless-nickel-immersion-gold-enig-market-fptzf/

Europe Liquid Crystal Epoxy Resin Market Size by Application Segment With CAGR 7.47% Forecast https://www.linkedin.com/pulse/europe-liquid-crystal-epoxy-resin-market-size-application-llx7f/

Europe Purity Metal Target Market Size & Share by Applications – 11.22% CAGR Outlook https://www.linkedin.com/pulse/europe-purity-metal-target-market-size-share-applications-slyjf/

Europe Indium Phosphide Wafer Market Size by End-Use Applications – 11.98% CAGR Report https://www.linkedin.com/pulse/europe-indium-phosphide-wafer-market-size-end-use-gts3f/?lipi=urn%3Ali%3Apage%3Ad_flagship3_publishing_published%3BrbycUCsMRiKpI9zglqWRkA%3D%3D

Europe Window Handles Market Size by End-Use Applications – 9.49% CAGR Report https://www.linkedin.com/pulse/europe-window-handles-market-size-end-use-applications-luwaf/

Europe PCR Packaging Market Size by End-Use Applications – 15.07% CAGR Report https://www.linkedin.com/pulse/europe-pcr-packaging-market-size-end-use-applications-gvk3f/

Europe Enterprise Network Infrastructure Market Size by End-Use Applications – 11.68% CAGR Report https://www.linkedin.com/pulse/europe-enterprise-network-infrastructure-market-size-cbzpf/

Europe Solenoid Valves For Semiconductors Market Size by End-Use Applications – 15.05% CAGR Report https://www.linkedin.com/pulse/europe-solenoid-valves-semiconductors-market-size-8mxcf/

Europe Electrician Pliers Market Size by End-Use Applications – 10.66% CAGR Report https://www.linkedin.com/pulse/europe-electrician-pliers-market-size-end-use-applications-klx3f/

Europe Welding Gas Market Size by End-Use Applications – 10.6% CAGR Report https://www.linkedin.com/pulse/europe-welding-gas-market-size-end-use-applications-toihf/

Europe Egg Free Mayonnaise Market Size by End-Use Applications – 9.94% CAGR Report https://www.linkedin.com/pulse/europe-egg-free-mayonnaise-market-size-end-use-applications-mkorf/

Europe Odorizing Systems Market Size by Application Segment With CAGR 12.54% Forecast https://www.linkedin.com/pulse/europe-odorizing-systems-market-size-application-segment-hblif/

7.6% CAGR Growth for Europe Mag Locks Market by Application Type https://www.linkedin.com/pulse/76-cagr-growth-europe-mag-locks-market-application-0e73f/?lipi=urn%3Ali%3Apage%3Ad_flagship3_publishing_published%3BcdsASH1kS8%2BzGlyX%2FetxGQ%3D%3D

7.38% CAGR Growth for Europe Palm Kernel Oil And Coconut Oil Based Natural Fatty Acids Market by Application Type https://www.linkedin.com/pulse/738-cagr-growth-europe-palm-kernel-oil-coconut-based-m6tvf/

7.96% CAGR Growth for Europe Robot Polishing Automatic Machine Market by Application Type https://www.linkedin.com/pulse/796-cagr-growth-europe-robot-polishing-automatic-machine-hlpzf/

14.05% CAGR Growth for Europe Dissolvable Metal Frac Plugs Market by Application Type https://www.linkedin.com/pulse/1405-cagr-growth-europe-dissolvable-metal-frac-plugs-xs85f/

14.61% CAGR Growth for Europe Plant-based Protein Bars Market by Application Type https://www.linkedin.com/pulse/1461-cagr-growth-europe-plant-based-protein-bars-market-etn1f/

8.08% CAGR Growth for Europe Marine Sonar Systems Market by Application Type https://www.linkedin.com/pulse/808-cagr-growth-europe-marine-sonar-systems-market-ebymf/

9.99% CAGR Growth for Europe Gallium Nitride (GaN) On Silicon (Si) Market by Application Type https://www.linkedin.com/pulse/999-cagr-growth-europe-gallium-nitride-gan-silicon-wyv5f/

16.31% CAGR Growth for Europe Linear Limit Switches Market by Application Type https://www.linkedin.com/pulse/1631-cagr-growth-europe-linear-limit-switches-market-868df/

Europe Screwing Machines Market Size by End-Use Applications – 15.49% CAGR Report https://www.linkedin.com/pulse/europe-screwing-machines-market-size-end-use-applications-8x2jf/

Application-Based Europe Thailand Pour Point Depressant Market Size with CAGR 14.6% Insights https://www.linkedin.com/pulse/application-based-europe-thailand-pour-point-hcwcf/?lipi=urn%3Ali%3Apage%3Ad_flagship3_publishing_published%3B90XT0Xy2T9a6E5UyGrcEeA%3D%3D

Application-Based Europe Bicycle Apparels Market Size with CAGR 11.08% Insights https://www.linkedin.com/pulse/application-based-europe-bicycle-apparels-market-pdihf/

Application-Based Europe Bagasse Food Container Market Size with CAGR 12.12% Insights https://www.linkedin.com/pulse/application-based-europe-bagasse-food-container-ylngf/

Application-Based Europe Crystal Tableware Market Size with CAGR 16.46% Insights https://www.linkedin.com/pulse/application-based-europe-crystal-tableware-mplhf/

Application-Based Europe Sublimation Printing Equipment Market Size with CAGR 14.16% Insights https://www.linkedin.com/pulse/application-based-europe-sublimation-printing-anpnf/

Application-Based Europe Skin (Aeronautics) Market Size with CAGR 16.41% Insights https://www.linkedin.com/pulse/application-based-europe-skin-aeronautics-market-zpvjf/

Application-Based Europe Grinding And Polishing Abrasive Market Size with CAGR 11.63% Insights https://www.linkedin.com/pulse/application-based-europe-grinding-polishing-yt03f/

8.08% CAGR Growth for Europe Smart Earbuds Case Charger Market by Application Type https://www.linkedin.com/pulse/808-cagr-growth-europe-smart-earbuds-case-charger-tlj8f/

Application-Wise Growth of Europe Semaglutide API Market | CAGR 13.83% Analysis https://www.linkedin.com/pulse/application-wise-growth-europe-semaglutide-07qqf/

Application-Wise Growth of Europe Connected Car Market | CAGR 13.49% Analysis https://www.linkedin.com/pulse/application-wise-growth-europe-connected-car-market-hblwf/?lipi=urn%3Ali%3Apage%3Ad_flagship3_publishing_published%3B94tCLRDvQFqQU981ob1ZCA%3D%3D

Application-Wise Growth of Europe TMJ Replacement System Market | CAGR 13.65% Analysis https://www.linkedin.com/pulse/application-wise-growth-europe-tmj-replacement-system-raqzf/

Application-Wise Growth of Europe Polyvinylidene Chloride (PVDC) Market | CAGR 11.97% Analysis https://www.linkedin.com/pulse/application-wise-growth-europe-polyvinylidene-chloride-ejojf/

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Transcript

Kassan: Internally at Databricks, I call myself the governess of data. The topics I end up covering are things like responsibility and capability maturity, and stuff like that. It sounds like I’m everyone’s nanny, hopefully more of the like Mary Poppins style than the ones that get taken away on umbrellas. I’ll talk a little bit about responsible AI, some of the things that we’re seeing now. How organizations are approaching that. A little bit of a regulation update. Obviously, there’s quite a lot going on in the space, and in particular, for FinTech, there are some specific things in there as well. Then, the response that we’re seeing in the industry and how that’s being approached.

Responsible AI

I was going to start with responsible AI. I like to lay the groundwork of what we’re talking about. One of the things to think about this, just to level set, 80% of companies are planning to increase their investment in responsible AI. Over the last few years, especially, we’ve seen a tremendous increase, of course, in the desire to use AI, and a parallel increase in the need to think about what that means from a risk perspective. How do you get the value of AI and the capabilities that it will drive for your organization and unlock that value, without creating a massive reputational risk for your company, or potentially business, and financial risks? When we think about responsible AI, we think of in multiple levels. A lot of people talk about the ethics. That’s where I started, actually. I was in data science ethics. I had a podcast for it for several years.

As a data scientist who came into the industry when things weren’t quite such a buzz, it was so important to see where could it go. There’s multiple levels. At the very bottom is where we see regulatory compliance. We have to follow the guidelines that are set in place to be compliant with regulation. Otherwise, we’re going to be out of business. That’s how it works. In the ethics space, this is like, what should we do? If we could make AI act the way we would act and have the same values that we have, what should we do? Getting there, as we may have seen in the last year and a half, is very challenging. Really, where we end up is, what can we do? What are the things that we can put in place to be responsible with AI and put in place guardrails that we actually can enforce, knowing that the aspiration is to get to ethics, but the realistic intention and goal is to be responsible. That’s where we’re at. That 80% of companies are looking to say, “Yes, I have to be compliant. That I’ve already invested in. What else can I do?” This is that next step.

I break this down to four levels. If you think about coming from the top down. At the top, you have the program, that’s really the, if we could do anything, if we could be the most ethical people that we intend to be, what does that look like for our company? It’s setting the stage on the ethical principles and the vision of what our organization wants to achieve and intends to be. This is where, at those top level, you typically see somewhere between four and six ethical principles in a given company. Some group of people, probably in the C-suite, maybe the board, are setting out some high-level principles, like, we want to be fair and unbiased. We want to be transparent. We want to be human centric. These kinds of very big, vague, floaty ideas.

Then, at some point, you have to translate that down. You say, that’s great and all. What does that mean to those of us who are actually in the organization? What is it we have to do to be whatever vague concept you’ve just thrown out there? That’s where you see policies. This is that translation down one level to say, what are the frameworks, what are the rules of the road? Are there specific things that that means we can or can’t do as an organization? Do we have to say, every time we’re going to create an AI, we need to evaluate it against this policy and say, is this allowed? Is this ok for us in our organization? Every organization is going to handle this a little differently.

Beneath that, the next level of translation is then, how do we create processes to enforce the policy? What does it look like to start enacting that on a daily basis? Do we have an AI review board that looks at this and says, does a given application of AI conform with the policy, conform with therefore the principles? Do we have auditing over time? How does this change other processes in our organization, like procurement, or like cybersecurity, for example? This is where you have all the different processes that the organization is going to operate under.

Then, at the bottom, you’ve got the practice, the actual hands to keyboard building these things. What is the way that we’re going to actually implement responsible AI? What are the tools and the templates and the techniques that we’re going to use to evaluate AI as we start to build it into our organization? What you find is that this middle section is what ends up being called AI governance. It’s governing the practice. It’s governing to make sure that these tools, templates, and techniques, abide by the processes and follow that process that is then aligned to the policy that then hopefully gets you closer to your principles. This is the stack of responsible AI.

In terms of principles, I’ve summarized from a lot of different frameworks these that end up being roughly what you find. It’s usually some subset of these. Basically, this is the picklist that ends up happening at the highest levels of, which of these do we think is most important? We’ve seen a lot more emphasis, for example, on security lately. Because there’s a lot of concern about, how do we make sure that IP is not infringed, that our information is not getting put out there, that our customers’ information is not going to be exposed? This safety and security ends up being a big part of that. Of course, we’ve also seen that in privacy as well. Now, with additional regulation, compliance is taking on more meaning, has more aspects to it. Other things that we find, for example, is efficiency.

Typically, building AI takes a lot of power, takes a lot of processing, takes a lot of money. Not the thing that most organizations, especially FinTechs, want to spend to do this. If they can get the capability less expensive, they want to. Efficiency is something that’s been talked about more because the energy costs of creating and maintaining and doing inference with AI, especially large language models, is becoming a bigger concern. How do we power these things? If efficiency becomes one of your principles, then you look at how you minimize that to still get the same outputs while not impacting the environment and also not impacting your budget.

Regulation Update

Quick update on regulation. Again, 77% of companies see regulatory compliance and regulation as a priority for AI. Of course, with the EU AI Act, that’s increasing. Anyone who does business with, or near, or on EU citizens, this is going to be a concern with the EU AI Act coming in. We’ve also seen new regulation elsewhere, and I’ll do a little bit of a mapping on that. Of course, in that ladder, the first step is, can we be compliant? With regulation changing, what it means to be compliant is changing. Of course, companies are saying, yes, we should probably still also be compliant with whatever happens. This is probably not up to date. It changes literally daily. We’re seeing regulation all over the place. The patterns that it takes is different.

In the States, what we’ve seen up to this point is more an indication that existing laws need to be abided, even if you’re using AI to do the thing. In Europe, they’re taking a different approach and saying, we’re going to have this risk based hierarchical view of how AI can and should be applied. In China, they’re saying, you can do AI as long as it is in accord with the party aspirations and how they think about the way they want their society to run. In fairness, that’s how responsible AI and regulation works. Everybody has their view of what does it mean to be responsible? What values should we be enforcing? Not universal, which makes it, again, very difficult if you’re operating in a multinational environment. We’re also starting to see additional regulations coming in in Japan, in India, in Brazil, lots of different approaches. In Australia, of course. Tons of different ways that this actually ends up implemented, and the things that you need to comply with will be different by jurisdiction.

I’ll go through a couple. Please understand that for a lot of them, preexisting laws do still apply. This is what I was talking about. In the states, if you look at the way they’re handling it, they basically say, if something was illegal before when a human was doing it, it’s still illegal if an AI does it. You’re not allowed to discriminate based on protected classes, if you’re a human. You’re not allowed to discriminate algorithmically if you’re using AI. That’s how that goes. Data and consumer protection still apply. You don’t get to just blast people’s information out there, because the AI accidentally did it, which we’ve seen. Intellectual property still applies. All the copyright disputes and IP infringements that have been spinning up in litigation still applies on AI. Anti-discrimination. Anything criminal.

For example, there’s a lot of worry about, could AI tell someone how to do something terrible that would constitute criminal behavior? There’s a lot of concern around, how do you guard against your AI saying something it shouldn’t, that it probably knows about, but you don’t want anyone to know it knows about, just based on how it was trained. There’s a lot of filtering that goes on for that. Then antitrust and unfair competition. Basically, with antitrust, it’s saying, you can’t use AI to create a non-competitive environment, despite the fact that the AI itself might be doing that.

EU AI Act: four categories of risk. You’ve probably heard about this. How many people have looked up the EU AI Act? It’s new to some people. Four categorizations. In the top left over there, unacceptable risk, is stuff that like it’s just disallowed, flat out disallowed. It’s things like behavioral profiling and all kinds of stuff with biometrics and stuff like that. The idea is that if you’re materially distorting behavior, or you’re doing things that are really privacy invading, those are prohibited. Just can’t do. Unless you’re the government or the military or a bunch of other things, just asterisks all over this slide. Imagine asterisks everywhere, like it’s snowing. Snowing caveats. In that category, hopefully in your organization, you’re not dealing with any of those things. If you are, probably think about ways to take them out now, because this is going to happen. This is going to come into effect in a year or so, year and a half. High risk.

In high risk, what it’s basically saying is, if there’s the potential for this to influence someone’s means of earning a living, means of living, health, safety, access to critical infrastructure, those kinds of things, you have to go through a very rigorous documentation process called the conformity assessment. This is where a fair bit of FinTech organizations are going to end up having something to do, and probably in the limited risk, which I’ll get to. The conformity assessment is a lot.

That said, finance has been doing impact assessments and compliance documentation for decades. I’ve been there. It sucks, but you do it. You put out reams of documentation about how you got to the conclusion you got to. Why this model was the best one of all the things you tested. Why these features were selected. What their relative importance is. How you’ve tested for disparate impact, all that good stuff. It’s that plus a bunch of other things that go into the conformity assessment. The documentation that you’ve done so far, drop in the bucket. My hope, personally, is that we can then train an LLM to help generate the documentation, and then we can figure out which bucket that falls into. I’m thinking minimal. That’s the high-risk group. There are some caveats on that as well.

Limited risk, basically, if it’s not one of the other two, but it interacts with a person, you have to tell the person, “You’re interacting with an AI”. The fun one I think about here is, a few years ago, Google did a demonstration of what was a Duo, or something like that, where they had an AI application that called and made a booking at a restaurant, or something like that. If that were the case under the EU AI Act, the phone call would sound like, “Hi, this is Google Duo calling. This is an AI. I would like to book a reservation for this particular person on this date and time”. Because you have to tell them. This is true then for all the chatbots that are being created and all these use cases that are coming in now where there’s an interaction with a person. Similarly, it would work for internal. Even if you’re not necessarily displacing customer service, for example, if you’re augmenting your customer service staff, you have to tell your customer service staff, this is an AI chatbot that’s giving you a script.

This is not a preprogrammed defined rules engine, this is an AI. Just so they’re aware. Then everything else is minimal risk. For example, things like fraud detection fall into minimal risk, according to the way that the current structure has been laid out. These are very vague. There are examples in the act, if you want to read through the 200 and some odd pages of it. I don’t recommend it unless you have insomnia, in which case, have at. The majority of what this will do, I think, again, not a lawyer, my own interpretation is a lot of how this is going to come together, will happen in litigation. As new applications come online once this is enforced, we will probably see a lot of the rules get a little bit more clear as to what they consider high risk, unacceptable risk, lower risk, and so forth. Because right now, there’s some vague understandings of what we think people are going to try and do with AI, but it’s not specific yet.

In the act, there are also specific rules around general-purpose AI and foundation models. If you’re training a general-purpose or a foundation model on your own, first of all, I would like your budget. Secondly, there are specific rules around how you have to be transparent, and how many flops you can have, and all kinds of crazy stuff. That’s for the few that want to actually build a model, which at Databricks we did. We had to actually look at that stuff.

The other one I wanted to talk about, specific to FinTechs, is consumer duty. This came in, I think, last year. It has some interesting implications for responsible AI. First of all, it says, design for good customer outcomes. How do you define a good customer outcome? How do you know that that customer outcome avoids foreseeable harm? This is going back to that conformity assessment, disparate impact assessment, all these things that you have to prove are doing the right thing for your customers. The next part is demonstrating your supply chain.

With AI, the supply chain gets a little nebulous, so you have to think about, what is your procurement process? How do you track what data is coming in, being used in your AI, how it’s being labeled, how it’s being featurized, how it’s being vectorized or chunked, or whatever you’re using, to be able to actually put it into an AI application. For FinTech, there’s this extra bit. Again, I think it’s probably mostly there for most financial services companies, including FinTech, because, again, we’ve been subject to this stuff for a long time. Things to think about with our new technologies is, how is this actually going to play out over time?

FinTech Response

It does bring us into the response, though, from FinTech. More stats, because I’m a numbers nerd. According to a survey of FinTechs, they expect a 10% to 30% revenue boost in the next 3 years, most leaders in FinTech, based on the use of generative AI specifically. This is not uncommon. I’ve heard this 30% thing bandied about. I think McKinsey had another one that was like, 30% efficiency from using generative AI. You’re going to save 30% of whatever costs, and all this stuff. Maybe. The reputation of FinTech is two things. There’s the disruption angle. From a technology perspective or digital native perspective, it’s also open source. When you think about how you’re going to achieve this 10% to 30% in a way that others aren’t, so you want to be disruptive. You don’t want to be the next J.P. Morgan, who’s saying, yes, we can incrementally improve our efficiency by 10%. FinTech is saying, no, we want to do something massively different, completely different from what the big guys are doing, disrupt it.

Often, especially in early stage, you want to go for something that you can build and control. It’s actually an advantage now, because when you talk about knowing and ensuring your supply chain for AI, being able to have transparency, driving towards responsibility. The more you use open source and can see the code and can see the data and can see the weights and can show all of that, the better you are able to take that, use it to your advantage, prove the supply chain. Go through the conformity assessments, and disrupt, so that you’re not the one sitting there going, yes, I will incrementally improve my efficiency, and I might get a 5% decrease in some sort of cost. We’re seeing, in FinTech, a lot of interest in the open-source models, a lot of interest in being able to build and fine-tune, especially for LLMs. Of course, that’s always been there for machine learning, and data science, and so forth. I’ve not yet met a FinTech using SaaS, which I’m very grateful for, but just taking advantage of what’s available.

That said, as a disruptor, there’s nothing holding you back from saying, we think there’s a 20% reduction in workforce that we could actually achieve. What’s interesting here is that it’s the unspoken bit in other industries, but in FinTech, it’s actually moving in that direction. There’s a lot of noise about it. Going from processes that had been manual through to augmenting people, and then eventually automating those capabilities. A great example of that was Klarna. A couple months ago, Klarna’s CEO, it was actually on their own website, on their blog, said, we have a chatbot that’s been handling two-thirds of our customer service requests over the last couple of months. It’s gotten better quality. There have been fewer issues where people had to come back and talk to somebody again.

We’re looking at that, and we’re seeing that it could replace 700 workers. They were public about this. Why? Because FinTech, most digital natives, are known for disrupting. They rely on technology. It’s that techno solutionism of being able to say, yes, we want to do this. Thinking back to those principles, does that make you human centric or not? These are the decisions that end up having ramifications for what you do with AI. If you’re saying we’re customer centric, we want to make sure that the humans that we’re serving are our customers, but that means potentially not serving our employees in the same way, not ensuring their continued work in this company.

Although, frankly, what he said was those were all outsourced people, so they don’t count as our employees, which, ethically vague. There’s a reason I always wear gray when I give talks about responsible AI, everything’s gray. This is something that’s very much happening now. I can tell you that they’re not the only company saying we think there’s a workforce reduction. I spoke with another very large organization not long ago that said we have 2000 analysts, but we think if we put in place the right tooling, we put in place the right AI, we could drop that to about 200. They’re not saying it publicly. They’re not telling their analysts, your job is on the line, but it’s still there. It’s happening now.

How do we move through this pattern together? From a responsibility perspective, first thing, establish your principles. That includes, how much transparency are you going to give, to whom, in what? These are the policies internally you need to set up. For organizations that have risk management, which FinTech should, extend your risk management framework to include AI. That’s happening now. They’re evaluating, what are the risks that we’re taking on when we put AI into place. Identify what I call no-fly zones. There are some organizations that are saying, we’re probably not in the high-risk camp most of the time, and we don’t really want to go through this conformity assessment stuff. If we could just never use AI in anything to do with HR, that’d be great. Because the moment it touches something like employment status, or pay, or performance, conformity assessment is required. Sometimes it’s just not worth it. Cross-functionally implementing responsible AI. There’s a lot in that one bullet.

This is something that, again, at the start, we’re seeing a lot more security getting involved. We’re seeing CISOs, legal teams, compliance, AI, governance, all coming together to figure out, what do we do? How do we safeguard the organization? How do we look at risk management differently? Do we bring in the risk officers in conjunction with all these other groups? Build your AI review board, including all these cross-functional folks, so that you can establish a holistic approach.

Then, set up practical processes. Saying, we’re just going to do a conformity assessment for every single AI that we ever have, just in case, probably not practical. May or may not need to do it, again, unless the magic LLM that happens someday is able to do all that documentation for you, which, here’s hoping. Try to think about what are all the teams that actually need to come together to help you solve for responsible AI. Especially in FinTech, a lot of this already exists. You’ve got risk management frameworks. You’ve got a model risk management capability. You’ve probably done some amount of documentation of this stuff before. You’ve got a lot of the components, all the people in different teams that could be part of this.

Questions and Answers

Ellis: You said 80% of companies are investing in ethical or responsible AI, does that mean 20% are investing in irresponsible AI?

Kassan: Twenty-percent probably have the hubris to think that they’ve already invested enough.

Ellis: When you talked about limited risk informing, what are you seeing with AIs dealing with AI? Because you talk about AIs dealing with humans a lot. Will you see a stage where AIs have to inform each AI that they’re dealing with an AI? How does that all work? Or what are you seeing with regulations around AIs dealing with AIs?

Kassan: I haven’t seen a tremendous amount of regulation around that area. What I have seen is more that AI will govern AI. It’s that adversarial approach of having an AI that says, explain to me why you said this, as a second buffer. Because a human can’t be there all the time to indicate, yes, this is a good response, or, no, it’s a bad response, in a generative nature. When you’re doing things like governing and checking and evaluating the responses after somebody’s prompted and saying, is this a response that we would want? It’s actually more scalable and effective to have another AI in place as the governor of that. That’s something I am seeing that’s come up quite a bit. It’s things like algorithmic red teaming. Yes, you could have someone try and sit there and type, but humans are constrained as to how quickly we can type and think up the next use case and the next thing we want to try and trick it to do. AIs can do that a lot faster. If we say to one AI, trick that one into saying this, it will find ways, and it’ll do it quick.

Participant 1: We started thinking about enforcing the policy of adding more use cases using generative AI. We came to a conclusion that we have to create a committee to approve every use case. What’s your takeaway about creating that type of committee?

Kassan: That’s the AI review board idea. A couple of things that that body would do. One is set up the policies, so making sure that you have that framework at the start. Also, looking at the processes. At what point do different business areas need to come to the board and say, we have this idea for a new AI application. Maybe they’ve already chatted with somebody who’s from the AI team, data science, machine learning, whatever it might be, to say, we think we want to solve it this way, so that the AI review board can then say, does this realistically conform with the policies, and processes, and so forth. It becomes that touchpoint. There are two issues with that, though. One is, if you don’t have a solid set of information on how you’re going to manage and mitigate risks, that can cause a lot of looping.

The other is, if they don’t meet often, that can cause a bit of a time suck. The two of those compound. You want to make sure that this is something where there’s enough of a framework there, and this comes back to the risk management framework. Understanding, categorizing, and quantifying the risks that you’re taking and understanding what can be done to mitigate them. Having enough information when you first go to that AI review board to say, here’s the stuff that you’re going to need as inputs to risk management. Here are the mitigations we’re planning. Here’s what we’re looking at, so that they can say, yes, go ahead. Then also just making sure they meet pretty frequently.

Participant 2: Do you think we will see a lot of chatbots giving financial advices in the future. What are the regulations in that area?

Kassan: There’s a lot of discussion around AI giving financial advice. There are regulations already in place about robo-advising. It’s been out there. Robo-advising has been out for some time, even without generative AI. I think the regulation will still apply. That said, there’s enough information for us to see that, yes, it’s something that, if the regulation allows in the jurisdiction, companies will absolutely be approaching it. I think the question there becomes one of, how many permutations of advice do you really want to offer? How customized does that become? What other information do you give access to that chatbot? For robo-advising, really, any of that, you’d want to have sufficient, up-to-date information so that it’s not giving advice on, for example, stock performances from six months ago. It’s looking at what’s happening now.

You want to make sure it’s constantly getting information. How many different places and how much information you’re going to feed it on an ongoing basis, because there’s a cost to that? It’s thinking about, what is that going to get you? How much do you want to do? Or, do you set it as almost like a rules-based engine, the way that a lot of robo-advisors do anyways, which is, instead of saying, these specific things would constitute a good portfolio for you. You say, you’re in this risk category, risk appetite category, so we recommend this fund, or whatever, and it’s a picklist. I think it’ll depend on the regulation. Like I said earlier, there are different approaches in every jurisdiction. It’s certainly a use case that comes up quite a bit.

Participant 3: There seems to be two schools of thought around bias, you either eliminate from the dataset beforehand, or you work through and let it be eliminated afterwards. What’s your thought on that?

Kassan: Neither is possible. Bias is something that we try to mitigate, but it’s very challenging, because the biases that you’re trying to take out mean that you’re putting in other biases. Good luck. It’s a vicious cycle. Bias is something that everyone has, various cognitive biases, contextual biases, and so forth. The more we try and do something about it, the more it’s just changing what bias is represented. It’s not to say don’t do it or don’t try to mitigate it, but just be aware that there will be biases. Certain of them are illegal in various countries, and those are the ones you definitely want to mitigate. That’s really what it comes down to.

Participant 4: When we get past the AIs coming for the job of the person operating the phone to the person who’s doing financial trading and moving up the stack. How do we avoid the financial war game scenario where the bots just trade away all the money?

Kassan: Generative AI is not necessarily going to be doing the trading immediately. That’s something that is a little bit different. You’re talking about different styles of agents at that point. It is helpful to think about what limits you put on these systems, so those no-fly zones. Do you allow AI to trade at certain levels? For example, how much do you automate? How much do you augment a human? Or, do you have a human in the loop where the AI says, I think it’d be prudent to do X, Y, and Z, but somebody has to approve it.

Participant 4: Do the regulations prevent someone from making the bad decision that says, I’m going to try and go fully automated on this, when the responsible thing is, keep a human in the loop.

Kassan: Some of it does. To the point earlier around robo-advising or financial advice, certain jurisdictions say you’re not allowed to give financial advice in a chatbot, for example. At that point, great. You’re not automating, you might be augmenting. Instead of a chatbot saying it directly to the person. You’ve got a financial advisor who types something in, and the chatbot says to them, here’s what I’d recommend for this client. Then they get to say, yes or no. Sometimes, depending on the jurisdiction, that’s what you’d end up seeing.

Participant 5: With LLMs being adopted quite widely, do you see that the onus being on being compliant, running against those companies that are building these LLMs, so they have like ISO, or some sort of certification that embeds that confidence that they’re compliant to a certain level.

Kassan: Compliance is an interesting one on this, because compliance is on a per application level at this point. You don’t get certified as a company that you are compliant with the AI regulation. You have to have every single AI certified, if you’re part of the conformity assessment. Every one of them has to go through that. You don’t get a blanket statement saying, stamp, you’re good. I think with respect to LLMs, the probability of things going more awry than in classical machine learning and data science is higher. I think there’s a higher burden of proof to say that we’ve done what we can to try and limit and to try to be responsible with it. It’s certainly acknowledged in all the regulations, but it’s going to be harder. Frankly, when you look at who’s on the hook, the actual liability of what happens, it’s still people. A lot of the regulation has actually been quite clear that it’s still a person, or people who are responsible for the actions of the AI.

Good example of that was the whole debacle with Air Canada and the bereavement policy that the AI said, yes, just go ahead and claim that it’s bereavement. You can say, within 90 days, and we’ll refund your money. They’re like, your AI said it. Go on, you got to honor it. I think there’s more of that. The regulation has been clearer, especially with some of the newer regulation coming out, saying it has to be a natural person that’s responsible. When it comes to financial services here, at least, as I understand it, it’s the MDs who carry that, which is a lot of risk when you look at it at scale.

Antitrust is an interesting one that I think about, not from the perspective of those implementing AI and trying to create anti-competitive environments for their potential competition. More so for the accessibility of AI as a competitive advantage that a lot of the organizations that we’re seeing that are startup or FinTech or digital native and whatnot, they’re going towards open source, partly because you have the ability to use it without having to spend millions of dollars to do it. It creates actually a more competitive environment to have open-source models and to be able to leverage some of these capabilities, so that if somebody does have a new, disruptive idea that would require the use of AI and require the use of LLMs, there’s a means of entry into that market. I personally think that it’s a helpful tool to have these kinds of open-source models to get new entrants into the market, so that it’s not reliant on this token-based economy of having to pay for a proprietary application for AI.

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