Data Suggests Google Indexing Rates Are Improving

 

New Research on Google Indexing Trends (2022-2025)

A recent study analyzing over 16 million webpages reveals that while Google indexing rates have improved, many pages remain unindexed, and over 20% of indexed pages are eventually deindexed. These findings highlight significant challenges faced by websites focused on SEO and indexing.

Research by IndexCheckr Tool IndexCheckr, a Google indexing tracking tool, allows users to monitor the indexing status of their content and external pages hosting backlinks. While the research may not reflect global Google indexing patterns, it closely aligns with trends observed by website owners focused on SEO and backlink tracking.

Understanding Web Indexing Web indexing involves search engines crawling and filtering internet content, removing duplicates or low-quality pages, and storing the remaining pages in a structured database called a Search Index. Google initially utilized the Google File System (GFS) and later upgraded to Colossus, a more advanced system capable of handling vast search data across thousands of servers.

Indexing Success Rates The study indicates that a significant portion of pages remain unindexed, although indexing rates have improved between 2022 and 2025. Notable findings include:

• 61.94% of pages in the dataset were not indexed.
• Google indexes 93.2% of successfully indexed pages within six months.
• Indexing rates have steadily improved from 2022 to 2025.

Deindexing Trends The research also sheds light on Google's rapid deindexing processes. Of all indexed pages, 13.7% are deindexed within three months, with an overall deindexing rate of 21.29%. However, 78.71% of indexed pages remain consistently in Google's index. The time-based cumulative deindexing percentages are as follows:

• 1.97% deindexed within 7 days.
• 7.97% deindexed within 30 days.
• 13.70% deindexed within 90 days.
• 21.29% deindexed after 90 days.

The research underscores the importance of early monitoring and optimization to mitigate deindexing risks. Although the risk decreases after three months, periodic audits remain crucial for maintaining long-term content visibility.

Effectiveness of Indexing Services The study also evaluates the efficacy of manual submission strategies through indexing tools. It found that only 29.37% of URLs submitted via these tools were successfully indexed, leaving 70.63% of submitted pages unindexed. This suggests limitations in current manual indexing approaches.

High Percentage of Pages Not Indexed While less than 1% of tracked websites were entirely unindexed, only 37.08% of all tracked pages were fully indexed. The data, derived from IndexCheckr subscribers, may not reflect broader internet-wide trends but offers valuable insights for SEO-focused website owners.

Google Indexing Improvements Since 2022 Despite some concerning statistics, the study reveals a positive trend: Google's indexing rates have steadily improved from 2022 to 2025. This suggests enhanced efficiency in Google's ability to process and include webpages.

Summary of Findings Complete deindexing of entire websites remains rare. However, over half of the pages analyzed struggle with indexing, likely due to site quality issues. Factors that may contribute to indexing challenges include:

• Commercial product pages with bulked-up content for search engines.
• Sites designed primarily to "feed the bot" rather than to provide value to users.

Google's search results, particularly for e-commerce, are becoming increasingly precise. SEO strategies that focus solely on entity optimization, keywords, and topical maps may fail to address the user-centric ranking factors that drive long-term success.

Data Shows Perplexity Cites Sources 2.5x More Than ChatGPT

 

AI Search Engine Citation Analysis: Key Insights

A recent report by xfunnel.ai reveals new insights into how major AI search engines reference web content. The study, which analyzed 40,000 responses containing 250,000 citations, highlights key differences in citation frequency, content types, and source quality. Here are the main findings:

Citation Frequency Varies by Platform

Researchers tested AI search engines across different buyer journey stages and observed variations in how frequently each platform cites external content:

• Perplexity: 6.61 citations per response
• Google Gemini: 6.1 citations per response
• ChatGPT: 2.62 citations per response

ChatGPT's lower citation frequency is attributed to its standard mode testing, which did not utilize search-enhanced features.

Third-Party Content Dominates Citations

Citations were classified into four categories:

• Owned Content: Company domains
• Competitor Content: Rival company domains
• Earned Content: Third-party and affiliate sites
• User-Generated Content (UGC): Reviews and forum posts

Earned content constitutes the largest share of citations across all platforms, with UGC increasing in prominence. Affiliate sites and independent blogs also play a significant role in AI-generated responses.

Citation Patterns Shift Along the Customer Journey

The study found that citation patterns evolve based on the query stage:

• Problem Exploration & Education: Higher citation rates from third-party editorial content
• Comparison Stage: Increased UGC citations from review platforms and forums
• Final Research & Evaluation: More direct citations from brand and competitor websites

Source Quality Distribution

AI search engines prioritize higher-quality sources but still reference a range of content levels:

• High-quality: ~31.5%
• Upper-mid quality: ~15.3%
• Mid-quality: ~26.3%
• Lower-mid quality: ~22.1%
• Low-quality: ~4.8%

UGC Source Preferences by Platform

Different AI platforms show distinct preferences for UGC sources:

• Perplexity: Favors YouTube and PeerSpot
• Google Gemini: Frequently cites Medium, Reddit, and YouTube
• ChatGPT: Often references LinkedIn, G2, and Gartner Peer Reviews

Leveraging Third-Party Citations for SEO

The findings highlight an underutilized opportunity for SEO professionals. While optimizing owned content remains important, the dominance of earned media citations suggests a broader strategy:

• Foster relationships with industry publications
• Create compelling content others want to reference
• Contribute guest articles to reputable sites
• Engage with preferred UGC platforms for each AI engine

By focusing on creating valuable, shareable content, brands can increase their chances of being cited across AI search engines.

Why It Matters

As AI search engines continue to shape how users find information, understanding citation patterns is crucial for maintaining visibility. Diversifying content strategies across owned, earned, and UGC platforms can enhance your presence while preserving SEO best practices.

Key Takeaway

To maximize visibility in AI search engines, invest in a balanced approach:

• Maintain high-quality owned content
• Secure mentions on trusted third-party sites
• Establish a presence on relevant UGC platforms

The data suggests that earning third-party citations may offer greater value than solely optimizing your own content for AI search visibility.

Sustainability is key in 2025 for businesses to advance AI efforts

 

The Future of AI: Balancing Innovation with Sustainability

Artificial intelligence has rapidly become a transformative force across industries, driving advancements in fields ranging from healthcare to environmental conservation. AI is helping doctors detect diseases earlier, optimising supply chains, and even contributing to cleaning up the world’s oceans. However, as AI technology continues to scale, so does its demand for computational power—raising concerns about its environmental impact.

Regardless of whether AI is powered by supercomputers, edge computing, or traditional data centres, the energy requirements and ecological footprint of these systems are becoming critical discussion points. With each breakthrough, new challenges emerge, particularly regarding sustainability and resource consumption.

The Environmental Cost of AI Innovation

The increasing energy consumption of AI systems is drawing scrutiny from global organisations. The United Nations Environment Programme (UNEP) has voiced concerns about rising e-waste and the cooling requirements of massive data centres. Similarly, academics have highlighted the growing carbon footprint associated with AI infrastructure, warning that unchecked expansion could hinder global sustainability efforts.

Governments worldwide are responding with regulations aimed at mitigating these impacts. For example, the European Union’s Circular Economy Action Plan (CEAP) sets new sustainability standards, requiring businesses to consider the environmental effects of their technological advancements. This regulatory landscape is pushing AI-driven organisations to prioritise energy efficiency and sustainable practices.

The Business Case for Sustainable AI

Beyond regulatory compliance, integrating sustainability into AI strategies has become a competitive advantage. Analysts predict that companies failing to adopt energy-efficient AI solutions risk falling behind. Gartner has named energy-efficient computing as a top technology trend for 2025, reinforcing the need for organisations to demonstrate environmentally conscious AI deployment.

Neglecting sustainability can also lead to reputational damage. Businesses that do not properly recycle electronic components or optimise energy consumption may face public scrutiny, loss of customer trust, and potential financial penalties. Conversely, organisations that take a proactive approach to sustainable AI can enhance their brand image, reduce operational costs, and future-proof their operations against market volatility.

Practical Steps Toward a Greener AI Future

To address these challenges, businesses can adopt sustainable AI frameworks that focus on three key areas:

1. Energy Efficiency: Leveraging hardware and software that minimise power consumption while maintaining performance.
2. Resource Optimisation: Implementing AI models that require fewer computational resources without sacrificing accuracy.
3. E-Waste Reduction: Establishing responsible recycling programs and extending the lifecycle of AI-related hardware.

A variety of solutions exist to help businesses balance sustainability with high-performance AI. ASUS, for instance, has partnered with Intel to develop energy-efficient servers that support both innovation and environmental responsibility. Companies that take the first step in integrating these technologies will be better positioned to meet regulatory expectations while fostering long-term growth.

Leading the Way in Sustainable AI

The tech industry is increasingly recognising the importance of sustainable AI. IDC has identified sustainable AI frameworks as a major industry trend, emphasising that organisations must prioritise energy efficiency, resource optimisation, and e-waste reduction. As AI innovation accelerates, businesses that embed sustainability into their core strategies will emerge as industry leaders.

The future of AI is not just about technological progress—it’s about ensuring that progress is responsible and sustainable. By embracing energy-efficient solutions and proactive sustainability measures, companies can drive innovation without compromising the health of the planet.

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Zebra Technologies and enterprise AI in the APAC

 

Enterprise AI Transformation Reaches a Tipping Point in APAC

The global enterprise AI transformation is accelerating, with the Asia Pacific region emerging as a key driver of change. At a time when CISQ estimates that poor software quality will cost U.S. businesses a staggering $2.41 trillion in 2022, the urgency for practical, results-driven AI implementation has never been greater. Zebra Technologies is stepping up to the challenge with ambitious plans to revolutionise frontline operations across the region.

Intelligent Automation: The Game Changer

While elements of Zebra’s AI strategy have been in development for years, the rapid evolution of intelligent automation is now reshaping frontline operations. Speaking at Zebra’s 2025 Kickoff in Perth, Australia, Tom Bianculli, Chief Technology Officer, highlighted the shift:

“We’re not just digitising workflows—we're integrating wearable technology with robotic processes, allowing frontline workers to interact with automation in ways that were unimaginable just five years ago.”

Real-World Impact: AI in Retail Operations

The tangible benefits of enterprise AI transformation are already evident. Zebra’s recent collaboration with a major North American retailer showcases AI’s potential to streamline operations. By combining traditional AI with generative AI, the solution enables rapid shelf analysis and automated task generation.

“You take a picture of a shelf, and within one second, traditional AI identifies all products, detects missing or misplaced items, and then hands the data to a Gen AI agent, which decides the next steps,” Bianculli explained.

This automation has significantly improved efficiency, cutting staffing needs by 25%. When stock shortages are detected, the system instantly assigns tasks to the right personnel, eliminating manual intervention and enhancing productivity.

APAC: A Leader in AI Adoption

The Asia Pacific region is at the forefront of enterprise AI transformation. According to IBM research presented at the briefing, 54% of APAC enterprises expect AI to drive long-term innovation and revenue growth. Key AI investment priorities for 2025 include:

• 21% focused on enhancing customer experiences
• 18% directed toward business process automation
• 16% invested in sales automation and customer lifecycle management

Ryan Goh, Senior Vice President and General Manager of Asia Pacific at Zebra Technologies, highlighted the practical applications already making a difference:

“Our e-commerce customers are leveraging ring scanners to scan packages more efficiently, significantly improving productivity over traditional scanning methods.”

Innovation at the Edge

Zebra’s AI deployment strategy focuses on cutting-edge solutions, including:

• AI-powered devices with native neural architecture for on-device processing
• Multimodal AI experiences that mimic human cognitive capabilities
• Generative AI agents optimizing workload distribution between edge and cloud

The company is also making strides in edge computing, with plans to deploy on-device language models. This is particularly relevant for environments with limited or no internet connectivity, ensuring AI-driven insights remain accessible under any conditions.

Market Dynamics Across APAC

AI adoption varies significantly across APAC markets. India, for example, is experiencing rapid AI growth, with its GDP projected to rise by 6.6% and manufacturing expected to grow by 7% year over year. The country’s commitment to AI is evident, with 96% of organisations surveyed by the World Economic Forum actively running AI programs.

Japan presents a different challenge, with a projected GDP growth of just 1.2% and slower AI adoption in certain sectors. However, unexpected use cases are emerging. “We used to think tablets were mainly for retail, but we’ve seen game-changing applications in manufacturing and customer self-service,” Goh noted.

Future Outlook: The Path to Full-Scale AI Deployment

According to Gartner, by 2027, 25% of CIOs will implement augmented connected workforce initiatives, cutting competency development time in half. Zebra is already paving the way with its generative AI-powered Z word companion, set to begin pilot deployments with select customers in Q2 of this year.

With a global presence spanning 120+ offices in 55 countries and a network of over 10,000 channel partners in 185 countries, Zebra is well-positioned to shape the future of enterprise AI transformation across APAC. As organisations shift from AI experimentation to large-scale deployment, the focus remains on practical innovations that drive measurable business impact and operational efficiency.

Learn More

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French initiative for responsible AI leaders

 

ESSEC Business School and Accenture Launch ‘AI for Responsible Leadership’ Initiative

ESSEC Business School and Accenture have unveiled a new initiative, ‘AI for Responsible Leadership,’ commemorating the 10th anniversary of the ESSEC Accenture Strategic Business Analytics Chair. This initiative aims to foster the responsible and ethical use of artificial intelligence (AI) in leadership, equipping current and future leaders with essential skills to navigate economic, environmental, and social challenges.

A Collaborative Effort

The initiative is backed by multiple institutions, businesses, and expert groups, including ESSEC Metalab for Data, Technology & Society and Accenture Research. Abdelmounaim Derraz, Executive Director of ESSEC Metalab, emphasised the transformative role of AI in business education, stating, “Technical subjects are continuously reshaping business schools, and AI has created opportunities for collaboration between partner companies, researchers, and other ecosystem members, such as students, think tanks, associations, and public service organisations.”

Over the past decade, the Chair has championed interdisciplinary collaboration, integrating insights from various domains to enhance AI’s role in leadership. The new initiative builds upon this foundation, promoting knowledge-sharing through workshops, discussions, and the introduction of a ‘barometer’ to monitor AI’s impact on responsible leadership.

Key Features and Goals

A crucial aspect of the initiative is its engagement with a wide network of institutions and academic publications. An annual Grand Prix will recognise outstanding projects that explore AI’s role in leadership.

Fabrice Marque, founder of the initiative and current ESSEC Accenture Strategic Business Analytics Chair, highlighted the long-standing efforts in leveraging AI for organisational success. “For years, we have explored the potential of data and artificial intelligence within organisations. Our collaborations with partners such as Accenture, Accor, Dataiku, Engie, Eurofins, MSD, and Orange have allowed us to test and implement innovative solutions. With this initiative, we are taking a significant step forward by uniting an engaged ecosystem to redefine leadership in the face of tomorrow’s challenges. Our goal is to make AI a driver of performance, innovation, and responsibility for leaders.”

Industry Perspectives

Aurélien Bouriot, Managing Director at Accenture and sponsor of the ESSEC/Accenture Chair, underscored the benefits of the initiative for all stakeholders, including Accenture employees actively participating in the program.

Additionally, Laetitia Cailleteau, Managing Director at Accenture and leader of Responsible AI & Generative AI for Europe, stressed the importance of AI literacy among future leaders. “AI is a cornerstone of the ongoing industrial transformation. Tomorrow’s leaders must grasp its technical, ethical, and human dimensions, along with associated risks, to maximise value creation and generate a positive impact for organisations, stakeholders, and society.”

Looking Ahead

As AI continues to reshape industries, ESSEC and Accenture’s initiative sets a new benchmark for responsible leadership. By equipping leaders with essential AI knowledge and ethical frameworks, the program aims to drive sustainable innovation and informed decision-making in an increasingly AI-driven world.

ChatGPT gains agentic capability for complex research

  

OpenAI Unveils Deep Research: A Breakthrough in AI-Powered Research Capabilities

OpenAI has introduced a groundbreaking agentic capability called Deep Research, designed to enable ChatGPT to conduct complex, multi-step research tasks online. This new feature reportedly accomplishes in minutes what might take human researchers hours or even days.

According to OpenAI, Deep Research represents a significant milestone in the company’s ongoing pursuit of artificial general intelligence (AGI).

“The ability to synthesise knowledge is a prerequisite for creating new knowledge,” OpenAI states. “For this reason, deep research marks a significant step toward our broader goal of developing AGI.”

A New Era of AI-Assisted Research

Deep Research empowers ChatGPT to autonomously find, analyse, and synthesise information from hundreds of online sources. With just a user prompt, the tool can generate comprehensive reports comparable to those produced by research analysts.

Built on a variant of OpenAI’s upcoming “o3” model, Deep Research aims to eliminate the time-consuming, labour-intensive process of information gathering. Whether for competitive industry analysis, informed policy reviews, or highly specific product recommendations, the tool delivers precise, well-documented results.

Every output includes full citations and transparent documentation, ensuring users can easily verify findings. OpenAI highlights that deep research excels in uncovering niche or non-intuitive insights, making it valuable across industries like finance, science, policymaking, and engineering. However, the company also envisions its usefulness for everyday users, such as shoppers seeking personalised recommendations.

One example from OpenAI CEO Sam Altman illustrates the tool’s effectiveness:

“I am in Japan right now and looking for an old NSX. I spent hours searching unsuccessfully for the perfect one. I was about to give up, and Deep Research just... found it.”

Seamless Integration with ChatGPT

Deep research is integrated directly into the ChatGPT interface. Users simply select the “Deep Research” option in the message composer and enter their query. They can also upload supporting files or spreadsheets to provide additional context.

Once initiated, the AI embarks on a rigorous multi-step process that may take 5–30 minutes to complete. A sidebar provides real-time updates on actions taken and sources consulted, allowing users to continue with other tasks until the final report is ready.

Reports are presented within the chat, offering detailed, well-documented insights. In the coming weeks, OpenAI plans to enhance these reports with embedded images, data visualisations, and graphs for improved clarity and context.

Unlike GPT-4o, which specialises in real-time, multimodal conversations, Deep Research prioritises in-depth analysis and rigorous citation. This positions it as a tool for those requiring research-grade insights rather than quick summaries.

Built for Real-World Challenges

Deep research leverages sophisticated training methodologies grounded in real-world browsing and reasoning tasks. The model was trained using reinforcement learning to autonomously plan and execute multi-step research processes, including adaptive refinement as new information emerges.

The tool can:

• Browse user-uploaded files
• Generate and iterate on graphs using Python
• Embed media such as generated images and web pages into responses
• Cite exact sentences or passages from sources

This extensive training has resulted in an AI capable of tackling complex, real-world problems.

To evaluate its capabilities, OpenAI tested Deep Research against a rigorous expert-level benchmark known as “Humanity’s Last Exam.” Comprising over 3,000 questions spanning disciplines from rocket science to linguistics, the benchmark assesses an AI’s ability to solve multifaceted problems.

Deep Research delivered record-breaking results, achieving an accuracy of 26.6%, far surpassing other models:

• GPT-4o: 3.3%
• Grok-2: 3.8%
• Claude 3.5 Sonnet: 4.3%
• OpenAI o1: 9.1%
• DeepSeek-R1: 9.4%
• Deep Research: 26.6% (with browsing + Python tools)

Additionally, Deep Research set a new state-of-the-art performance on the GAIA benchmark, which evaluates reasoning, multi-modal fluency, and tool-use proficiency. It secured the top score of 72.57%.

Challenges and Limitations

Despite its impressive capabilities, deep research is not without its challenges. OpenAI acknowledges that the system still has limitations, including occasional hallucinations (incorrect or misleading information), difficulty distinguishing authoritative sources from speculative content, and overconfidence in uncertain findings.

Users may also experience minor formatting errors in reports and citations, as well as occasional delays in task initiation. However, OpenAI expects these issues to improve through iterative updates and increased usage.

Gradual Rollout and Future Enhancements

OpenAI is rolling out deep research gradually, starting with Pro users, who will receive up to 100 queries per month. Plus and Team tiers will follow, with Enterprise access arriving thereafter.

Currently, residents of the UK, Switzerland, and the European Economic Area do not have access to the feature, though OpenAI is working on expanding availability to these regions.

In the coming weeks, Deep Research will be integrated into ChatGPT’s mobile and desktop platforms. Looking ahead, OpenAI plans to connect the tool to subscription-based or proprietary data sources, further enhancing its reliability and personalisation.

Additionally, OpenAI envisions integrating deep research with its operator chatbot, which can take real-world actions. This future enhancement could enable ChatGPT to seamlessly handle tasks that require both in-depth online research and real-world execution.

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Ursula von der Leyen: AI race ‘is far from over’

 

Europe Carves Its Own Path in the Global AI Race

European Commission President Ursula von der Leyen made it clear at the AI Action Summit in Paris that Europe has no intention of playing catch-up in the global AI race. While the United States and China are often perceived as frontrunners, von der Leyen emphasized that the race "is far from over" and that Europe possesses distinct strengths that position it as a leader in the field.

Shaping the Future of AI

“This is the third summit on AI safety in just over a year,” von der Leyen remarked. “In that same period, three new generations of increasingly powerful AI models have emerged. Some experts predict that within a year, AI models could approach human reasoning.”

She underscored the shift from previous summits, which focused on establishing AI safety principles, to the current summit’s emphasis on action. “We have built a shared consensus that AI must be safe, uphold our values, and benefit humanity. Now, this summit is about action—and that is exactly what we need right now.”

As AI’s impact continues to grow, von der Leyen urged Europe to define its vision for the technology, integrating AI into key economic sectors and addressing pressing global challenges. She argued that Europe has a unique opportunity to lead through strategic investment and innovation.

A European Approach to AI

Von der Leyen rejected the idea that Europe is lagging behind its competitors. “Too often, I hear that Europe is late to the race, while the U.S. and China have surged ahead. I disagree,” she stated. “The frontier is always shifting, and global leadership is still within reach.”

Rather than mimicking other regions, she called for Europe to leverage its own strengths, particularly in science and technology. “Instead of chasing others’ successes, we should focus on what we do best. Europe has long been a leader in scientific and technological innovation, and we must build on that foundation.”

She highlighted three core pillars of the “European brand of AI” that distinguish it from global competitors:

1. A focus on high-complexity, industry-specific applications.
2. A cooperative, collaborative approach to innovation.
3. A commitment to open-source principles.

“This summit demonstrates that Europe has its own distinct AI approach,” von der Leyen affirmed. “It is already driving innovation and adoption, and momentum is building.”

Accelerating AI Innovation: Factories and Gigafactories

To maintain its competitive edge, Europe must rapidly expand its AI infrastructure. One crucial element of this strategy is Europe’s investment in computational resources. The continent already hosts some of the world’s fastest supercomputers, and these are now being utilized through the establishment of “AI factories.”

“In just a few months, we have launched 12 AI factories,” von der Leyen revealed. “We are investing €10 billion in these projects—this is not just a promise; it is happening right now. It represents the largest public investment in AI worldwide and is set to unlock more than ten times that amount in private investment.”

Beyond AI factories, von der Leyen announced an even more ambitious initiative: AI gigafactories, modeled on the scale of CERN’s Large Hadron Collider. These facilities will provide the necessary infrastructure for training AI systems on an unprecedented scale and will serve as hubs for collaboration between researchers, entrepreneurs, and industry leaders.

“We are creating the computational power infrastructure,” von der Leyen explained. “We welcome talent from around the world, and industries will be able to collaborate and federate their data.”

She stressed that while AI development requires competition, collaboration remains essential. “AI needs both competition and cooperation,” she noted, emphasizing that AI gigafactories will serve as “safe spaces” for joint innovation efforts.

Building Trust Through the AI Act

Ensuring AI safety and trustworthiness remains a core priority for Europe. Von der Leyen reiterated the importance of the EU AI Act, positioning it as a unifying framework to replace fragmented national regulations across member states.

“The AI Act will create a single set of safety rules across the European Union—covering 450 million people—rather than 27 different national regulations,” she stated, acknowledging that businesses must also be supported in navigating these regulations. “At the same time, we must simplify processes and reduce red tape. And we will.”

Securing €200 Billion in AI Investments

To finance Europe’s ambitious AI agenda, von der Leyen highlighted the EU AI Champions Initiative, which has already secured €150 billion from industry leaders, investors, and technology providers.

During the summit, she announced a complementary initiative—InvestAI—which will contribute an additional €50 billion. Together, these efforts will mobilize a staggering €200 billion in public-private AI investments.

“We will focus on industrial and mission-critical AI applications,” she stated. “This will be the world’s largest public-private partnership dedicated to the development of trustworthy AI.”

Ethical AI: A Global Responsibility

Von der Leyen concluded her address by framing Europe’s AI ambitions within a broader ethical context. She argued that responsible AI development is a shared global responsibility.

“Cooperative AI can be valuable far beyond Europe, including for our partners in the Global South,” she emphasized, promoting inclusivity in AI advancements.

She expressed strong support for the newly launched AI Foundation, which aims to ensure equitable access to AI technologies. “AI can be a gift to humanity. But we must ensure that its benefits are widespread and accessible to all.”

“We want AI to be a force for good. We want AI that fosters collaboration and benefits everyone. That is our path—our European way.”

See Also: AI Action Summit: Leaders Call for Unity and Equitable Development

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ChatGPT-4 vs. ChatGPT-3.5: Which to use? ChatGPT-4 vs. ChatGPT-3.5: AI App Comparison | ChatGPT 3.5 vs 4 vs 4o - Key Differences to Consider

 

OpenAI offers two versions of its chatbot: ChatGPT-4 and ChatGPT-3.5, each designed to meet different user needs.

ChatGPT-4 is the more advanced model, providing superior accuracy and reasoning capabilities. On the other hand, ChatGPT-3.5 remains a strong choice, particularly for users seeking a free AI tool. Choosing between the two depends on the user’s requirements—whether they need a powerful AI for complex tasks or a straightforward chatbot for everyday use.

Both models share the same foundational AI principles but have key distinctions. ChatGPT-4 features enhanced reasoning, a larger context window, and multimodal capabilities, making it better suited for complex problem-solving and content creation. In contrast, ChatGPT-3.5 is ideal for general-purpose tasks and is freely accessible, making it a practical option for users who don’t require advanced features.

Who Should Choose ChatGPT-4?

ChatGPT-4 is ideal for users who require a high-performance AI capable of handling both text and image inputs. Its ability to manage longer conversations makes it valuable for users seeking context-rich interactions. Some subscription plans even offer internet browsing, allowing for real-time information retrieval.

However, access to ChatGPT-4 requires a paid subscription, starting at $20 per month for individual users, with higher-tier plans available for teams and enterprises. These plans provide additional benefits such as an expanded context window and enhanced performance. Still, the cost may not be justified for users with basic AI needs.

Who Should Choose ChatGPT-3.5?

ChatGPT-3.5 is a great option for users looking for a free AI chatbot without subscription fees. It can handle a range of general tasks, including answering questions, drafting content, and offering conversational support.

Although it lacks multimodal capabilities and has a smaller context window than ChatGPT-4, it remains a reliable tool for many everyday applications. Accessing ChatGPT-3.5 is simple—users only need to create an OpenAI account to start using it via the web or mobile apps. Additionally, it supports voice interactions on mobile devices, making it convenient for hands-free use.

Businesses and professionals in need of a scalable AI solution will likely prefer ChatGPT-4 due to its sophisticated responses, advanced reasoning, and enterprise-oriented features. Its ability to process multimodal inputs, analyze data, and support extended conversations makes it a more effective tool for professional and research-based applications.

Choosing Between ChatGPT-4 and ChatGPT-3.5

The decision ultimately depends on the user’s needs. ChatGPT-4 is best for those requiring greater accuracy and advanced reasoning, making it ideal for professionals, researchers, and businesses. Meanwhile, ChatGPT-3.5 is a user-friendly and accessible option for handling a broad range of tasks without a financial commitment.

Are There Better AI Alternatives?

While both ChatGPT-4 and ChatGPT-3.5 are powerful AI models, they may not be the perfect fit for everyone. Users looking for a free, multimodal AI tool with extensive real-time web search capabilities might prefer other options. Likewise, individuals seeking AI solutions specifically tailored to coding and development may benefit from models optimized for those tasks. OpenAI’s chatbots are general-purpose, meaning they may not always meet the needs of users requiring highly specialized AI applications.

For those exploring alternatives, several strong competitors exist in the AI chatbot space:

• Google Gemini (formerly Bard) integrates deeply with Google Search and offers robust multimodal capabilities. Many users appreciate its accessibility and free-tier offerings.
• Anthropic Claude is well-regarded for ethical AI development and security. It also features one of the largest context windows available, making it a strong choice for long-form content creation.
• Microsoft Copilot integrates seamlessly with Microsoft 365 applications and Bing, offering an AI assistant tailored for productivity and development workflows.

Ultimately, the best AI chatbot depends on individual needs, whether for professional use, casual interactions, or industry-specific applications.

DeepSeek: What lies under the bonnet of the new AI chatbot? DeepSeek founders

 

Tumbling Stock Market and the Rise of DeepSeek AI

The release of China’s DeepSeek AI-powered chatbot has sent shockwaves through the technology industry. Rapidly surpassing OpenAI’s ChatGPT as the most downloaded free iOS app in the U.S., its debut also triggered a record-breaking $600 billion (£483 billion) loss in Nvidia’s market value in just one day.

The Innovation Behind DeepSeek

The turmoil surrounding DeepSeek stems from its cutting-edge "large language model" (LLM), which boasts reasoning capabilities comparable to top U.S. models like OpenAI’s GPT-4 but at a fraction of the training and operational cost. DeepSeek has achieved this efficiency through advanced computational strategies that minimise the time and memory required to train its model, R1. According to reports, R1’s base model V3 required 2.788 million GPU hours to train at an estimated cost of under $6 million (£4.8 million), compared to the over $100 million (£80 million) investment needed for GPT-4.

Impact on Nvidia and the AI Industry

Despite the financial blow to Nvidia, DeepSeek’s training process still relied on approximately 2,000 Nvidia H800 GPUs. These chips, modified to comply with U.S. export regulations, were likely stockpiled before the Biden administration imposed tighter restrictions in October 2023. Working within these limitations, DeepSeek has devised innovative methods to maximise its hardware’s efficiency.

Reducing AI’s computational cost could also alleviate environmental concerns. Data centres powering AI models consume vast amounts of electricity and water, primarily for cooling. While AI companies rarely disclose their carbon footprint, estimates suggest that ChatGPT alone emits over 260 metric tonnes of CO2 per month—comparable to 260 flights from London to New York. If DeepSeek’s efficiency claims hold true, its advancements could set a precedent for more sustainable AI development.

A Rapid Rise to Prominence

Founded by Liang Wenfeng in 2023, DeepSeek’s meteoric rise has surprised many. The company’s success is partly attributed to its use of a "mixture of experts" model, where smaller, specialised models handle distinct tasks. This technique was also employed in Mistral AI’s Mixtral 8x7B model in 2023. Additionally, DeepSeek has openly shared some of its unsuccessful attempts to enhance reasoning, such as Monte Carlo Tree Search, providing valuable insights for future AI advancements.

Openness and the Future of AI

Unlike OpenAI’s proprietary models, DeepSeek has released its model’s "weights"—the numerical parameters derived from training—along with technical documentation. This transparency allows researchers worldwide to analyse and adapt the model, fostering a more open AI development ecosystem. However, certain critical details, such as training datasets and code, remain undisclosed.

DeepSeek’s breakthrough demonstrates that cutting-edge AI doesn’t necessarily require vast financial or computational resources. As AI development becomes more efficient, smaller companies may challenge Big Tech’s dominance in the field. Former U.S. President Donald Trump has called DeepSeek’s emergence "a wake-up call" for the U.S. tech industry. Yet, this shift may ultimately benefit Nvidia and other tech giants by increasing demand for AI-powered solutions and the hardware that supports them.

The AI landscape is evolving rapidly, with companies like DeepSeek playing an increasingly significant role. As innovation continues, the impact of smaller players on the industry should not be underestimated.

DeepSeek founders

Liang Wenfeng: The Visionary Behind DeepSeek’s AI Revolution

Liang Wenfeng, the founder and CEO of DeepSeek, is a key figure in China’s rapidly evolving artificial intelligence (AI) sector. Born in 1985 in Guangdong, China, he pursued a degree in electronics at Zhejiang University, where he cultivated a deep interest in machine learning and its applications in finance.

Before venturing into AI, Liang made his mark in the financial industry as a hedge fund entrepreneur. His transition to AI reflects a strong belief in technology’s potential to drive innovation and transform industries. Through DeepSeek, he aims to establish China as a global leader in AI, contributing to the country’s growing influence in cutting-edge technological advancements.

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What is DeepSeek and why is it disrupting the AI sector? Who builds DeepSeek?

In the rapidly evolving landscape of artificial intelligence (AI) and data analytics, one name is increasingly standing out: **DeepSeek**. This cutting-edge technology is not just another buzzword in the tech industry; it represents a paradigm shift in how we approach data analysis, machine learning, and decision-making processes. In this article, we’ll explore what DeepSeek is, its unique capabilities, and how it’s poised to transform industries across the globe.

**What is DeepSeek?**

DeepSeek is an advanced AI-driven platform designed to extract actionable insights from vast and complex datasets. Unlike traditional data analysis tools, DeepSeek leverages deep learning algorithms, natural language processing (NLP), and predictive analytics to uncover patterns, trends, and correlations that would otherwise remain hidden. Its ability to process unstructured data—such as text, images, and audio—sets it apart from conventional systems, making it a game-changer for businesses and researchers alike.

At its core, DeepSeek is about **depth and precision**. It doesn’t just skim the surface of data; it dives deep, seeking out the most relevant and valuable information to drive smarter decisions.

 **Key Features of DeepSeek**

1. **Advanced Deep Learning Capabilities**  

   DeepSeek’s neural networks are trained to handle massive datasets with unparalleled accuracy. Whether it’s analyzing customer behavior, predicting market trends, or optimizing supply chains, DeepSeek’s algorithms continuously learn and adapt, ensuring that insights remain relevant and actionable.

2. **Natural Language Processing (NLP)**  

   One of DeepSeek’s standout features is its ability to understand and process human language. This makes it invaluable for tasks like sentiment analysis, customer feedback interpretation, and even automated report generation. By bridging the gap between human communication and machine understanding, DeepSeek empowers organizations to make data-driven decisions faster.

3. **Real-Time Analytics**  

   In today’s fast-paced world, real-time insights are critical. DeepSeek’s ability to process and analyze data in real-time allows businesses to respond to emerging trends, threats, and opportunities instantly. This is particularly useful in industries like finance, healthcare, and e-commerce, where timing is everything.

4. **Scalability and Flexibility**  

   DeepSeek is designed to scale with your needs. Whether you’re a startup or a multinational corporation, its modular architecture ensures that it can handle datasets of any size and complexity. Additionally, its flexibility allows it to integrate seamlessly with existing systems, minimizing disruption and maximizing ROI.

5. **Explainable AI (XAI)**  

   One of the challenges of traditional AI systems is their "black box" nature, where decisions are made without clear explanations. DeepSeek addresses this issue by providing transparent and interpretable results, enabling users to understand the reasoning behind its insights.

 **Applications of DeepSeek Across Industries**

1. **Healthcare**  

   DeepSeek is revolutionizing healthcare by enabling predictive diagnostics, personalized treatment plans, and efficient resource allocation. For example, it can analyze patient records, medical imaging, and genomic data to identify early signs of diseases and recommend tailored interventions.

2. **Finance**  

   In the financial sector, DeepSeek is being used for fraud detection, risk assessment, and portfolio optimization. Its ability to analyze market trends and predict economic shifts gives investors and institutions a competitive edge.

3. **Retail and E-Commerce**  

   DeepSeek helps retailers understand customer preferences, optimize inventory management, and enhance the shopping experience through personalized recommendations. By analyzing browsing patterns and purchase histories, it can predict demand and reduce waste.

4. **Manufacturing**  

   In manufacturing, DeepSeek is driving the adoption of Industry 4.0 by enabling predictive maintenance, quality control, and supply chain optimization. Its real-time analytics capabilities ensure that production lines run smoothly and efficiently.

5. **Marketing and Advertising**  

   DeepSeek’s NLP and sentiment analysis tools are transforming how brands engage with their audiences. By analyzing social media trends, customer reviews, and campaign performance, it helps marketers create more targeted and effective strategies.

 **Why DeepSeek is a Game-Changer**

1. **Democratizing Data Science**  

   DeepSeek’s user-friendly interface and explainable AI make it accessible to non-technical users, democratizing data science and enabling more people to harness the power of AI.

2. **Driving Innovation**  

   By uncovering hidden insights and enabling faster decision-making, DeepSeek is fostering innovation across industries. It empowers organizations to stay ahead of the curve and adapt to changing market dynamics.

3. **Enhancing Efficiency**  

   DeepSeek’s ability to automate complex analytical tasks reduces the time and resources required for data processing. This allows businesses to focus on strategic initiatives rather than getting bogged down by manual analysis.

4. **Future-Proofing Businesses**  

   As AI continues to evolve, DeepSeek ensures that organizations are well-equipped to handle the challenges and opportunities of tomorrow. Its scalability and adaptability make it a long-term investment for any forward-thinking business.

 **The Future of DeepSeek**

The potential of DeepSeek is virtually limitless. As AI technology continues to advance, we can expect DeepSeek to incorporate even more sophisticated features, such as enhanced multimodal learning (combining text, image, and audio analysis) and greater integration with IoT devices. Moreover, its applications will expand into new domains, such as climate modeling, urban planning, and education.

In a world increasingly driven by data, DeepSeek is not just a tool—it’s a strategic asset. By unlocking the full potential of AI and data analytics, it’s helping organizations navigate complexity, seize opportunities, and create a brighter future.

 **Conclusion**

DeepSeek represents the next frontier in AI and data analytics. Its ability to process, analyze, and interpret data with unmatched depth and precision is transforming industries and empowering decision-makers like never before. Whether you’re a business leader, researcher, or tech enthusiast, DeepSeek is a technology worth watching—and embracing.

As we move further into the age of AI, one thing is clear: the future belongs to those who can harness the power of data. And with DeepSeek, that future is closer than ever.

DeepSeek is a Chinese company dedicated to making AGI a reality. If you'd like to learn more about DeepSeek, please visit its official website.

why is it disrupting the AI sector?

DeepSeek is making waves in the AI sector due to its innovative approaches and cutting-edge technologies. Here are a few reasons why it is considered disruptive: 1. **Advanced Research and Development**: DeepSeek invests heavily in R&D, pushing the boundaries of what AI can achieve. Their focus on creating more efficient, scalable, and intelligent systems sets them apart from traditional AI companies. 2. **Focus on AGI**: Unlike many AI companies that concentrate on narrow AI applications, DeepSeek is dedicated to achieving Artificial General Intelligence (AGI). This ambitious goal has the potential to revolutionize various industries by creating systems that can perform any intellectual task that a human can do. 3. **Interdisciplinary Expertise**: DeepSeek brings together experts from various fields, including computer science, neuroscience, and cognitive psychology. This interdisciplinary approach allows them to develop more holistic and human-like AI systems. 4. **Ethical AI Development**: DeepSeek places a strong emphasis on ethical considerations in AI development. They are committed to creating AI that benefits humanity while minimizing risks and ensuring transparency and accountability. 5. **Strategic Partnerships and Collaborations**: By collaborating with leading academic institutions, tech companies, and industry leaders, DeepSeek is able to leverage a wide range of expertise and resources, accelerating their progress and impact in the AI sector. 6. **Innovative Applications**: DeepSeek is known for developing AI applications that address real-world problems in novel ways. Their solutions often integrate multiple AI technologies, providing comprehensive and effective outcomes. 7. **Global Reach**: With a presence in key markets around the world, DeepSeek is able to influence and shape the global AI landscape. Their international perspective allows them to address diverse challenges and opportunities in the AI sector. Overall, DeepSeek's commitment to advancing AGI, combined with their innovative approaches and ethical considerations, positions them as a disruptive force in the AI industry.

Who is the owner of ChatGPT? How much does ChatGPT cost per day? Does ChatGPT save conversations?

 Who is the owner of ChatGPT?

OpenAI, a team of engineers, researchers, and scientists' artificial intelligence research laboratory, created the AI language model known as ChatGPT. Elon Musk, Sam Altman, Greg Brockman, Ilya Sutskever, John Schulman, and Wojciech Zaremba were among the prominent technology executives who helped establish OpenAI in 2015.

The mission of the organization is to advance artificial intelligence in a way that is both secure and beneficial to humanity. OpenAI studies robotics, computer vision, and reinforcement learning, among other AI-related fields, in addition to creating language models like ChatGPT.

OpenAI is a non-profit organization that is supported by grants from the government, corporate partnerships, and philanthropic donations. The organization's research and development activities are meant to be open to the wider scientific community. This is done with the intention of promoting the creation of AI systems that are good for society and are ethical.

How much does ChatGPT cost per day?

OpenAI created the language model known as ChatGPT; it is not offered for sale as a product or service. All things being equal, it is accessible for use through different Programming interface administrations given by OpenAI, which ordinarily charge in light of utilization.

OpenAI's API services are priced according to a variety of factors, including the complexity of the model being used, the number of API requests, and so on. It is difficult to provide a specific cost per day for using ChatGPT because the pricing can vary significantly based on the specific API used and the amount of usage.

However, OpenAI does provide a limited amount of access to language models like ChatGPT through a free tier of its API services. Developers can test and experiment with the model at no cost with this free tier. OpenAI offers paid plans with more access and support for users who need more time to use it.

Does ChatGPT save conversations?

ChatGPT, as an AI language model, is unable to save or store conversations on its own. However, depending on how ChatGPT is implemented, the application or platform that uses it might be able to save conversations.

For instance, if ChatGPT is incorporated into a chatbot application, the chatbot may be programmed to save the user-to-chatbot conversations for a variety of purposes, such as enhancing the chatbot's performance or analyzing user behavior. In such a scenario, the chatbot application would save the conversations rather than ChatGPT itself.

It is important to note that user data collection and storage are governed by a variety of privacy laws and ethical considerations. Before collecting or storing users' data, developers and businesses that use ChatGPT or any other AI language model in their applications or platforms must ensure compliance with applicable privacy laws and obtain user consent.

How is ChatGPT different from Google search? Where does ChatGPT get its data?

 How is ChatGPT different from Google search?

Google search and ChatGPT are two distinct technologies with distinct applications.

ChatGPT is a language model that responds to user input with human-like language generated by deep learning algorithms. It is made to look like a human conversation and can respond to a wide range of inputs. Most of the time, ChatGPT is used to answer questions, give advice, or have a casual conversation.

On the other hand, Google Search is a search engine that shows users a list of web pages and other online resources that are related to their search query. Algorithms are used by Google Search to rank web pages and other online resources based on how relevant they are to the search query. Most of the time, Google search is used to find information, do research on a subject, or buy things.

In conclusion, Google search focuses on finding relevant online resources, whereas ChatGPT is focused on producing responses in natural language. Although some of their capabilities may be comparable, they are fundamentally distinct technologies with distinct applications.

Where does ChatGPT get its data?

ChatGPT gets its information from a wide assortment of sources, including books, articles, and sites. The large and diverse corpus of text that serves as the training data for ChatGPT is typically curated by the model's creators.

The underlying rendition of ChatGPT, for instance, was prepared on a dataset of over 40GB of text information, which incorporated various sources, for example, books, Wikipedia articles, and site pages. The training data were carefully chosen to include a wide range of topics and writing styles, assisting the model in learning to produce responses in natural language that are appropriate for various contexts.

ChatGPT can be tailored to specific applications or domains using additional datasets in addition to the initial training data. A medical chatbot that can answer questions about health and wellness could be created, for instance, by fine-tuning a ChatGPT model on a dataset of medical text.

It is important to note that the performance of ChatGPT can be significantly affected by the variety and quality of the training data. To ensure that the training data is of high quality and contains a diverse range of text that is representative of human language, developers typically devote a significant amount of time to curating and preprocessing it.

How does ChatGPT work?

Chatgpt

ChatGPT is an enormous language model made by OpenAI. ChatGPT, a language model, uses machine learning algorithms to comprehend user input and generate language that is human-like.

ChatGPT is made to look like a human conversation, and it can talk about anything from answering questions to telling jokes and giving advice. Its training on a vast amount of text data, including books, articles, and websites, is what enables it to generate responses in natural language.

ChatGPT has a number of potential uses, including personal assistants, language translation, and customer service. It can also be used as a learning tool because it lets users ask questions and get answers in a way that sounds like a conversation.

Nevertheless, it is essential to keep in mind that ChatGPT is still a machine and is constrained in its capacity to comprehend and interpret language. It may occasionally produce responses that are nonsensical or irrelevant because its responses are generated based on statistical patterns in its training data. Use ChatGPT with caution and verify any information it provides before acting, just like you would with any AI tool.

How does ChatGPT work?

ChatGPT is a sort of language model that utilizes profound learning calculations to create human-like language in light of client input. How does it work?

• Data on training: ChatGPT is taught from a lot of text data, like articles, books, and websites. The purpose of this data is to instruct the model on how to recognize language patterns and comprehend the connections between words and phrases.

• Encoding: The text is first encoded into a numerical representation that the model can comprehend before the user enters a message. To accomplish this, the text is typically broken up into individual words or subwords and given a unique numerical value through a process known as tokenization.

• Prediction: The model responds to the user's message by utilizing its understanding of language patterns. This is accomplished by using the patterns it has learned from the training data to predict the most likely sequence of words that will follow the user's message.

• Decoding: The sequence of words generated by the model are then decoded into natural language text that can be displayed to the user. Methods like beam search and sampling are used to accomplish this, allowing the model to generate a wide range of responses.

• Iteration: Through an iterative training and evaluation process in which the model is continuously updated with new data and feedback to enhance its accuracy and capacity to generate language that is human-like, ChatGPT's responses can be improved.

In general, the vast amount of text data it has trained on enables ChatGPT to recognize and apply language patterns, which are the foundation of its ability to generate responses in natural language. Because of this, it is able to imitate human conversation and offer a wide range of responses to various inputs.

How to earning money from chatgpt

OpenAI created ChatGPT, a large language model that uses natural language processing to communicate with users. It understands and responds to user inquiries in a human-like manner through the use of machine learning algorithms. ChatGPT can answer a wide range of questions about a variety of subjects because it has been trained on a large amount of data, such as books, websites, and other informational sources.

ChatGPT can respond to both general and in-depth inquiries. History, science, literature, and current events are just a few of the areas it can cover. ChatGPT allows users to ask for advice, definitions, explanations, and other information.

The capacity of ChatGPT to learn and grow over time is one of its primary advantages. It can learn from the data and improve its responses as more users interact with the system and ask questions. Because of this, ChatGPT is always changing and getting better, making it a useful tool for people who want to stay up to date and informed.

ChatGPT can be accessed through a number of different platforms, such as social media platforms, messaging apps, and web-based interfaces. ChatGPT offers conversational interaction, allowing users to ask questions and receive responses in real time.

ChatGPT can be used for customer service, education, and research, among other things. It can be used to educate customers, respond to inquiries from students, and conduct research on a variety of subjects.

In general, ChatGPT is an innovative and potent tool that provides a novel approach to information interaction. It is a useful resource for users who want to remain informed and engaged with the world around them due to its capacity to learn and improve over time.

ChatGPT is not a platform that provides users with direct monetization opportunities because it is an AI language model. However, you can potentially make money using ChatGPT in a number of different ways:

• Providing services for consulting: You could use ChatGPT to provide consulting services if you are an expert in a particular field. You can use ChatGPT to communicate with potential customers and advertise your services through your website or social media channels.

• Marketing via affiliates: You could promote products or services with which you are affiliated by using ChatGPT. You could potentially earn commissions on any sales that result from providing information and answering questions about these products or services.

• Producing content: ChatGPT could serve as an inspiration for content creation. You could, for instance, produce blog posts or videos that investigate inquiries or subject matters that are frequently brought up during conversations with ChatGPT.

• Research: ChatGPT can be used for research on a variety of subjects. You might be able to identify trends and patterns that could help inform your research by analyzing the queries and questions that users submit to ChatGPT.

It's important to note that the aforementioned tactics aren't limited to ChatGPT; they can also be used with other platforms and tools. Even though ChatGPT does not provide direct opportunities for monetization, it can be a useful tool for developing expertise, coming up with content ideas, and connecting with potential clients and customers.