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Grand Theft Auto V hits 210M units, Red Dead 70M shortly before GTA VI’s debut - Related to 70m, off, do, auto, grand

Grand Theft Auto V hits 210M units, Red Dead 70M shortly before GTA VI’s debut

Grand Theft Auto V hits 210M units, Red Dead 70M shortly before GTA VI’s debut

Take-Two Interactive today revealed more about the sales figures behind its most popular titles, Grand Theft Auto V and Red Dead Redemption 2. , the two games have now sold 210 million and 70 million units respectively. That means Grand Theft Auto V sold five million units since the last quarter and Red Dead 2 sold three million units.

GTA V’s sales are largely consistent over the last several quarters — the title sold five million units over the previous quarter as well. RDR2 has actually upped its sales, having sold only two million units over the previous quarter.

Take-Two also noted Grand Theft Auto Online’s performance during the quarter with its holiday upgrade, growing by 10%. Red Dead Redemption 2 is “currently enjoying its highest level of concurrent players on Steam” and Red Dead Online also had its own holiday bonuses with the launch of the Halloween pass.

It’s been just over a year since the official announcement of Grand Theft Auto VI, which is still set for a fall 2025 launch date — Take-Two’s research reiterates the date alongside all of its other releases for the upcoming year. Despite the lead time, it doesn’t not appear that the announcement of a sequel has caused a drop-off in sales for GTA V.

Even as OpenAI continues clinging to its assertion that the only path to AGI lies through massive financial and energy expenditures, independent resea......

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Turns out, it’s not that hard to do what OpenAI does for less

Turns out, it’s not that hard to do what OpenAI does for less

Even as OpenAI continues clinging to its assertion that the only path to AGI lies through massive financial and energy expenditures, independent researchers are leveraging open-source technologies to match the performance of its most powerful models — and do so at a fraction of the price.

Last Friday, a unified team from Stanford University and the University of Washington presented that they had trained a math and coding-focused large language model that performs as well as OpenAI’s o1 and DeepSeek’s R1 reasoning models. It cost just $50 in cloud compute credits to build. The team reportedly used an off-the-shelf base model, then distilled Google’s Gemini [website] Flash Thinking Experimental model into it. The process of distilling AIs involves pulling the relevant information to complete a specific task from a larger AI model and transferring it to a smaller one.

What’s more, on Tuesday, researchers from Hugging Face released a competitor to OpenAI’s Deep Research and Google Gemini’s (also) Deep Research tools, dubbed Open Deep Research, which they developed in just 24 hours. “While powerful LLMs are now freely available in open-source, OpenAI didn’t disclose much about the agentic framework underlying Deep Research,” Hugging Face wrote in its announcement post. “So we decided to embark on a 24-hour mission to reproduce their results and open-source the needed framework along the way!” It reportedly costs an estimated $20 in cloud compute credits, and would require less than 30 minutes, to train.

Hugging Face’s model subsequently notched a 55% accuracy on the General AI Assistants (GAIA) benchmark, which is used to test the capacities of agentic AI systems. By comparison, OpenAI’s Deep Research scored between 67 – 73% accuracy, depending on the response methodologies. Granted, the 24-hour model doesn’t perform quite as well as OpenAI’s offering, but it also didn’t take billions of dollars and the energy generation capacity of a mid-sized European nation to train.

These efforts follow news from January that a team out of University of California, Berkeley’s Sky Computing Lab managed to train their Sky T1 reasoning model for around $450 in cloud compute credits. The team’s Sky-T1-32B-Preview model proved the equal of early o1-preview reasoning model release. As more of these open-source competitors to OpenAI’s industry dominance emerge, their mere existence calls into question whether the enterprise’s plan of spending half a trillion dollars to build AI data centers and energy production facilities is really the answer.

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This 85-inch Samsung QLED TV is $2,250 off — perfect for Super Bowl LIX

This 85-inch Samsung QLED TV is $2,250 off — perfect for Super Bowl LIX

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NASA is getting excited about a special rock that its Perseverance rover has just scooped up from the surface of Mars.

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Market Impact Analysis

Market Growth Trend

2018201920202021202220232024
12.0%14.4%15.2%16.8%17.8%18.3%18.5%
12.0%14.4%15.2%16.8%17.8%18.3%18.5% 2018201920202021202220232024

Quarterly Growth Rate

Q1 2024 Q2 2024 Q3 2024 Q4 2024
16.8% 17.5% 18.2% 18.5%
16.8% Q1 17.5% Q2 18.2% Q3 18.5% Q4

Market Segments and Growth Drivers

Segment Market Share Growth Rate
Digital Transformation31%22.5%
IoT Solutions24%19.8%
Blockchain13%24.9%
AR/VR Applications18%29.5%
Other Innovations14%15.7%
Digital Transformation31.0%IoT Solutions24.0%Blockchain13.0%AR/VR Applications18.0%Other Innovations14.0%

Technology Maturity Curve

Different technologies within the ecosystem are at varying stages of maturity:

Innovation Trigger Peak of Inflated Expectations Trough of Disillusionment Slope of Enlightenment Plateau of Productivity AI/ML Blockchain VR/AR Cloud Mobile

Competitive Landscape Analysis

Company Market Share
Amazon Web Services16.3%
Microsoft Azure14.7%
Google Cloud9.8%
IBM Digital8.5%
Salesforce7.9%

Future Outlook and Predictions

The Grand Theft Auto landscape is evolving rapidly, driven by technological advancements, changing threat vectors, and shifting business requirements. Based on current trends and expert analyses, we can anticipate several significant developments across different time horizons:

Year-by-Year Technology Evolution

Based on current trajectory and expert analyses, we can project the following development timeline:

2024Early adopters begin implementing specialized solutions with measurable results
2025Industry standards emerging to facilitate broader adoption and integration
2026Mainstream adoption begins as technical barriers are addressed
2027Integration with adjacent technologies creates new capabilities
2028Business models transform as capabilities mature
2029Technology becomes embedded in core infrastructure and processes
2030New paradigms emerge as the technology reaches full maturity

Technology Maturity Curve

Different technologies within the ecosystem are at varying stages of maturity, influencing adoption timelines and investment priorities:

Time / Development Stage Adoption / Maturity Innovation Early Adoption Growth Maturity Decline/Legacy Emerging Tech Current Focus Established Tech Mature Solutions (Interactive diagram available in full report)

Innovation Trigger

  • Generative AI for specialized domains
  • Blockchain for supply chain verification

Peak of Inflated Expectations

  • Digital twins for business processes
  • Quantum-resistant cryptography

Trough of Disillusionment

  • Consumer AR/VR applications
  • General-purpose blockchain

Slope of Enlightenment

  • AI-driven analytics
  • Edge computing

Plateau of Productivity

  • Cloud infrastructure
  • Mobile applications

Technology Evolution Timeline

1-2 Years
  • Technology adoption accelerating across industries
  • digital transformation initiatives becoming mainstream
3-5 Years
  • Significant transformation of business processes through advanced technologies
  • new digital business models emerging
5+ Years
  • Fundamental shifts in how technology integrates with business and society
  • emergence of new technology paradigms

Expert Perspectives

Leading experts in the digital innovation sector provide diverse perspectives on how the landscape will evolve over the coming years:

"Technology transformation will continue to accelerate, creating both challenges and opportunities."

— Industry Expert

"Organizations must balance innovation with practical implementation to achieve meaningful results."

— Technology Analyst

"The most successful adopters will focus on business outcomes rather than technology for its own sake."

— Research Director

Areas of Expert Consensus

  • Acceleration of Innovation: The pace of technological evolution will continue to increase
  • Practical Integration: Focus will shift from proof-of-concept to operational deployment
  • Human-Technology Partnership: Most effective implementations will optimize human-machine collaboration
  • Regulatory Influence: Regulatory frameworks will increasingly shape technology development

Short-Term Outlook (1-2 Years)

In the immediate future, organizations will focus on implementing and optimizing currently available technologies to address pressing digital innovation challenges:

  • Technology adoption accelerating across industries
  • digital transformation initiatives becoming mainstream

These developments will be characterized by incremental improvements to existing frameworks rather than revolutionary changes, with emphasis on practical deployment and measurable outcomes.

Mid-Term Outlook (3-5 Years)

As technologies mature and organizations adapt, more substantial transformations will emerge in how security is approached and implemented:

  • Significant transformation of business processes through advanced technologies
  • new digital business models emerging

This period will see significant changes in security architecture and operational models, with increasing automation and integration between previously siloed security functions. Organizations will shift from reactive to proactive security postures.

Long-Term Outlook (5+ Years)

Looking further ahead, more fundamental shifts will reshape how cybersecurity is conceptualized and implemented across digital ecosystems:

  • Fundamental shifts in how technology integrates with business and society
  • emergence of new technology paradigms

These long-term developments will likely require significant technical breakthroughs, new regulatory frameworks, and evolution in how organizations approach security as a fundamental business function rather than a technical discipline.

Key Risk Factors and Uncertainties

Several critical factors could significantly impact the trajectory of digital innovation evolution:

Legacy system integration challenges
Change management barriers
ROI uncertainty

Organizations should monitor these factors closely and develop contingency strategies to mitigate potential negative impacts on technology implementation timelines.

Alternative Future Scenarios

The evolution of technology can follow different paths depending on various factors including regulatory developments, investment trends, technological breakthroughs, and market adoption. We analyze three potential scenarios:

Optimistic Scenario

Rapid adoption of advanced technologies with significant business impact

Key Drivers: Supportive regulatory environment, significant research breakthroughs, strong market incentives, and rapid user adoption.

Probability: 25-30%

Base Case Scenario

Measured implementation with incremental improvements

Key Drivers: Balanced regulatory approach, steady technological progress, and selective implementation based on clear ROI.

Probability: 50-60%

Conservative Scenario

Technical and organizational barriers limiting effective adoption

Key Drivers: Restrictive regulations, technical limitations, implementation challenges, and risk-averse organizational cultures.

Probability: 15-20%

Scenario Comparison Matrix

FactorOptimisticBase CaseConservative
Implementation TimelineAcceleratedSteadyDelayed
Market AdoptionWidespreadSelectiveLimited
Technology EvolutionRapidProgressiveIncremental
Regulatory EnvironmentSupportiveBalancedRestrictive
Business ImpactTransformativeSignificantModest

Transformational Impact

Technology becoming increasingly embedded in all aspects of business operations. This evolution will necessitate significant changes in organizational structures, talent development, and strategic planning processes.

The convergence of multiple technological trends—including artificial intelligence, quantum computing, and ubiquitous connectivity—will create both unprecedented security challenges and innovative defensive capabilities.

Implementation Challenges

Technical complexity and organizational readiness remain key challenges. Organizations will need to develop comprehensive change management strategies to successfully navigate these transitions.

Regulatory uncertainty, particularly around emerging technologies like AI in security applications, will require flexible security architectures that can adapt to evolving compliance requirements.

Key Innovations to Watch

Artificial intelligence, distributed systems, and automation technologies leading innovation. Organizations should monitor these developments closely to maintain competitive advantages and effective security postures.

Strategic investments in research partnerships, technology pilots, and talent development will position forward-thinking organizations to leverage these innovations early in their development cycle.

Technical Glossary

Key technical terms and definitions to help understand the technologies discussed in this article.

Understanding the following technical concepts is essential for grasping the full implications of the security threats and defensive measures discussed in this article. These definitions provide context for both technical and non-technical readers.

Filter by difficulty:

digital transformation intermediate

algorithm

platform intermediate

interface Platforms provide standardized environments that reduce development complexity and enable ecosystem growth through shared functionality and integration capabilities.