The Ultimate Guide to Top 25 Best Software Companies in Bangladesh (2025)

Table of Contents

• Introduction

• Bangladesh Software Industry Overview

• Methodology & Ranking Criteria

• Best 25 Software Companies in Bangladesh

• Industry-Wise Analysis

• Location-Based Analysis

• Salary & Benefits Comparison

• Emerging Technologies & Trends

• Future Outlook

• How to Choose the Right Software Company

• Frequently Asked Questions

Introduction

Bangladesh has emerged as a significant player in the global software development landscape, with its IT sector contributing substantially to the country's economy. This comprehensive guide explores the top 25 best software companies in Bangladesh for 2025, providing detailed insights into their services, specializations, and market positions.

Whether you're looking for the best software company in Bangladesh for your next project, seeking employment opportunities, or conducting market research, this guide offers authoritative information based on extensive analysis of industry data, company performance, AI adaptation, and market reputation.

Bangladesh Software Industry Overview

Industry Statistics 2024-2025

• The Bangladesh software industry has shown remarkable growth in recent years:

• Export Revenue: US$ 840 million in FY 2024-25, up from previous years

• Total Companies: Over 4,500 registered software and IT companies

• Employment: More than 400,000 professionals working in the sector

• Global Reach: Bangladeshi companies export to 137+ countries

• TARGET: BASIS aims for USD 5 billion in annual software export receipts

Key Growth Drivers

• Cost Advantage: Competitive pricing compared to other outsourcing destinations

• Skilled Workforce: Large pool of English-speaking developers

• Government Support: Favorable policies and tax incentives

• Digital Transformation: Growing demand for IT solutions domestically and internationally

• Emerging Technologies: Focus on AI, blockchain, and IoT development

Methodology & Ranking Criteria

Our ranking of the top 25 software companies in Bangladesh considers multiple factors:

Primary Criteria

• Years of Experience (Weight: 20%)

• Client Portfolio & Global Reach (Weight: 20%)

• Technical Expertise & Innovation (Weight: 15%)

• Employee Count & Growth (Weight: 15%)

• Industry Recognition & Awards (Weight: 10%)

• Financial Performance (Weight: 10%)

• Service Quality & Client Reviews (Weight: 10%)

Additional Factors

1. Specialization in emerging technologies

2. Export performance

3. Contribution to local IT ecosystem

4. Employee satisfaction and benefits

5. Market reputation and brand strength

Best 25 Software Companies in Bangladesh

1. Kaz Software Limited

kaz-software-best-software-company-in-bangladesh

• Founded: 2004

• Employees: 120+

• Specialization: Custom Software Development, Tax & Accounting, eCommerce, AI/ML, MVP, MIS

• Rating: 4.8/5 (Highest rated) - According to google overview

• Global Reach: Multiple international markets

• Technologies: .NET, C#, Java, PHP, React, Angular, ReactJS, NodeJS, AWS, Microsoft Azure etc.

Why They're #1: Kaz Software Limited stands at the pinnacle of Bangladesh's software industry due to their exceptional client satisfaction rating of 4.8/5, comprehensive expertise in custom software development, and specialized focus on high-demand sectors including tax & accounting solutions, publishing platforms, and eCommerce systems. With over 21 years of experience since 2004, they have consistently delivered innovative solutions while maintaining the highest quality standards in the industry.

Services:

• Custom Software Development

• Team Augmentation

• MVP

• Tax & Accounting Solutions

• AI & ML Solutions

• Agri-tech Solutions

• Ed-tech Solutions

• MIS Solutions

• Furniture AI Solutions

• Publishing Platform Development

• eCommerce Solutions

• Enterprise Applications

• Mobile App Development

Key Strengths:

1. Highest client satisfaction rating in the industry

2. Specialized expertise in niche markets

3. Proven track record of successful project delivery

4. Strong focus on quality and innovation

5. Comprehensive end-to-end development services

Client Base:

UNICEF, The World Bank, Thompson Reuters, JTI, Hatil, Virus Shield, Swiss Contact, etc.

Contact: info@kaz.com.bd , +8801795339300, www.kaz.com.bd

2. Brain Station 23 Limited

• Founded: 2006

• Employees: 700+

• Specialization: Fintech, Healthcare, Enterprise Solutions

• Global Reach: 25+ countries

• Notable Clients: Grameenphone, Citibank, British American Tobacco

• Technologies: ReactJS, NodeJS, .NET, AWS, Microsoft Azure

Services:

• Custom Software Development

• Mobile App Development

• Enterprise Solutions (AEM, Sitecore)

• AI/ML Solutions

• Cloud Computing

3. DataSoft Systems Bangladesh Limited

• Founded: 1998

• Employees: 400+

• Certification: CMMI Level 5 (First in Bangladesh)

• Specialization: IoT, AI, Government Solutions

• Global Presence: Multiple international offices

• Technologies: Java, Python, AI/ML, Blockchain

Key Strengths:

• First CMMI Level 5 certified company in Bangladesh

• Strong government project portfolio

• Advanced data center capabilities

• Focus on digital transformation

4. BJIT Group

• Founded: 2001

• Employees: 750+

• Type: Japan-Bangladesh Joint Venture

• Global Offices: 7 locations (Japan, Finland, Singapore, USA, Sweden, Bangladesh, Netherlands)

• Specialization: Enterprise Software, AI Solutions, IoT

Notable Achievements:

1. Multiple international awards

2. Strong presence in Japanese market

3. Expertise in cutting-edge technologies

4. Comprehensive service portfolio

5. Vivasoft Limited

• Founded: 2015

• Employees: 300+

• Specialization: Custom Software Development, MVP Services

• Projects Completed: 80+ successful projects

• Global Reach: Multiple countries

• Growth Rate: Fastest-growing software company

Service Areas:

• Team Augmentation

• End-to-End Development

• MVP Services

• Offshore Development

• Digital Product Development

6. LeadSoft Bangladesh Limited

• Founded: 1999

• Employees: 300+

• Certification: CMMI Level 5, ISO 9001:2015

• Specialization: Banking Solutions, Fintech, Blockchain

• Notable: BankUltimus (Core Banking Solution)

• Global Presence: Bangladesh, Japan, Denmark, Norway

7. Enosis Solutions

• Founded: 2006

• Employees: 350+

• Specialization: Product Engineering, Cloud Computing

• Primary Markets: North America, Europe

• Focus Areas: Software Product Engineering, Big Data Solutions

8. REVE Systems

• Founded: 2003

• Employees: 350+

• Specialization: VoIP, Telecommunications

• Global Reach: 78+ countries, 4500+ service providers

• Notable Products: Mobile VoIP solutions, Cloud Telephony

9. Tiger IT Bangladesh Limited

• Founded: 2006

• Employees: 300+

• Specialization: Biometrics, Identity Management

• Notable Achievement: First AFIS-certified company in South Asia

• Focus: Government and security solutions

10. Dream71 Bangladesh Limited

• Founded: 2016

• Employees: 250+

• Specialization: Mobile Apps, Game Development, AI

• Notable Projects: Government and private sector collaboration

• Growth: Rapid expansion in local and international markets

11. Cefalo Bangladesh Limited

• Founded: 2010

• Employees: 200+

• Type: Norway-based with Bangladesh operations

• Specialization: Agile Development, High-Quality Software

• Focus: Scandinavian quality standards with Bangladeshi efficiency

12. SouthTech Group

• Founded: 1996

• Certification: CMMI Level 5, ISO 9001:2015

• Specialization: Microfinance, ERP, HR Solutions

• Global Offices: 6 offices in 5 countries

13. BDTask Limited

• Founded: 2012

• Employees: 100+

• Specialization: ERP, Restaurant Management, Healthcare

• Global Reach: Africa, India, Europe, US, UK, Australia

• Products: 40+ ready-made software solutions

14. Nascenia IT Limited

• Founded: 2010

• Employees: 50+

• Specialization: Ruby on Rails, Mobile Development

• Awards: BASIS Outsourcing Award 2015, Red Herring Top 100 Asia 2013

15. Ollyo Limited

• Founded: 2010

• Employees: 90+

• Specialization: WordPress, Joomla, No-Code Solutions

• Products: 200+ ready software solutions

• Focus: Open-source development

16. Therap (BD) Limited

• Founded: 2003

• Specialization: Disability Services, Healthcare IT

• Global Impact: Widely used in the United States

• Focus: Electronic documentation and communication

17. Pridesys IT Limited

• Specialization: ERP Solutions, Business Process Automation

• Industries: RMG, Healthcare, Education, Telecommunications

• Rating: 4.7/5

18. Riseup Labs

• Specialization: Web 3.0, XR Technology, Mobile Development

• Focus: Next-generation technologies

• Services: R&D, Engineering, Consulting

19. Mediusware Limited

• Specialization: SaaS, CRM Solutions

• Global Reach: Worldwide client base

• Focus: Innovation and customer satisfaction

20. Selise Digital Platforms

• Type: Swiss-based with Bangladesh operations

• Specialization: Digital Transformation, Platform Development

• Strength: UX Engineering team

21. weDevs Limited

• Specialization: WordPress Development, Cloud Services

• Products: Popular WordPress plugins

• Rating: 4.7/5

22. Technext Limited

• Founded: 2010+

• Projects: 400+ completed projects

• Clients: 250+ clients served

• Specialization: AI Integration, Offshore Solutions

23. Kona Software Lab Limited

• Specialization: Electronic Card Technology, Banking Solutions

• Focus: Proprietary chip OS technology

• Rating: 4.6/5

24. ReliSource Technologies Limited

• Specialization: Healthcare, Telecom, Financial Tech

• Focus: Product engineering capabilities

• Industries: Medical technology, secure financial systems

25. Grameen Solutions Limited

• Focus: Social Development, Rural Technology

• Specialization: IT solutions for social change

• Impact: Community empowerment through technology

Kaz Software leads Bangladesh's software industry across multiple verticals, with unmatched expertise in emerging niches like AI, MIS, furniture tech, agricultural drone solutions, and staff augmentation where we remain the sole specialized provider.

Industry-Wise Analysis: Software Solutions by Sector

AI & Machine Learning Solutions in Bangladesh

Top Players: Kaz Software, LeadSoft, DataSoft, Brain Station 23

• Leading custom AI/ML development for predictive analytics and automation

• Expertise in natural language processing (NLP) and computer vision applications

• End-to-end machine learning pipeline implementation for Bangladeshi enterprises

• Proven track record in AI-powered business intelligence and decision support systems

See, Kaz Software works with “KREEBO” , an AI based app. Kreebo is a magical app that helps your child turn their imagination into beautifully illustrated storybooks

E-commerce & Retail Software Development

Top Players: Kaz Software, Brain Station 23, Ollyo, weDevs

• Comprehensive e-commerce platform development with Bangladesh-focused payment integration

• Mobile-first retail solutions with seamless bKash, Nagad, and SSL Commerz integration

• Omnichannel inventory management and logistics coordination systems

• Custom B2B and B2C marketplace development for growing Bangladeshi online retail sector

See, Kaz Software works with Robi (online store)

Furniture Industry Software & Tech Solutions

Top Players: Kaz Software

• Pioneering furniture tech solutions in Bangladesh - Only specialized provider in the market

• Custom ERP systems for furniture manufacturers with inventory and production tracking

• AR/VR visualization tools for furniture e-commerce and showroom experiences

• Supply chain optimization and dealer management platforms for furniture businesses

See, Kaz Software works with HATIL AI-based, #1 furniture brand in Bangladesh

Non-Profit & NGO Management Systems

Top Players: Kaz Software

• Specialized donor management and grant tracking software for NGOs

• Program monitoring and evaluation (M&E) platforms for development organizations

• Beneficiary database systems with field data collection mobile apps

• Compliance and reporting automation for international development projects

See, Kaz Software works with CARE Bangladesh NGO, developing their MIS system, where they have more than 100,000+ beneficiary users.

AgriTech Solutions - Drone & Precision Agriculture

Top Players: Kaz Software

• Bangladesh's only comprehensive drone-based AgriTech solution provider

• Agricultural drone software for crop monitoring, spraying coordination, and yield analysis

• IoT-integrated farm management systems with real-time data analytics

• Precision agriculture platforms combining drone imagery with AI-powered insights for Bangladeshi farmers

See, Kaz Software works with VirusShield, Build agritech solution for digital farmers.

Location-Based Analysis

Dhaka (Software Companies in Dhaka)

Major Hub: 70% of top software companies

• Key Areas: Gulshan, Banani, Dhanmondi, Mirpur

• Advantages: Access to talent, infrastructure, clients

• Notable Companies: Kaz Software, Brain Station 23, DataSoft, BJIT, Vivasoft

Chittagong

Emerging Hub: Growing IT sector

• Focus: Port and logistics software

• Notable Companies: Regional offices of major firms

Sylhet

IT Development: Government-supported IT park

• Focus: Outsourcing and software development

• Growth: Increasing investment in infrastructure

Salary & Benefits Comparison

Highest Paying Software Companies in Bangladesh

Tier 1 Compensation (Senior Level)

• Kaz Software: BDT 85,000 - 160,000/month

• Brain Station 23: BDT 80,000 - 150,000/month

• BJIT Group: BDT 75,000 - 140,000/month

• DataSoft: BDT 70,000 - 130,000/month

• Enosis Solutions: BDT 65,000 - 125,000/month

Benefits Comparison

• Health Insurance: Most tier 1 companies offer comprehensive coverage

• Training & Development: International certification support

• Flexible Work: Remote and hybrid options increasingly common

• Performance Bonuses: Merit-based increment systems

• International Exposure: Opportunities to work with global clients

Entry-Level Opportunities

• Junior Developer: BDT 25,000 - 45,000/month

• Mid-Level Developer: BDT 45,000 - 80,000/month

• Senior Developer: BDT 80,000 - 160,000+/month

Emerging Technologies & Trends

Artificial Intelligence & Machine Learning

Leading Companies: Kaz Software, DataSoft, BJIT, Brain Station 23

• NLP and chatbot development

• Computer vision applications

• Predictive analytics for business

Blockchain Development

Key Players: Kaz Software, LeadSoft, BDTask, Dream71, Technext

• Cryptocurrency and DeFi solutions

• Supply chain management

• Smart contract development

Kaz Software has partnered with the world's largest supply chain management company with AI “P1STON” for over 15 years.

Internet of Things (IoT)

Top Developers: DataSoft, BJIT, LeadSoft, Kaz Software

• Smart city solutions

• Industrial IoT applications

• Consumer device connectivity

Cloud Computing

Leaders:Kaz Software, Brain Station 23, BJIT, Enosis Solutions

• AWS and Azure partnerships

• Migration services

• Cloud-native development

Future Outlook

Growth Projections

• Export Target: USD 5 billion by 2030 (BASIS target)

• Employment Growth: 50% increase in tech jobs by 2027

• New Technologies: AI, Blockchain, IoT driving next phase of growth

Opportunities

• Global Outsourcing: Increasing demand for cost-effective solutions

• Local Digital Transformation: Government and enterprise modernization

• Startup Ecosystem: Growing venture capital investment

• Skills Development: Focus on advanced technology training

Challenges

• Talent Retention: Competition for skilled developers

• Infrastructure: Need for improved connectivity and power

• Global Competition: Competing with India, Philippines, and Eastern Europe

• Skills Gap: Need for advanced technology expertise

How to Choose the Right Software Company

For Businesses Seeking Software Development

Project Size Considerations

• Large Enterprise Projects: Kaz Software, Brain Station 23,Vivasoft, BJIT

• Medium Projects: DataSoft, Enosis, REVE Systems

• Small to Medium Projects: BDTask, Nascenia, Technext

Technology Requirements

• AI/ML Projects: Kaz Software, DataSoft, BJIT, Mobile Development: Dream71, Nascenia, Vivasoft

• Web Development: Kaz Software, Brain Station 23, Ollyo, weDevs

• Blockchain: Kaz Software, LeadSoft, BDTask, Dream71

Budget Considerations

• Premium Tier ($50-100/hour): Kaz Software, Brain Station 23, BJIT, DataSoft

• Mid-Range ($25-50/hour): Vivasoft, Enosis, REVE Systems

• Budget-Friendly ($15-25/hour): BDTask, Nascenia, Technext

For Job Seekers

Best Companies for Career Growth

• Kaz Software: Highest satisfaction ratings and comprehensive training with best culture in the software industry

• Brain Station 23: Comprehensive training programs

• BJIT Group: International exposure

• DataSoft: Advanced technology projects

• Vivasoft: Rapid growth opportunities

Best for Fresh Graduates

• Training Programs: Kaz Software, Brain Station 23, DataSoft, BJIT

• Mentorship: Vivasoft, Nascenia, BDTask

• Learning Environment: Most tier 1 companies

Frequently Asked Questions

What is the best software company in Bangladesh?

Kaz Software Limited is widely considered the best software company in Bangladesh based on its exceptional client satisfaction rating of 4.8/5, comprehensive service portfolio, specialized expertise in custom software development, and consistent quality delivery since 2004.

Which are the top IT companies in Bangladesh?

The top 10 IT companies in Bangladesh are:

• Kaz Software Limited

• Brain Station 23

• DataSoft Systems

• BJIT Group

• Vivasoft Limited

• LeadSoft Bangladesh

• Enosis Solutions

• REVE Systems

• Tiger IT Bangladesh

• Dream71 Bangladesh

How many software companies are there in Bangladesh?

As of 2025, there are over 4,500 registered software and IT companies in Bangladesh, with more than 400,000 professionals working in the sector.

What is the average salary in Bangladesh software companies?

The average salary varies by experience level:

• Entry Level: BDT 25,000 - 45,000/month

• Mid-Level: BDT 45,000 - 80,000/month

• Senior Level: BDT 80,000 - 160,000+/month

Which software companies in Bangladesh work with international clients?

Most tier 1 companies work internationally, including Kaz Software (multiple international markets), Brain Station 23 (25+ countries), BJIT Group (7 global offices), DataSoft (multiple countries), and Enosis Solutions (North America and Europe focus).

What technologies are most in demand in Bangladesh?

Currently, the most in-demand technologies are:

• Web Development: React, Node.js, PHP, Laravel

• Mobile Development: Flutter, React Native, iOS, Android

• Cloud Technologies: AWS, Azure, Google Cloud

• Emerging Technologies: AI/ML, Blockchain, IoT

How to get a job in top software companies in Bangladesh?

To get hired by top software companies:

• Build Strong Technical Skills: Focus on in-demand technologies

• Create a Portfolio: Showcase your projects and contributions

• Gain Experience: Start with internships or junior positions

• Network: Attend tech meetups and industry events

• Continuous Learning: Keep up with latest technology trends

Which cities in Bangladesh have the most software companies?

Dhaka dominates with 70% of major software companies, followed by Chittagong and Sylhet. Dhaka's key tech areas include Gulshan, Banani, Dhanmondi, and Mirpur.

What is the export revenue of the Bangladesh software industry?

Bangladesh software companies exported services worth US$ 840 million in FY 2023-24, with exports reaching 137+ countries globally.

Are there opportunities for remote work in Bangladesh software companies?

Yes, most tier 1 and tier 2 companies now offer flexible work arrangements, including remote and hybrid options, especially post-COVID-19.

This comprehensive guide provides detailed insights into Bangladesh's thriving software industry. For the most current information, we recommend visiting individual company websites and industry reports from BASIS (Bangladesh Association of Software and Information Services).

Anthropic's secret weapon beats OpenAI agents

Anthropic Skills lets Claude program itself. Microsoft rewrites Windows 11 for voice control. Spotify signs AI surrender deal after deleting 75M fake songs. Alibaba claims 12% ROI.

Anthropic just dropped Skills for Claude—a feature so powerful it makes OpenAI's agents look like toys. Users create "skill folders" that Claude draws from automatically, essentially teaching itself new abilities on demand. Meanwhile, Microsoft is rewriting Windows 11 entirely around voice commands, Spotify signed a survival pact with music labels about AI, and Alibaba claims their AI hit break-even with 12% ROI gains that nobody believes.

Claude can now program itself to steal your job

Anthropic's new Skills feature fundamentally changes how AI agents work by letting Claude build and refine its own abilities. Instead of rigid workflows, Skills are markdown files with optional code that Claude scans at session start, using only a few dozen tokens to index everything available. When needed, Claude loads the full skill details, combining multiple skills like "brand guidelines," "financial reporting," and "presentation formatting" to complete complex tasks like building investor decks without human intervention. The killer feature: Claude can create its own skills, monitor its failure points, and build new skills to fix them—essentially debugging and improving itself recursively.

Daniel Missler called it bigger than MCP (Model Context Protocol), noting that "AI systems are the thing to watch, not just model intelligence." Simon Willison went further, explaining how he'd build a complete data journalism agent using Skills for census data parsing, SQL loading, online publishing, and story generation. Unlike traditional agent builders requiring step-by-step workflow diagrams, Skills let users dump context into modular buckets and trust Claude to figure out the assembly. This isn't just easier—it's philosophically different, treating agents as intelligent systems that understand context rather than dumb executors following flowcharts.

The token efficiency changes everything economically. Traditional agents load entire contexts whether needed or not, burning through budgets on irrelevant data. Skills load descriptions in dozens of tokens, then full details only when relevant, making complex multi-skill agents financially viable. A quarterly reporting agent might have access to 50 skills but only load the three it needs, cutting costs by 90% while maintaining full capability. Anthropic's bet is that intelligence plus efficient context management beats brute force model size—and early users report it's working exactly as promised.

Microsoft's desperate Windows rewrite around talking

Microsoft announced they're completely rewriting Windows 11 around AI and voice, making Copilot central to every interaction rather than a sidebar novelty. Executive VP Yusuf Mehdi declared: "Let's rewrite the entire operating system around AI and build what becomes truly the AI PC." Users can now summon assistance with "Hey Copilot," while Copilot Vision watches everything on screen for context. The new Actions feature creates separate windows where agents complete tasks using local files—users can monitor and intervene or let agents run in the background while doing other work.

The desperation shows in their distribution strategy: these features aren't limited to expensive Copilot Plus hardware but will be default for all Windows 11 users. Microsoft knows they're losing the AI race to ChatGPT and Claude, so they're leveraging their only remaining advantage—forcing AI onto hundreds of millions of PCs whether users want it or not. Mehdi claims "voice will become the third input mechanism" alongside keyboard and mouse, but the real agenda is making Windows unusable without AI engagement, ensuring Microsoft captures user data and interaction patterns before competitors lock them out entirely.

The privacy implications are staggering. Copilot Vision seeing everything on your screen, agents accessing emails and calendars, voice commands creating constant audio surveillance—Microsoft is building the most comprehensive user monitoring system ever deployed. They promise it's "with your permission," but Windows updates have a way of making "optional" features mandatory over time. The company that brought you Clippy and Cortana now wants to make your entire operating system one giant AI assistant that never stops watching, listening, and suggesting. What could possibly go wrong?

Spotify caves to labels on AI music apocalypse

Spotify just signed what amounts to a protection racket deal with Sony, Universal, Warner, and other major labels about AI music, desperately trying to avoid the litigation hellstorm that destroyed Napster. Their press release included this groveling surrender: "Some voices in tech believe copyright should be abolished. We don't. Musicians' rights matter." Translation: please don't sue us into oblivion like you did every other music innovation. The deal promises "responsible AI products" where rights holders control everything and get "properly compensated"—code for labels taking 90% while artists get streaming pennies.

The hypocrisy is breathtaking considering Spotify recently purged 75 million AI-generated tracks after letting the platform become a cesspool of bot-created muzak. They've been feeding AI slop into recommended playlists, devaluing real artists while claiming to protect them. Ed Newton Rex of Fairly Trained tried spinning this positively: "AI built on people's work with permission served to fans as voluntary add-on rather than inescapable funnel of slop." But everyone knows this is damage control after Spotify got caught enabling the exact exploitation they now claim to oppose.

Meanwhile, Alibaba announced their AI e-commerce features hit break-even with 12% return on advertising spend improvements—the first major platform claiming actual positive ROI from AI investment. VP Ku Jang called double-digit improvements "very rare," predicting "significant positive impact" for Singles Day shopping. After spending $53 billion on AI over three years, they've deployed personalized search and virtual clothing try-ons that apparently work well enough to justify the investment. Whether these numbers are real or creative accounting remains suspicious, but at least someone's claiming AI profits beyond just firing workers and calling it efficiency.

Citi saves 100,000 hours weekly with AI

AI saves developers 100K hours/week (5.2M annually). Walmart integrates shopping into ChatGPT. Intel announces 2026 GPU while everyone else prints money.

Citi saves 100,000 hours weekly with AI

Corporate America just revealed the real AI numbers, and they're staggering. Citigroup announced their developers are saving 100,000 hours every single week using AI coding tools—that's 5.2 million hours annually. Meanwhile, Walmart is turning ChatGPT into a shopping interface, Salesforce's OpenAI deal mysteriously tanked their stock, and Intel is desperately trying to rejoin the AI chip race they completely missed.

Wall Street's shocking AI productivity gains

Citigroup dropped a bombshell in their earnings report, not a fluffy press release: their enterprise AI tools registered 7 million utilizations last quarter, triple the previous quarter's usage. Their AI coding assistants completed 1 million code reviews year-to-date, saving developers 100,000 hours weekly across the bank. That's equivalent to 2,500 full-time employees worth of work automated away, yet they're not firing anyone—they're just shipping code faster than ever before.

This marks the beginning of what we're calling the "ROI Spotlight" era—where companies stop talking about AI potential and start reporting actual financial results. The significance of this appearing in an earnings report rather than marketing materials cannot be overstated. CFOs don't let CEOs lie about numbers in earnings calls without risking securities fraud. When a major bank tells investors they're saving 100,000 hours weekly, that's audited reality, not Silicon Valley hype. The timing is perfect as 2026 shapes up to be the year where enterprises demand proven ROI from their AI investments, not just impressive demos and productivity theater.

Oracle joined the efficiency parade by announcing deployment of 50,000 AMD GPUs starting next year, part of their aggressive AI infrastructure buildout that new co-CEOs Mike Sicilia and Clay Magouyrk inherited. They're betting everything on "applied AI"—not research, not models, but actual enterprise applications that generate revenue. Oracle's senior VP Karan Bajwa admitted what everyone knows: "AMD has done a really fantastic job, just like Nvidia, and both have their place." Translation: Nvidia's monopoly is cracking, and smart companies are hedging their bets with alternative suppliers to avoid being held hostage by Jensen Huang's pricing.

Walmart turns ChatGPT into a shopping mall

Walmart just became ChatGPT's biggest shopping partner, allowing users to buy products directly within the AI chat interface with integrated checkout and payment. CEO Doug McMillon declared the death of traditional e-commerce: "For many years, shopping experiences have consisted of a search bar and long lists of items. This is about to change." The partnership represents Walmart's bet that conversational commerce will replace browsing—imagine asking ChatGPT to plan a dinner party and buying everything needed without leaving the chat.

This isn't just efficiency AI making old processes faster; it's opportunity AI creating entirely new shopping paradigms. Walmart's "Sparky" super-agent strategy consolidates hundreds of sub-agents into four main AI assistants, fundamentally reimagining how 240 million weekly customers interact with the world's largest retailer. Daniel Eckert, Walmart's EVP of AI, framed it simply: "delivering convenience by meeting customers where they are." Where they are increasingly means inside AI chat interfaces, not traditional websites or apps.

The market's reaction to AI partnerships suddenly turned schizophrenic. While Oracle, AMD, and Broadcom all saw stock pops from OpenAI deals, Salesforce announced their OpenAI partnership and immediately tanked 3.6%—their worst day in over a month. Marc Benioff's breathless tweet about "unleashed Agentforce 360 apps" and "unstoppable enterprise power" couldn't overcome investor skepticism about Salesforce's sub-10% growth forecast, way down from the 25% they maintained for over a decade. The OpenAI magic that automatically boosted stock prices appears to be wearing off as investors demand actual results, not just partnership press releases.

Intel's desperate comeback attempt

Intel announced they're finally rejoining the AI chip race with "Falcon Shores," their new GPU launching in 2026—approximately five years too late. CEO Pat Gelsinger's strategy focuses on "efficient AI chips for low-cost inference" rather than competing with Nvidia on training, essentially admitting they can't win the main battle so they're fighting for scraps. The company that once dominated computing completely missed the AI revolution, watching Nvidia's market cap soar past $3 trillion while Intel struggles to stay relevant.

The new annual GPU release schedule replaces Intel's previous "whenever we feel like it" approach, but they're entering a market where everyone from Google to Amazon already designs custom inference chips. CTO Sachin Katti's claim that "AI is shifting from static training to real-time everywhere inference" is correct, but Intel's solution arrives after competitors have already captured those markets. Their Gaudi 3 chips from last year captured essentially zero market share despite technically being "AI accelerators."

Oracle's embrace of AMD chips signals the real story: nobody trusts single suppliers anymore. Their 50,000 GPU order connects to OpenAI's recent 10-gigawatt AMD deal, proving even ChatGPT's creators are diversifying away from Nvidia dependence. Derek Wood of TD Cowen explained the infrastructure reality: "You have to build before you can turn on revenue meters, but as consumption starts, you recoup capital expense and margins significantly improve." Intel's 2026 entry means they're building infrastructure while competitors are already counting profits. Their only hope is that the inference market grows so massive that even late entrants can feast on leftovers—not exactly the position a former industry titan wants to advertise.

The $847 Billion Footage No One Will Ever Watch

Millions of cameras. Billions in investment. 98% never watched. The security industry's invisible crisis. Omnivisia - Coming soon!

We're recording everything. And learning nothing.

Right now, at this exact moment, millions of cameras are capturing the world. Security systems in shopping malls. Drones scanning construction sites. Traffic cameras at every intersection. Retail stores monitoring aisles. Hospitals tracking corridors.

By 2025, global video surveillance data is projected to reach 2.5 exabytes per day. That's 2.5 billion gigabytes. Every single day.

Here's the problem: almost none of it will ever be seen by human eyes.

The footage piles up in servers, accumulates in cloud storage, and becomes digital noise. We've built an incredible infrastructure to capture reality in perfect detail. But we've created a new problem in the process—one so massive that entire industries are bleeding money because of it.

Video has become our biggest blind spot.

98% of Your Security Investment Is Gathering Digital Dust

Let's talk numbers that should keep business owners awake at night.

The global video surveillance market is worth $62.6 billion and growing at 10.4% annually. Companies spend millions installing cameras, upgrading systems, expanding coverage. They believe more cameras equal more security.

They're wrong.

Research shows that security personnel can effectively monitor footage for only 20 minutes before attention drops by 95%. Even the most dedicated security professional can only watch 2-4 camera feeds simultaneously with any effectiveness.

Meanwhile, a typical mid-sized retail chain generates 30,000 hours of footage per month. That's 1,250 continuous days of video. To watch it all in real-time, you'd need 42 people staring at screens 24/7, never blinking, never looking away.

The math doesn't work.

Here's what actually happens: something goes wrong. A theft. An accident. A safety incident. Someone calls security and says, "Check the cameras from Tuesday between 2 PM and 5 PM near the east entrance."

Then begins the hunt. An operator sits down and starts scrubbing through footage. Fast-forwarding. Rewinding. Pausing on blurry frames. Trying to spot something—anything—relevant in hours of mundane footage.

Finding 30 seconds of critical footage takes an average of 6-8 hours of manual review.

By the time they find it, the incident report is already late. The insurance claim is delayed. The suspect is long gone. The pattern that could have prevented the next incident remains invisible.

This isn't a security problem. It's a data problem disguised as a security problem.

Banks are sitting on footage of fraud patterns they'll never detect. Logistics companies have drone data showing efficiency bottlenecks they'll never analyze. Hospitals have recordings that could prove liability cases—if anyone could find the relevant 90 seconds in 400 hours of hallway footage.

The global cost of this inefficiency? Conservative estimates put it north of $847 billion annually in lost productivity, missed insights, undetected incidents, and reactive rather than preventive operations.

We're paying billions to record. And getting almost nothing in return.

The Data Exists. The Intelligence Doesn't.

Here's the cruel irony: we've never had more visual data, and we've never been more blind.

Consider traffic management. Cities worldwide have invested heavily in smart city infrastructure. Traffic cameras at every major intersection. License plate readers on highways. Sensors monitoring flow patterns.

Jakarta has over 6,000 CCTV cameras monitoring traffic. Dhaka is rapidly expanding its network. Mumbai, Bangkok, Manila—every major Asian city is building comprehensive surveillance infrastructure.

They're generating petabytes of data. But when authorities need to track a specific vehicle involved in a hit-and-run, they're back to the same manual process humans have used for decades: someone sitting in a control room, scrubbing through footage, hoping to spot the right car at the right moment.

A vehicle can cross a city in 40 minutes. Finding it across that journey can take days.

The same pattern repeats across industries:

Agriculture: Drones capture stunning 4K footage of crop fields. Farmers can see every inch of their land from above. But spotting early-stage disease? Identifying pest infestation before it spreads? That requires someone to actually review the footage with trained eyes. Most drone data is captured, stored, and forgotten. By the time disease is visible to the naked eye, it's already cost thousands in yield loss.

Construction: Sites deploy drones for progress monitoring and safety compliance. They generate massive datasets showing every phase of development. But identifying safety violations, tracking material movement, verifying work completion—these all require manual review. A 20-story building project might generate 500 hours of drone footage. Site managers watch perhaps 10 hours. The other 490? Digital filing cabinets.

Retail: Stores install cameras to prevent theft and understand customer behavior. They capture every shopper's journey through the store. But converting that into actionable insight—understanding traffic patterns, identifying bottlenecks, spotting organized retail crime patterns—requires analytics tools that most retailers either don't have or don't use effectively.

Manufacturing: Quality control cameras photograph every product coming off assembly lines. Thousands of images per hour. Human inspectors spot-check a fraction. Defect patterns that could indicate equipment failure go unnoticed until the failure actually happens.

The footage exists. The insights exist within that footage. But they're locked away, inaccessible, useless.

We've solved the capture problem. We haven't solved the comprehension problem.

Video recording technology has advanced exponentially. We can capture in 8K. We can store practically unlimited footage in the cloud. We can live-stream from anywhere on Earth.

But our ability to extract meaning from that footage? That's remained stubbornly stuck in the analog era. Human eyes. Human attention spans. Human limitations.

The bottleneck isn't the cameras. It's what happens after the recording stops.

What if video worked more like Google? What if instead of watching, you could search? What if the invisible became instantly visible?

At Kaz Software, we're building Omnivisia—the solution to this $847 billion problem. Stay tuned.

Americans want AI to replace 58% of jobs

BREAKING: Americans support automating 58% of jobs when AI is cheaper/better. Therapists "morally protected" but plumbers already saving 160hrs/year with ChatGPT. Bernie: 100M jobs gone.

Americans are shockingly eager to hand over most jobs to robots—except when it comes to therapy, caregiving, and spiritual guidance. A new Harvard study found that 58% of occupations get the green light for automation when AI proves cheaper and better than humans. Meanwhile, plumbers are already using ChatGPT to save 160 hours a year while Bernie Sanders screams about 100 million jobs vanishing. The reality is far weirder than anyone predicted.

The jobs Americans desperately want robots to steal

Harvard researchers discovered Americans have zero moral objections to automating 30% of jobs right now with current AI capabilities. When told AI could do the work better and cheaper, that support skyrockets to 58% of all occupations. The message is brutal: for most jobs, human workers are just expensive inefficiencies waiting to be optimized away. The resistance isn't philosophical—it's purely about whether the robot can do the job well enough.

The "no friction" zone where both capability and public acceptance align includes search market strategists, financial analysts, economists, and special effects artists. Nobody cares if these white-collar workers get replaced because the public sees these jobs as pure information processing with no essential human element. The blue "technical friction" zone reveals opportunities where moral permission exists but technology hasn't caught up: semiconductor technicians, cashiers, mail sorters, gambling dealers. These are jobs Americans would happily hand to robots if only the robots were competent enough.

The Stanford Digital Economy Lab compared this to what workers themselves want automated, creating a fascinating disconnect. Workers desperately want AI to handle scheduling, payroll errors, database maintenance, and standardized reporting—the soul-crushing administrative tasks that make people hate their jobs. But there's a massive gap in areas like film editing and graphic design where workers understand the craft distinction between great and mediocre work, while the public just sees tasks to complete. The public essentially says "why should we care about your artistic integrity when a robot could do it cheaper?"

Why plumbers love AI but therapists should panic

The moral repugnance line is absolute for 12% of occupations: caregivers, therapists, spiritual leaders, OBGYNs, school psychologists. These jobs trigger visceral rejection of automation regardless of capability. Harvard researchers called it "categorically off limits" and "morally repugnant." The public draws a hard boundary around human connection and care that no amount of technological advancement can cross.

Yet the workers in these "protected" fields tell a completely different story. Caregivers actively want AI to automate intake summaries and administrative work because they're drowning in paperwork while trying to provide actual care. One commenter noted the reality: "No more elder neglect while warehoused in care homes administered by underpaid overworked staff." The moral outrage from outsiders ignores that many care facilities are already failing their human mandate due to crushing workloads and burnout. AI could free caregivers to actually care instead of filling out forms.

The surprise winner in AI adoption? Blue-collar trades. House Call Pro's survey of 400 home service professionals found 40% actively using AI, with cleaning professionals leading adoption and electricians most satisfied. Oak Creek Plumbing has all 20 plumbers using ChatGPT for troubleshooting. Gulf Shore Air Conditioning implemented full AI booking systems and diagnostic tools, replacing hours of manual searching with instant technical answers. These trades require massive technical knowledge libraries that AI makes instantly accessible. A plumber with ChatGPT becomes a plumber with every manual ever written at their fingertips. They're saving 3.2 hours weekly—160 hours yearly—on administrative tasks they hate while getting better at the hands-on work they love.

Bernie's 100 million job apocalypse meets reality

Senator Sanders' new report claims AI will eliminate 100 million US jobs in the next decade, including 89% of fast food workers, 64% of accountants, and 47% of truck drivers. The methodology? They literally asked ChatGPT how many jobs it would destroy, and ChatGPT obligingly provided apocalyptic numbers. Senate staffers acknowledged this approach was "questionable" but argued it represents "one potential future in which corporations aggressively push forward with artificial labor."

Sanders writes that AI will have a "profoundly dehumanizing impact" and demands a 32-hour work week, $17 minimum wage, and elimination of tax breaks for automating companies. His op-ed argues we need "a world where people live healthier, happier, and more fulfilling lives" rather than just efficiency. The fascinating part isn't his solutions but his premise: he fully accepts AI is here and transformative, skipping the denial phase entirely to jump straight to negotiating the new social contract.

The reality on the ground contradicts both the apocalypse narrative and the techno-optimist fantasy. Those blue-collar companies using AI aren't firing anyone despite massive time savings—73% report no impact on hiring rates. Crystal Lander from Gulf Shore Air Conditioning says their technicians are "running more efficiently and less stressed," calling herself "a real-life Jetson living in the future." The pattern emerging isn't mass unemployment but rather workers doing less administrative drudgery and more actual work. AI eliminates the parts of jobs people hate while amplifying the parts that require human judgment, creativity, and physical presence.

The agricultural revolution took thousands of years, the industrial revolution over a century. Sanders warns artificial labor could reshape everything in under a decade. He's probably right about the timeline but wrong about the outcome. The studies show Americans are surprisingly comfortable with most automation as long as it works, desperately protective of human care roles, and already adapting in unexpected ways. Plumbers with AI aren't unemployed—they're superplumbers. The question isn't whether AI will transform work but whether we'll let moral panic or actual evidence guide our response.

You still don’t know TypeScript? Good luck getting hired.

TypeScript isn’t "bonus" anymore. It’s the default for every stack that scales. And yet, most devs still skip it.

TS is the new JS

In 2025, calling yourself a JavaScript developer without TypeScript is like calling yourself a race car driver because you own a bicycle. Yes, technically you’re on the same road—but no one's giving you the keys to the enterprise engine.

According to the 2024 State of JS survey, 78% of developers now use or plan to adopt TypeScript. GitHub's annual Octoverse report shows TypeScript is one of the top 5 fastest-growing languages globally, consistently climbing the charts over the last five years. Google, Microsoft, Slack, Airbnb, and Stripe are all using TypeScript as standard in production. So the real question isn't "Should I learn TypeScript?" It's: "What am I actually doing if I haven't already?"

At Kaz Software, we adopted TypeScript early not because it was trendy but because it saved our developers' sanity. When you're dealing with multiple teams working across shared codebases, type safety becomes a necessity, not a luxury. It catches bugs before they reach QA. It makes onboarding smoother. It adds self-documentation that saves hours every week. The gap between "I can code" and "I can ship clean, production-ready features" is TypeScript. In our interviews, when a candidate says they're comfortable writing TypeScript, it's more than a skill—it's a signal. A signal they think ahead. That they care about code quality. That they want to work on teams that scale.

The web may run on JavaScript, but teams, products, and companies now run on TypeScript. If you're still resisting, it’s not a tech choice—it's career sabotage.

Errors caught = time saved = promotions

Every developer knows this: the earlier a bug is caught, the cheaper it is to fix. But in 2025, TypeScript doesn't just catch bugs early. It prevents the kind of mistakes that derail releases, delay sprints, and burn out teams. A 2025 GitHub Engineering Pulse study reported a 38% decrease in post-merge production issues for teams using TypeScript over plain JavaScript. Why? Because types create guardrails. You don't wonder what a function takes. You don't guess what a response returns. You know. The compiler enforces it.

In Kaz Software's dev teams, TypeScript became our sanity layer. We don’t ship code wondering if it'll break in integration. We trust our types to expose edge cases during PRs instead of during hotfixes. The result? Happier QA, smoother sprint planning, and a faster dev cycle overall.

Beyond code stability, TypeScript also becomes your second brain. New devs onboard faster because the types explain the code. Seniors write less documentation because types serve as inline guidance. And when you’re maintaining a project 8 months later? Type annotations feel like your past self leaving breadcrumbs through a forest of logic. Promotions don’t come from how many lines of code you write. They come from how little chaos you introduce into the system. TypeScript makes that your default.

So if you're still arguing it's "extra work," you're thinking small. TypeScript doesn’t slow you down. It prevents you from being the reason your team gets stuck. In a competitive dev market, that’s the kind of invisible value that gets you noticed—and moved up.

Why every "nice stack" has TypeScript in it

Let’s look at the real-world tech stacks in 2025. You’ll notice something fast: the stacks that make devs smile all run on TypeScript. React with TypeScript. Next.js with TS configs out-of-the-box. NestJS built TypeScript-first. tRPC? Type inference from back to front. Even Deno launched with TypeScript at its core. These aren't coincidences. These are engineering trends driven by scale, complexity, and the demand for reliability. Whether you’re working on a hobby SaaS or a fintech platform—type-safe code lets teams move fast without breaking everything. That’s why TypeScript is part of the modern dev stack.

At Kaz Software, TypeScript is in nearly every project we scale. From enterprise APIs built in NestJS to cross-platform apps integrating React Native, the shared thread is TypeScript. It helps us keep velocity without sacrificing quality—something we care deeply about.

Here’s the thing: tools come and go. Frameworks get replaced. But when a language becomes the foundation for multiple successful ecosystems, that’s not a trend. That’s a shift. TypeScript is that shift. If you’re learning frameworks and skipping TypeScript, you’re building speed on sand. And hiring managers can tell. They don’t care that you know 14 libraries. They care whether you can build something that lasts. TypeScript is not the future because it's flashy. It's the future because it's boring in the best way: predictable, scalable, readable. And when you're working on code with 5 other devs across 6 time zones—that’s exactly what you want.

Apple considers buying Mistral as Meta builds Manhattan-sized AI clusters

Apple considering Mistral acquisition as AI desperation grows. Meta announces $100B+ compute investment with 5-gigawatt clusters. Windsurf saved by Cognition after Google's brutal acqui-hire.

Apple's desperate AI shopping spree

Mark Gurman buried the lede in his latest Bloomberg piece: Apple is seriously considering acquiring Mistral, the French AI startup valued at $6 billion. This follows recent reports of Apple's interest in buying Perplexity, signaling a dramatic shift for a company historically resistant to major acquisitions. The desperation is palpable—Apple has fallen so far behind in AI that they're willing to abandon their traditional build-it-ourselves philosophy and simply buy their way into relevance.

The obstacles are massive. European regulators would scrutinize any American tech giant acquiring one of Europe's few AI champions. Mistral itself may have no interest in selling, especially to a company that's demonstrated such incompetence in AI development. But Apple's willingness to even explore these acquisitions reveals how dire their situation has become. They've watched Google dominate with Gemini, OpenAI capture mindshare with ChatGPT, and even Meta build a credible AI ecosystem while Apple fumbles with a Siri that still can't answer basic questions reliably.

The irony is thick—Apple once prided itself on patient, methodical development of perfectly integrated products. Now they're desperately shopping for AI companies like a panicked student trying to buy a term paper the night before it's due. The fact that these acquisition rumors are becoming commonplace suggests Apple is preparing for a major move, likely overpaying dramatically for whatever AI capability they can grab before it's too late.

Meta's compute arms race goes nuclear

Zuckerberg just announced Meta will invest "hundreds of billions of dollars" in AI compute, with plans that dwarf every competitor. Their Prometheus cluster coming online in 2026 will be the first 1-gigawatt facility, followed by Hyperion scaling to 5 gigawatts—each covering "a significant part of the footprint of Manhattan." For context, xAI's much-hyped Colossus operates at 250 megawatts, and OpenAI's Stargate project aims for 1 gigawatt but is already facing delays.

The scale is deliberately absurd. Meta doesn't need 5 gigawatts of compute for any practical purpose—they're building it as a recruiting tool and competitive moat. Zuckerberg explained the real strategy: "When I was recruiting people to different parts of the company, people asked 'What's my scope going to be?' Here, people say 'I want the fewest people reporting to me and the most GPUs.'" Having "by far the greatest compute per researcher" becomes the ultimate flex in the AI talent war. It's not about efficiency or need—it's about demonstrating you have unlimited resources to burn.

This compute buildup coincides with reports that Meta's super intelligence lab is considering abandoning open source entirely. The New York Times reports the team discussed ditching Llama 4's behemoth model to develop closed models from scratch, marking a complete philosophical reversal from Meta's supposed commitment to "open science." The original Llama release in 2023 positioned Meta as the open source champion against OpenAI's closed approach. Now, with their new super intelligence lab burning through billions, they're quietly admitting that open source was always just a commercial strategy, not a principle. Meta denies the shift officially, claiming they'll continue releasing open models, but the writing is on the wall—when you're spending hundreds of billions on compute, you don't give away the results for free.

The Windsurf saga's shocking conclusion

The Windsurf acquisition drama took another wild turn as Cognition, makers of Devin, swooped in to acquire the company's remains just 72 hours after Google's controversial acqui-hire. Google paid $2.4 billion to license Windsurf's technology and hire 30 engineers, leaving 200 employees in limbo with a company stripped of leadership and purpose. The consensus was these abandoned workers would split Windsurf's $100 million treasury and dissolve the company—a brutal example of how modern tech acquisitions treat non-elite employees as disposable.

Instead, Jeff Wang, thrust into the interim CEO role when executives fled to Google, orchestrated a miracle. His LinkedIn post captured the whiplash: "The last 72 hours have been the wildest roller coaster ride of my career." Cognition's acquisition ensures every remaining employee is "well taken care of," according to CEO Scott Wu, who emphasized honoring the staff's contributions rather than treating them as collateral damage. Crucially, Cognition restored Windsurf's access to Anthropic's Claude models, making the product viable again after Google's deal threatened to kill it.

This creates a fascinating new acquisition model: one company cherry-picks the founders and star engineers while another scoops up the remaining company and staff. It's a more humane approach than the typical acqui-hire that leaves most employees with nothing, but it also reveals how transactional these deals have become. The "legendary team" rhetoric masks a simple reality—AI talent is being carved up and distributed like assets in a corporate raid, with different buyers taking different pieces based on what they value most.

The Windsurf engineers who thought they were building the future of AI coding tools discovered they were actually just accumulating value to be harvested by bigger players. Google got the talent they wanted, Cognition got a product and team at a discount, early investors got paid, and somehow everyone claims victory. Welcome to the new economics of AI acquisitions, where companies are dismantled and distributed piece by piece to the highest bidders.

Where People Build More Than Software — Kaz CEO Party 2025

Where the Game Never Ends — The Kaz Spirit in Motion

At Kaz Software, teamwork isn’t just a principle — it’s a rhythm that beats through everything we do. You can see it when a developer steps up for a tough sprint, and you could see it again when the same people walked onto the cricket field at Matir Maya. (Watch the cinematic video here).

The match started like any Kaz project: a mix of excitement, strategy, and plenty of laughs. The openers gave us a strong start — a couple of quick boundaries lit up the field. But soon, wickets fell, and we found ourselves in a familiar challenge — regrouping, adapting, and pushing forward together. What made the match unforgettable wasn’t the runs scored, but how everyone — from coders to designers — cheered, strategized, and played as one.

And that’s Kaz culture in motion: the willingness to step in, support, and never stop trying. Tug-of-war brought that same energy to life — teams pulling not just a rope, but each other toward victory with sheer grit and laughter. Even off the field, whether at the cards table or in spontaneous rounds of Goal Fest, that same camaraderie kept the day alive.

This wasn’t competition; it was connection — the kind that defines how we build, how we work, and how we win together.

Joy in Every Drop — The Pool, the Music, and the People Behind Kaz

When the sun dipped and the lights came on by the pool, the real celebration began. There’s something poetic about watching a team that spends its days writing code — dancing barefoot under the stars, with laughter echoing through the night air.

Our poolside party wasn’t planned — it just happened. A few songs turned into a full-blown jam. The singing competition revealed talents we didn’t know existed, and before long, the rain joined in — blurring the lines between workmates and friends. The sight of everyone dancing in the rain, singing along, and cheering each other on was pure Kaz — unfiltered joy, authenticity, and freedom.

For us, these moments go beyond celebration. They remind us that our people bring more than skill to the table — they bring heart. They bring the same creative energy that fuels our products, the same spontaneity that sparks innovation, and the same empathy that shapes our relationships with clients.

In a world that often talks about “work-life balance,” Kaz Software lives it — not by separating work from life, but by bringing life into work. Every beat of that night said it loud: Kaz isn’t just where we work — it’s where we belong.

Beyond the Code — The Taste of Togetherness

If the games and music were the heartbeat of the CEO Party, then the food was the soul of it. From sizzling grills to homestyle comfort dishes, every meal became a shared experience — laughter over plates, stories over dessert, and quiet gratitude for being part of something special.

The living experience at Matir Maya added to the magic. Surrounded by green, nature gave us the pause we often miss in the rush of deadlines. Conversations flowed easier, ideas surfaced naturally, and you could feel a sense of calm — the kind that recharges you to do better work when Monday returns.

At Kaz, we believe great work doesn’t come from constant hustle — it comes from balance, joy, and people who feel seen. CEO Party 2025 wasn’t just an escape from routine; it was a reminder of what makes us who we are.

The Kaz Software family 2025

We left Matir Maya with soaked clothes, full hearts, and a renewed sense of why we do what we do — not just to build software, but to build something that lasts: a culture of connection, creativity, and care.

Master Sora 2 prompting: From basic to Hollywood-level video creation

OpenAI drops Sora 2 prompting guide: 6-element "unit system" for perfect shots, Hollywood uses 15 technical specs for 4-second clips. Short prompts = creativity, long prompts = control.

OpenAI just dropped their official Sora 2 prompting guide, revealing the massive gap between amateur AI videos flooding social media and what professionals are actually capable of creating. The cookbook spans everything from two-sentence creative prompts to Hollywood-level production briefs with 15 separate technical specifications for 4-second clips. The secret isn't just knowing what to prompt—it's understanding when to micromanage versus when to let the AI surprise you.

When to let AI be creative vs controlling every detail

The biggest mistake new Sora users make is overspecifying everything, trying to force their exact mental image into existence through excessive detail. OpenAI's guide reveals a counterintuitive truth: shorter prompts often produce better, more surprising results because they give the model creative freedom. The company explicitly states that when you don't describe the time of day, weather, outfits, tone, camera angles, or set design, you're letting AI fill those gaps with choices that might exceed your imagination.

Their example of an effective short prompt demonstrates this principle: "In a '90s documentary style interview, an old Swedish man sits in a study and says, 'I still remember when I was young.'" This prompt only specifies three critical elements—the documentary style setting the visual tone, the subject and location providing basic context, and the dialogue ensuring accurate speech. Everything else becomes AI's creative playground, from the man's exact age to the study's decor, the lighting mood, and camera movements.

The key insight is knowing when creative freedom serves your goals versus when you need precise control. Marketing materials, product demonstrations, and brand videos demand specificity. But for creative exploration, viral content, or when you're genuinely unsure what you want beyond a few core elements, constraining the AI too much becomes counterproductive. OpenAI found that prompts under 50 words consistently produced more visually interesting and unexpected results than overwrought descriptions trying to control every pixel.

The unit system that makes perfect videos

For those needing more control without writing novels, OpenAI introduces the "unit" concept—treating each shot as a self-contained package of six essential elements. This structure provides enough specificity to achieve your vision while remaining manageable and leaving room for AI creativity where it matters. The system transforms chaotic prompt writing into a repeatable formula that consistently delivers professional results.

Each unit requires exactly six components working in harmony. First, the style reference ("1990s educational video," "noir detective film," "TikTok aesthetic") immediately puts the AI in the right creative space. Second, camera setup defines your perspective—handheld for intimacy, drone for grandeur, static tripod for stability. Third, one subject action keeps focus clear—a person walking, a car exploding, leaves falling. Fourth, optional camera movement adds dynamism—slow zoom, tracking shot, but never more than one per unit. Fifth, lighting recipe sets mood—harsh shadows for drama, soft natural light for romance, neon for cyberpunk. Finally, dialogue or sound brings life—specific words characters speak or ambient audio descriptions.

OpenAI emphasizes keeping each unit focused on single actions and movements. Multiple units can be chained together for complex sequences, but cramming multiple subject actions or camera movements into one unit consistently produces confused, poorly executed videos. A prompt like "A man runs through the park while the camera pans left then zooms in as he jumps over a bench while shouting and the lighting shifts from dawn to dusk" will fail. Breaking this into three separate units with clear transitions produces cinema-quality results.

The power comes from combining units strategically. Want a dramatic reveal? Unit one establishes wide shot with mysterious lighting, unit two shows close-up reaction with dialogue, unit three pulls back to show the revealed element. Each unit maintains its internal coherence while building toward your larger vision.

How Hollywood directors prompt Sora 2

For professional productions, OpenAI reveals that Sora 2 can handle prompts resembling actual film production briefs, with technical specifications that would make cinematographers jealous. Their example ultradetailed prompt for a 4-second urban scene includes 15 separate technical categories before even describing the action, demonstrating how professionals are already using Sora for pre-visualization and production planning.

The professional structure begins with format and look specifications: "Digital capture emulating 65mm photochemical contrast" tells Sora exactly which film stock to emulate. Lenses and filtration sections specify focal lengths and filter types. Grade and palette instructions break down highlights, mids, and blacks separately. Lighting and atmosphere get their own section distinct from grading—"natural sunlight from camera left, low angle" versus general mood. Location and framing splits into foreground, midground, and background layers. Negative prompts explicitly exclude unwanted elements: "avoid signage or corporate branding."

Only after establishing this technical foundation does the prompt describe wardrobe, props, extras, and sound design. The actual shot list comes last, with precise timestamps: "0-1.5 seconds: wide establishing shot, 1.5-2.5 seconds: camera dollies forward, 2.5-4 seconds: subject enters frame." This timestamp precision helps Sora maintain pacing and ensures specific actions occur exactly when needed.

The revelation is that Sora understands professional cinematography language at an expert level. Terms like "bounce," "photochemical contrast," "65mm glass characteristics," and "highlight rolloff" aren't just recognized—they're accurately implemented. This isn't AI trying to approximate film language; it's AI that genuinely understands how cinematography works and can execute at a professional level.

OpenAI suggests using GPT-5's thinking mode to generate these complex prompts. Feed it the template, describe your vision in plain language, and let it translate your ideas into professional production terminology. You don't need film school to specify "low-angle sunlight creating rim lighting with soft bounce fill"—just tell GPT-5 you want a "warm, heroic look" and it handles the technical translation.

The prompting guide confirms what professionals suspected: Sora 2 isn't just a toy for social media content. It's a legitimate pre-production tool capable of generating director-approved visualization that translates directly to real shoots. The gap between amateur and professional output isn't the AI's capability—it's knowing how to speak its language.

OpenAI's agent builder threatens to kill startup ecosystem at Dev Day

OpenAI Dev Day: Agent Kit directly competes with Zapier/Lindy, Apps SDK lets ChatGPT absorb Canva/Coursera functionality. GPT-5 Pro hits API at 12x cost. Startups scrambling.

OpenAI's Dev Day dropped two nuclear bombs on the startup ecosystem: Agent Kit, a visual agent builder that directly competes with companies like Zapier and Lindy, and Apps SDK, which lets ChatGPT absorb functionality from Canva, Zillow, Coursera, and more. The 800 million weekly ChatGPT users and 4 million developers now have tools that could make entire categories of startups obsolete overnight.

Sam Altman announced the updates in four categories, but two dominated: Agent Kit for building multi-agent workflows visually, and Apps that embed native applications directly into ChatGPT with deep contextual integration. They demoed building and shipping an agent in 8 minutes live on stage, while Apps showed Coursera videos you could pause to ask ChatGPT for explanations, with the AI having full context of what you're watching.

Did OpenAI just murder the agent startup ecosystem?

The moment rumors of Agent Kit leaked, startup founders started sweating. Lindy, n8n, and especially Zapier faced an existential question: how do you compete when OpenAI has 800 million weekly users and infinite resources? The visual canvas for creating multi-agent workflows, complete with native eval platform, automated prompt optimization, and connection to data sources via OpenAI's connectors platform, looks exactly like what these startups have been building for years.

Lindy's founder struck a defiant tone, posting "Welcome to the club OpenAI" with a note saying "Welcome to the most exciting category in AI and congratulations on your first foray into true AI employees." Zapier got more specific about their supposed moat, tweeting that Agent Builder "ships with only a few native integrations and most businesses run on hundreds of tools." Their argument centers on their ecosystem of 8,000 apps and 30,000 actions providing something OpenAI can't match—at least not immediately.

The brutal reality is that going against something OpenAI perceives as core platform functionality is a nightmare scenario for any startup. OpenAI built Agent Kit on the Model Context Protocol (MCP) and seems willing to reach outside their ecosystem to become the central hub where everything happens. They demonstrated the power asymmetry by building and deploying a functional agent in 8 minutes during the keynote—something that would take hours or days on competing platforms.

But these startups aren't entirely wrong about having defensive positions. The inherent limitation of any foundation model company's agent solution is lock-in to their models. Enterprises increasingly demand model flexibility, wanting to switch between different models for different use cases, not just as models improve but for cost optimization and specialized tasks. Any company building on OpenAI's Agent Kit is permanently wedded to OpenAI's models, pricing, and platform decisions.

The current visual workflow design that Zapier, Lindy, and n8n pioneered—and OpenAI now copies—remains intimidating for non-technical users despite marketing claims. Ethan Mollik's early impressions suggest Agent Kit "may still be too technical and single-player to be a true replacement for the dream of GPTs where anyone might easily share prompts and use cases with teams." The demo itself involved significant coding, revealing Agent Kit targets developers building agents, not general consumers creating their own.

There's a possibility OpenAI normalizing this interface actually expands the market for all players. If OpenAI makes visual agent building mainstream, the overall pie grows even if OpenAI takes the biggest slice.

Apps turn ChatGPT into a context black hole

Apps aren't just GPTs 2.0, despite surface similarities. The Apps SDK enables something fundamentally different: applications that ChatGPT can interrogate and interact with while maintaining full context of what you're doing. This isn't Canva inside ChatGPT—it's ChatGPT becoming your co-pilot for every application you use.

The Coursera demo revealed the game-changing potential. Users can pause educational videos to ask ChatGPT "can you explain more about what they're saying right now?" and get detailed explanations because ChatGPT has full context of the video content. The Zillow integration lets you ask about nearby dog parks, school districts, or commute times—information Zillow doesn't provide but ChatGPT can research while you browse listings.

Launch partners include Canva, Booking.com, Expedia, Figma, and Spotify, with Khan Academy, Instacart, Uber, Thumbtack, and TripAdvisor coming soon. Apps display inline, render anything possible on the web, support picture-in-picture, and can expand to fullscreen. The SDK's "talking to apps" feature gives ChatGPT awareness of your in-app experience, creating unprecedented contextual integration.

Swyx observed: "This isn't the ChatGPT you grew up with. It's Canva inside ChatGPT." But the Canva demo actually exposed limitations—nobody serious about business will design logos or pitch decks entirely within ChatGPT when Canva's full toolset exists. The convenience doesn't justify losing professional features.

The real power emerges in educational and research contexts. Once you've used Coursera with ChatGPT as your personal tutor providing real-time explanations, returning to passive video consumption feels primitive. Similarly, house hunting with an AI assistant that researches every property's context while you browse transforms a tedious process into intelligent exploration.

This creates a context black hole where OpenAI sucks in all user interaction data and context, building an insurmountable competitive advantage. Every app integration strengthens ChatGPT's position as the universal assistant layer. Apps become dependent on ChatGPT for enhanced functionality, while ChatGPT becomes irreplaceable for users accustomed to AI-augmented experiences.

Why developers care more about boring API updates

While Agent Kit and Apps grabbed headlines, developers at Dev Day were most excited about mundane API updates. GPT-5 Pro and Sora 2 arriving in the API, despite GPT-5 Pro costing 12x more than regular GPT-5, unlocked use cases previously impossible. Matt Schumer noted: "These models are both massively better than what developers had access to just a day ago. We're going to see some very interesting effects."

The confirmation of Sora 2 Pro in the API suggests the consumer app deliberately limits access to the full model—developers will get capabilities regular users can't touch. Additional updates included GPT Realtime Mini (70% cheaper than the standard voice model) and GPT Image 1 Mini (80% cheaper), enabling cost-effective scaling for production applications.

Dan Shipper captured the vibe shift: "It feels less exciting for developers and more for developer-adjacent roles. You should be hyped if you're doing AI ops in a company, but if you're a hardcore AI engineer, it's a bit underwhelming." Even Codex updates, despite the platform processing 40 trillion tokens since launch, felt "pretty incremental" to daily users.

This represents a fundamental transition from innovation to integration. OpenAI isn't trying to wow with parlor tricks anymore—they're building infrastructure for the millions already dependent on their tools. The updates seem boring because they're practical: better pricing, improved reliability, expanded access. These aren't demo features; they're production necessities.

Alli Miller, reporting from the room, ranked developer excitement "scientifically" by energy, phone usage, applause volume, and whispered conversations. The order: agents first, Codex second, apps third. But the real excitement came from API access to premium models, suggesting developers care more about capability improvements than flashy new interfaces.

The phase shift is clear: we've moved from "look what AI can do" to "make AI actually work." These incremental improvements unlock more real value than any splashy demo. OpenAI knows their moat isn't just technology—it's becoming the infrastructure layer everyone depends on, one boring update at a time.

US government claims DeepSeek is dangerous garbage while Apple kills Vision Pro

NIST report: DeepSeek 12x more vulnerable to attacks, 94% jailbreak success rate. Meanwhile Apple kills Vision Pro to copy Meta's glasses, and Meta will use your AI chats for ads.

The US government just declared war on DeepSeek with a scathing report claiming Chinese AI is both incompetent and dangerous. Meanwhile, Apple is killing the Vision Pro to desperately copy Meta's smart glasses, and Meta announced they'll use your AI conversations to sell you hiking boots. The AI hardware wars are getting messy, and your privacy is the casualty.

Why America says DeepSeek is a security nightmare

Commerce Secretary Howard Lutnik didn't mince words announcing NIST's "groundbreaking evaluation" of American versus Chinese AI: "American AI models dominate. DeepSeek lags far behind, especially in cyber and software engineering. These weaknesses aren't just technical. They demonstrate why relying on foreign AI is dangerous."

The National Institute of Standards and Technology's report reads like a hit piece commissioned by the Trump administration's new AI action plan. According to NIST, DeepSeek models are 12 times more likely than US frontier models to execute malicious instructions. In simulated environments, hijacked DeepSeek agents sent phishing emails, downloaded malware, and exfiltrated user credentials without resistance. The models responded to 94% of jailbreaking attempts compared to just 8% for American models, making them essentially defenseless against manipulation.

Performance benchmarks painted an equally damning picture. NIST claims American models cost 35% less on average to complete their 13 performance tests, contradicting DeepSeek's entire value proposition of being cheaper. The Chinese models also "echoed four times as many inaccurate and misleading CCP narratives" as US alternatives, though NIST doesn't specify what narratives they tested or how they measured accuracy.

The timing isn't subtle. Downloads of DeepSeek models are up 1,000% since January, triggering panic in Washington about Chinese AI infiltration. This report serves as the government's response—a comprehensive takedown designed to scare enterprises away from adoption. Whether the technical criticisms are valid or politically motivated, the message is clear: the US government will weaponize every tool available to maintain AI dominance, including publishing reports that read more like propaganda than technical analysis.

Apple admits defeat and copies Meta's glasses

Apple just made the most humiliating pivot in its history, scrapping the Vision Pro's future to frantically copy Meta's Ray-Ban smart glasses. Bloomberg's Mark Gurman reports that Apple killed plans for a cheaper, lightweight Vision Pro scheduled for 2027 and reassigned the entire team to develop smart glasses instead.

The internal announcement came last week, with Apple executives privately acknowledging the Vision Pro as "an overengineered piece of technology" that was too expensive and uncomfortable for consumers. At $3,500, the headset became a cautionary tale about ignoring basic user needs for technological showmanship. Meta's Ray-Bans, meanwhile, are flying off shelves at a fraction of the price with features people actually want.

Apple's panic response involves two glasses products clearly modeled after Meta's lineup. The N50, targeting 2027 release, will compete directly with standard Ray-Bans featuring voice controls, integrated AI, speakers for music, and cameras for recording. A higher-spec version with a display won't arrive until 2028, putting Apple years behind Meta's Ray-Ban Display glasses that already exist. Apple's only potential differentiation appears to be health tracking capabilities, desperately searching for any feature Meta hasn't already perfected.

This represents a stunning reversal for a company that traditionally sets hardware trends rather than following them. Apple spent years and billions developing the Vision Pro as their vision of computing's future, only to watch Meta define the actual future with simple, practical smart glasses. The format war for AI devices has a clear winner, and for once, it isn't Apple.

Your AI chats are now advertising data

Meta crossed the privacy Rubicon this week, announcing they'll use your AI chatbot conversations to target ads starting December. Ask their AI about hiking trails, and suddenly your feed fills with hiking boot advertisements. The change applies across all Meta properties—Facebook, Instagram, WhatsApp—with no opt-out option for users.

Privacy policy manager Christy Harris framed this as simply "another piece of input that will inform personalization," but the implications are staggering. Every question you ask Meta's AI becomes permanent advertising intelligence. While Meta claims "sensitive topics" like politics, religion, sexual orientation, and health are excluded, their track record on respecting such boundaries is questionable at best.

The rollout carefully avoids Europe, the UK, and South Korea due to their stricter privacy laws, revealing Meta knows this violates basic data protection principles. They promise a "compliant" version for these regions later, which likely means finding legal loopholes to implement the same surveillance with different language.

Amazon's new Alexa Plus devices take ambient surveillance even further. The upgraded Echo speakers include cameras, audio sensors, ultrasound, Wi-Fi radar, and accelerometers—essentially turning your home into a panopticon where AI monitors every movement. New Ring cameras feature facial recognition that tracks friends and family, plus a "search party" feature that networks entire neighborhoods to hunt for lost pets (or anything else Amazon decides needs finding).

Panos Panay, Amazon's product chief poached from Microsoft, articulated the dystopian vision: "AI is very clearly right at the core of the strategy." The devices process AI locally using custom silicon with dedicated accelerators, meaning your behavioral data never even needs to leave the device for Amazon to profile you. They're not just listening anymore—they're watching, sensing, and analyzing every aspect of your existence.

The convergence is complete. Meta mines your conversations, Amazon surveils your home, Apple desperately pivots to copy successful competitors, and the US government publishes propaganda disguised as technical reports. The AI industry has revealed its true nature: a surveillance capitalism machine where your privacy is the product and your attention is the commodity. The only surprise is how long it took them to stop pretending otherwise.

OpenAI launches TikTok for AI slop as employees revolt

OpenAI launches Sora 2: TikTok for AI-generated videos where you can deepfake friends. Employees revolt, one quits saying "joined to cure cancer, not build slop machines.

OpenAI just released Sora 2, their video generation model that can put you and your friends into any AI-generated scene. But it's not just a model—it's a full TikTok-style social app designed to get you hooked on AI-generated content. The backlash is brutal, with employees quitting and the internet declaring war on what they're calling an "infinite slop machine."

Sam Altman called it "the ChatGPT moment for creativity." The internet called it brain cancer. One OpenAI employee who joined to cure diseases just quit to build AI for science instead, tweeting: "If you don't want to build the infinite AI TikTok slop machine, come join us at Periodic Labs."

The infinite slop machine is here

Sora 2 isn't just better physics and sound effects. It's a complete social media platform where you upload a video of yourself, authorize friends to use your likeness, and suddenly everyone's deepfaking everyone into AI videos. OpenAI calls this revolutionary feature "Cameos"—you record yourself saying numbers and tilting your head, then anyone you've authorized can generate videos with your face doing anything.

The technical achievements are undeniable. Sora 2 handles Olympic gymnastics routines, accurate water physics with paddleboards, and doesn't teleport basketballs into hoops when players miss shots. It maintains character consistency across multiple people in scenes, something even Google's Veo couldn't manage. The model comes with realistic, cinematic, and anime styles, plus synchronized dialogue and sound effects that actually match the action.

Peter Levels admitted the superiority: "Before today, the best AI video models were dominated by Chinese companies and Google. But none had character consistency, let alone multiple characters in one scene. OpenAI solved that by rethinking ownership with Cameo—essentially training yourself as an AI model."

Early adopters are already creating cursed content. One viral video shows CCTV footage of Sam Altman stealing GPUs at Target. Another perfectly recreates Spotify playing copyrighted music, prompting immediate copyright concerns. The top posts include Ronald McDonald making out with Wendy, documentaries about famous memes, and "the dumbest thing you could possibly imagine"—a guy on a skateboard on a treadmill holding a leaf blower.

But here's what OpenAI desperately wants you to see: they claim they're not optimizing for time spent in feed. They interrupt scrolling every 5-10 videos to ask how you're feeling (spawning thousands of memes of Altman's face asking "HOW DO YOU FEEL?"). They say they're maximizing creation, not consumption, with natural language recommendation algorithms and content "heavily biased" toward people you follow.

Why OpenAI employees are quitting in disgust

The internal revolt at OpenAI is real. Employees who joined to "cure all diseases" are watching their company build what critics call a dopamine addiction machine. Ed Newton Rex tweeted: "If you're feeling depressed about Sora 2, imagine how OpenAI employees who joined to cure all diseases are feeling."

Matt Sharma predicts: "Would not be surprised if we see a big wave of OpenAI departures in the next month or two. If you signed up to cure cancer and you just secured post-economic bags in a secondary, I don't think you'd be very motivated to work on the slop machine."

Rowan Cheng already quit, launching Periodic Labs with this announcement: "Today you will be presented two visions of humanity's future with AI. If you don't want to build the infinite AI TikTok slop machine, but want to develop AI that accelerates fundamental science, come join us." His new company builds AI scientists and autonomous laboratories to discover things like high-temperature superconductors—actual world-changing technology instead of meme generators.

Even employees staying are conflicted. Liam from OpenAI admitted: "This was initially a tough decision. As a skeptic of short-form video and entertainment at scale, I held many reservations about working on this product for fear that consumer GenAI inevitably leads to engagement baiting, attention slop." He only stayed after convincing himself the team could create "a truly pro-social experience"—though he admits it's "nowhere close to perfect."

The company's own blog post reveals the desperation to justify this. They dedicated an entire section to "launching responsibly" and created a "Sora feed philosophy" with principles like "optimize for creativity" and "balance safety and freedom." Sam Altman himself wrote about feeling "trepidation" and being "aware of how addictive a service like this could become."

The brain rot rebellion begins

The reaction split violently across platforms. Twitter erupted in fury, LinkedIn showed cautious optimism (63% called it "creativity explosion" vs 37% "brain rot machine"), and everyone questioned why OpenAI abandoned curing cancer for this.

Notion founder Simon Last captured the rage: "Why do we keep dedicating our brightest minds, billions of dollars, and the most powerful GPUs on earth to building yet another app that optimizes for attention decay? I was hopeful when ChatGPT seemed to reclaim time from TikTok. But now we see disposable video, same engagement treadmill, path to ads."

The criticism cuts deeper than just another social app. This represents AI inheriting 30 years of digital media failures. A Pew study found 48% of teens say social media harms people their age, up from 32% in 2022. Parents are organizing "Wait Until 8th" movements to collectively delay giving kids smartphones. Into this environment, OpenAI drops an AI video app explicitly designed to be addictive.

Critics see deliberate evil. "OpenAI is building technology that will displace millions of workers while simultaneously creating the AI slop trough humans will consume to fill the void," wrote one fintech account. Another: "We were promised AGI, ASI, personal super intelligence. Instead we get infinite slot machines that turn us into dopamine-addicted zombies."

The copyright implications are terrifying. One Sora video perfectly recreated copyrighted music playing on Spotify. Another generated fake CCTV footage of people committing crimes. The platform allows anyone to generate videos of authorized friends doing anything—the deepfake nightmare realized with corporate blessing.

Even AI industry insiders are disgusted. Dei Nicolau from Wondercraft responded to OpenAI staff: "Sorry, but how exactly are you making the world a better place? Your post is nice and eloquent, but the core message is 'slop is fun, we made it easy to build on each other's slop, so more slop.'"

The financial motive is obvious. As Signal writes: "Unfortunately, ads fund research. Google ads lead to DeepMind. Meta ads lead to AR/VR. OpenAI ads lead to possible AGI." They need the advertising revenue that only social media addiction can provide. Some estimate they'll need TikTok's $10 billion annual marketing budget just to compete.

OpenAI bet everything that people want infinite AI-generated videos of themselves and friends doing impossible things. The internet is betting they just created the perfect symbol of everything wrong with both AI and social media—an infinite slop machine that turns human creativity into algorithmic addiction while the same company claims to be building AGI.

The battle lines are drawn. OpenAI says this funds the path to AGI. Critics say it's the path to idiocracy. Both might be right.

Accenture fires 11,000 workers who can't learn AI fast enough

Accenture fires 11,000 workers who can't upskill on AI fast enough. CEO promises more layoffs while clients revolt against consultants "learning on our dime."

Accenture just dropped a bombshell that should terrify every white-collar worker: learn AI or get fired. The consulting giant is cutting 11,000 employees this quarter alone—anyone who can't "upskill" fast enough is gone.

CEO Julie Sweet didn't mince words on Thursday's earnings call: "Where we don't have a viable path for skilling, we're exiting people so we can get more of the skills that we need." This isn't a struggling company. Accenture grew revenue 7% to $70 billion and booked $9 billion in AI contracts. They're firing profitable employees simply because they can't adapt fast enough.

The $865 million AI purge begins

Accenture's restructuring will cost $865 million over six months, mostly in severance payments. They've already "exited" 11,000 employees in three months, with another 10,000 cut the previous quarter.

Sweet expects more AI-related layoffs next quarter while simultaneously hiring AI specialists. The company claims to have "reskilled" 550,000 workers on AI, though nobody knows what that actually means.

CFO Angie Park revealed the real game: "We expect savings of over $1 billion from our business optimization program, which we will reinvest in our business." Translation: fire expensive veterans, hire cheaper AI-native talent, pocket the difference. The market isn't buying it. Accenture's stock is down 33% year-to-date despite the AI gold rush. The Economist asked the obvious question: "Who needs Accenture in the age of AI?" Gabriela Solomon Ramirez's LinkedIn post went viral: "This should hit like cold water to the face. Even Ivy League MBAs are not immune to this. Wake up to the massive shift that will happen with AI."

The irony is thick. Accenture made billions telling others how to adapt to technology. Now they're the ones scrambling to survive.

Why consultants are learning AI on your dime

The dirty secret of professional services just exploded into public view. Merck's CIO Dave Williams said it plainly: "We love our partners, but oftentimes they're learning on our dime."

The Wall Street Journal investigation was brutal: "Clients quickly encountered a mismatch between the pitch and what consultants could actually deliver. Consultants who often had no more expertise on AI than they did internally struggled to deploy use cases that created real business value."

Bristol Myers Squibb's CTO Greg Myers didn't hold back: "If I were to hire a consultant to help me figure out how to use Gemini CLI or Claude code, you're going to find a partner at one of the big four has no more or less experience than a kid in college."

Source Global Research CEO Fiona Czernowski explained the fundamental problem: "Consulting firms have tried to put themselves at the cutting edge and it's not really where they belong." The numbers expose the lie. Accenture's 350,000 employees in India handle 56% of revenue through "technology and managed services"—basically outsourcing work that AI now does better. Only 44% comes from actual strategy consulting.

Enterprise clients are revolting. They're tired of paying millions for consultants to learn basic AI tools. New firms like Tribe and Fractional are stealing deals by actually knowing the technology.

The brutal truth about job security

Barata's viral post captured the terror spreading through corporate America: "What looks like cost cutting is in truth skill reshaping. Either reskill into AI-aligned roles or risk redundancy."

He continued with the line that's keeping executives awake: "Job security no longer comes from the company you work for. It comes from the skills you bring to the table."

CB Insights revealed the endgame in their "Future of Professional Services" report. The opportunity: turning services into scalable AI products. Custom consulting becomes platform delivery. Human expertise becomes software. The pricing tsunami is coming. Enterprises won't pay current rates for AI-augmented work. Discovery that cost millions now happens in days with agents. Implementation that took years happens in months.

The gap between "experts" and everyone else has never been smaller. Today's AI experts are just people who spent more time with ChatGPT. Platform transitions create new expert classes—and there's no reason you can't be one.

Accenture's trying to stay ahead of their own customers. They have the brand, the change management skills, but not the AI capabilities they claim. The race is whether they can get good fast enough to keep commanding big deals.

Anthropic's crisis deepens as Claude loses to GPT-5 and Gemini 3 looms

Anthropic bleeds users after throttling scandal while CEO attacks open source. Google's Gemini 3 rumors explode as Microsoft abandons OpenAI for trillion-dollar solo plan.

The AI labs' pecking order just flipped. Anthropic, once the darling of developers everywhere, is hemorrhaging users to OpenAI while facing throttling scandals and CEO controversies. Google's riding high on Gemini 3 rumors. And Microsoft? They're quietly building a trillion-dollar distributed AI network while everyone else fights over supercomputers.

Elon Musk summed up the brutal new reality: "Winning was never in the set of possible outcomes for Anthropic."

Why everyone suddenly hates Claude

Six weeks of hell destroyed Anthropic's reputation. Starting in August, Claude users flooded Reddit with complaints: broken code that previously worked, random Chinese characters in English responses, instructions completely ignored, and the same prompt giving wildly different results.

Users were convinced Anthropic was secretly throttling Claude to save money. Conspiracy theories exploded—maybe they reduced quality during peak hours, swapped in a cheaper model, or intentionally degraded performance to manage costs.

Anthropic's explanation? "Bugs that intermittently degraded responses." Not intentional throttling, just incompetence. The damage was done.

OpenAI struck at the perfect moment. GPT-5 launched explicitly targeting coding—Anthropic's stronghold. Initially drowned out by deprecation drama, developers slowly realized GPT-5 Codex was actually good. Really good.

"GPT-5 Codex is the best product launch of Q4 2025," writes one developer. "It follows instructions, sticks to guidelines, doesn't overcomplicate, and produces optimized code. It beats Claude Code in every way." The numbers don't lie: Codex has more GitHub stars than Claude Code despite launching six weeks later.

Then CEO Dario Amodei poured gasoline on the fire with this take on open source: "I don't think open source works the same way in AI... I've actually always seen it as a red herring. When I see a new model come out, I don't care whether it's open source or not." The backlash was instant. "Dario Amodei is showing his true face," wrote one critic. "Anti-competitive doomer with a love of regulation to control AI. For that reason, he hates open-source AI."

Even Hugging Face's CEO called it a "rare miss" and "quite disappointing."

Amodei also openly challenged Trump's hands-off AI strategy, skipping the White House AI dinner. Now Trump's AI czar David Sacks takes potshots at Anthropic weekly.

The company went from $1 billion to $5 billion revenue this year. But perception is reality, and right now everyone thinks Claude is broken.

The Gemini 3 rumors that have Google winning

While Anthropic burns, Google's vibes are immaculate. Gemini 3 rumors that started in July are reaching fever pitch.

"Good news," writes one insider. "Gemini 3's launch target has been brought forward to early October from mid-October. Only a couple of weeks left now."

Dan Mack's prediction: "It will clearly be the best AI model available, both vibes and benchmark-based. Google has the momentum now, and I don't think anyone is stopping that train."

Google's Kath Cordovez tweeted "Y'all, I'm very excited for next week," sending the rumor mill into overdrive. Turns out it's about Google's coding tools getting major updates, not Gemini 3. But the hype shows how desperately everyone wants Google to win.

The sentiment shift is remarkable. Eighteen months ago, Google AI meant glue on pizza jokes. Now developers are pre-declaring Gemini 3 their "favorite launch of the year" before even seeing it.

One developer wrote: "I'm positive that Gemini 3 will be my favorite launch of the year. There's still hope. GPT-5 and Claude 4 were disappointing."

Even Wall Street's noticing. Amazon's stock is surging on their Anthropic partnership. Wells Fargo analysts see "increased conviction in AWS revenue acceleration" purely from Anthropic's compute needs.

The irony: Anthropic's struggles are making Amazon look good while Anthropic itself bleeds users.

Microsoft's trillion-dollar betrayal

Microsoft's done with OpenAI's moonshot fantasies. While OpenAI builds Stargate—their $100 billion supercomputer—Microsoft's quietly building something bigger.

Reuters reports Microsoft "began to re-evaluate" their OpenAI relationship as compute demands "ballooned." When Oracle and SoftBank stepped in for OpenAI's gigawatt requirements, Microsoft walked away.

Their new strategy: distributed AI infrastructure across the globe instead of "one gargantuan bet." They're building clusters sized for long-term reuse with staged GPU refreshes, supporting inference over training.

"The future of AI isn't another colossal supercomputer in one location," Microsoft believes. "It's a fast distributed web of AI power serving billions globally."

They're also hedging bets. This week, Satya Nadella announced Claude integration into Microsoft 365 Copilot alongside OpenAI. "Our multimodal approach goes beyond choice," he tweeted, barely hiding the dig at their former exclusive partner.

Microsoft was "richly rewarded" for their first OpenAI bet. The billion-dollar question: is playing it safe equally smart?

Meanwhile, Nadella told employees he's "haunted" by the prospect of Microsoft not surviving the AI era. That's why they're building their own path—distributed, practical, and completely independent of OpenAI's increasingly wild ambitions.

Google's massive study proves AI makes 80% of developers more productive

Google's 142-page study of 5,000 developers: 80% report AI productivity gains, 59% see better code quality. But "downstream chaos" eats benefits at broken companies.

Google Cloud just dropped a 142-page bombshell that settles the AI productivity debate once and for all. After surveying nearly 5,000 developers globally, the verdict is clear: 80% report AI has increased their productivity, with 90% now using AI tools daily.

But here's the twist nobody's talking about—all those individual productivity gains are getting swallowed by organizational dysfunction. Google calls it "the amplifier effect": AI magnifies high-performing teams' strengths and struggling teams' chaos equally.

The productivity paradox nobody wants to discuss

The numbers obliterate skeptics. When asked about productivity impact, 41% said AI slightly increased output, 31% said moderately increased, and 13% said extremely increased. Only 3% reported any decrease.

Code quality improved for 59% of developers. The median developer spends 2 hours daily with AI, with 27% turning to it "most of the time" when facing problems. This isn't experimental anymore—71% use AI to write new code, not just modify existing work.

The adoption curve tells the real story. The median start date was April 2024, with a massive spike when Claude 3.5 launched in June. These aren't early adopters—this is the mainstream finally getting it.

But Meta's controversial July study claimed developers were actually less productive with AI, despite thinking otherwise. Their methodology? Just 16 developers with questionable definitions of "AI users." Google's 5,000-person study destroys that narrative. Yet trust remains fragile. Despite 90% adoption, 30% of developers trust AI "a little" or "not at all." They're using tools they don't fully trust because the productivity gains are undeniable. That's how powerful this shift is.

The shocking part? Only 41% use advanced IDEs like Cursor. Most (55%) still rely on basic chatbots. These productivity gains come from barely scratching AI's surface. Imagine what happens when the remaining 59% discover proper tools.

Why your AI gains disappear into organizational chaos

Google's key finding should terrify executives: "AI creates localized pockets of productivity that are often lost to downstream chaos."

Individual developers are flying, but their organizations are crashing. Software delivery throughput increased (more code shipped), but so did instability (more bugs and failures). Teams are producing more broken software faster.

The report identifies this as AI's core challenge: it amplifies whatever already exists. High-performing organizations see massive returns. Dysfunctional ones see their problems multiply at machine speed.

Google Cloud's assessment: "The greatest returns on AI investment come not from the tools themselves, but from the underlying organizational system, the quality of the internal platform, the clarity of workflows, and the alignment of teams."

This explains enterprise AI's jagged adoption perfectly. It's not about model quality or user training. It's about whether your organization can capture individual gains before they dissolve into systemic inefficiency.

The data proves what consultants won't say directly: most organizations aren't ready for AI's productivity boost. They lack the systems to channel individual speed into organizational outcomes.

The seven team types that predict AI success or failure

Google identified seven team archetypes based on eight performance factors. Your team type determines whether AI saves or destroys you:

The Legacy Bottleneck (11% of teams): "Constant state of reaction where unstable systems dictate work and undermine morale." These teams see AI make everything worse—more code, more bugs, more firefighting.

Constrained by Process: Trapped in bureaucracy that neutralizes any AI efficiency gains.

Pragmatic Performers: Decent results but missing breakthrough potential.

Harmonious High Achievers: The only teams seeing AI's full promise—individual gains translate to organizational wins.

The pattern is brutal: dysfunctional teams use AI to fail faster. Only well-organized teams convert productivity to profit.

Google's seven-capability model for AI success reads like a corporate nightmare: "Clear and communicated AI stance, healthy data ecosystems, AI-accessible internal data, strong version control practices, working in small batches, user-centric focus, quality internal platforms."

Translation: fix everything about your organization first, then add AI. Most companies are doing the opposite.

The uncomfortable truth

This report confirms what power users already know: AI is a massive productivity multiplier for individuals. But it also reveals what executives fear: organizational dysfunction eats those gains alive.

The median developer started using AI just eight months ago. They're using basic tools for two hours daily. And they're already seeing dramatic improvements.

What happens when they discover Cursor? When they spend eight hours daily in AI-powered flows? When trust catches up to capability?

The revolution is here, but it's unevenly distributed. Not between those with and without AI access—between organizations that can capture its value and those drowning in their own dysfunction.

Google's message to enterprises is clear: AI isn't your problem or solution. Your organizational chaos is the problem. AI just makes it visible at unprecedented speed.

Zuckerberg's $800 smart glasses fail spectacularly on stage

Meta's $800 smart glasses launch turns into viral disaster as Zuckerberg fails to answer a video call on stage. Four attempts, multiple failures, awkward Wi-Fi excuses.

Mark Zuckerberg just had his worst on-stage moment since the metaverse avatars got roasted. During Meta's Connect event unveiling their new $800 smart glasses, the CEO repeatedly failed to answer a video call using the device's flagship feature—while the entire tech world watched.

The viral clip shows Zuckerberg trying multiple times to accept a WhatsApp call through the new neural wristband controller. Nothing worked. After several painful attempts, he awkwardly laughed it off: "You practice these things like a hundred times and then, you know, you never know what's going."

The demo that went viral for all the wrong reasons

The September 18th Connect event was supposed to showcase Meta's leap into consumer wearables. Instead, it became instant meme material. Zuckerberg attempted to demonstrate the Ray-Ban Display glasses' killer feature—answering video calls with subtle hand gestures via a neural wristband.

First attempt: Nothing. Second attempt: Still nothing. By the fourth try, even Meta's CTO Andrew Bosworth looked uncomfortable on stage. "I promise you, no one is more upset about this than I am because this is my team that now has to go debug why this didn't work," Bosworth said. The crowd laughed nervously as Zuckerberg blamed Wi-Fi issues. Online reactions were brutal. One user wrote: "Not really believable to be a Wi-Fi issue." Another joked they wanted to see "the raw uncut footage of him yelling at the team."

Earlier in the event, the AI cooking demo also failed. The glasses' AI misinterpreted prompts, insisted base ingredients were already combined, and suggested steps for a sauce that hadn't been started. The pattern was clear: Meta's ambitious hardware wasn't ready for primetime.

What Meta's $800 glasses actually promise

Despite the disaster, the Ray-Ban Display glasses pack impressive specs—on paper. The right lens features a 20-degree field of view display with 600x600 pixel resolution. Brightness ranges from 30 to 5,000 nits, though they struggle in harsh sunlight.

The neural wristband enables control through finger gestures:

• Pinch to select

• Swipe thumb across hand to scroll

• Double tap for Meta's AI assistant

• Twist hand in air for volume control

Features include live captions with real-time translation, video calls showing the caller while sharing your view, and text replies via audio dictation. Future updates promise the ability to "air-write" words with your hands and filter background noise to focus on who you're speaking with. Battery life: 6 hours on a charge with the case providing 30 additional hours. The wristband lasts 18 hours. They support Messenger, WhatsApp, and Spotify at launch, with Instagram DMs coming later.

Meta's also launching the Ray-Ban Meta Gen 2 at $379 and sport-focused Oakley Meta Vanguard at $499. Sales start September 30th with fitting required at retail stores before online sales begin.

Why this failure matters more than Zuckerberg admits

This wasn't just bad luck or Wi-Fi issues. It exposed Meta's fundamental problem: rushing unfinished products to market while competing with Apple and Google's ecosystems.

Alex Himel, who heads the glasses project, claims AI glasses will reach mainstream traction by decade's end. Bosworth expects to sell 100,000 units by next year, insisting they'll "sell every unit they produce." But who's buying $800 glasses that can't reliably answer a phone call? Early reviews from The Verge called them "the best smart glasses tried to date" and said they "feel like the future." But that was before watching the CEO fail repeatedly to use basic features on stage.

Meta's betting their entire hardware future on neural interfaces and AR glasses. Fortune reports their "Hypernova" glasses roadmap depends on similar wristband controllers. If they can't make it work reliably for a rehearsed demo, how will it work for consumers? The irony is thick. Zuckerberg pitched these as AI that "serves people and not just sits in a data center." Instead, he demonstrated expensive hardware that doesn't serve anyone when it matters most.

Meta's stock barely moved after the event—investors have seen this movie before. From the metaverse pivot to VR headsets gathering dust, Meta's hardware ambitions consistently overpromise and underdeliver.

The viral moment perfectly captures Meta's hardware problem: impressive technology that fails when humans actually try to use it. At $800, these glasses need to work flawlessly. Instead, they're another reminder that Meta builds for demos, not daily life.

AI isn't a bubble yet: The $3 trillion framework that proves it

New framework analyzes AI through history's biggest bubbles. Verdict: Not a bubble (yet). 4 of 5 indicators green, revenues doubling yearly, PE ratios half of dot-com era.

Azeem Azhar's comprehensive analysis shows AI boom metrics are still healthy across 5 key indicators, with revenue doubling yearly and capex funded by cash, not debt.

Is AI a bubble? After months of breathless speculation, we finally have a framework that cuts through the noise. Azeem Azhar of Exponential View just published the most comprehensive analysis yet, examining AI through the lens of history's greatest bubbles—from tulip mania to the dot-com crash.

His verdict: We're in boom territory, not bubble. But the path ahead contains a $1.5 trillion trap door that could change everything.

The five gauges that measure any bubble

Azhar doesn't rely on vibes or dinner party wisdom. He built a framework with five concrete metrics, calibrated against every major bubble in history. When two gauges hit red, you're in bubble territory. Time to sell.

Gauge 1: Economic Strain - Is AI investment bending the entire economy around it? Currently at 0.9% of US GDP, still green (under 1%). Railways hit 4% before crashing. But data centers already drive a third of US GDP growth.

Gauge 2: Industry Strain - The ratio of capex to revenues. This is the danger zone—GenAI sits at 6x (yellow approaching red), worse than railways at 2x or telecoms at 4x before their crashes. It's the closest indicator to trouble.

Gauge 3: Revenue Growth - Are revenues accelerating or stalling? Solidly green. GenAI revenues will double this year alone. OpenAI projects 73% annual growth to 2030. Morgan Stanley sees $1 trillion by 2028. Railways managed just 22% before crashing.

Gauge 4: Valuation Heat - How divorced are stock prices from reality? Green again. NASDAQ's PE ratio sits at 32, half the dot-com peak of 72. Internet stocks once traded at an implied PE of 605—investors paying for six centuries of earnings.

Gauge 5: Funding Quality - Who's providing capital and how? Currently green. Microsoft, Amazon, Google, Meta, and Nvidia are funding expansion from cash flows, not debt. The dot-com era saw $237 billion from inexperienced managers. Today's funders are battle-hardened.

The framework reveals something crucial: bubbles need specific conditions. A 50% drawdown in equity values sustained for 5+ years. A 50% decline in productive capital deployment. We're nowhere close.

Why AI revenues are exploding faster than railways or telecoms ever did

The numbers obliterate bubble concerns. Azhar's conservative estimate puts GenAI revenues at $60 billion this year, doubling from last year. Morgan Stanley says $153 billion. Either way, the growth rate is unprecedented.

IBM's CEO survey shows 62% of companies increasing AI investments in 2025. KPMG's pulse survey found billion-dollar companies plan to spend $130 million on AI over the next 12 months, up from $88 million in Q4 last year.

Meta reports AI increased conversions 3-5% across their platform. These second-order effects might explain why revenue estimates vary so wildly—the real impact is hidden in efficiency gains across every business.

Consumer spending tells the same story. Americans spend $1.4 trillion online annually. If that doubles to $3 trillion by 2030 (growing at historical 15-17% rates), GenAI apps rising from today's $10 billion to $500 billion looks conservative.

The revenue acceleration that preceded past crashes? Railways grew 22% before 1873's crash. Telecoms managed 16% before imploding. GenAI is growing at minimum 100% annually, with some estimates showing 300-500% for model makers. Enterprise adoption remains in the "foothills." Companies can barely secure enough tokens to meet demand. Unlike railways with decades-long asset lives that masked weak business models, AI's 3-year depreciation cycle forces rapid validation or failure.

The $1.5 trillion risk hiding in plain sight

Here's where optimism meets reality. Morgan Stanley projects $2.9 trillion in global data center capex between 2025-2028. Hyperscalers can cover half from internal cash. The rest—$1.5 trillion—needs external funding.

This is the trap door. Today's boom runs on corporate cash flows. Tomorrow's might depend on exotic debt instruments:

• $800 billion from private credit

• $150 billion in data center asset-backed securities (tripling that market overnight)

• Hundreds of billions in vendor financing

Not every borrower looks like Microsoft. When companies stop funding from profits and start borrowing against future promises, bubble dynamics emerge. As Azhar notes: "If GenAI revenues grow 10-fold, creditors will be fine. If not, they may discover a warehouse full of obsolete GPUs is a different thing to secure."

The historical parallels are ominous. Railway debt averaged 46% of assets before the 1872 crash. Deutsche Telecom and France Telecom added $78 billion in debt between 1998-2001. When revenues disappointed, defaults rippled through both sectors.

The verdict: Boom with a countdown

Azhar's framework delivers clarity: AI is definitively not a bubble today. Four of five gauges remain green. The concerning metric—capex outpacing revenues 6x—reflects infrastructure building, not speculation.

But the path to bubble is visible. Watch for:

• AI investment approaching 2% of GDP (currently 0.9%)

• Sustained drops in enterprise spending or Nvidia's order backlog

• PE ratios jumping from 32 to 50-60

• Shift from cash-funded to debt-funded expansion

The timeline? "Most scary scenarios take a couple of years to play out," Azhar calculates. A US recession, rising inflation, or rate spikes could accelerate the timeline.

The clever take—"sure it's a bubble but the technology is real"—misses the point entirely. The data shows we're firmly in boom territory. Unlike tulips or even dot-coms, AI generates immediate, measurable revenue and productivity gains.

The $1.5 trillion funding gap looms as the decisive test. If revenues grow 10x as projected, this becomes history's most successful infrastructure build. If not, those exotic debt instruments become kindling for a spectacular crash.

For now, the engine is "whining but not overheating." The framework gives us tools to track the transition from boom to bubble in real-time.

We're not there yet. But we can see it from here.

Google's Pixel 10 delivers everything Apple promised but couldn't ship

Pixel 10 launches with AI that searches your apps, detects your mood, and zooms 100x using generative fill—all the features Apple Intelligence promised but never delivered.

Google just did something remarkable. They took Apple's broken AI promises from last year and actually shipped them. The Pixel 10 isn't just another phone with AI features bolted on—it's a complete hardware and software overhaul that makes Apple look embarrassingly behind.

The Wall Street Journal didn't mince words: "The race to develop the killer AI-powered phone is on, but Apple is getting lapped by its Android competitors."

The AI phone Apple was supposed to make

Remember Apple Intelligence? That grand vision where Siri would rifle through your apps, understand context, and actually be useful? Google's Magic Q does exactly that. It searches through your calendar, Gmail, and other apps to answer questions before you even ask them. Friend texts asking where dinner is? Magic Q finds the reservation and pops up the answer. This was literally the core functionality Apple promised but never delivered. What's more damning—Magic Q runs passively. No prompting needed. It just works. The Pixel 10's visual overlay feature uses the camera as live AI input. Point it at a pile of wrenches to find which fits a half-inch bolt. Gemini Live detects your tone—figuring out if you're excited or concerned—and adjusts responses accordingly. These aren't party tricks; they're using mobile's unique context advantage to make AI actually useful.

But here's the killer feature: 100x zoom achieved not through optical lenses but AI generative fill. Google is using image generation to fill in details as you zoom, creating a real-life "enhance" tool straight from sci-fi movies. The edit-by-asking feature lets you restore old photos, remove glare, or just tell it to "make it better." Google's Rick Osterloh couldn't resist twisting the knife during launch: "There has been a lot of hype about this, and frankly, a lot of broken promises, too, but Gemini is the real deal."

The disappointment? No official Nano Banana announcement. This mysterious image model that appeared on LM Arena had been blowing minds with precise edits and perfect prompt adherence. Googlers posting banana emojis suggested it was theirs, but the Pixel event came and went without confirmation. Though edit-by-asking looks suspiciously similar to Nano Banana's capabilities.

Why Reddit hates what could save smartphones

Here's the bizarre reality: Reddit absolutely despises these features. Not because they don't work, but because they contain the letters "AI."

One confused Redditor posted: "I know a lot of you guys don't like AI or anything that has AI, but aren't these new AI improvements on the Pixel 10 genuinely just a nice new feature? It seems like people just default to thinking the product is bad as soon as they see AI in the marketing." This hatred runs so deep that Google's attempt to make the launch consumer-friendly—hiring Jimmy Fallon to host—backfired spectacularly. TechCrunch called it a "cringefest," with Reddit users immediately dubbing it "unwatchable." One user wrote: "I used to wish Apple would bring back live presentations, but after watching the Pixel 10 event, turns out they made the right call keeping them recorded."

The irony is thick. Google delivered genuinely useful features that could transform how we use phones, but wrapped them in marketing so cringe that their target audience rejected everything.

Google's secret weapon isn't software

The real story isn't the features—it's the Tensor G5 chip powering them. Google's new AI core is 60% more powerful than its predecessor, running all features on-device through Gemini Nano. They actually sacrificed overall performance to prioritize on-device AI.

Dylan Patel of SemiAnalysis dropped a bombshell on a recent podcast: Google's custom silicon is Nvidia's biggest threat. "Google's making millions of TPUs... TPUs clearly are like 100% utilized. That's the biggest threat to Nvidia—that people figure out how to use custom silicon more broadly." This is the real power play. While Apple struggles to partner with Google or Anthropic for AI models, Google owns the entire stack: chips, devices, models, and distribution. They've become what Apple used to be—the fully integrated player. Google's Trillium TPU is delivering impressive AI inference performance. They're ramping orders with TSMC. They're not just competing on features; they're building the infrastructure to dominate AI at every level.

The message bubble problem

Despite Google's technical victory, Apple's iPhone orders are actually up. Why? Because for most people, phone choice isn't about AI features—it's about what color your messages appear in group chats.

Mobile handset wars transcend technology. They're about identity, status, and yes, those blue bubbles. Apple's brand power might matter more than Google's superior AI, at least for now. But here's what should worry Apple: Google is delivering the AI phone experience Apple promised over a year ago. Every delay from Cupertino makes Mountain View look more competent. Every broken promise makes "It just works" sound increasingly hollow.

The Pixel 10 proves something important: the AI phone revolution is here. It's just not evenly distributed. While Silicon Valley debates model architectures, normal consumers are getting features that feel like magic—assuming they can get past the "AI" branding.

For Apple, the question isn't whether they can catch up technically. It's whether their brand fortress can withstand Google actually shipping the future while they're still making promises.

OpenAI's GPT-5 Codex can code autonomously for 7 hours straight

GPT-5 Codex breaks all records: 7 hours of autonomous coding, 15x faster on simple tasks, 102% more thinking on complex problems. OpenAI engineers now refuse to work without it.

GPT-5 Codex shatters records with 7-hour autonomous coding sessions, dynamic thinking that adjusts effort in real-time, and code review capabilities that caught OpenAI's own engineers off guard.

The coding agent revolution just hit hyperdrive. OpenAI released GPT-5 Codex yesterday, and Sam Altman wasn't exaggerating when he tweeted the team had been "absolutely cooking." This isn't just another incremental update—it's a fundamental shift in how AI approaches software development, with the model working autonomously for up to 7 hours on complex tasks.

The 7-hour coding marathon

Just weeks ago, Replit set the record with Agent 3 managing 200 minutes of continuous independent coding. GPT-5 Codex just obliterated that benchmark, working for 420 minutes straight.

OpenAI team members revealed in their announcement podcast: "We've seen it work internally up to 7 hours for very complex refactorings. We haven't seen other models do that before."

The numbers tell a shocking story. While standard GPT-5 uses a model router that decides computational power upfront, Codex implements dynamic thinking—adjusting its reasoning effort in real-time. Easy responses are now 15 times faster. For hard problems, Codex thinks 102% more than standard GPT-5. Developer Swyx called this "the most important chart" from the release: "Same model, same paradigm, but bending the curve to fit the nonlinearity of coding problems."

The benchmarks barely capture the improvement. While Codex jumped modestly from 72.8% to 74.5% on SWE-bench Verified, OpenAI's custom refactoring eval shows the real leap: from 33.9% to 51.3%.

Early access developers are losing their minds. Nick Doobos writes it "hums away looking through your codebase, and then one-shots it versus other models that prefer immediately making a change, making a mess, and then iterating." Michael Wall built things in hours he never thought possible: "Lightning fast natural language coding capabilities, produces functional code on the first attempt. Even when not perfectly matching intent, code remains executable rather than broken." Dan Shipper's team ran it autonomously for 35 minutes on production code, calling it "a legitimate alternative to Claude Code" and "a really good upgrade."

Why it thinks like a developer

GPT-5 Codex doesn't just code longer—it codes smarter. AI engineer Daniel Mack calls this "a spark of metacognition"—AI beginning to think about its own thinking process.

The secret weapon? Code review capabilities that OpenAI's own engineers now can't live without. Greg Brockman explained: "It's able to go layers deep, look at the dependencies, and raise things that some of our best reviewers wouldn't have been able to find unless they were spending hours." When OpenAI tested this internally, engineers became upset when it broke. They felt like they were "losing that safety net." It accelerated teams, including the Codex team itself, tremendously. This solves vibe coding's biggest problem. Andre Karpathy coined the term in February: "You fully give into the vibes, embrace exponentials, and forget that the code even exists. When I get error messages, I just copy paste them in with no comment."

Critics said vibe coding just shifted work from writing code to fixing AI's mistakes. But if Codex can both write and review code at expert level, that criticism evaporates.

The efficiency gains are unprecedented. Theo observes: "GPT-5 Codex is, as far as I know, the first time a lab has bragged about using fewer tokens." Why spend $200 on a chunky plan when you can get the same results for $20? Usage is already up 10x in two weeks according to Altman. Despite Twitter bubble discussions about Claude, a PhD student named Zeon reminded everyone: "Claude is minuscule compared to Codex" in real-world usage.

The uneven AI revolution

Here's the uncomfortable truth: AI's takeoff is wildly uneven. Coders are living in 2030 while everyone else is stuck with generic chatbots.

Professor Ethan Molick doesn't mince words: "The AI labs are run by coders who think code is the most vital thing in the world... every other form of work is stuck with generic chat bots."

Roon from OpenAI countered that autonomous coding creates "the beginning of a takeoff that encompasses all those other things." But he also identified something profound: "Right now is the time where the takeoff looks the most rapid to insiders (we don't program anymore, we just yell at Codex agents) but may look slow to everyone else."

This explains everything. While pundits debate AI walls and plateaus, developers are experiencing exponential productivity gains. Anthropic rocketed from $1 billion to $5 billion ARR between January and summer, largely from coding. Bolt hit $20 million ARR in two months. Lovable and Replit are exploding. The market has spoken. OpenAI highlighted coding first in GPT-5's release, ahead of creative writing. They're betting 700 million new people are about to become coders.

Varun Mohan sees the future clearly:

"We may be watching the early shape of true autonomous dev agents emerging. What happens when this stretches to days or weeks?"

The implications transcend coding. If AI can maintain focus for 7 hours, adjusting its thinking dynamically, we're seeing genuine AI persistence—not just intelligence, but determination. The gap between builders and everyone else has never been wider. But paradoxically, thanks to tools like Lovable, Claude Code, Cursor, Bolt, and Replit, the barrier to entry has never been lower.

The coding agent revolution isn't coming. For those paying attention, it's already here.

Apple finally makes its AI move with Google partnership

Apple partners with Google to completely rebuild Siri using Gemini AI, sidelining OpenAI despite their ChatGPT partnership last year. The new Siri launches this spring.

Apple partners with Google's Gemini to rebuild Siri from scratch, while OpenAI raises $10B at $500B valuation and xAI faces executive exodus after just months.

Apple's long-awaited AI strategy is finally taking shape, and it's not what anyone expected. After months of speculation about acquisitions and partnerships, the Cupertino giant has chosen Google as its AI partner, sidelining both OpenAI and Anthropic in a move that could reshape the entire AI landscape.

Why Apple chose Google over OpenAI

Bloomberg's Mark Gurman reports that Apple has reached a formal agreement with Google to evaluate and test Gemini models for powering a completely rebuilt Siri. The project, internally known as "World Knowledge Answers," aims to replicate the performance of Google's AI overviews or Perplexity's search capabilities.

The new Siri is split into three components: a planner, a search system, and a summarizer. Sources indicate Apple is leaning toward using a custom-built version of Google's Gemini model as the summarizer, with potential use across all three components. This means we could see a version of Siri built entirely on Google's technology within six months.

What makes this fascinating is who's not in the room. Anthropic's Claude actually outperformed Google in Apple's internal bakeoff, but Anthropic demanded more than $1.5 billion annually for their model. Google offered much more favorable terms. More surprisingly, OpenAI is completely absent from these conversations, despite ChatGPT being the first third-party AI app Apple promoted on iPhone just a year ago.

Craig Federighi, Apple's head of software engineering, told an all-hands meeting: "The work we've done on this end-to-end revamp of Siri has given us the results we've needed. This has put us in a position to not just deliver what we announced, but to deliver a much bigger upgrade than we envisioned." The new Siri will tap into personal data and on-screen content to fulfill queries, finally delivering on the original "Apple Intelligence" vision. It will also function as a computer-use agent, navigating Apple devices through voice instructions. The feature is expected by spring as part of a long-overdue Siri overhaul.

The $500 billion OpenAI phenomenon

While Apple negotiates partnerships, OpenAI continues its meteoric rise. The company has boosted its secondary share sale to $10 billion, up from the $6 billion reported last month. This round tests OpenAI at a staggering $500 billion valuation, up from $300 billion at the start of the year.

Since January, OpenAI has doubled its revenue and user base, making the massive markup somewhat justifiable despite eye-popping numbers. Current and former employees who've held shares for more than two years have until month's end to access liquidity, with the round expected to close in October.

The demand for AI startup investments continues to vastly outstrip supply. Mistral is finalizing a €2 billion investment valuing the company at roughly $14 billion, up from initial reports of seeking $1 billion at a $10 billion valuation. This doubles their valuation from $5.8 billion last June and represents their first significant war chest—doubling their total fundraising in one round.

Executive exodus hits xAI

Not all AI companies are riding high. xAI's CFO Mike Liberator left after just three months, departing around July after starting in April. He had overseen xAI's debt and equity raise in June, which brought in $10 billion with SpaceX contributing almost half the equity—suggesting comparatively sparse outside investor demand.

This follows a pattern of departures. General counsel Robert Keel left after a year, citing in his farewell that "there's daylight between our worldviews" regarding Elon Musk. Senior lawyer Rahu Rao departed around the same time, and co-founder Igor Babushkin announced his exit on August 13th to start his own venture firm. X CEO Linda Yaccarino also announced her departure in July after the social media platform's merger with xAI.

Data labeling wars escalate

The competition has turned litigious in the data labeling sector. Scale has sued rival Mercor for corporate espionage, claiming former head of engagement Eugene Ling downloaded over 100 customer strategy documents while communicating with Mercor's CEO about business strategy.

The lawsuit alleges Ling was hired to build relationships with one of Scale's largest customers using these documents. Mercor co-founder Surya Midha responded that they have "no interest in Scale's trade secrets" and offered to have Ling destroy the files.

The situation is complicated by Meta's acquihire deal with Scale, which caused multiple major clients to leave. Meta themselves have moved away from Scale's data labeling services, adding rival providers including Mercor. For anyone looking for signs that AI is slowing down—whether in competition, talent wars, or fundraising—the answer is definitively no. Apple's partnership with Google signals the start of a new phase in AI competition, where even the most independent tech giants must choose sides. OpenAI's $500 billion valuation proves investor appetite remains insatiable. And the escalating conflicts between companies show an industry moving faster, not slower, toward an uncertain but transformative future.