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දැන් වැඩ කරද්දි සතුටක් නැහැ (Developing IT)
- Thread starter webDevNinja
- Start date
knowledge workers la okkoma iwarai bn
transition period eke kiyak hari hoyagena wena sector ekekta bahapalla
transition period eke kiyak hari hoyagena wena sector ekekta bahapalla
Here's the breakdown by industry:
Extreme impact
Logistics is the hardest hit — UPS alone announced ~48,000 cuts under its "Network of the Future" initiative, saying automation and AI-enabled logistics allowed it to move more volume with fewer workers, closing 93 facilities in the process.
IT Consulting saw Accenture cut ~11,000 roles and TCS made large reductions as well, with restructuring tied to how work is changing inside these firms.
Technology & Software — Oracle announced up to ~30,000 cuts partly attributed to AI adoption, while IBM confirmed thousands of back-office roles replaced by AI agents.
High impact
Finance is seeing measurable decline — employment growth in marketing consulting, graphic design, office administration, and telephone call centers has fallen below trend amid reduced labor demand due to AI efficiency gains. Citigroup was among the major names affected.
Media / Journalism and Data & Analytics saw significant job posting declines — the data and analytics sector saw a 13.2% decline in new openings in 2025, and scientist/researcher postings dropped ~22.2%.
The big picture
Employers reported about 55,000 layoffs attributed to AI in 2025, and CFO surveys suggest a 9x increase in AI-related layoffs in 2026. At least 8 companies announced AI-related layoffs affecting 10,000+ employees each, including Accenture, Amazon, Citigroup, Dell, Intel, Microsoft, TCS, and UPS.
The World Economic Forum's 2025 Future of Jobs Report found that 41% of employers worldwide intend to reduce their workforce in the next five years due to AI.
software wasn't exactly engineering to begin with
it is much more akin to a craft or a trade or a technical skill
a skill that you gained with hands on experience + years on the job
software engineering කියන ජොබ් title එකම හෙන බොරුවක්
it is much more akin to a craft or a trade or a technical skill
a skill that you gained with hands on experience + years on the job
software engineering කියන ජොබ් title එකම හෙන බොරුවක්
Its not really AI can do all what a dev can do. Its only to have enough funding/budget to invest in AI processes. But we need ppl to think & architec solutions. Human brain is not replaceable. .
human brains wala amuthu magic ekk naha bro
go chess wage human brain eka use krala karana hama game ekkma machine walta humans lata wada hodata karanna puluwan dn godk kal idala
human language thama thibba barrier eka dn ekath wisaduna kiyanne human brains walin kiyala karanna amuthu mewwa ekk ithuru wenne naha
Yes there's a truth of what you are saying here. But my point is still a GPT model can't do all what we do. I'm a CTO & I get pressure from the board to use more GPTs & reduce dev usage. We already put away some Indians devs from our R&D team. I always had some issues with Indians. But imagine, there is a Github repo & we need to clone this & debug for issues. when we have humans, I only need to tell this in Github issues & they will do the necessary steps. But with AI GPTs we can't do it. still we need to provide step by step istructions for a task. Even the best Claude model:Opus. It wil take 50+ years to replace us entirely if they invented AGI.Vibe coding is ok for small static websites, But I'm talking enterprise systems like Healthcare systems which stores 10 million+ patient data + diagnostic data with 6TB+ AWS S3 files (Xray, prescriptions, etc) scanns for legal purposes & audits.
balamu mokd wenne kiyala issrahata
ඔය ලෙගසි ලෙගසි ගාන ඒව ඔය විදිහටම පවත්වාගෙන යයි කියල හිතන්න අමාරුයි
ඉස්සරහට ඇති වෙන ලෝකෙ හරියටම මේකයි කියල කියන්න බෑ ඉතින්
හැබැයි AI වලට විරුද්දව bet කරන එක නම් ගොන් වැඩක් වගේ
හිතපන් අස්ස කරත්ත දුවන කාලෙ කාර් එක ආවට පස්සෙ ඔය කාර් එකේ කොච්චර නම් අඩු පාඩු තියෙන්නැද්ද
ඒ කාලෙ අස්ස කරත්ත කාරයොත් හිතා ඉන්නැති මේ හුචක්කු වලට නම් කවදාවත් අපිව රිප්ලේස් කරන්න වෙන්නෙ නැ කියල
හුචක්කුව උනත් හැමදාම හුචක්කුව විදිහට තියෙන්නෙ නෑනෙ ඕක තමයි Ai වලටත් වෙන්නෙ
AI දවසින් දවස් දියුණු වෙනව
major categories of powerful AI that companies are actively building right now
The ultimate goal for many frontier labs. AGI refers to a type of artificial intelligence capable of processing information and performing actions in the same ways human beings can — not just doing what it was created to do, but learning and carrying out tasks outside of those parameters.
While current AI excels at specific tasks such as language translation, image recognition, or data analysis, AGI will possess the flexibility to understand, learn, and apply knowledge across any intellectual domain — mirroring human versatility in problem-solving and abstract thinking.
OpenAI's strategy is centered around one single objective: developing AGI, progressing from conversational AI to fully operational systems. The AGI race is fierce — the AGI market already has over 520 companies and 370 startups, and is expected to grow to over $116 billion by 2035.
This is arguably the hottest category right now. AI agents are autonomous software components capable of perceiving their digital environment, making decisions, and taking actions toward specific goals with limited or no human oversight. This technology will fundamentally change how companies operate, as AI agents will soon begin to take over complex workflows and accelerate decision-making cycles.
Three key features distinguish modern agents: persistent memory (maintaining context across multi-week processes), tool integration (browsing the web, executing code, querying databases, sending emails), and verifiable output through multi-component pipelines that reduce errors and enable trust in autonomous actions.
The AI agent market is growing at a CAGR of 46.3%, projected to reach $52.62 billion by 2030 from $7.84 billion in 2025.
These models will be able to perceive and act in a world much more like a human — bridging language, vision, and action all together. In the near future, we're going to start seeing multimodal digital workers that can autonomously complete different tasks, like interpreting complex healthcare cases.
Daily AI use will move beyond text to multimodal systems, with voice agents that remember context and offer more human-like, continuous interaction.
Physical AI represents any physical process learning from and applying AI — such as robots, drones, autonomous vehicles, and smart devices — where AI software directly controls physical behavior and adapts based on real-world feedback.
During the next decade, the intersection of agentic AI systems with physical AI robotic systems will result in robots whose "brains" are agentic AIs — able to adapt to new environments, plan multistep tasks, recover from failure, and operate under uncertainty.
The numbers are striking: while AI-powered humanoid robots meant for industrial use are still in early stages, annual unit shipments are estimated at 5,000–7,000 in 2025, potentially reaching 15,000 in 2026. Looking further ahead, UBS estimates that by 2035, there will be 2 million humanoids in the workplace, a number expected to increase to 300 million by 2050.
Companies leading this charge include Tesla (Optimus), Agility Robotics (Digit), Boston Dynamics (Atlas), and Physical Intelligence Inc., which is building AI software to help robots learn any task.
Many AI practitioners believe that today's AI models will need to grow beyond words and develop an understanding of the spatial and physical world. Fei-Fei Li's startup World Labs is building a form of "world model" capable of processing sensory data and developing a physics-based understanding of the real world — essentially AI that understands how reality works, not just how language works.
AI systems are beginning to possess the ability to understand and model the real physical world through "Next-State Prediction" — a breakthrough that provides a new foundation for complex tasks such as autonomous driving simulation and robotic environmental interaction.
IBM is building a quantum-centric supercomputing architecture that combines quantum computing with powerful high-performance computing and AI infrastructure. AMD and IBM are exploring how to integrate AMD CPUs, GPUs, and FPGAs with IBM quantum computers to efficiently accelerate a new class of emerging algorithms outside the current reach of either paradigm working independently.
Beyond AGI lies superintelligence — AI that surpasses human intelligence across every domain. In November 2025, Microsoft CEO Mustafa Suleyman said Microsoft was building "humanist superintelligence" — super-smart AI designed to help everyone. Meanwhile, Ilya Sutskever's startup Safe Superintelligence (SSI) has raised over $2 billion with a singular focus on building safe superintelligent systems.
AGI houses the potential to automate 60–80% of cross-functional activities by 2030, up from the 15–20% automation achievable with current AI systems. Innovation cycles could drop from the current 6–12 months to just 1–3 months.
In short, companies aren't just building smarter chatbots — they're building AI that reasons, acts, moves, and eventually thinks at or beyond human level. The race is on across every dimension: cognitive, physical, multimodal, and quantum.
1. Artificial General Intelligence (AGI)
The ultimate goal for many frontier labs. AGI refers to a type of artificial intelligence capable of processing information and performing actions in the same ways human beings can — not just doing what it was created to do, but learning and carrying out tasks outside of those parameters.
While current AI excels at specific tasks such as language translation, image recognition, or data analysis, AGI will possess the flexibility to understand, learn, and apply knowledge across any intellectual domain — mirroring human versatility in problem-solving and abstract thinking.
OpenAI's strategy is centered around one single objective: developing AGI, progressing from conversational AI to fully operational systems. The AGI race is fierce — the AGI market already has over 520 companies and 370 startups, and is expected to grow to over $116 billion by 2035.
2. Agentic AI
This is arguably the hottest category right now. AI agents are autonomous software components capable of perceiving their digital environment, making decisions, and taking actions toward specific goals with limited or no human oversight. This technology will fundamentally change how companies operate, as AI agents will soon begin to take over complex workflows and accelerate decision-making cycles.
Three key features distinguish modern agents: persistent memory (maintaining context across multi-week processes), tool integration (browsing the web, executing code, querying databases, sending emails), and verifiable output through multi-component pipelines that reduce errors and enable trust in autonomous actions.
The AI agent market is growing at a CAGR of 46.3%, projected to reach $52.62 billion by 2030 from $7.84 billion in 2025.
3. Multimodal AI
These models will be able to perceive and act in a world much more like a human — bridging language, vision, and action all together. In the near future, we're going to start seeing multimodal digital workers that can autonomously complete different tasks, like interpreting complex healthcare cases.
Daily AI use will move beyond text to multimodal systems, with voice agents that remember context and offer more human-like, continuous interaction.
4. Physical AI & Humanoid Robots
Physical AI represents any physical process learning from and applying AI — such as robots, drones, autonomous vehicles, and smart devices — where AI software directly controls physical behavior and adapts based on real-world feedback.
During the next decade, the intersection of agentic AI systems with physical AI robotic systems will result in robots whose "brains" are agentic AIs — able to adapt to new environments, plan multistep tasks, recover from failure, and operate under uncertainty.
The numbers are striking: while AI-powered humanoid robots meant for industrial use are still in early stages, annual unit shipments are estimated at 5,000–7,000 in 2025, potentially reaching 15,000 in 2026. Looking further ahead, UBS estimates that by 2035, there will be 2 million humanoids in the workplace, a number expected to increase to 300 million by 2050.
Companies leading this charge include Tesla (Optimus), Agility Robotics (Digit), Boston Dynamics (Atlas), and Physical Intelligence Inc., which is building AI software to help robots learn any task.
5. World Models
Many AI practitioners believe that today's AI models will need to grow beyond words and develop an understanding of the spatial and physical world. Fei-Fei Li's startup World Labs is building a form of "world model" capable of processing sensory data and developing a physics-based understanding of the real world — essentially AI that understands how reality works, not just how language works.
AI systems are beginning to possess the ability to understand and model the real physical world through "Next-State Prediction" — a breakthrough that provides a new foundation for complex tasks such as autonomous driving simulation and robotic environmental interaction.
6. Quantum-Enhanced AI
IBM is building a quantum-centric supercomputing architecture that combines quantum computing with powerful high-performance computing and AI infrastructure. AMD and IBM are exploring how to integrate AMD CPUs, GPUs, and FPGAs with IBM quantum computers to efficiently accelerate a new class of emerging algorithms outside the current reach of either paradigm working independently.
7. Superintelligence
Beyond AGI lies superintelligence — AI that surpasses human intelligence across every domain. In November 2025, Microsoft CEO Mustafa Suleyman said Microsoft was building "humanist superintelligence" — super-smart AI designed to help everyone. Meanwhile, Ilya Sutskever's startup Safe Superintelligence (SSI) has raised over $2 billion with a singular focus on building safe superintelligent systems.
The Big Picture
AGI houses the potential to automate 60–80% of cross-functional activities by 2030, up from the 15–20% automation achievable with current AI systems. Innovation cycles could drop from the current 6–12 months to just 1–3 months.
In short, companies aren't just building smarter chatbots — they're building AI that reasons, acts, moves, and eventually thinks at or beyond human level. The race is on across every dimension: cognitive, physical, multimodal, and quantum.
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