#Generative AI in Manufacturing Sector

[lisakeller22]

Applications of Gen AI in Manufacturing Processes

Generative AI is becoming an increasingly important aspect of the manufacturing industry. Read the blog to know how Gen AI is being used to optimize manufacturing processes.

[johnsongray22]

How can the manufacturing sector leverage generative AI for informed decision-making, enhanced productivity, and improved product quality? Go through the blog to learn about the use cases and benefits of  generative AI in the manufacturing industry.

[srbachchan]

DAY 6274

Jalsa, Mumbai Aopr 20, 2025 Sun 11:17 pm

🪔 ,

April 21 .. birthday greetings and happiness to Ef Mousumi Biswas .. and Ef Arijit Bhattacharya from Kolkata .. 🙏🏽❤️🚩.. the wishes from the Ef family continue with warmth .. and love 🌺

The AI debate became the topic of discussion on the dining table ad there were many potent points raised - bith positive and a little indifferent ..

The young acknowledged it with reason and able argument .. some of the mid elders disagreed mildly .. and the end was kind of neutral ..

Blessed be they of the next GEN .. their minds are sorted out well in advance .. and why not .. we shall not be around till time in advance , but they and their progeny shall .. as has been the norm through generations ...

The IPL is now the greatest attraction throughout the day .. particularly on the Sunday, for the two on the day .. and there is never a debate on that ..

🤣

.. and I am most appreciative to read the comments from the Ef on the topic of the day - AI .. appreciative because some of the reactions and texts are valid and interesting to know .. the aspect expressed in all has a legitimate argument and that is most healthy ..

I am happy that we could all react to the Blog contents in the manner they have done .. my gratitude .. such a joy to get different views , valid and meaningful ..

And it is not the end of the day or the debate .. some impressions of the Gen X and some from the just passed Gen .. and some that were never ever the Gen are interesting as well :

The Printing Press (15th Century)

Fear: Scribes, monks, and elites thought it would destroy the value of knowledge, lead to mass misinformation, and eliminate jobs. Reality: It democratized knowledge, spurred the Renaissance and Reformation, and created entirely new industries—publishing, journalism, and education.

⸻

Industrial Revolution (18th–19th Century)

Fear: Machines would replace all human labor. The Luddites famously destroyed machinery in protest. Reality: Some manual labor jobs were displaced, but the economy exploded with new roles in manufacturing, logistics, engineering, and management. Overall employment and productivity soared.

⸻

Automobiles (Early 20th Century)

Fear: People feared job losses for carriage makers, stable hands, and horseshoe smiths. Cities worried about traffic, accidents, and social decay. Reality: The car industry became one of the largest employers in the world. It reshaped economies, enabled suburbia, and created new sectors like travel, road infrastructure, and auto repair.

⸻

Personal Computers (1980s)

Fear: Office workers would be replaced by machines; people worried about becoming obsolete. Reality: Computers made work faster and created entire industries: IT, software development, cybersecurity, and tech support. It transformed how we live and work.

⸻

The Internet (1990s)

Fear: It would destroy jobs in retail, publishing, and communication. Some thought it would unravel social order. Reality: E-commerce, digital marketing, remote work, and the creator economy now thrive. It connected the world and opened new opportunities.

⸻

ATMs (1970s–80s)

Fear: Bank tellers would lose their jobs en masse. Reality: ATMs handled routine tasks, but banks actually hired more tellers for customer service roles as they opened more branches thanks to reduced transaction costs.

⸻

Robotics & Automation (Factory work, 20th century–today)

Fear: Mass unemployment in factories. Reality: While some jobs shifted or ended, others evolved—robot maintenance, programming, design. Productivity gains created new jobs elsewhere.

The fear is not for losing jobs. It is the compromise of intellectual property and use without compensation. This case is slightly different.

I think AI will only make humans smarter. If we use it to our advantage.

That’s been happening for the last 10 years anyway

Not something new

You can’t control that in this day and age

YouTube & User-Generated Content (mid-2000s onward)

Initial Fear: When YouTube exploded, many in the entertainment industry panicked. The fear was that copyrighted material—music, TV clips, movies—would be shared freely without compensation. Creators and rights holders worried their content would be pirated, devalued, and that they’d lose control over distribution.

What Actually Happened: YouTube evolved to protect IP and monetize it through systems like Content ID, which allows rights holders to:

Automatically detect when their content is used

Choose to block, track, or monetize that usage

Earn revenue from ads run on videos using their IP (even when others post it)

Instead of wiping out creators or studios, it became a massive revenue stream—especially for musicians, media companies, and creators. Entire business models emerged around fair use, remixes, and reactions—with compensation built in.

Key Shift: The system went from “piracy risk” to “profit partner,” by embracing tech that recognized and enforced IP rights at scale.

This lead to higher profits and more money for owners and content btw

You just have to restructure the compensation laws and rewrite contracts

It’s only going to benefit artists in the long run ‎

Yes

They can IP it

That is the hope

It’s the spread of your content and material without you putting a penny towards it

Cannot blindly sign off everything in contracts anymore. Has to be a lot more specific.

Yes that’s for sure

“Automation hasn’t erased jobs—it’s changed where human effort goes.”

Another good one is “hard work beats talent when talent stops working hard”

Which has absolutely nothing to with AI right now but 🤣

These ladies and Gentlemen of the Ef jury are various conversational opinions on AI .. I am merely pasting them for a view and an opinion ..

And among all the brouhaha about AI .. we simply forgot the Sunday well wishers .. and so ..

my love and the length be of immense .. pardon

Amitabh Bachchan

[allthecanadianpolitics]

The federal government will be investing $2.4 billion to accelerate Canada’s artificial intelligence (AI) sector, Prime Minister Justin Trudeau announced Sunday. The investment will be divided between a number of measures meant to advance job growth in the AI and tech industry and boost businesses’ productivity. “This announcement is a major investment in our future, in the future of workers, in making sure that every industry, and every generation, has the tools to succeed and prosper in the economy of tomorrow,” Trudeau said in a press release Sunday. Majority of the funds, $2 billion, will go toward increasing access to computing and technological infrastructure. Another $200 million is being invested into AI start-ups to accelerate the technology in “critical sectors” such as health care, agriculture and manufacturing, the release says. Additional funds will be put toward helping small and medium-sized businesses incorporate AI, with another $50 million being committed to help train workers whose jobs may be disrupted by the technology.

Continue Reading.

Tagging: @politicsofcanada

[canmom]

how much power does tech really use, compared to other shit?

my dash has been full of arguing about AI power consumption recently. so I decided to investigate a bit.

it's true, as the Ars Technica article argues, that AI is still only one fairly small part of the overall tech sector power consumption, potentially comparable to things like PC gaming. what's notable is how quickly it's grown in just a few years, and this is likely to be a limit to how much more it can scale.

I think it is reasonable to say that adding generative AI at large scale to systems that did not previously have generative AI (phones, Windows operating system etc.) will increase the energy cost. it's hard to estimate by how much. however, the bulk of AI energy use is in training, not querying. in some cases 'AI' might lead to less energy use, e.g. using an AI denoiser will reduce the energy needed to render an animated film.

the real problem being exposed is that most of us don't really have any intuition for how much energy is used for what. you can draw comparisons all sorts of ways. compare it to the total energy consumption of humanity and it may sound fairly niche; compare it to the energy used by a small country (I've seen Ireland as one example, which used about 170TWh in 2022) and it can sound huge.

but if we want to reduce the overall energy demand of our species (to slow our CO2 emissions in the short term, and accomodate the limitations of renewables in a hypothetical future), we should look at the full stack. how does AI, crypto and tech compare to other uses of energy?

here's how physicist David McKay broke down energy use per person in the UK way back in 2008 in Sustainable Energy Without The Hot Air, and his estimate of a viable renewable mix for the UK.

('Stuff' represents the embedded energy of manufactured goods not covered by the other boxes. 'Gadgets' represents the energy used by electronic devices including passive consumption by devices left on standby, and datacentres supporting them - I believe the embodied energy cost of building them falls under 'stuff' instead.)

today those numbers would probably look different - populations change, tech evolves, etc. etc., and this notably predates the massive rise in network infrastructure and computing tech that the Ars article describes. I'm sure someone's come up with a more up-to-date SEWTHA-style estimate of how energy consumption breaks down since then, but I don't have it to hand.

that said, the relative sizes of the blocks won't have changed that much. we still eat, heat our homes and fly about as much as ever; electric cars have become more popular but the fleet is still mostly petrol-powered. nothing has fundamentally changed in terms of the efficiency of most of this stuff. depending where you live, things might look a bit different - less energy on heating/cooling or more on cars for example.

how big a block would AI and crypto make on a chart like this?

per the IEA, crypto used 100-150TWh of electricity worldwide in 2022. in McKay's preferred unit of kWh/day/person, that would come to a worldwide average of just 0.04kWh/day/person. that is of course imagining that all eight billion of us use crypto, which is not true. if you looked at the total crypto-owning population, estimated to be 560 million in 2024, that comes to about 0.6kWh/day/crypto-owning person for cryptocurrency mining [2022/2024 data]. I'm sure that applies to a lot of people who just used crypto once to buy drugs or something, so the footprint of 'heavier' crypto users would be higher.

I'm actually a little surpised by this - I thought crypto was way worse. it's still orders of magnitude more demanding than other transaction systems but I'm rather relieved to see we haven't spent that much energy on the red queen race of cryptomining.

the projected energy use of AI is a bit more vague - depending on your estimate it could be higher or lower - but it would be a similar order of magnitude (around 100TWh).

SEWTHA calculated that in 2007, data centres in the USA added up to 0.4kWh/day/person. the ars article shows worldwide total data centre energy use increasing by a factor of about 7 since then; the world population has increased from just under 7 billion to nearly 8 billion. so the amount per person is probably about a sixfold increase to around 2.4kWh/day/person for data centres in the USA [extrapolated estimate based on 2007 data] - for Americans, anyway.

however, this is complicated because the proportion of people using network infrastructure worldwide has probably grown a lot since 2007, so a lot of that data centre expansion might be taking place outside the States.

as an alternative calculation, the IEA reports that in 2022, data centres accounted for 240-340 TWh, and transmitting data across the network, 260-360 TWh; in total 500-700TWh. averaged across the whole world, that comes to just 0.2 kWh/day/person for data centres and network infrastructure worldwide [2022 data] - though it probably breaks down very unequally across countries, which might account for the huge discrepancy in our estimates here! e.g. if you live in a country with fast, reliable internet where you can easily stream 4k video, you will probably account for much higher internet traffic than someone in a country where most people connect to the internet using phones over data.

overall, however we calculate it, it's still pretty small compared to the rest of the stack. AI is growing fast but worldwide energy use is around 180,000 TWh. humans use a lot of fucking energy. of course, reducing this is a multi-front battle, so we can still definitely stand to gain in tech. it's just not the main front here.

instead, the four biggest blocks by far are transportation, heating/cooling and manufacturing. if we want to make a real dent we'd need to collectively travel by car and plane a lot less, insulate our houses better, and reduce the turnover of material objects.

[darkmaga-returns]

Shifting AMD's server-grade processor line to TSMC's Arizona facility marks the first time the U.S. company will produce these chips domestically, eliminating the supply chain risks associated with manufacturing at TSMC's Taiwan-based fabs. 

"We want to have a very resilient supply chain, so Taiwan continues to be a very important part of that supply chain, but the United States is also going to be important and we're expanding our work there, including our work with TSMC and other key supply chain partners," Su said. 

News that AMD's fifth-generation EPYC will be produced in America comes one day after Nvidia unveiled new initiatives aimed at strengthening America's chip manufacturing sector:

Nvidia is localizing AI chip and supercomputer manufacturing in the U.S. for the first time, partnering with TSMC, Foxconn, Wistron, Amkor, and SPIL.

Over 1 million square feet of manufacturing space has been commissioned for Blackwell chips and AI supercomputers in Arizona and Texas.

Mass production of these chips is expected within 12–15 months.

Total AI infrastructure by Nvidia could total $500 billion over the next four years.

Restoring U.S. chipmaking capacity is critical for several reasons, but national security stands above all. China can easily disrupt chip supply chains in Taiwan—something that could send shockwaves around the world, impacting U.S. defense production of missiles, tanks, and other critical systems, many of which rely on chips fabricated overseas. 

If the U.S. intends to compete—and win—in the 2030s, the ongoing expansion of domestic chip manufacturing is not just welcome news; it's essential for survival.

[transmutationisms]

naaaai (not another ask about ai) but would u happen to have any useful sources or links for regarding the labour involved in training models? both in the initial "big boom" and of current developments. it's one of the critiques i find worthwhile but i find the way that it's talked about by most leftists or communists to be extremely lacking, in the same vein as the environmental damage arguments (not contextualized enough, doubtful of their understanding of the actual technology, wayyy too much focus on soul). and it's just (unfortunately predictably) completely absent in any tech space discussion

im particularly curious on training/building/creating the models specifically, though ofc if u have stuff regarding the actual physical labour involved, or on the maintenance/finetuning, id appreciate anything u can recommend. my (inexperienced) understanding is that the brunt of the work offloaded to underpaid workers was in preparing the datasets, but in one way or another that's becoming less true (focused on other areas of ai development instead, better automation, the datasets already exist, the base models already exist and dont need billions of datasets, weird secrecy stuff). like i don't doubt that google or meta or whatnot are still extorting, abusing, and mistreating workers, but i cant help but feel that it's much worse in the areas of social media or in the sector of physical labour instead?

i know the onus of proof isn't on u as an uninvolved tumblr user but ive found your posts and recs on it deeply useful in the past, and also i cant handle scrolling through hackernews/tech twitter/bsky/god forbid lesswrong for any longer. SORRY THAT THIS READS AS A STUDENT THING i swear it isnt im just eternally cursed with email to professor writing syndrome. ty love ur poasts

im not the right person to ask for reading on this sorry, i keep up with broad strokes but it's not a topic i really care enough to deep dive on. you can check my tech tag there are at least some news articles i reblogged in there. i mean there's also a lot written in general about the labour conditions in tech mining and manufacturing broadly, and on content moderators at socmed companies. but yeah you would have to poke around or ask someone else for material on this specifically about llm datasets

[mariacallous]

Tariffs, another chaotic venture of the barely four-month-old Trump administration, are set to rollick every sector of the economy and nearly all the goods and services people use across the world. But tariffs could also cause the tech in your phone and other devices you use every day to stagnate as supply chains are hit by the rise in costs and companies scramble to balance the books by cutting vital development research.

Let’s get a couple important caveats out of the way here, starting with the possibility that the US might just come to its senses and back down on tariffs after all. President Trump promises he won't, of course, but he has now enacted a 90-day delay on higher tariffs for all countries except China, which has had its tariffs hiked from 34 to 145 percent.

While the tariff reprieve may ease pressures elsewhere, it is terrible news for Big Tech, which has supply chains that rely heavily on Chinese companies and Chinese-made components. Some companies have already gotten very creative about trying to dodge those additional costs, like Apple, which Reuters reports airlifted about 600 tons of iPhones to India in an effort to avoid Trump’s tariffs.

Whether tech leaders more broadly can yet negotiate special exemptions that allow their products to swerve these costs remains to be seen, but if they don’t, sky-high tariffs are likely to limit what new technologies companies can cram into their devices while keeping costs low.

“There's absolutely a threat to innovation,” says Anshel Sag, a principal analyst at Moor Insights and Strategies. “Companies have to cut back on spending, which generally means cutting back on everything.”

Smartphones in particular are at risk of soaring in price, given that they are the single largest product category that the US imports from China. Moving the wide variety of manufacturing capabilities needed to produce them in the US would cost an amount of money that’s almost impossible to calculate—if the move would even be possible at all.

The trouble tariffs cause smartphone makers will come as they try to battle rising costs while making their products ever more capable. Apple spent nearly $32 billion on research and development costs in 2024. Samsung spent $24 billion on R&D that same year. Phone companies need their devices to dazzle and excite users so they upgrade to the shiny new edition each and every year. But people also need to be able to afford these now near essential products, so striking a balance in the face of exponentially high tariffs creates problems.

“As companies shift their engineering teams to focus on cost reductions rather than creating the next best thing, the newest innovation—does that hurt US manufacturers?” asks Shawn DuBravac, chief economist at the trade association IPC. “Are we creating an environment where foreign manufacturers can out innovate US manufacturers because they are not having to allocate engineering resources to cost reduction?”

If that’s how it goes down, the result will be almost the exact opposite effect of what Trump claims he intended to do by implementing tariffs in the first place. Yet sadly it’s a well-known fact of business that R&D is one of the first budgets to be cut when profits are at risk. If US manufacturers are forced to keep costs low enough to entice customers in this new regime, it’ll more than likely mean innovation falters.

“Rather than focusing on some new AI application, they might want to focus on reengineering this product so that they're able to shave pennies here and pennies there and reduce production cost,” DuBravac says. “What ends up happening is you say, ‘Ah, you know what? We're not going to launch that this year. We're going to wait 12 months. We’re going to wait for the cost to fall.’”

Sag says that a lower demand—likely caused because people will have less money as we potentially careen toward a recession—also leads to a slowdown of the refresh cycle of a product. Less people buying a thing means less need to make more of the thing. Some products may get to the point where there is just no market for them anymore.

He points to product categories such as folding phones, which after six years of adjustment and experimentation at high price points have finally started to come into their own. The prices have come down as well, meaning folding phones are nearly at the phase of being at an attractive price point for more regular buyers.

It has been rumored that Apple has a folding phone close to debuting, but who knows how that plays out in a world where Apple is subject to the same trade tariffs as everyone else with a heavy reliability on China production? A complicated or potentially risky device might be delayed, or be deemed too ambitious, because tariff costs forced budgets elsewhere.

“It definitely affects product cycles and which features get made—and even which configurations of which chips get shipped,” Sag says. “The ones that are more cost optimized will probably get used more.”

[beardedmrbean]

BEIJING (Reuters) - After quitting the education industry last August due to China's crackdown on private tutoring, He Ajun has found an unlikely second life as an unemployment influencer.

The Guangzhou-based vlogger, 32, offers career advice to her 8,400 followers, charting her journey through long-term joblessness. "Unemployed at 31, not a single thing accomplished," she posted last December.

He is now making around 5,000 yuan ($700) per month through ads on her vlogs, content editing, private consultations and selling handicrafts at street stalls.

"I think in future freelancing will be normalised," said He. "Even if you stay in the workplace, you'll still need freelancing abilities. I believe it will become a backup skill, like driving."

China is under instruction to unleash "new productive forces", with government policies targeting narrow areas of science and technology including AI and robotics.

But critics say that has meant weak demand in other sectors and risks leaving behind a generation of highly educated young people, who missed the last boom and graduated too late to retrain for emerging industries.

A record 11.79 million university graduates this year face unprecedented job scarcity amid widespread layoffs in white-collar sectors including finance, while Tesla, IBM and ByteDance have also cut jobs in recent months.

Urban youth unemployment for the roughly 100 million Chinese aged 16-24 spiked to 17.1% in July, a figure analysts say masks millions of rural unemployed.

China suspended releasing youth jobless data after it reached an all-time high of 21.3% in June 2023, later tweaking criteria to exclude current students.

Over 200 million people are currently working in the gig economy and even that once fast-growing sector has its own overcapacity issues. A dozen Chinese cities have warned of ride-hailing oversaturation this year.

Redundancies have even spread to government work, long considered an "iron rice bowl" of lifetime employment.

Last year Beijing announced a 5% headcount reduction and thousands have been laid off since, according to official announcements and news reports. Henan province trimmed 5,600 jobs earlier this year, while Shandong province has cut nearly 10,000 positions since 2022.

Meanwhile, analysts say China's 3.9 million vocational college graduates are mostly equipped for low-end manufacturing and service jobs, and reforms announced in 2022 will take years to fix underinvestment in training long regarded as inferior to universities.

China currently faces a shortage of welders, joiners, elderly caregivers and "highly-skilled digital talent", its human resources minister said in March.

Yao Lu, a sociologist at Columbia University, estimates about 25% of college graduates aged 23-35 are currently in jobs below their academic qualifications.

Many of China's nearly 48 million university students are likely to have poor starting salaries and contribute relatively little in taxes throughout their lifetimes, said one Chinese economist who asked not to be named because of the sensitivity of the issue.

"Although they cannot be called a 'lost generation', it is a huge waste of human capital," the person said.

'DOING THREE PEOPLE'S JOBS'

Chinese President Xi Jinping in May urged officials to make job creation for new graduates a top priority. But for younger workers unemployed or recently fired, the mood is bleak, nine people interviewed by Reuters said.

Anna Wang, 23, quit her state bank job in Shenzhen this year due to high pressure and frequent unpaid overtime. For a salary of about 6,000 yuan per month, "I was doing three people's jobs," she said.

Her ex-colleagues complain about widespread pay cuts and transfers to positions with unmanageable workloads, effectively forcing them to resign. Wang now works part-time jobs as a CV editor and mystery shopper.

At a July briefing for foreign diplomats about an agenda-setting economic meeting, policymakers said they have been quietly urging companies to stop layoffs, one attendee told Reuters.

Olivia Lin, 30, left the civil service in July after widespread bonus cuts and bosses hinted at further redundancies. Four district-level bureaus were dissolved in her city of Shenzhen this year, according to public announcements.

"The general impression was that the current environment isn't good and fiscal pressure is really high," she said.

Lin now wants a tech job. She has had no interview offers after a month of searching. "This is completely different from 2021, when I was guaranteed one job interview a day," she said.

REDUCED STIGMA

Shut out of the job market and desperate for an outlet, young Chinese are sharing tips for surviving long-term unemployment. The hashtags "unemployed", "unemployment diary" and "laid off" received a combined 2.1 billion views on the Xiaohongshu platform He uses.

Users describe mundane daily routines, count down the days since being fired, share awkward chat exchanges with managers or dole out advice, sometimes accompanied by crying selfies.

The increasing visibility of jobless young people "increases broader social acceptance and reduces stigma surrounding unemployment", said Columbia's Lu, allowing otherwise isolated youth to connect and "perhaps even redefine what it means to be unemployed in today's economic climate".

Lu said unemployed graduates understood blaming the government for their plight would be both risky and ineffective. Rather, she said, they were more likely to slip into "an internalisation of discontent and blame" or "lying flat".

He, the influencer, thinks graduates should lower their ambitions.

"If we have indeed entered 'garbage time', then I think young people could accumulate skills or do something creative, such as selling things via social media or making handicrafts."

[acuvate-updates]

How Agentic AI & RAG Revolutionize Autonomous Decision-Making

In the swiftly advancing realm of artificial intelligence, the integration of Agentic AI and Retrieval-Augmented Generation (RAG) is revolutionizing autonomous decision-making across various sectors. Agentic AI endows systems with the ability to operate independently, while RAG enhances these systems by incorporating real-time data retrieval, leading to more informed and adaptable decisions. This article delves into the synergistic relationship between Agentic AI and RAG, exploring their combined impact on autonomous decision-making.

Overview

Agentic AI refers to AI systems capable of autonomous operation, making decisions based on environmental inputs and predefined goals without continuous human oversight. These systems utilize advanced machine learning and natural language processing techniques to emulate human-like decision-making processes. Retrieval-Augmented Generation (RAG), on the other hand, merges generative AI models with information retrieval capabilities, enabling access to and incorporation of external data in real-time. This integration allows AI systems to leverage both internal knowledge and external data sources, resulting in more accurate and contextually relevant decisions.

Read more about Agentic AI in Manufacturing: Use Cases & Key Benefits

What is Agentic AI and RAG?

Agentic AI: This form of artificial intelligence empowers systems to achieve specific objectives with minimal supervision. It comprises AI agents—machine learning models that replicate human decision-making to address problems in real-time. Agentic AI exhibits autonomy, goal-oriented behavior, and adaptability, enabling independent and purposeful actions.

Retrieval-Augmented Generation (RAG): RAG is an AI methodology that integrates a generative AI model with an external knowledge base. It dynamically retrieves current information from sources like APIs or databases, allowing AI models to generate contextually accurate and pertinent responses without necessitating extensive fine-tuning.

Know more on Why Businesses Are Embracing RAG for Smarter AI

Capabilities

When combined, Agentic AI and RAG offer several key capabilities:

Autonomous Decision-Making: Agentic AI can independently analyze complex scenarios and select effective actions based on real-time data and predefined objectives.

Contextual Understanding: It interprets situations dynamically, adapting actions based on evolving goals and real-time inputs.

Integration with External Data: RAG enables Agentic AI to access external databases, ensuring decisions are based on the most current and relevant information available.

Enhanced Accuracy: By incorporating external data, RAG helps Agentic AI systems avoid relying solely on internal models, which may be outdated or incomplete.

How Agentic AI and RAG Work Together

The integration of Agentic AI and RAG creates a robust system capable of autonomous decision-making with real-time adaptability:

Dynamic Perception: Agentic AI utilizes RAG to retrieve up-to-date information from external sources, enhancing its perception capabilities. For instance, an Agentic AI tasked with financial analysis can use RAG to access real-time stock market data.

Enhanced Reasoning: RAG augments the reasoning process by providing external context that complements the AI's internal knowledge. This enables Agentic AI to make better-informed decisions, such as recommending personalized solutions in customer service scenarios.

Autonomous Execution: The combined system can autonomously execute tasks based on retrieved data. For example, an Agentic AI chatbot enhanced with RAG can not only answer questions but also initiate actions like placing orders or scheduling appointments.

Continuous Learning: Feedback from executed tasks helps refine both the agent's decision-making process and RAG's retrieval mechanisms, ensuring the system becomes more accurate and efficient over time.

Read more about Multi-Meta-RAG: Enhancing RAG for Complex Multi-Hop Queries

Example Use Case: Customer Service

Customer Support Automation Scenario: A user inquiries about their account balance via a chatbot.

How It Works: The Agentic AI interprets the query, determines that external data is required, and employs RAG to retrieve real-time account information from a database. The enriched prompt allows the chatbot to provide an accurate response while suggesting payment options. If prompted, it can autonomously complete the transaction.

Benefits: Faster query resolution, personalized responses, and reduced need for human intervention.

Example: Acuvate's implementation of Agentic AI demonstrates how autonomous decision-making and real-time data integration can enhance customer service experiences.

2. Sales Assistance

Scenario: A sales representative needs to create a custom quote for a client.

How It Works: Agentic RAG retrieves pricing data, templates, and CRM details. It autonomously drafts a quote, applies discounts as instructed, and adjusts fields like baseline costs using the latest price book.

Benefits: Automates multi-step processes, reduces errors, and accelerates deal closures.

3. Healthcare Diagnostics

Scenario: A doctor seeks assistance in diagnosing a rare medical condition.

How It Works: Agentic AI uses RAG to retrieve relevant medical literature, clinical trial data, and patient history. It synthesizes this information to suggest potential diagnoses and treatment options.

Benefits: Enhances diagnostic accuracy, saves time, and provides evidence-based recommendations.

Example: Xenonstack highlights healthcare as a major application area for agentic AI systems in diagnosis and treatment planning.

4. Market Research and Consumer Insights

Scenario: A business wants to identify emerging market trends.

How It Works: Agentic RAG analyzes consumer data from multiple sources, retrieves relevant insights, and generates predictive analytics reports. It also gathers customer feedback from surveys or social media.

Benefits: Improves strategic decision-making with real-time intelligence.

Example: Companies use Agentic RAG for trend analysis and predictive analytics to optimize marketing strategies.

5. Supply Chain Optimization

Scenario: A logistics manager needs to predict demand fluctuations during peak seasons.

How It Works: The system retrieves historical sales data, current market trends, and weather forecasts using RAG. Agentic AI then predicts demand patterns and suggests inventory adjustments in real-time.

Benefits: Prevents stockouts or overstocking, reduces costs, and improves efficiency.

Example: Acuvate’s supply chain solutions leverage predictive analytics powered by Agentic AI to enhance logistics operations

How Acuvate Can Help

Acuvate specializes in implementing Agentic AI and RAG technologies to transform business operations. By integrating these advanced AI solutions, Acuvate enables organizations to enhance autonomous decision-making, improve customer experiences, and optimize operational efficiency. Their expertise in deploying AI-driven systems ensures that businesses can effectively leverage real-time data and intelligent automation to stay competitive in a rapidly evolving market.

Future Scope

The future of Agentic AI and RAG involves the development of multi-agent systems where multiple AI agents collaborate to tackle complex tasks. Continuous improvement and governance will be crucial, with ongoing updates and audits necessary to maintain safety and accountability. As technology advances, these systems are expected to become more pervasive across industries, transforming business processes and customer interactions.

In conclusion, the convergence of Agentic AI and RAG represents a significant advancement in autonomous decision-making. By combining autonomous agents with real-time data retrieval, organizations can achieve greater efficiency, accuracy, and adaptability in their operations. As these technologies continue to evolve, their impact across various sectors is poised to expand, ushering in a new era of intelligent automation.

[phuketattorney]

Thailand SMART Visa

Thailand’s Smart Visa program represents a strategic initiative by the Thai government to attract top-tier foreign talent, investors, and entrepreneurs in targeted high-value industries. Unlike conventional work visas, the Smart Visa offers longer validity, reduced bureaucratic hurdles, and exclusive privileges tailored for professionals in technology, innovation, and advanced industries.

This comprehensive guide provides an in-depth, expert-level analysis of the Smart Visa, covering:

Visa categories and eligibility criteria

Application process and required documentation

Key benefits and limitations

Strategic advantages for businesses and individuals

Long-term residency pathways

1. Understanding the Smart Visa: Purpose and Target Sectors

Launched in 2018 by the Thailand Board of Investment (BOI) in collaboration with the Digital Economy Promotion Agency (DEPA), the Smart Visa is designed to: ✔ Accelerate Thailand’s transition into a digital and innovation-driven economy ✔ Attract foreign expertise in AI, robotics, biotech, fintech, and advanced manufacturing ✔ Encourage high-value investment in priority industries

Targeted Industries

The Smart Visa is available for professionals and businesses in the following sectors:

Next-generation automotive (EVs, smart mobility)

Smart electronics and IoT

Advanced agriculture and biotechnology

Automation and robotics

Digital and direct-to-consumer (DTC) startups

Financial technology (Blockchain, digital banking)

Aerospace and aviation tech

2. Smart Visa Categories: Which One Fits Your Profile?

The Smart Visa is divided into four distinct categories, each with specific eligibility criteria:

A. Smart-T (Talent Visa) – For High-Skilled Professionals

✔ Who qualifies?

Experts in AI, machine learning, cybersecurity, biotech, or advanced engineering

Minimum salary of 200,000 THB/month (lower thresholds possible for BOI-backed companies)

Must be employed by a Thai company in a BOI-promoted sector

✔ Key benefits:

No work permit required

Permission to work for multiple companies (with approval)

B. Smart-I (Investor Visa) – For High-Net-Worth Investors

✔ Who qualifies?

Minimum investment of 20 million THB in a Thai tech company or startup

Investment must align with BOI’s priority sectors

✔ Key benefits:

No minimum stay requirement

Family members eligible for dependent visas

C. Smart-E (Executive Visa) – For Senior Corporate Leaders

✔ Who qualifies?

C-level executives or directors in BOI-promoted companies

Minimum salary of 200,000 THB/month

✔ Key benefits:

Fast-tracked immigration processing

Exemption from re-entry permits

D. Smart-S (Startup Visa) – For Tech Entrepreneurs

✔ Who qualifies?

Founders of registered startups in Thailand

Must be endorsed by DEPA or a BOI-approved incubator

Minimum 50,000 USD funding or participation in a recognized accelerator

✔ Key benefits:

Access to Thai startup ecosystem and funding networks

Easier business registration processes

3. Step-by-Step Application Process

Step 1: Determine Eligibility & Gather Documents

For Employees (Smart-T, Smart-E):

Employment contract

Company’s BOI certification (if applicable)

Proof of salary (tax documents, bank statements)

For Investors (Smart-I):

Proof of investment (bank transfer, share certificates)

BOI investment approval letter

For Startups (Smart-S):

Business registration documents

Proof of funding (venture capital, accelerator acceptance)

Step 2: Submit Application to the Smart Visa Unit

Applications can be filed online or at the One Start One Stop Investment Center (OSOS) in Bangkok.

Processing time: 3-4 weeks.

Step 3: Visa Issuance & Entry into Thailand

Initial visa validity: Up to 4 years (renewable).

No 90-day reporting required (unlike standard visas).

4. Key Benefits: Why Choose the Smart Visa?

FeatureSmart VisaStandard Work VisaVisa ValidityUp to 4 years1 year (renewable)Work PermitNot requiredRequired90-Day ReportingExemptMandatoryDependent VisasSpouse & children eligibleSpouse eligible (with restrictions)Income Tax BenefitsPossible exemptionsStandard taxation

Additional Perks:

✔ Multiple re-entry permits without additional paperwork ✔ Spouse can work legally (subject to approval) ✔ Fast-tracked permanent residency pathway

5. Challenges & Considerations

A. Strict Eligibility Requirements

High salary thresholds (200,000 THB/month for Smart-T/E)

BOI/DEPA endorsement mandatory (limits flexibility for non-tech professionals)

B. Limited Scope Outside Tech & Investment

Traditional industries (e.g., hospitality, education) excluded

No provisions for freelancers or digital nomads

C. Bureaucratic Hurdles for Startups

Startup Visa requires accelerator backing, which can be competitive

6. Long-Term Strategic Advantages

A. Gateway to Permanent Residency & Citizenship

After 3+ years, Smart Visa holders can apply for permanent residency.

Elite Visa upgrade possible for long-term stays beyond 4 years.

B. Access to Thailand’s Booming Tech Ecosystem

Eastern Economic Corridor (EEC) offers tax breaks for tech firms.

Growing VC funding in AI, fintech, and biotech.

C. Regional Business Expansion

Thailand’s strategic ASEAN location makes it ideal for scaling businesses across Southeast Asia.

7. Expert Tips for a Successful Application

✔ Consult with a BOI-certified lawyer to ensure compliance. ✔ Maintain clear financial records (especially for investment visas). ✔ Prepare for potential immigration interviews (some offices require in-person verification).

Conclusion

The Thailand Smart Visa is one of the most attractive long-term visa options for high-skilled professionals, investors, and startup founders. With 4-year validity, work permit exemptions, and a streamlined process, it offers unparalleled advantages over traditional visas.

However, its strict eligibility criteria mean it is best suited for those in tech, advanced industries, or with significant investment capital. For qualifying individuals, it provides a direct pathway to Thailand’s innovation economy and long-term residency.

Final Recommendation:

If you work in AI, robotics, biotech, or digital startups, the Smart Visa is ideal.

For investors, the 20M THB threshold is steep but offers long-term stability.

Startups should secure accelerator backing early to qualify for the Smart-S visa.

[fatemamitu]

The AI Revolution: Understanding, Harnessing, and Navigating the Future

What is AI

In a world increasingly shaped by technology, one term stands out above the rest, capturing both our imagination and, at times, our apprehension: Artificial Intelligence. From science fiction dreams to tangible realities, AI is no longer a distant concept but an omnipresent force, subtly (and sometimes not-so-subtly) reshaping industries, transforming daily life, and fundamentally altering our perception of what's possible.

But what exactly is AI? Is it a benevolent helper, a job-stealing machine, or something else entirely? The truth, as always, is far more nuanced. At its core, Artificial Intelligence refers to the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction. What makes modern AI so captivating is its ability to learn from data, identify patterns, and make predictions or decisions with increasing autonomy.

The journey of AI has been a fascinating one, marked by cycles of hype and disillusionment. Early pioneers in the mid-20th century envisioned intelligent machines that could converse and reason. While those early ambitions proved difficult to achieve with the technology of the time, the seeds of AI were sown. The 21st century, however, has witnessed an explosion of progress, fueled by advancements in computing power, the availability of massive datasets, and breakthroughs in machine learning algorithms, particularly deep learning. This has led to the "AI Spring" we are currently experiencing.

The Landscape of AI: More Than Just Robots

When many people think of AI, images of humanoid robots often come to mind. While robotics is certainly a fascinating branch of AI, the field is far broader and more diverse than just mechanical beings. Here are some key areas where AI is making significant strides:

Machine Learning (ML): This is the engine driving much of the current AI revolution. ML algorithms learn from data without being explicitly programmed. Think of recommendation systems on streaming platforms, fraud detection in banking, or personalized advertisements – these are all powered by ML.

Deep Learning (DL): A subset of machine learning inspired by the structure and function of the human brain's neural networks. Deep learning has been instrumental in breakthroughs in image recognition, natural language processing, and speech recognition. The facial recognition on your smartphone or the impressive capabilities of large language models like the one you're currently interacting with are prime examples.

Natural Language Processing (NLP): This field focuses on enabling computers to understand, interpret, and generate human language. From language translation apps to chatbots that provide customer service, NLP is bridging the communication gap between humans and machines.

Computer Vision: This area allows computers to "see" and interpret visual information from the world around them. Autonomous vehicles rely heavily on computer vision to understand their surroundings, while medical imaging analysis uses it to detect diseases.

Robotics: While not all robots are AI-powered, many sophisticated robots leverage AI for navigation, manipulation, and interaction with their environment. From industrial robots in manufacturing to surgical robots assisting doctors, AI is making robots more intelligent and versatile.

AI's Impact: Transforming Industries and Daily Life

The transformative power of AI is evident across virtually every sector. In healthcare, AI is assisting in drug discovery, personalized treatment plans, and early disease detection. In finance, it's used for algorithmic trading, risk assessment, and fraud prevention. The manufacturing industry benefits from AI-powered automation, predictive maintenance, and quality control.

Beyond these traditional industries, AI is woven into the fabric of our daily lives. Virtual assistants like Siri and Google Assistant help us organize our schedules and answer our questions. Spam filters keep our inboxes clean. Navigation apps find the fastest routes. Even the algorithms that curate our social media feeds are a testament to AI's pervasive influence. These applications, while often unseen, are making our lives more convenient, efficient, and connected.

Harnessing the Power: Opportunities and Ethical Considerations

The opportunities presented by AI are immense. It promises to boost productivity, solve complex global challenges like climate change and disease, and unlock new frontiers of creativity and innovation. Businesses that embrace AI can gain a competitive edge, optimize operations, and deliver enhanced customer experiences. Individuals can leverage AI tools to automate repetitive tasks, learn new skills, and augment their own capabilities.

However, with great power comes great responsibility. The rapid advancement of AI also brings forth a host of ethical considerations and potential challenges that demand careful attention.

Job Displacement: One of the most frequently discussed concerns is the potential for AI to automate jobs currently performed by humans. While AI is likely to create new jobs, there will undoubtedly be a shift in the nature of work, requiring reskilling and adaptation.

Bias and Fairness: AI systems learn from the data they are fed. If that data contains historical biases (e.g., related to gender, race, or socioeconomic status), the AI can perpetuate and even amplify those biases in its decisions, leading to unfair outcomes. Ensuring fairness and accountability in AI algorithms is paramount.

Privacy and Security: AI relies heavily on data. The collection and use of vast amounts of personal data raise significant privacy concerns. Moreover, as AI systems become more integrated into critical infrastructure, their security becomes a vital issue.

Transparency and Explainability: Many advanced AI models, particularly deep learning networks, are often referred to as "black boxes" because their decision-making processes are difficult to understand. For critical applications, it's crucial to have transparency and explainability to ensure trust and accountability.

Autonomous Decision-Making: As AI systems become more autonomous, questions arise about who is responsible when an AI makes a mistake or causes harm. The development of ethical guidelines and regulatory frameworks for autonomous AI is an ongoing global discussion.

Navigating the Future: A Human-Centric Approach

Navigating the AI revolution requires a proactive and thoughtful approach. It's not about fearing AI, but rather understanding its capabilities, limitations, and implications. Here are some key principles for moving forward:

Education and Upskilling: Investing in education and training programs that equip individuals with AI literacy and skills in areas like data science, AI ethics, and human-AI collaboration will be crucial for the workforce of the future.

Ethical AI Development: Developers and organizations building AI systems must prioritize ethical considerations from the outset. This includes designing for fairness, transparency, and accountability, and actively mitigating biases.

Robust Governance and Regulation: Governments and international bodies have a vital role to play in developing appropriate regulations and policies that foster innovation while addressing ethical concerns and ensuring the responsible deployment of AI.

Human-AI Collaboration: The future of work is likely to be characterized by collaboration between humans and AI. AI can augment human capabilities, automate mundane tasks, and provide insights, allowing humans to focus on higher-level problem-solving, creativity, and empathy.

Continuous Dialogue: As AI continues to evolve, an ongoing, open dialogue among technologists, ethicists, policymakers, and the public is essential to shape its development in a way that benefits humanity.

The AI revolution is not just a technological shift; it's a societal transformation. By understanding its complexities, embracing its potential, and addressing its challenges with foresight and collaboration, we can harness the power of Artificial Intelligence to build a more prosperous, equitable, and intelligent future for all. The journey has just begun, and the choices we make today will define the world of tomorrow.

[awbsblog]

Profitable Business Ideas for 2025

As we step into 2025, the business landscape is evolving rapidly. With advancing technology, changing consumer needs, and increasing digital adoption, new opportunities are emerging. Whether you want to start a full-time venture or a side hustle, choosing the right business is key to success. Here are some of the most promising business ideas for 2025.

1. Artificial Intelligence-Based Businesses

AI is revolutionizing industries, and businesses that integrate AI solutions will thrive. Some ideas include:

AI-Driven Virtual Assistants for Businesses

AI-Powered Personalized Shopping Experiences

AI Tools for Content Generation and Automation

2. Sustainable & Eco-Friendly Ventures

With rising environmental concerns, sustainable businesses are in high demand. You can start:

Zero-Waste Packaging Solutions

Renewable Energy Consulting & Solar Panel Installation

Eco-Friendly Fashion and Accessories

3. E-Commerce & Online Selling

The online marketplace continues to expand. If you want to start an e-commerce business, consider:

Customized Print-on-Demand Products

Niche Subscription Boxes (organic food, pet treats, etc.)

Selling Digital Products like E-books & Templates

4. Health & Wellness Businesses

The health sector is booming as people become more health-conscious. Profitable options include:

Virtual Fitness Training & Home Workout Plans

Organic & Herbal Supplement Business

Meditation & Mental Health Coaching

5. Digital Marketing & Branding Services

Businesses are investing heavily in their online presence. You can offer:

SEO & Content Marketing Solutions

Social Media Management & Growth Strategies

PPC Advertising & Influencer Marketing Services

6. Freelancing & Remote Work Opportunities

The freelance industry is thriving. If you have a skill, you can monetize it through:

Professional Blogging & Copywriting Services

Graphic Design & Website Development

Virtual Assistant & Administrative Support

7. Online Education & Coaching

E-learning is growing exponentially, and you can take advantage by offering:

Creating & Selling Digital Courses

Career Guidance & Resume Writing Services

Language & Soft Skills Training

8. Tech Startups & Software Development

Tech solutions are in high demand, making software development a lucrative field. Ideas include:

SaaS (Software as a Service) Solutions for Businesses

Mobile Apps for Personal Productivity & Business Management

Web3 & Blockchain-Based Platforms

9. Food & Beverage Industry Innovations

Food-related businesses continue to evolve with consumer preferences. Some trending ideas are:

Cloud Kitchen & Food Delivery Business

Organic & Plant-Based Snack Manufacturing

Specialty Coffee, Tea, or Juice Bars

10. Pet Care & Accessories Business

The pet industry is booming, providing great business opportunities. Consider:

Selling Organic & Handmade Pet Products

Professional Pet Grooming Services

Personalized Pet Clothing & Accessories

Conclusion

The year 2025 offers numerous business opportunities across various sectors. To succeed, choose a business that aligns with your skills, interests, and market demand. By staying innovative and customer-focused, you can build a profitable and sustainable business.

[sarkariresultdude]

China Recruitment Results 2025: Trends, Insights, and Analysis

 As the arena's second-biggest economy, China is still a primary player within the international exertions marketplace. The today's recruitment effects from 2025 display key trends and insights across industries, demographics, and regions. Companies, activity seekers, and policymakers alike can gain from know-how these shifts, as they replicate China's evolving economic landscape, expertise priorities, and marketplace demands.

Recruitment Process In China 

1. Strong Recovery in Recruitment Activity

In 2025, China’s recruitment market noticed a incredible rebound, following years of pandemic-associated disruptions and financial uncertainty. According to statistics from a couple of human resources and exertions market tracking agencies, general job openings in China increased through about 12% 12 months-on-12 months. This growth turned into frequently driven via sectors which include generation, renewable power, superior production, and modern-day offerings, which includes finance and healthcare.

The surge in recruitment pastime is basically attributed to China’s push closer to monetary modernization and innovation, aligning with the government’s "14th Five-Year Plan" and its vision for incredible development. Furthermore, easing COVID-19 restrictions inside the past two years has revitalized domestic demand, especially in urban centers like Shanghai, Shenzhen, and Beijing, wherein expertise demand stays high.

2. Sector-by using-Sector Breakdown

Technology Sector

China’s tech enterprise stays one in every of the most important recruiters in 2025, with hiring increasing with the aid of 15% in comparison to 2024. Companies running in regions such as synthetic intelligence (AI), semiconductor production, cloud computing, and 5G/6G network infrastructure are main the demand. In precise, the AI and automation sectors skilled document-breaking recruitment, as agencies throughout numerous industries put into effect virtual transformation techniques.

Manufacturing and New Energy

Advanced manufacturing—together with robotics, aerospace, and electric vehicles (EVs)—recorded an eleven% uptick in hiring. With China striving to grow to be a global leader in EV production and inexperienced technology, recruitment in battery generation, renewable energy engineering, and environmental technology has also elevated. The expansion of sun and wind electricity initiatives in inland provinces which include Inner Mongolia and Xinjiang has opened new activity opportunities out of doors main metropolitan hubs.

Financial and Business Services

Financial offerings confirmed a moderate but consistent 7% increase in hiring, in particular in fintech, funding banking, and risk management roles. The fast adoption of virtual finance systems and the growth of inexperienced finance initiatives contributed to this upward fashion. Similarly, prison and compliance departments saw a surge in call for, as stricter regulatory requirements and international exchange dynamics precipitated corporations to strengthen their internal controls.

Healthcare and Life Sciences

China’s growing old populace and the authorities's focus on enhancing healthcare infrastructure have boosted hiring within the medical and pharmaceutical sectors. Hospitals, biotech firms, and healthtech startups elevated recruitment via nine% yr-on-12 months. Special emphasis become placed on roles associated with scientific research, clinical trials, and public fitness management, reflecting China's ambitions to beautify its healthcare resilience.

Three. Regional Disparities in Recruitment

While Tier 1 towns like Beijing, Shanghai, Guangzhou, and Shenzhen hold to dominate in phrases of activity vacancies, there was a major uptick in hiring in Tier 2 and Tier 3 towns, which includes Chengdu, Hangzhou, Xi’an, and Suzhou. The government’s urbanization strategy and nearby improvement rules are riding this shift. Inland provinces and less-advanced regions are actually attracting extra investment, main to activity advent in industries along with logistics, e-trade, and smart production.

This geographic diversification is also related to the upward thrust of far off work, as agencies come to be more bendy in hiring talent from diverse locations. As a end result, skilled specialists are now not limited to standard financial hubs and are finding competitive possibilities in rising cities.

4. Recruitment Challenges: Skills Gaps and Talent Shortages

Despite the overall high quality recruitment results, several sectors pronounced continual demanding situations, specially regarding skills shortages in high-tech and specialised fields. For instance, the semiconductor enterprise keeps to stand a essential gap in skilled engineers and researchers, while the inexperienced electricity area is struggling to find sufficient skilled task managers and technical experts.

Soft abilties consisting of leadership, go-cultural communique, and trouble-fixing also continue to be in excessive demand, mainly as Chinese organizations make bigger their global operations. Talent shortage has led to accelerated competition among employers, riding up salaries for niche roles and prompting groups to make investments extra heavily in inner schooling and improvement packages.

Five. Demographic Shifts: Youth Employment and Aging Workforce

Youth employment remains a complicated problem in China. While job opportunities for younger graduates have grown along financial recuperation, excessive competition and high expectancies hold to pose demanding situations. The countrywide young people unemployment charge stood at about 14% in early 2025, slightly decrease than in 2024 but nonetheless a subject for policymakers.

In reaction, the authorities has expanded employment subsidies, vocational education initiatives, and entrepreneurship programs focused on young human beings. Additionally, more college students are choosing internships, apprenticeships, and industry-connected educational pathways to decorate employability earlier than commencement.

Meanwhile, the getting old group of workers provides its very own set of challenges. Industries including manufacturing, logistics, and healthcare are increasingly more searching out ways to preserve older employees through re-skilling applications and flexible work preparations.

6. Trends in Hiring Practices

Recruitment practices in China are evolving, with organizations leveraging AI-pushed recruitment equipment, virtual exams, and facts analytics to streamline hiring processes. Many organizations now prioritize candidate experience, the use of era to lessen time-to-lease and improve engagement at some point of the recruitment cycle.

Campus recruitment remains a key approach for principal agencies, mainly in sectors which includes generation, finance, and engineering. However, there may be a developing desire for hiring candidates with realistic revel in, main to greater collaboration between universities and companies to offer industry-relevant guides and internships.

Diversity and inclusion are also gaining traction. Companies are increasingly dedicated to gender balance and hiring talent from numerous backgrounds, which include ethnic minorities and worldwide candidates, specially within the tech and R&D sectors.

7. Outlook for 2025 and Beyond

Looking in advance, China’s recruitment panorama is predicted to remain dynamic. The persisted improvement of emerging sectors consisting of quantum computing, biotechnology, smart towns, and the metaverse will create new employment opportunities, specially for skills with interdisciplinary ability sets.

Policy shifts, which includes similarly liberalization of the hard work market and supportive measures for small and medium corporations (SMEs), may also stimulate job advent. Additionally, the emphasis on sustainable improvement and digital innovation is in all likelihood to reshape hiring priorities, with an growing awareness on inexperienced jobs and virtual literacy.

However, geopolitical uncertainties, change tensions, and worldwide monetary fluctuations will remain key elements influencing China’s hard work marketplace within the close to destiny. Businesses and activity seekers alike will need to stay agile, adapting to changing financial situations and technological advancements.

[darkmaga-returns]

Short of dot-com type of a bust, spending on AI data centers will continue to skyrocket, with attendant energy demand. Utility companies should be in panic mode to increase generating capacity, but they are not. The resulting squeeze will drive consumer prices through the roof and put exorbitant strain on the electric grid. But rest assured that AI companies will suffer no outages. ⁃ TN Editor

The rapid growth of data centers to support AI is significantly increasing global electricity demand.

This surge in demand threatens to outpace the development of renewable energy sources.

International regulations are needed to ensure tech companies use clean energy and minimize their impact on climate goals.

The global electricity demand is expected to grow exponentially in the coming decades, largely due to an increased demand from tech companies for new data centers to support the rollout of high-energy-consuming advanced technologies, such as artificial intelligence (AI). As governments worldwide introduce new climate policies and pump billions into alternative energy sources and clean tech, these efforts may be quashed by the increased electricity demand from data centers unless greater international regulatory action is taken to ensure that tech companies invest in clean energy sources and do not use fossil fuels for power.

The International Energy Agency (IEA) released a report in October entitled “What the data centre and AI boom could mean for the energy sector”. It showed that with investment in new data centers surging over the past two years, particularly in the U.S., the electricity demand is increasing rapidly – a trend that is set to continue.

The report states that in the U.S., annual investment in data center construction has doubled in the past two years alone. China and the European Union are also seeing investment in data centers increase rapidly. In 2023, the overall capital investment by tech leaders Google, Microsoft, and Amazon was greater than that of the U.S. oil and gas industry, at approximately 0.5 percent of the U.S. GDP.

The tech sector expects to deploy AI technologies more widely in the coming decades as the technology is improved and becomes more ingrained in everyday life. This is just one of several advanced technologies expected to contribute to the rise in demand for power worldwide in the coming decades.

Global aggregate electricity demand is set to increase by 6,750 terawatt-hours (TWh) by 2030, per the IEA’s Stated Policies Scenario. This is spurred by several factors including digitalization, economic growth, electric vehicles, air conditioners, and the rising importance of electricity-intensive manufacturing. In large economies such as the U.S., China, and the EU, data centers contribute around 2 to 4 percent of total electricity consumption at present. However, the sector has already surpassed 10 percent of electricity consumption in at least five U.S. states. Meanwhile, in Ireland, it contributes more than 20 percent of all electricity consumption.

[shituationist]

I think companies like Google and Microsoft have incentive to embellish and even falsify the results of their machine learning projects, but I do worry that sufficiently capable (we won't say "conscious," because that doesn't matter) AI agents are going to replace human workers in services while "advanced economies" have made their economies dependent on the existence of a service sector with high-paying jobs (on a global scale) buttressing a comfortable middle class existence for the average adult.

This isn't just an AI thing. Professionals in the US and Western Europe advocate more restrictive work visa and credentialing programs in order to protect their salaries. This also goes for unskilled workers, who are afraid of the influx of Latin American labor in the US (especially when bosses can hire these workers under the table and pay them less, while paying no payroll taxes on that labor).

The whole existence of the labor market is increasingly at odds with human needs and the technical horizon of the world economy. Coupling livelihood to wages has also been a monstrous feature of our society, but it is becoming more and more monstrous as the number of jobs which provide a consistently decent standard of living seem to be increasingly the purview of machines.

Theorists like Steven Wolfram seem to think that "computational irreducibility" will keep plenty of jobs around forever, but I don't know. Automation in manufacturing didn't increase the total number of manufacturing jobs in the US and Europe. People didn't go from making the tenth part of the head of a pin to the thousandath part. People who were displaced by automation and offshoring were instead shuffled into the service sector, but as automation hits that sector, where's the next sector for them to go to?

Socialist planned economies struggled with this too, but because of more conscious political objectives, since an employment guarantee (an artefact of the appropriation of capitalist, specifically Fordist, thinking) was part of their social contract, and automation would reduce the number of jobs or otherwise make workers redundant (but you couldn't just fire them, especially since things like housing were often tied to jobs), so there was a conservative impulse outside of the military production sector to keep jobs labor intensive. Political ossification and the gerontocracy ensured that people with no adequate knowledge of on-the-ground conditions would be able to reach a ministerial position and make the Soviet state unable to undertake needed reforms (so they ended up privatizing everything and sending millions to an early grave with the liquidation of the planned economy instead). Glushkov's OGAS project is a testament to this: early digitalization of the planned economy was killed because ministers didn't want to hand over any control to a computer network, even if that computer network would have breathed new life into the planned economy or made it more competitive with its imperialist rivals (bear in mind here that many ex-Soviet ministers would go on to become oligarchs after privatization was pursued).

I don't know. I'm a little scared and therefore motivated to be skeptical of recent advances in artificial intelligence. "Artificial General Intelligence" has seemingly been redefined from "scary superintelligence that's gonna became conscious and kill us all" to "computer program that can complete any task that a human can do", which is a more realistic goal and arguably feasible from a materialist vantage point (even if not commercially profitable). It isn't there yet, and there's reasons to think the current approach could hit a wall, but even without breaching that kind of "intelligence", the impact of automation in services could mean that service workers are increasingly subject to the precarity that American manufacturing workers have been and which such bromides as "learn to code" have been intended to address.

Every day I am more convinced that our choice is between socialism or barbarism: a global planned economy in which our basic and social needs are addressed through collective, non-commercial, association(s), in which opportunities to make our lives meaningful through work have become "life's prime want"; or a capitalist world-system which mericlessly throws people into the industrial reserve army of labor, making people's lives subject to impersonal forces completely and totally beyond their control, preserving the class structure at all costs and bribing the remnants of the labor aristocracy and bureaucracy with patronage while potentially billions suffer. Worst case scenario is we only get the former through the latter.

(I would appreciate it if you have anything to say about this post, don't do it through a reblog but through a reply or DM, just for my anxiety's sake 🥺)