
Song Shiqiang of Slkor: Trials and Reflections on AI Large Model Text Writing
Key Features of AI Model DeepSeek: Search, Q&A, and Writing
From my observations in Huaqiangbei, technologies like AI, the metaverse, humanoid robots, and autonomous driving have improved efficiency in certain sectors, but they haven't yet triggered a "Kondratieff cycle" of large-scale economic growth. Instead, they've caused job losses in some fields. Recently, Zhu Xiaohu of GSR Ventures poured cold water on the AI hype. As the founder of Kinghelm and Slkor (www.slkormicro.com), I'd like to share my practical experiences, insights, and reflections on using these models, particularly in writing.
Key Features of AI Model DeepSeek: Search, Q&A, and Writing
Last month, my article Applying Economics, Sociology, and Management Theories to Drive Slkor and Kinghelm's Rapid Growth! surpassed 10 million reads. It explored macroeconomic theories from the Austrian School, integrating Joseph Schumpeter's "innovation," Friedrich Hayek's "trust in markets," Adam Smith's "division of labor and free exchange," Max Weber's organizational behavior theories, and Peter Drucker's "knowledge worker" management principles into our business practices. A sophisticated Huaqiangbei businesswoman with both beauty and brains took a liking to the article and decided to "optimize" it using DeepSeek-R1. Here's the result:
DeepSeek's Optimization of Song Shiqiang's Economics Article
Title: How This Company Used Economics to "Level Up" — Kinghelm and Slkor's Growth Playbook
Opening: A business miracle in tough times. While most companies complain about "hard times," Kinghelm (electronic components) and Slkor (semiconductors) have soared like rockets. Founder Song Shiqiang credits economics textbooks. By mastering three "killer moves," they've carved out a niche.
Move 1: Innovation as Fruit Farming
l Tech Roots: Slkor partnered with top Korean teams to develop SiC chips for EVs, enhancing battery life and charging speed.
l Management Watering: Weekly "Idea King" contests and quarterly "Tech Showdowns" keep creativity flowing.
l Profit Fertilizer: Engineers earn up to 8% profit shares from successful innovations.
Song Shiqiang Reviews Kinghelm 's Al Customer Service Robot
Move 2: Team as Lego Blocks
l Skill Mapping: Employees get "competency profiles" for optimal team assembly.
l Supply Chain Duplex: A smart factory in Jiangxi runs 24/7, while partnerships with German firms like Würth Elektronik ensure material backups.
Move 3: Simplify Management
l Task Delivery: An AI dispatch system assigns tasks like food orders.
l Error Fund: R&D teams get a "mistake budget," uncovering 3 hidden opportunities last year.
Song Shiqiang
Secret Weapons:
l Telescope: Betting on EV chips 5 years ago.
l Piggy Bank: Saving 3% profits yearly for crises.
l Network: A 300-supplier alliance sharing tech intel.
Conclusion:
Kinghelm's website (www.kinghelm.net) isn't just a catalog—it's a "playbook" showing how textbook theories translate into real profits. Their story proves that economics isn't abstract; applied right, it lets businesses "cheat-code" their way to success!
Song Shiqiang Reviews SLKOR's Al Customer Service Robot
AI Hallucinations?
The optimized article included fictional elements, like a smart factory in Jiangxi and a partnership with Würth Elektronik. Perhaps AI "hallucinated" these as future pathways. Is the model seeing what we can't?
Testing Doubao's AI
The New Eight-Legged Essay?
AI-generated content feels homogenized, mirroring the rigid "eight-legged essays" of imperial exams. Modern templates add SWOT analysis and "dialectical unity" conclusions. Asking Baidu's AI about this phenomenon, it agreed:
Baidu AI on AI's "Eight-Legged" Tendencies
Key Issues:
l Buzzword Overload: Terms like "empowerment" and "iteration" mask hollow content.
l Homogenized Thinking: Over-reliance on AI erases originality.
l Misinformation Risks: AI's "confident nonsense" could poison databases.
Root Causes:
l Metrics Gone Wild: Forcing AI adoption quotas breeds superficial use.
l Creative Laziness: Treating AI as a shortcut undermines human insight.
l Anthropomorphism: Mistaking AI for true intelligence leads to blind trust.
Solutions:
l Human-AI Checks: Mandate manual verification for critical documents.
l Ethical Guidelines: Limit AI's role in policymaking and education.
Historical Parallels: Guange Style and Eight-Legged Essays
The Ming Dynasty's rigid "eight-legged essays" stifled creativity, producing officials who "knew nothing of finance or governance." Similarly, AI risks becoming the "Guange calligraphy" of our era—standardized but lifeless.
Conclusion: AI's relationship with humans mirrors early industrialization: machines handle grunt work, but creativity remains human. We must uphold "tools serve, humans lead" to avoid an "AI eight-legged" dystopia.
Baidu Al on Historical Lessons
Al in 3D Design and ERP
Bright Spots Ahead AI's potential is undeniable. Tsinghua's Prof. Zhao Min showcased New Dimension's AI-driven 3D design for lightweight cars. Huaqiangbei's Longway ERP integrated AI for faster component sourcing. Our Kinghelm and Slkor AI Customer Service Robot, developed by Tsinghua prodigy Dr. Ni on Kouzi's framework, slashed costs. More breakthroughs await.
Personal Note: Once the "God of War" of Huaqiangbei, I've mellowed into a tea-sipping, bead-twirling retiree. My recent visit to Taiyuan's Tianlong Temple deepened my love for Northern Wei stone Buddhas. Testing Baidu's AI on niche topics like the "Qingzhou Smile" yielded decent answers, though details on "wet drapery" carvings were sparse—likely due to scant data. At least there were no ads!
Final Thoughts: As a novice user, observer, and thinker, I hope sharing these reflections fosters dialogue, guiding AI toward a future that truly serves humanity.
Media Contact
Company Name: Shenzhen Kinghelm Electronics Co., Ltd.
Contact Person: Support
Email: Send Email
Phone: +86 0755-83975897
Address: 2010, Block A, Bairuida Building Vanke City Community Bantian Avenue, Longgang District
City: Shenzhen
Country: China
Website: www.kinghelm.net

Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles


Globe and Mail
35 minutes ago
- Globe and Mail
2 Artificial Intelligence (AI) Stocks to Buy Before They Soar 150% and 735%, According to Certain Wall Street Analysts
Key Points Nvidia and Tesla are two of the three best-performing stocks in the S&P 500 since January 2020, notching returns of 2,690% and 1,010%, respectively. Nvidia has a dominant market position in data center GPUs and generative AI networking equipment, and the rise of physical artificial intelligence (AI) should be a major tailwind. Tesla's electric car business is struggling with market share losses, but CEO Elon Musk says the company will dominate the robotaxi market in the future. 10 stocks we like better than Nvidia › Nvidia (NASDAQ: NVDA) and Tesla (NASDAQ: TSLA) rank among the three best-performing stocks in the S&P 500 (SNPINDEX: ^GSPC) so far this decade, and artificial intelligence (AI) has been a major tailwind for both companies. Since January 2020, Nvidia shares have added 2,690% due to soaring demand for AI chips. Meanwhile, Tesla shares have added 1,010% due to excitement about self-driving cars and autonomous robots. In both cases, some Wall Street analysts expect more fireworks in the years ahead. Beth Kindig at the I/O Fund thinks Nvidia stock will reach $410 per share by 2030. That implies 150% upside from its current share price of $164. Tasha Keeney at Ark Invest thinks Tesla stock will reach $2,600 per share by 2029. That implies about 735% upside from its current share price of $310. Here's what investors should know about these companies. Nvidia: 150% implied upside Beth Kindig, lead technology analyst at the I/O Fund, thinks Nvidia will trade at $410 per share by 2030, which implies a market value of $10 trillion. The investment thesis centers on rapidly growing demand for artificial intelligence (AI) hardware and software in data centers, as well as edge devices like autonomous cars and robots. Nvidia is best known for its graphics processing units (GPUs), chips also known as artificial intelligence accelerators. It holds over 90% market share in data center GPUs, and analysts at TD Cowen expect the company to maintain the same level of dominance through the end of the decade, with AI chip sales increasing 160% during that period. However, investors need to understand Nvidia is more than a chipmaker. The company also leads the market for generative AI networking gear and it has a burgeoning cloud services business. "We stopped thinking of ourselves as a chip company long ago," CEO Jensen Huang told attendees at the annual shareholder meeting in June. Importantly, while generative AI is currently the largest source of demand for Nvidia AI infrastructure, the company is well positioned to benefit as the physical AI boom unfolds. Physical AI refers to autonomous machines like cars and robots that understand, interact with, and navigate the real world. "We're working toward a day where there will be billions of robots, hundreds of millions of autonomous vehicles, and hundreds of thousands of robotic factories that can be powered by Nvidia technology," Jensen Huang explained to shareholders last month. So, can Nvidia reach $410 per share by 2030? I think so. That implies annual returns of 18%. Grand View Research estimates AI spending will increase at 36% annually through the end of the decade, which means Nvidia could achieve similar annual earnings growth. In that scenario, the stock could hit $410 per share in late 2030 at a reasonable valuation of 22 times earnings. For context, the stock currently trades at 53 times earnings, which itself is a substantial discount to the three-year average of 80 times earnings. Tesla: 735% implied upside Ark Invest analysts led by Tasha Keeney expect Tesla to trade at $2,600 per share by 2029, which implies a market value of $8.3 trillion. Their investment thesis centers on robotaxis, which are expected to account for 63% of revenue by the end of that period. Meanwhile, electric cars (26%), energy storage (10%), and insurance (1%) will comprise the remaining portion. While Alphabet 's Waymo is currently the market leader, Tesla theoretically has an edge in autonomous driving technology. Its full self-driving (FSD) software is powered entirely by computer vision, rather than a costly array of lidar, radar, and cameras like Waymo. For context, Tesla says its dedicated robotaxi (the Cybercab) will cost less than $30,000, but Waymo sensors alone can cost as much as $100,000. Also, Tesla has more camera-equipped vehicles on the road collecting data than every other automaker combined. That data advantage should translate into better AI models. Indeed, Ark Invest says Teslas in FSD mode can drive 3,200 miles per crash on surface streets, which makes them an estimated 16 times safer than an average driver and six times safer than Waymo. Tesla recently started its first autonomous ride-sharing service in Austin, Texas. CEO Elon Musk says robotaxis could be a material source of revenue by late next year, and he thinks Tesla will eventually have 99% market share in what could be a multitrillion-dollar industry. Indeed, Tom Narayan at RBC Capital estimates marketwide robotaxi revenue will reach $1.7 trillion by 2040. While that outcome is plausible, I would be remiss not to mention Tesla's woes. It has lost substantial market share in electric cars in the past year due to its aging product lineup and Elon Musk's political activities. In fact, Tesla deliveries dropped 13% in the first and second quarters, despite a 35% increase in global electric car sales year to date through May, according to Morgan Stanley. So, can Tesla reach $2,600 per share by 2029? I doubt it. While I think autonomous driving technology will be a big catalyst for the company, Ark's target price implies the stock will return 60% annually over the next four-plus years. That means Tesla's earnings would need to increase at 60% annually during the same period just to maintain its already-expensive valuation of 170 times earnings. Should you invest $1,000 in Nvidia right now? Before you buy stock in Nvidia, consider this: The Motley Fool Stock Advisor analyst team just identified what they believe are the 10 best stocks for investors to buy now… and Nvidia wasn't one of them. The 10 stocks that made the cut could produce monster returns in the coming years. Consider when Netflix made this list on December 17, 2004... if you invested $1,000 at the time of our recommendation, you'd have $674,432!* Or when Nvidia made this list on April 15, 2005... if you invested $1,000 at the time of our recommendation, you'd have $1,005,854!* Now, it's worth noting Stock Advisor 's total average return is1,049% — a market-crushing outperformance compared to180%for the S&P 500. Don't miss out on the latest top 10 list, available when you join Stock Advisor. See the 10 stocks » *Stock Advisor returns as of July 7, 2025 Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool's board of directors. Trevor Jennewine has positions in Nvidia and Tesla. The Motley Fool has positions in and recommends Alphabet, Nvidia, and Tesla. The Motley Fool has a disclosure policy.


Winnipeg Free Press
2 hours ago
- Winnipeg Free Press
Future of finance
Opinion Artificial intelligence is undeniably among the coolest technologies in recent memory — making science fiction science fact. It's not a stretch to see how it will change everything, including banking. It's also easy to understand people's sense of unease, given the many dystopian science fiction tales involving AI. Prairie folk are among the most skeptical of AI as a life-improving technology, a new study by TD shows, finding 35 per cent of Manitoba and Saskatchewan respondents trust AI as a technology. That's compared with the national average of 43 per cent. This scepticism may stem from unfamiliarity, with 40 per cent of all respondents giving themselves a 'C' in AI-related knowledge. This is all fine and somewhat informative, but why is a Big Bank interested in our AI familiarity and trust? Like other financial institutions, TD has been working with the technology on behind-the-scenes processes and looking to offer consumer-friendly AI tools. 'The first thing to address is how AI can make supporting our customers better,' says Aitor del Coso, vice-president of analytics, insights and AI, Canadian personal banking at TD. That consumers are a bit suspicious of AI is understandable, especially with respect to their money, he says. But both consumers and financial institutions recognize the potential for AI-enabled banking tools can be a positive — helping us save, budget, manage debt and invest, presumably better. Already, TD plans to introduce a generative AI search engine for its employees to find solutions for clients within its own datasets — i.e. its own banking protocols — later this year, he says. It also has evolving AI tools for portfolio managers and analysts to be able to gain better insights and make better investing decisions. In the next few years, if not sooner, consumer-facing tools will emerge. 'Right now, we're working on ensuring there are the right guardrails in place,' says del Coso. 'We don't want it providing the wrong advice or insights.' He notes most financial institutions like TD already do a good job of serving clients through existing means. Decades of trust have been built while carefully layering on new technologies and services. The last thing any bank wants to do is introduce a clunky tool — particularly using a technology prone to hallucinating erroneous responses. Indeed, AI can go off the rails more than we realize. One study by Columbia Journalism Review found all generative AI search tools averaged making errors about 60 per cent of the time when citing its sources when asked a news-related question. Its potential for mistakes is why major financial institutions are going slow, even though they have collectively hit a wall with customer satisfaction in a recent survey. 'There is really not much movement year-over-year, in terms of improvement,' says Sean Gelles, senior director in payments intelligence at J.D. Power. Speaking about the findings in J.D. Power's recent Canadian consumer online banking and mobile app studies, he adds most consumers are generally satisfied with the banks' performance. Yet the banks pack together in scoring, ranging about a B- to B+. The scores have been steady in recent years with little growth. AI could change things, however. 'There hasn't been a lot of innovation in that space,' Gelles says, adding a new generative AI tool could put the 'wow' back into banking. Yet banks' trepidation makes sense given these are massive, complex organizations. Most banks are not into risk-taking first-movers — unlike big tech companies, which have embraced generative AI. That said, banks 'are investing heavily in AI,' he notes, pointing to fraud detection as one current use for the technology. The innovation elsewhere is coming to the financial industry. Small fintech companies are already pushing it. One area is the fast-growing buy-now-pay-later (BNPL) market. An old concept for large purchases like appliances, AI has powered BNPL to be used at the checkout in-person or online quickly for much smaller buys. It is a great option so long as you can pay the instalments on time, because then you don't pay interest, says Natasha Macmillan, senior director of everyday banking at AI is key to the speed of approval, facilitating underwriting in real-time of the loans to consumers to make purchases. It can also match consumers with BNPL who are most suitable for this type of loan. While BNPL can help people on tight budgets stretch their finances further in a pinch, BNPL providers 'bank on people overextending themselves,' Macmillan notes, adding interest rates can reach 35 per cent when people fail to pay on time. Wednesdays A weekly dispatch from the head of the Free Press newsroom. The loans do not affect credit ratings yet — though one provider in the U.S. is starting soon. No doubt it might be warranted given BNPL's growth, expected to be a $15- billion market in Canada by 2030. It's already a much bigger deal in the U.S., where about half of consumers use credit cards to extend purchasing power — and run balances. A recent New York Times article noted Americans took out US$100 billion in BNPL loans in 2023. One report from its federal Consumer Financial Protection Bureau found two-thirds of those loans went to risky borrowers, stacking these loans onto credit card debt. All of this points to consumers needing to beware of new-fangled technologies in finance. Based on the TD survey findings, it seems the people of the Prairies have good reason to be wary. Joel Schlesinger is a Winnipeg-based freelance journalist. joelschles@


CTV News
3 hours ago
- CTV News
CTV National News: How people can protect themselves from smishing attacks
Watch It is getting harder for people to spot smishing attacks as scammers turn to AI to make their text messages seem more legit. John Vennavally-Rao explains.