#AI implementation in IT companies

[bixels]

As cameras becomes more normalized (Sarah Bernhardt encouraging it, grifters on the rise, young artists using it), I wanna express how I will never turn to it because it fundamentally bores me to my core. There is no reason for me to want to use cameras because I will never want to give up my autonomy in creating art. I never want to become reliant on an inhuman object for expression, least of all if that object is created and controlled by manufacturing companies. I paint not because I want a painting but because I love the process of painting. So even in a future where everyone’s accepted it, I’m never gonna sway on this.

if i have to explain to you that using a camera to take a picture is not the same as using generative ai to generate an image then you are a fucking moron.

[munchboxart]

I hope Adobe becomes bankrupt

[simantopia]

honestly if sims RLY wants to improve and be good competition for inzoi. they shouldn't just focus on what makes inzoi so great -- but instead focus on what made sims so great. they should make sims 5 and go back to the roots, take the best things from sims 2 + sims 3 and then add even more.

the problem with sims 4 is that it took way too many steps back. i often feel like sims 4 is sims 3 and sims 3 is sims 4 because of how basic sims 4 feels in comparison. fuck, sims 4 even feels like it came before 2 with some of its stuff. (lack of animations, bland graphics such as flat snow?? unresponsive characters/simulation, lackluster "emotions", still lacking content such as cars and for 10 years until now, burglars. not to mention the stuff it lacked at launch)

like yes, sims 3 didn't have everything that sims 2 had -- but it didn't matter because it makes up with all the stuff it adds. the only thing i can say sims 4 "100%" improves is the build buy, and even it isn't as good as it could be because it lacks the sims 3's design tool / color wheel.

sims 5 should take the very best from these two games, and then add + improve on the franchise as a whole. (another problem with sims 4 is that it doesn't rly add either, it takes in order to sell it to you in another overpriced dlc)

but they won't. even after their stupid inzoi survey, i'm sure they're planning on doing what they've always done. which is doing the most low effort thing in order to trick players into thinking they've done something bigger.

[tippenfunkaport]

Just... the fact that it's, "We sold your data to AI companies!" and they didn't even pretend for 5 seconds that it was a profit share or revenue stream users could opt into really says it all.

[shoechoe]

i find the common implication that Google of all companies wasn't already using up a ton of water and energy before its implementation of AI annoying but it is true that the AI results feature is bad and useless and therefore an extra waste of water and energy for no practical purpose. i would have no problem with posts that just say that instead of bizarrely wording it like "well google USED to be fine but NOW thanks to AI it uses a million billion tons of water"

[chirpn]

AI Chatbot Development Company Offered by Chirpn for Intelligent Customer Engagement

Wondering how to enhance customer engagement and automate support using conversational AI? Chirpn, a leading AI chatbot development company, builds intelligent, NLP-powered chatbots that deliver seamless, real-time interactions across websites, apps, and social platforms. Our chatbots understand user intent, handle queries efficiently, and provide personalized responses—24/7. Whether you're in e-commerce, healthcare, finance, or any service industry, Chirpn customizes chatbot solutions that align with your brand voice and operational needs. With a focus on user experience and automation, our AI chatbots help you reduce costs, improve satisfaction, and boost business performance through smarter communication.

[aisbusinesscorp]

Why Choose AIS Business Corp in 2025?

1. Proven Expertise & Industry Experience

With a decade-long track record of successful SAP implementations, AIS Business Corp has established itself as a trusted partner for businesses worldwide. Their team of SAP-certified consultants possesses deep domain knowledge and technical proficiency, ensuring seamless digital transformations.

2. Tailored SAP Solutions

AIS Business Corp understands that every business is unique. By offering customized SAP solutions, the company ensures that organizations can address their specific pain points and achieve optimal efficiency.

3. Commitment to Innovation

In 2025, businesses need partners who are ahead of the curve. AIS Business Corp continuously invests in research and development to integrate emerging technologies such as AI, IoT, and block chain with SAP solutions, delivering next-gen business transformation.

4. Seamless Cloud Integration

Cloud adoption is no longer an option – it’s a necessity. AIS Business Corp specializes in cloud-based SAP solutions, enabling businesses to operate with enhanced agility, security, and scalability.

5. End-to-End SAP Support

From initial consultation to implementation and ongoing support, AIS Business Corp provides a full spectrum of SAP services. Their 24/7 support ensures businesses experience minimal disruptions and maximum efficiency.

Source Link:

[aiscorp]

Empowering Businesses with Staffing Services – SAP Silver Partner in Chennai

Why Staffing Services Matter in SAP Ecosystem

SAP solutions are powerful, but they require knowledgeable experts for successful implementation, customization, and maintenance. Whether a business is migrating to SAP S/4HANA, enhancing its current SAP Business One setup, or integrating SAP modules with third-party applications, the quality of staffing directly impacts project outcomes.

However, hiring full-time SAP professionals can be time-consuming and costly. That’s why many organizations prefer flexible staffing models—contractual, project-based, or temporary—to access top-tier SAP expertise without long-term commitments. This is exactly what AIS Business Corp offers.

AIS Business Corp: Bridging the Talent Gap

As an SAP Silver Partner, AIS Business Corp combines deep technical expertise with strategic staffing capabilities. The company has a large pool of SAP-certified consultants, developers, and project managers who are available for deployment across various industries including manufacturing, retail, logistics, pharma, and finance.

Their staffing services include:

Contract Staffing: Hire SAP professionals on short-term or project-based contracts.

Permanent Staffing: Source top SAP talent for long-term organizational roles.

Project Outsourcing: End-to-end management of SAP projects with dedicated resources.

Remote Staffing: Access skilled SAP consultants remotely for global operations.

With a thorough understanding of client needs, AIS ensures that only pre-screened, qualified candidates are presented—saving time and improving workforce quality.

Expertise Across SAP Domains

AIS Business Corp’s staffing services cover a wide spectrum of SAP functions:

SAP S/4HANA

SAP Business One

SAP FICO, MM, SD, PP, HR modules

SAP ABAP and BASIS administration

SAP Analytics, BOBJ, and Fiori

Their consultants are experienced not only in technical execution but also in understanding domain-specific requirements, ensuring a strong alignment between business goals and system capabilities.

Why Choose AIS Business Corp?

SAP-Certified Expertise: Backed by the credibility of being a recognized SAP Silver Partner.

Industry Experience: Proven track record in deploying SAP talent across multiple verticals.

Customized Staffing Models: Flexible hiring plans tailored to business requirements.

Quick Turnaround Time: Rapid deployment of resources to meet urgent project needs.

Client-Centric Approach: Emphasis on long-term relationships and client satisfaction.

For organizations SAP Silver Partner in Chennai and beyond looking to strengthen their SAP teams with qualified, dependable professionals, AIS Business Corp Pvt. Ltd. stands out as the partner of choice. Their blend of technical excellence and staffing proficiency makes them a valuable ally in any SAP-driven business journey.

[solutionmindfire]

Driving Digital Transformation: Mindfire Solutions' Expertise in Mobile App Development, AI Services, and DevOps

In today's rapidly evolving technological landscape, businesses must adapt swiftly to maintain a competitive edge. Mindfire Solutions, a leading mobile app development company, offers a comprehensive suite of services, including AI development services and DevOps expertise, to help organizations navigate digital transformation effectively.

Mobile App Development: Crafting Tailored Solutions

As a seasoned mobile app development company, Mindfire Solutions specializes in creating custom applications that cater to diverse business needs.

Their portfolio showcases a range of successful projects across various industries:

Shipment Management Solution: Developed a cross-platform mobile app to streamline logistics and enhance real-time tracking capabilities.

Healthcare Management System: Built a comprehensive mobile application integrating IoT devices for real-time patient monitoring, improving healthcare delivery.

E-commerce Platform for Spray Foam Business: Created a user-friendly mobile app facilitating seamless online shopping experiences for customers.

These projects underscore Mindfire's ability to deliver scalable, secure, and user-centric mobile applications that drive business growth.

AI Development Services: Empowering Intelligent Decision-Making

Mindfire Solutions' AI development services enable businesses to harness the power of artificial intelligence and machine learning for enhanced decision-making and operational efficiency.

Their expertise spans various AI applications:

AI-based Cost Estimation from HVAC Symbols: Implemented machine learning algorithms to automate cost estimation processes, reducing manual errors and improving accuracy.

AI Roof Visualization Tool for Construction: Developed an AI-powered tool that generates accurate roof visualizations, aiding construction planning and client presentations.

RAG Based Chatbot to Boost Efficiency: Created a chatbot utilizing Retrieval-Augmented Generation (RAG) to provide precise responses, enhancing customer service efficiency.

These solutions demonstrate Mindfire's commitment to delivering AI-driven innovations that streamline operations and provide actionable insights.

DevOps Expertise: Enhancing Operational Agility

Mindfire Solutions' DevOps expertise ensures seamless integration between development and operations, fostering a culture of continuous improvement and rapid deployment.

Their DevOps services have led to significant improvements in various projects:

DevOps to Scale Health Insurance Platform: Implemented CI/CD pipelines and automated testing, resulting in faster release cycles and improved system reliability.

DevOps for Delivery Network: Optimized infrastructure and deployment processes, enhancing the scalability and performance of the delivery network.

DevOps for Scalable Infrastructure: Established robust DevOps practices to support scalable infrastructure, ensuring high availability and performance.

These initiatives highlight Mindfire's ability to implement DevOps strategies that accelerate development cycles and improve operational efficiency.

Conclusion

Mindfire Solutions stands out as a versatile mobile app development company with a strong foothold in AI development services and DevOps expertise. Their proven track record across various industries showcases their ability to deliver customized solutions that drive digital transformation.

To explore how Mindfire Solutions can assist your business in achieving its digital goals, visit their official website.

[jcmarchi]

AI’s Real Value Is Built on Data and People – Not Just Technology

New Post has been published on https://thedigitalinsider.com/ais-real-value-is-built-on-data-and-people-not-just-technology/

AI’s Real Value Is Built on Data and People – Not Just Technology

The promise of AI expands daily – from driving individual productivity gains to enabling organizations to uncover powerful new business insights through data. While the potential of AI appears limitless and its impact easy to imagine, the journey to a truly AI-powered ecosystem is both complex and challenging. This journey doesn’t begin and end with implementing, adopting or even consistently using AI – it ends there. Realizing the full value of an AI solution ultimately depends on the quality of the data and the people who implement, manage and apply it to drive meaningful results.

Data: The Cornerstone of AI Success

Data, the organizational constant. Whether it’s a Mom-and-Pop convenience store or an enterprise organization, every business runs on data (financial records, inventory, security footage  etc.) The   management, accessibility and governance of this data is the cornerstone to realizing AI’s full  potential  within an organization. Gartner recently noted that 63% of organizations either lack confidence or are unsure about if their existing data practice or management structure is sufficient for successful adoption of AI. Enabling an organization to unlock  the full potential of AI requires a well thought out Data Practice. From collection, storage, synthesis, analysis, security, privacy, governance, and access control – a framework and methodology must be in place to leverage AI properly.  Additionally, it is essential to mitigate the risks and unintended consequences. Bottom line, data is the cornerstone of analytics and the fuel for your AI.

The access your AI solution has to your data determines its potential to deliver – so much so, we’re seeing the emergence of new functions tailored specifically to it, the Chief Data Officer (CDO). Simply put, if an AI solution is introduced to an environment with “free-floating” data accessible to anyone – it will be error-prone, biased, non-compliant, and very likely to expose sensitive and private information. Conversely, when  the data environment is rich, structured, accurate, within a framework and methodology for how the organization uses its data – AI can return immediate benefits and save numerous hours on modeling, forecasting, and propensity development. Built around the data cornerstone are access rights and governance policies for data, which present its own concern – the human element.

People: The Underrated Factor in AI Adoption

IDC recently shared that 45% of CEOs and over 66% of CIOs surveyed conveyed a hesitancy around technology vendors not completely understanding the downside risk potential of AI. These leaders are justified in their caution. Arguably, the consequences of age-old IT risks remain similar with governed AI (i.e., downtime, operational seizures, costly cyber-insurance premiums, compliance fines, customer experience, data-breaches, ransomware, and more.) and are amplified by the integration of AI into IT. The concern comes from the lack of understanding around the root-causes for those consequences or for those that are not aware, the angst that comes with associate AI enablement serving as the catalyst for those consequences.

The pressing question is, “Should I invest in this costly IT tool that can vastly improve my business’s performance at every functional level at the risk of IT implosion due to lack of employee readiness and enablement?” Dramatic? Absolutely – business risk always is, and we already know the answer to that question. With more complex technologies and elevated operational potential, so too must the effort to enable teams to use these tools legally, properly, efficiently, and effectively.

The Vendor Challenge

The lack of confidence in technology vendors’ understanding goes beyond subject matter expertise and reflects a deeper issue: the inability to clearly articulate the specific risks that an organization can and will face with improper implementations and unrealistic expectations.

The relationship between an organization and technology vendors is much like that of a patient and a healthcare practitioner. The patient consults a healthcare practitioner with symptoms seeking a diagnosis and hoping for a simple and cost-effective remedy. In preventative situations, the healthcare practitioner will work with the patient on dietary recommendations, lifestyle choices, and specialized treatment to achieve specified health goals. Similarly, there’s an expectation that organizations will receive prescriptive solutions from technology vendors to solve or plan for technology implementations. However, when organizations are unable to provide prescriptive risks specific to given IT environments, it exacerbates the uncertainty of AI implementation.

Even when IT vendors effectively communicate the risks and potential impacts of AI, many organizations are deterred by the true total cost of ownership (TCO) involved in laying the necessary foundation. There’s a growing awareness that successful AI implementation must begin within the existing environment – and only when that environment is modernized can organizations truly unlock the value of AI integration. It’s similar to assuming that anyone can jump into the cockpit of an F1 supercar and instantly win races. Any reasonable person knows that success in racing is the result of both a skilled driver and a high-performance machine. Likewise, the benefits of AI can only be realized when an organization is properly prepared, trained, and equipped to adopt and implement it.

Case in Point: Microsoft 365 Copilot

Microsoft 365 Copilot is a great example of an existing AI solution whose potential impact and value have often been misunderstood or diluted due to customers’ misaligned expectations – in how AI should be implemented and what they believe it should do, rather than understanding what it can do. Today, more than 70% of Fortune 500 companies are already leveraging Microsoft 365 Copilot. However, the widespread fear that AI will replace jobs is largely a misconception when it comes to most real-world AI applications. While job displacement has occurred in some areas – such as fully automated “dark warehouses” – it’s important to distinguish between AI as a whole and its use in robotics. The latter has had a more direct impact on job replacement.

In the context of Modern Work, AI’s primary value lies in enhancing performance and amplifying expertise – not replacing it. By saving time and increasing functional output, AI enables more agile go-to-market strategies and faster value delivery. However, these benefits rely on critical enablers:

A mature Data Practice

Strong Access Management and Governance

Robust Security measures to mitigate risks

People enablement around responsible AI use and best practices

Here are a few examples of AI-driven functional improvements across business areas:

Sales Leaders can generate propensity models using customer lifecycle data to drive cross-sell and upsell strategies, improving customer retention and value.

Corporate Strategy & FP&A Teams gain deeper insights thanks to time saved analyzing business units, enabling better alignment with corporate goals.

Accounts Receivable Teams can manage payment cycles more efficiently with faster access to actionable data, improving outreach and customer engagement.

Marketing Leaders can build more effective, sales-aligned go-to-market strategies by leveraging AI insights on sales performance and opportunities.

Operations Teams can reduce time spent reconciling Finance and Sales data, minimizing chaos during end-of-quarter or end-of-year processes.

Customer Success & Support Teams can cut down response and resolution times by automating workflows and simplifying key steps.

These examples only scratch the surface of AI’s potential to drive functional transformation and productivity gains. Yet, realizing these benefits requires the right foundation – systems that allow AI to integrate, synthesize, analyze, and ultimately deliver on its promise.

Final Thought: No Plug-and-Play for AI

Implementing AI to unlock its full potential isn’t as simple as installing a program or application. It’s the integration of an interconnected web of autonomous functions that permeate your entire IT stack – delivering insights and operational efficiencies that would otherwise require significant manual effort, time and resources.

Realizing the value of an AI solution is grounded in building a data practice, maintaining a robust access and governance framework, and securing the ecosystem – a topic that requires its own deep dive.

The ability for technology vendors to a valued partner will be dependent on both marketing and enablement, focused on debunking myths and calibrating expectations on what harnessing the potential of AI truly means.

[diabetickart]

How to Select an AI Company That Aligns with Your Vision

Incorporating AI into your business strategy starts with choosing the right partner. This blog focuses on helping businesses align their vision with the expertise of AI companies. It emphasizes the importance of clear communication, transparent pricing models, and collaborative approaches. The article discusses how to ensure seamless integration, customize solutions, and protect sensitive data. By addressing both technical and business perspectives, this guide provides a balanced approach to selecting an AI company that meets your expectations and drives meaningful results.

Read more:

How to Choose the Right AI Company for Your Business Needs

[gagande]

PureCode AI review | The decision between Node.js and Next.js

The decision between Node.js and Next.js primarily hinges on the project requirements. For projects requiring rapid development, Node.js’s flexibility, due to the absence of strict conventions, can result in longer development times as utilities may need to be implemented manually.

[rjas16]

Think Smarter, Not Harder: Meet RAG

How do RAG make machines think like you?

Imagine a world where your AI assistant doesn't only talk like a human but understands your needs, explores the latest data, and gives you answers you can trust—every single time. Sounds like science fiction? It's not.

We're at the tipping point of an AI revolution, where large language models (LLMs) like OpenAI's GPT are rewriting the rules of engagement in everything from customer service to creative writing. here's the catch: all that eloquence means nothing if it can't deliver the goods—if the answers aren't just smooth, spot-on, accurate, and deeply relevant to your reality.

The question is: Are today's AI models genuinely equipped to keep up with the complexities of real-world applications, where context, precision, and truth aren't just desirable but essential? The answer lies in pushing the boundaries further—with Retrieval-Augmented Generation (RAG).

While LLMs generate human-sounding copies, they often fail to deliver reliable answers based on real facts. How do we ensure that an AI-powered assistant doesn't confidently deliver outdated or incorrect information? How do we strike a balance between fluency and factuality? The answer is in a brand new powerful approach: Retrieval-Augmented Generation (RAG).

What is Retrieval-Augmented Generation (RAG)?

RAG is a game-changing technique to increase the basic abilities of traditional language models by integrating them with information retrieval mechanisms. RAG does not only rely on pre-acquired knowledge but actively seek external information to create up-to-date and accurate answers, rich in context. Imagine for a second what could happen if you had a customer support chatbot able to engage in a conversation and draw its answers from the latest research, news, or your internal documents to provide accurate, context-specific answers.

RAG has the immense potential to guarantee informed, responsive and versatile AI. But why is this necessary? Traditional LLMs are trained on vast datasets but are static by nature. They cannot access real-time information or specialized knowledge, which can lead to "hallucinations"—confidently incorrect responses. RAG addresses this by equipping LLMs to query external knowledge bases, grounding their outputs in factual data.

How Does Retrieval-Augmented Generation (RAG) Work?

RAG brings a dynamic new layer to traditional AI workflows. Let's break down its components:

Embedding Model

Think of this as the system's "translator." It converts text documents into vector formats, making it easier to manage and compare large volumes of data.

Retriever

It's the AI's internal search engine. It scans the vectorized data to locate the most relevant documents that align with the user's query.

Reranker (Opt.)

It assesses the submitted documents and score their relevance to guarantee that the most pertinent data will pass along.

Language Model

The language model combines the original query with the top documents the retriever provides, crafting a precise and contextually aware response. Embedding these components enables RAG to enhance the factual accuracy of outputs and allows for continuous updates from external data sources, eliminating the need for costly model retraining.

How does RAG achieve this integration?

It begins with a query. When a user asks a question, the retriever sifts through a curated knowledge base using vector embeddings to find relevant documents. These documents are then fed into the language model, which generates an answer informed by the latest and most accurate information. This approach dramatically reduces the risk of hallucinations and ensures that the AI remains current and context-aware.

RAG for Content Creation: A Game Changer or just a IT thing?

Content creation is one of the most exciting areas where RAG is making waves. Imagine an AI writer who crafts engaging articles and pulls in the latest data, trends, and insights from credible sources, ensuring that every piece of content is compelling and accurate isn't a futuristic dream or the product of your imagination. RAG makes it happen.

Why is this so revolutionary?

Engaging and factually sound content is rare, especially in today's digital landscape, where misinformation can spread like wildfire. RAG offers a solution by combining the creative fluency of LLMs with the grounding precision of information retrieval. Consider a marketing team launching a campaign based on emerging trends. Instead of manually scouring the web for the latest statistics or customer insights, an RAG-enabled tool could instantly pull in relevant data, allowing the team to craft content that resonates with current market conditions.

The same goes for various industries from finance to healthcare, and law, where accuracy is fundamental. RAG-powered content creation tools promise that every output aligns with the most recent regulations, the latest research and market trends, contributing to boosting the organization's credibility and impact.

Applying RAG in day-to-day business

How can we effectively tap into the power of RAG? Here's a step-by-step guide:

Identify High-Impact Use Cases

Start by pinpointing areas where accurate, context-aware information is critical. Think customer service, marketing, content creation, and compliance—wherever real-time knowledge can provide a competitive edge.

Curate a robust knowledge base

RAG relies on the quality of the data it collects and finds. Build or connect to a comprehensive knowledge repository with up-to-date, reliable information—internal documents, proprietary data, or trusted external sources.

Select the right tools and technologies

Leverage platforms that support RAG architecture or integrate retrieval mechanisms with existing LLMs. Many AI vendors now offer solutions combining these capabilities, so choose one that fits your needs.

Train your team

Successful implementation requires understanding how RAG works and its potential impact. Ensure your team is well-trained in deploying RAG&aapos;s technical and strategic aspects.

Monitor and optimize

Like any technology, RAG benefits from continuous monitoring and optimization. Track key performance indicators (KPIs) like accuracy, response time, and user satisfaction to refine and enhance its application.

Applying these steps will help organizations like yours unlock RAG's full potential, transform their operations, and enhance their competitive edge.

The Business Value of RAG

Why should businesses consider integrating RAG into their operations? The value proposition is clear:

Trust and accuracy

RAG significantly enhances the accuracy of responses, which is crucial for maintaining customer trust, especially in sectors like finance, healthcare, and law.

Efficiency

Ultimately, RAG reduces the workload on human employees, freeing them to focus on higher-value tasks.

Knowledge management

RAG ensures that information is always up-to-date and relevant, helping businesses maintain a high standard of knowledge dissemination and reducing the risk of costly errors.

Scalability and change

As an organization grows and evolves, so does the complexity of information management. RAG offers a scalable solution that can adapt to increasing data volumes and diverse information needs.

RAG vs. Fine-Tuning: What's the Difference?

Both RAG and fine-tuning are powerful techniques for optimizing LLM performance, but they serve different purposes:

Fine-Tuning

This approach involves additional training on specific datasets to make a model more adept at particular tasks. While effective for niche applications, it can limit the model's flexibility and adaptability.

RAG

In contrast, RAG dynamically retrieves information from external sources, allowing for continuous updates without extensive retraining, which makes it ideal for applications where real-time data and accuracy are critical.

The choice between RAG and fine-tuning entirely depends on your unique needs. For example, RAG is the way to go if your priority is real-time accuracy and contextual relevance.

Concluding Thoughts

As AI evolves, the demand for RAG AI Service Providers systems that are not only intelligent but also accurate, reliable, and adaptable will only grow. Retrieval-Augmented generation stands at the forefront of this evolution, promising to make AI more useful and trustworthy across various applications.

Whether it's a content creation revolution, enhancing customer support, or driving smarter business decisions, RAG represents a fundamental shift in how we interact with AI. It bridges the gap between what AI knows and needs to know, making it the tool of reference to grow a real competitive edge.

Let's explore the infinite possibilities of RAG together

We would love to know; how do you intend to optimize the power of RAG in your business? There are plenty of opportunities that we can bring together to life. Contact our team of AI experts for a chat about RAG and let's see if we can build game-changing models together.

[bonediggercharleston]

I am very wary of people going "China does it better than America" because most of it is just reactionary rejection of your overlord in favor of his rival, but this story is 1. absolutely legit and 2. way too funny.

US wants to build an AI advantage over China, uses their part in the chip supply chain to cut off China from the high-end chip market.

China's chip manufacturing is famously a decade behind, so they can't advance, right?

They did see it as a problem, but what they then did is get a bunch of Computer Scientists and Junior Programmers fresh out of college and funded their research in DeepSeek. Instead of trying to improve output by buying thousands of Nvidia graphics cards, they tried to build a different kind of model, that allowed them to do what OpenAI does at a tenth of the cost.

Them being young and at a Hedgefund AI research branch and not at established Chinese techgiants seems to be important because chinese corporate culture is apparently full of internal sabotage, so newbies fresh from college being told they have to solve the hardest problems in computing was way more efficient than what usually is done. The result:

American AIs are shook. Nvidia, the only company who actually is making profit cause they are supplying hardware, took a hit. This is just the market being stupid, Nvidia also sells to China. And the worst part for OpenAI. DeepSeek is Open Source.

Anybody can implement deepseek's model, provided they have the hardware. They are totally independent from DeepSeek, as you can run it from your own network. I think you will soon have many more AI companies sprouting out of the ground using this as its base.

What does this mean? AI still costs too much energy to be worth using. The head of the project says so much himself: "there is no commercial use, this is research."

What this does mean is that OpenAI's position is severely challenged: there will soon be a lot more competitors using the DeepSeek model, more people can improve the code, OpenAI will have to ask for much lower prices if it eventually does want to make a profit because a 10 times more efficient opensource rival of equal capability is there.

And with OpenAI or anybody else having lost the ability to get the monopoly on the "market" (if you didn't know, no AI company has ever made a single cent in profit, they all are begging for investment), they probably won't be so attractive for investors anymore. There is a cheaper and equally good alternative now.

AI is still bad for the environment. Dumb companies will still want to push AI on everything. Lazy hacks trying to push AI art and writing to replace real artists will still be around and AI slop will not go away. But one of the main drivers of the AI boom is going to be severely compromised because there is a competitor who isn't in it for immediate commercialization. Instead you will have a more decentralized open source AI field.

Or in short: