Modular Design and Upgradability

At the heart of the M2 is a chassis that accepts different tool heads. The base model includes a 10W laser diode capable of cutting over 300 thinner materials. For those who also want color printing, a $749 bundle adds the CMYK Inkjet Printing Module. This module delivers about 400 pages per ink cartridge, allowing users to print designs onto paper, wood, or felt before cutting or engraving them. For heavier materials, a $1,149 bundle includes a 20W laser that can cut thicker substrates. Additionally, an infrared laser module is available for $499, optimized for engraving metals like stainless steel and aluminum.

The modular approach means the M2 can evolve with a user's skills. Beginners might start with basic cutting and printing, then later upgrade to more powerful lasers without buying a whole new machine. This stands in contrast to many competing all-in-one crafters that lock users into a fixed set of capabilities.

Dual Cameras and AI Material Detection

What really sets the M2 apart from earlier xTool models, such as the M1 Ultra, is the addition of two cameras: a 5-megapixel panoramic camera and a 2-megapixel close-up camera. These work together to photograph the material placed inside the machine, enabling accurate positioning of designs and cutting patterns. The images are processed by xTool Studio desktop software, which overlays the design onto a live view of the material. This eliminates the guesswork of aligning projects manually.

To further simplify the process for novices, the M2 leverages artificial intelligence to detect the type of material inserted. The AI analyzes visual cues from the cameras and suggests or automatically applies the optimal settings for cutting, engraving, or scoring. For instance, if you place a sheet of 3mm birch plywood, the machine will adjust laser power and speed to achieve a clean cut without charring. For paper, it may choose a lower power scoring setting to create fold lines. This feature significantly reduces the trial-and-error phase that often frustrates new crafters.

Laser Cutting and Engraving Capabilities

The M2's laser module can perform three functions: cutting, engraving, and scoring. Cutting completely severs material, engraving removes a thin layer for permanent marks, and scoring creates a shallow groove that makes folding easier. The 10W laser can cut through materials like cardboard, balsa wood, fabric, and thin acrylic up to about 5mm thick. The 20W upgrade doubles the cutting depth and can handle thicker acrylic, hardwoods, and even some metals when used with the infrared module.

Engraving quality is enhanced by the machine's stable gantry and precise motion control. The 20W laser can engrave detailed images, text, and patterns onto coated metals, stone, and glass. The infrared laser, with its different wavelength, is particularly effective on bare metals, producing high-contrast marks without the need for marking sprays.

Printing Module and Sticker Making

The CMYK inkjet module allows full-color printing directly onto materials up to about 12 inches wide. This is ideal for creating custom stickers, labels, fabric patches, and personalized gifts. Users can design artwork in software, then print it — and immediately cut around the shapes using the laser. The combination of print then cut is a popular workflow for Etsy sellers and hobbyists making stickers, decals, and cardstock crafts. The printing module uses standard CMYK ink cartridges, and the machine supports borderless printing for edge-to-edge coverage.

The printed output is water-resistant once dry, though for outdoor applications, lamination is recommended. xTool also offers a roll feeder accessory for continuous printing on longer materials, though this is sold separately.

Integrated Air Pump and Ventilation

More powerful laser cutters often need external air pumps and filtration systems to remove smoke and fumes. The M2 comes with an integrated air pump that blows compressed air onto the cutting surface to reduce flame and charring. However, the exhaust is simply vented into the room. xTool recommends using the machine in a well-ventilated area, and for heavy use, they suggest pairing it with the SafetyPro AP2 Air Purifier, priced at $999. This external unit filters smoke and odors before releasing the air, making the setup safe for indoor use. While the purifier costs more than the M2 itself, it may be necessary for frequent cutters working with acrylic, leather, or wood.

Comparison with Competitors

The desktop crafting market includes established players like Cricut, Glowforge, and LaserPecker. Cricut machines are limited to cutting (with a blade) and can score, but they do not offer built-in laser engraving or printing without additional tools. Glowforge lasers are powerful but more expensive (starting around $2,000) and lack a print head. The xTool M2 fills a gap by offering both printing and laser functions in one box at a lower entry price. Its dual-camera alignment system rivals the "Passthrough" feature on higher-end Cricut models, while the AI material detection is a unique selling point not found on most competitors.

The M2's main drawback is its size: it measures roughly 20 inches wide, 15 inches deep, and 8 inches tall, making it larger than a typical home printer. It requires dedicated desk space. Additionally, the base model's 10W laser may not satisfy users who need to cut thick acrylic or metal frequently; they would need the more expensive 20W or infrared bundles.

Software and Workflow

xTool provides its own desktop software, xTool Studio, for Windows and macOS. It includes design tools, import options for SVG, DXF, PNG, and JPG files, and direct control over the M2's functions. The software can integrate with popular design apps like Adobe Illustrator, Inkscape, and LightBurn through plugin extensions. Users can also send designs wirelessly over Wi-Fi or via USB tethering. The dual-camera preview allows drag-and-drop placement of designs onto the material image, simplifying alignment for print-then-cut projects. The AI material setting can be further fine-tuned manually.

Pricing and Availability

The xTool M2 Color Craft Laser launched today at a base price of $599, with a temporary discount to $549 for the first week. The bundles are:

• Base 10W laser bundle: $599 (discount to $549 first week) — includes the machine with 10W laser diode for cutting thin materials.
• CMYK printing bundle: $749 — adds the inkjet module for color printing.
• 20W laser bundle: $1,149 — includes the 20W laser for cutting thicker materials and improved engraving.
• Infrared laser module (add-on): $499 — for metal engraving.

The machine is available directly from xTool's website and through select retailers. Shipping begins immediately. xTool also offers a variety of accessories: a roll feeder, honeycomb worktable, rotary attachment for cylindrical objects, and an enclosure with exhaust adapter.

Real-World Applications

The M2's versatility makes it suitable for numerous applications. Small business owners can produce custom merchandise keychains, earrings, coasters, and phone cases. Hobbyists can create personalized gifts like engraved photo frames, printed greeting cards, and model parts. Educators can use it in STEM classes to teach design-to-manufacturing workflows. The AI material detection is especially helpful in school settings where students need consistent results.

The dual-camera system also enables precise alignment for multi-step projects. For example, you could print a full-color design on wood, then laser-cut the shape, and finally engrave a second layer. All steps are coordinated within the same machine, eliminating the need to transfer between devices.

Safety and Operational Considerations

Laser cutters produce intense light and fumes. The M2 includes a class 1 laser enclosure that contains the beam during operation, making it safe for household use as long as the lid is closed. The integrated air pump helps to clear smoke, but for materials like PVC or vinyl, the fumes can be corrosive and toxic — xTool explicitly warns against cutting such materials. Users should always follow material safety guidelines. The internal cameras also serve as a safety feature, allowing the user to monitor the job without opening the lid.

The M2 measures noise levels around 60 dB during operation, comparable to a typical inkjet printer. The integrated fan and pump add some noise, but it is not disruptive.

Future Prospects

xTool has a history of releasing firmware updates and new accessories for its machines. The M2's modular design suggests that future tool heads could be developed, such as a blade cutter for vinyl or a 3D pen-like extruder. The company has not announced any such plans yet, but the community expects continued evolution. The M2 might also integrate with third-party software like LightBurn directly, expanding its appeal to advanced users.

With the M2, xTool is positioning itself as a strong competitor in the affordable multi-craft space. The combination of color printing, laser cutting, and engraving — plus AI-driven ease of use — makes it a compelling option for anyone from beginners to seasoned makers. The price point undercuts many competitors while offering more features. As the crafting market grows, machines like the M2 lower the barrier to entry for starting a side business or exploring creative hobbies.

The dual-camera alignment system is likely to become a standard feature in future crafting machines, as it dramatically reduces setup time. The AI material detection, while not perfect, continues to improve with software updates. For now, the xTool M2 represents a smart investment for those looking to consolidate multiple tools into one device without sacrificing quality or versatility.

Source: The Verge News

How It Works

Customers who sign up for Shift's program receive a professional cleaner at no charge. The cleaner, employed by a partner agency, wears a specially designed hat that contains a camera. This device captures everything the cleaner sees and does, from scrubbing floors to wiping counters. The footage is then processed by Shift's AI systems to generate training data for robots.

Shift's co-CEO and co-founder Bercan Kilic describes the hat as a 'magic hat' because it records the cleaner's point of view. Although the hat is not fashionable, it serves a critical function: turning each cleaning job into a data-gathering mission. According to Kilic, the data collected is what funds the free service. 'You get a spotless apartment. We get training data. Everyone wins,' the company's website states.

Privacy and Security Measures

Privacy is a major concern for anyone allowing cameras into their home. Shift addresses this by promising that all footage is anonymized. Faces, names, and personal information such as screens on computers or ID cards are blurred or removed before the data is used for AI training. The company also notes that cleaners are vetted by its partner agencies, though they are not direct employees of Shift.

The company emphasizes that the more challenging the cleaning environment, the better. An FAQ on its website states that 'more challenging cleaning environments can be especially useful' for training robots. However, cleaners have the right to decline any task they are not comfortable performing.

Target Cities and Expansion Plans

Initially, Shift is offering the free cleaning service only in New York City. Kilic says the service will be available 'very soon' in San Francisco, London, Zurich, and Munich. The free cleanings are limited to a 'limited time,' but the model fits into a broader trend of using recordings of human tasks to train AI systems and robots.

Broader Context: The Value of Training Data

Shift's approach is part of a growing market for human-generated data. The company already pays tens of thousands of people across 15 countries to record their daily activities through its app. These recordings help train AI models for tasks ranging from cooking to assembly. The cleaning service is a natural extension of this data collection strategy, offering a more direct and structured way to gather high-quality footage of household chores.

The market for AI training data is immense. As robots and AI systems become more sophisticated, the need for real-world examples of human tasks increases. Companies are constantly seeking ways to obtain this data cheaply and ethically. Shift's model, while innovative, is not without controversy. Critics raise concerns about privacy, worker rights, and the potential for exploitation. However, Shift positions itself as a transparent and ethical alternative to other data collection methods.

Historical Context: From Datasets to Robots

The use of human recordings to train AI is not new. For decades, researchers have relied on datasets of images, videos, and sensor readings to teach machines how to recognize objects and perform tasks. However, recent advances in machine learning and robotics have created a need for more detailed and task-specific data. Companies like Google, Amazon, and Tesla have used similar methods, but often rely on internal employees or crowdsourced workers. Shift's approach is distinctive because it offers a direct benefit to the data provider: free cleaning.

Potential Applications Beyond Cleaning

Shift's video suggests that the company eventually plans to expand into other areas such as plumbing, cooking, and building. This indicates a long-term vision of creating a comprehensive library of human task recordings that can be used to train robots for virtually any home service. The first step is cleaning, which is a relatively simple and repetitive task that provides a strong foundation for robot learning.

Robots that can clean homes have been a long-standing goal of robotics. Companies like iRobot have succeeded with vacuuming robots, but more complex tasks such as mopping, dusting, and dishwashing remain difficult. By recording human cleaners, Shift hopes to gather the precise movements, decisions, and techniques needed to teach robots these higher-level skills.

Challenges and Limitations

While the idea is appealing, several challenges exist. First, the quality of the training data depends heavily on the consistency of the cleaners and the variety of environments. Second, privacy concerns may deter many potential customers. Third, the cost of providing free cleaning may be unsustainable in the long run unless the data generates significant revenue. Shift has not disclosed its financials but suggests that the value of the data justifies the expense.

Additionally, the robot training itself is far from complete. Even with excellent data, teaching a robot to perform general cleaning in a real home requires years of development. Shift's data collection is a necessary but not sufficient step. The company likely plans to partner with robotics firms or develop its own hardware later.

Industry Reception and Future Outlook

The announcement has generated mixed reactions online. Some see it as a brilliant use of the sharing economy, while others worry about the erosion of privacy. Tech analysts note that Shift is following a well-trodden path of offering free services in exchange for data, akin to how social media platforms operate. The difference here is the tangible and immediate benefit to the consumer: a clean home.

As Shift expands to more cities and possibly additional services, it will need to navigate regulatory frameworks related to data privacy and worker classification. The company's success will depend on its ability to maintain trust and deliver consistent quality. For now, the 'magic hat' is a curious experiment that could shape the future of both cleaning and AI training.

Source: The Verge News

As reported, if you search the query “is it 2027 next year,” Google's AI Overviews occasionally produces a response stating that 2027 is two years from now. This is obviously incorrect in the context of a 2025 calendar year (assuming the search is conducted in 2025 as of the original article’s publication date). The error is not only factually wrong but also self-referential: the AI has been citing old Instagram and Reddit posts that were themselves making fun of earlier wrong answers to the same question.

This is not an isolated incident. In fact, the same bug has been documented before, with users pointing out that Google’s AI seems to struggle with simple date-related queries. The recurrence suggests that Google has not yet fully resolved the underlying issue that causes these hallucinations, even after earlier public mockery.

Background on Google AI Overviews

Google AI Overviews, launched in early 2024 as a replacement for the earlier “Search Generative Experience” (SGE), uses large language models to generate concise answers directly on the search results page. The goal is to save users time by providing immediate, synthesized information without requiring them to click through multiple links. However, the system has been plagued by inaccuracies, including recommending using glue to keep cheese on pizza, suggesting rocks as a healthy snack, and now failing basic date math.

The feature is part of Google’s broader push to integrate generative AI into its core products, following the success of ChatGPT and competing AI assistants. Yet the quality control challenges remain severe. In September 2024, Google publicly acknowledged that AI Overviews could “make mistakes” but assured users that improvements were underway. Despite these promises, the 2027 confusion indicates that persistent blind spots exist.

Why does the AI get this question wrong?

Large language models, including the ones powering Google AI Overviews, do not perform arithmetic calculations in the traditional sense. Instead, they predict the next most likely token based on patterns learned from training data. When asked “is it 2027 next year,” the model may have seen examples in its training set where people debated whether 2027 was the following year or a later one. Without robust symbolic reasoning, the model can easily output “two years” if it associates 2027 with a future point that feels distant relative to the current year. Additionally, the model may be confused by ambiguous phrasing: “next year” is relative, and if the model does not anchor the current year correctly, it can drift into error.

In this case, the AI even cited social media posts that were originally making fun of the same mistake. This is a classic example of a feedback loop: the model finds training data that contains errors and amplifies them, because it lacks a mechanism to evaluate factual accuracy against a reliable external knowledge base.

Broader implications for AI reliability

The continued failure to compute something as simple as “what year is next year” is embarrassing for a company that positions itself as the world’s leading AI provider. It underscores a fundamental limitation of current transformer-based models: they are not rule-based systems and cannot guarantee basic logical consistency. For real-world applications like search, this type of error can erode user trust. If people cannot rely on an AI for basic facts, they may become wary of using it for more complex queries, such as medical or financial advice.

Moreover, the persistence of the bug—despite being publicly ridiculed on Instagram and Reddit—suggests that Google has not prioritized fixing the issue. It may be a symptom of a larger engineering challenge: patching hallucinations in large language models is notoriously difficult because the errors are not traceable to a specific wrong line of code but are emergent behaviors from the training data.

Notably, Google has also been criticized for not showing AI Overviews when users specifically try to trick the system—for example, by using the word “disregard.” A search for “disregard” still does not trigger an AI overview, indicating that Google has implemented heuristic filters to avoid showing the feature for potentially manipulative queries. Yet the basic arithmetic failure remains unfiltered.

What Google says and what users experience

In official statements, Google has consistently said it is working to improve AI Overviews. The company has implemented safeguards to reduce the generation of obviously false statements, but the 2027 error shows that those safeguards are not comprehensive. In a blog post from December 2025, Google claimed that AI Overviews had improved accuracy by 40% compared to the launch version. However, such anecdotal failures like the 2027 confusion suggest either that the improvements did not cover date-related queries or that the metric used to measure accuracy is insufficiently granular.

User feedback on forums like Hacker News and X (formerly Twitter) reflects a growing skepticism. Some users have taken to adding explicit commands like “think step by step” to their queries to force the model to reason more carefully, a trick that works with some large language models but not consistently with Google’s system.

The future of AI-powered search

Missteps like this highlight the tension between innovation and reliability. Google’s push to integrate generative AI into search is driven by competitive pressure from rivals like Microsoft Copilot, ChatGPT Search, and Perplexity AI. All of these services offer AI-generated answers, but each faces similar challenges with factual accuracy. However, because Google processes the vast majority of global search traffic, its errors are more visible and can have broader consequences.

For now, users encountering the 2027 bug are likely to dismiss the AI Overview as useless and scroll past it to find traditional web results. That behavior—ignoring the AI feature—is the opposite of what Google intends. The company wants AI Overviews to become the primary way people interact with search, increasing engagement and opening new advertising avenues. But if the feature consistently gets basic facts wrong, users will learn to disregard it, defeating its purpose.

Some researchers argue that the best path forward is hybrid systems that combine large language models with symbolic reasoning modules or verified knowledge graphs. Google has the resources to implement such a system, but it has not yet done so for AI Overviews. Instead, the system relies primarily on the pattern-matching capabilities of the language model, augmented by a limited set of safety filters.

Earlier examples of AI Overviews errors

The 2027 confusion is just the latest in a long list. In 2024, AI Overviews infamously suggested that a person could safely eat one small rock per day for minerals. It also recommended using non-toxic glue to make cheese stick to pizza. Both errors went viral, prompting Google to manually curate the training data and add guardrails. Yet the 2027 error shows a different type of mistake: a logical error rather than an absurd health or recipe tip. Logical errors are harder to catch because they often appear plausible on the surface. For a user who does not already know that 2027 is not two years from 2025, the AI’s answer could be misleading.

Another earlier incident involved an AI Overview stating that President Obama was a Muslim, a blatantly false claim that forced Google to publicly apologize. Errors of this magnitude damage Google’s reputation and invite regulatory scrutiny. In Europe, the EU’s Digital Services Act already requires platforms to mitigate systemic risks from their algorithms, and AI-generated misinformation could fall under that mandate.

How to avoid AI hallucinations when trusting search

For users who want reliable information, the best practice remains to verify AI-generated answers against primary sources. The original article notes that the AI cited Instagram and Reddit posts that were themselves mocking the error, showing that the model cannot distinguish between a joke and a factual correction. Until Google solves this problem, caution is advised for any query where factual precision matters.

Some power users employ custom prompts or browser extensions that disable AI Overviews entirely. Google itself offers the option to turn off the feature through Search Labs, but it is not widely advertised. Given the frequency of these errors, the company might benefit from allowing users to opt out more easily.

The situation also underscores the importance of user feedback. Each time a user flags an erroneous AI Overview, it can help improve the model. However, the visible persistence of the 2027 error suggests that either the feedback loop is too slow or that the model’s training regime does not prioritize these specific corrections.

Looking ahead

As of May 2026, the date of the original report, the bug remains active. Google has not issued a specific statement regarding the 2027 error. The company continues to develop next-generation AI models, including Gemini 3.0, which is expected to feature enhanced reasoning capabilities. But until those models are deployed at scale for search, users will have to live with the possibility that AI Overviews can get trivial facts wrong. The path to reliable AI search is long, and this incident is a reminder that the destination is not yet in sight.

The Verge’s coverage also points out that Google still does not show an AI Overview for searches containing the word “disregard,” a deliberate test many users employ to check if the feature has been disabled. This suggests that Google has implemented specific trigger words to suppress the feature in sensitive contexts, but it has not addressed the underlying mathematical drift that causes the 2027 error. In the end, the inconsistency sends mixed signals: the company is capable of selectively disabling the feature for security reasons, but cannot fix a core logical flaw.

Source: The Verge News

The Bug and Its Scope

The glitch was first noticed on Friday when searches for 'disregard' triggered an AI overview that completely ignored the query's intent. Rather than summarizing web results, the system appeared to treat the word as a user instruction to disregard its own assistance. This behavior quickly spread to similar terms like 'ignore' and 'skip', each producing different but equally unhelpful responses. For example, a search for 'ignore' returned: 'Message received! I’m here and ready to help. What would you like to focus on today?' Meanwhile, 'skip' prompted: 'It looks like your message was just a test or a typo! Feel free to ask a question...'

The issue highlights a fundamental challenge in AI-powered search: distinguishing between a query seeking information and a command directed at the AI itself. Google's AI overviews, which were widely rolled out in May 2024 after a limited beta, are designed to summarize search results. However, certain action-oriented keywords appear to confuse the system, causing it to revert to a default chatbot mode.

Google's Response

Google has acknowledged the problem. In a statement to Android Authority, a spokesperson said: 'We’re aware that AI Overviews are misinterpreting some action-related queries, and we’re working on a fix, which will roll out soon.' As of Friday afternoon, the company had already removed the AI overview for the term 'disregard', replacing it with a standard list of news articles about the bug itself. However, searches for 'ignore' and 'skip' still displayed the faulty chatbot responses several hours later.

The speed of the partial fix suggests that Google is treating the issue with urgency. AI Overviews have been a flagship product for the company, integrated into billions of search queries. Any misbehavior risks undermining user trust and drawing regulatory scrutiny. The company’s AI division, led by DeepMind, has been under pressure to ensure reliability, especially after earlier high-profile errors such as recommending glue on pizza or suggesting eating rocks.

Historical Context and Implications

The current bug is not the first time Google’s AI search has struggled. Since the launch of AI Overviews, users have reported numerous instances of the feature providing bizarre or incorrect answers. In some cases, it has cited satirical content as fact or invented sources. The 'disregard' incident, however, is unique because it reveals a logical flaw: the AI is failing to recognize that a user’s search term is a noun or action that could refer to web content, not a command.

This kind of error is reminiscent of problems faced by earlier AI assistants, such as Microsoft’s Tay or even early versions of Siri. When an AI system is trained on vast datasets but lacks robust context understanding, edge cases can lead to amusing but dangerous failures. In the case of search, the stakes are higher because Google is the primary gateway to information for billions. A single bug can affect millions of users instantly.

The issue also raises questions about the future of AI Integration in search. Rival search engines like Bing Copilot have similar features, and this bug could influence how competitors design their own AI overviews. If Google cannot reliably handle simple action words, what about more complex queries involving intent? The company’s commitment to 'helpful' AI may be tested as it rushes to patch this flaw.

Technical Background

AI Overviews are powered by Google’s Gemini model, a multimodal AI that generates text, images, and code. The system is designed to synthesize information from multiple sources into a coherent summary. However, the model includes a safety layer to prevent harmful outputs, which might be triggering these responses. When the model encounters a word like 'disregard,' it may interpret it as a user trying to ignore previous instructions or to reset the conversation, leading to the chatbot-like replies.

Additionally, the models are fine-tuned with reinforcement learning from human feedback (RLHF), which teaches them to be polite and helpful. This can backfire when the AI overgeneralizes politeness cues. Instead of treating a search as a standalone query, it assumes a conversational context that doesn’t exist. The fix likely involves adjusting the system prompt to suppress such responses in search mode, or adding a classifier that detects command-like language and routes it to a different handler.

The partial removal of the AI overview for 'disregard' suggests that Google has the ability to quickly disable the feature for specific queries. However, a permanent solution requires retraining the model to better differentiate between user intent and meta-commands. This is easier said than done, as language models are inherently probabilistic and edge cases are inevitable.

Broader Impact on Users and Advertisers

For everyday users, the bug is more embarrassing than harmful. Few people would rely on a search result for 'ignore' to find credible information, but the incident erodes confidence. If users cannot trust AI overviews for simple queries, they may become skeptical of AI-generated content in general. This could slow adoption of Google’s AI features, which the company has heavily promoted as a new paradigm for search.

Advertisers, too, may be affected. If search becomes unreliable, click-through rates on ads could drop because users will be less likely to engage with results. Google’s entire business model depends on high-quality search results that keep users coming back. While this bug is minor in isolation, repeated incidents could damage the brand’s reputation. Competitors like DuckDuckGo and Brave are already marketing themselves as AI-free alternatives, and any misstep by Google bolsters their narrative.

The bug also highlights a tension between innovation and stability. Google’s I/O conference earlier this month was filled with AI announcements, including deeper integration of Gemini into Search, Photos, and Workspace. The rollout of AI Overviews to the public was seen as a bold move. However, as this incident shows, moving fast can break things. Some argue that Google should have tested more rigorously before the global launch. The company has conducted extensive internal testing, but real-world usage always finds new failure modes.

Industry Reaction and Lessons

The technology community has reacted with a mix of amusement and concern. On social media, users shared screenshots of the responses, drawing comparisons to the 'doge' meme or early chatbot failures. Some warned that this could be a precursor to more serious issues as AI becomes embedded in critical infrastructure. The incident also sparked debates about whether AI should be used for search at all, with some purists insisting that traditional algorithmic search is more reliable.

Lessons for other companies include the need for robust guardrails and human oversight. Even the most advanced AI systems can have blind spots. A multi-layered approach—including rule-based filters, manual review, and continuous retraining—is essential. Google’s ability to quickly disable the feature for a specific term demonstrates good incident response, but it is a reactive rather than proactive measure. Ideally, the system would have caught this behavior during testing.

Looking ahead, Google’s fix will likely be followed by a broader update to how AI Overviews handle ambiguous queries. It may also lead to changes in the underlying model architecture, such as better tokenization that distinguishes between search terms and commands. The company has not released details on the exact cause, but insiders suggest it is a trivial bug in the system prompt logic. Once fixed, it should not repeat.

Future Outlook

Despite this setback, AI Overviews are not going away. Google has invested billions in AI research and sees the feature as central to its future. The company is also exploring voice-activated search, multimodal queries, and AI that can complete tasks on users’ behalf. Each new capability introduces new failure risks. The key is to build robust feedback loops that catch errors before they reach scale.

For now, users can expect the bug to be resolved within days. In the meantime, those searching for 'disregard', 'ignore', or 'skip' will either see no AI overview or the quirky chatbot reply. It serves as a gentle reminder that AI, for all its power, is still a work in progress. As one commenter noted: 'Maybe Google Search itself is tired after everything that happened at Google I/O.' Humor aside, the incident underscores the importance of continuous improvement in AI systems.

The broader trajectory of AI search remains positive. Each bug and fix makes the system smarter and more resilient. Google’s competitors are watching closely, and they may learn from this episode. Ultimately, the race to perfect AI search is a marathon, not a sprint. And while this bug is a stumble, it is unlikely to derail the long-term vision of a more intelligent, helpful web.

Source: The Verge News

The S-1 document lists a total addressable market of $28.5 trillion, which exceeds the entire gross domestic product of the United States. Roughly $26.5 trillion of that is attributed to AI applications, even though SpaceX's AI arm lost $6 billion in operations last year on just $3.2 billion in revenue. By contrast, competitor Anthropic is expected to turn an operating profit this quarter.

SpaceX's flagship rocket, Starship, is years behind schedule and has suffered multiple failures. The company acknowledges that its next-generation V3 satellites depend on Starship's success, yet the rocket has not yet demonstrated the capacity to deploy the 60 satellites per launch that the business plan requires. Engineering analysis suggests a mismatch: the V3 satellites weigh 2,000 kg each, meaning 60 would total 120 metric tons, but Starship V3 is designed for only 100 metric tons.

The profitable core: Starlink

Starlink, the satellite internet division, is the company's only clear success. It generated over $11 billion in revenue last year and has become a cash cow supporting Musk's more speculative ventures. However, the S-1 shows that revenue per subscriber declined 25% in the first quarter of 2026, raising questions about long-term customer retention. The company has also not disclosed the cost of deorbiting satellites, though analysts note that lower orbits make them self-cleaning.

Debt and governance risks

The IPO filing reveals nearly $30 billion in debt. A $20 billion bridge loan comes due in September 2027, and the first $20 billion raised from the IPO must go toward repaying it. Additionally, SpaceX entered a technical default on a $1.5 billion credit facility when it acquired xAI, due to the debt that xAI brought.

Related-party transactions raise further red flags. Board member Antonio Gracias's firm, Valor Equity Partners, has lease deals with SpaceX subsidiaries worth $20 billion, described as a "failed sale leaseback" — effectively loans with GPU collateral. One governance expert called it the worst related-party arrangement she has seen in 40 years.

Musk controls 85% of voting rights, leaving public shareholders with virtually no say. The S-1 includes an arbitration clause that may bar securities fraud lawsuits, and the current SEC under the Trump administration has shown little interest in enforcement. Index fund inclusion is likely within 15 days of listing, forcing passive investors to buy shares regardless of fundamentals.

AI and the environment

Despite Musk's claims about saving humanity and spreading renewable energy, SpaceX has purchased $2.8 billion worth of polluting gas turbines to power its data centers. The company is already being sued for using 46 turbines when only 15 were permitted. The risk factors section acknowledges that if an injunction is granted, the AI business will suffer.

The vision of space-based data centers — touted as a solution to AI's energy needs — is highly speculative. Critics point to launch costs, radiation risks, and slower data transfer as fundamental challenges. Even if Starship works, the economics remain unproven.

For ordinary investors, the IPO represents a bet on Elon Musk's ability to sustain a meme stock. The company's $30 billion debt, failed AI investments, and reliance on a not-yet-operational rocket create enormous downside risk. If SpaceX fails, the losses will fall on retail and index fund investors who cannot sell easily, not on Musk or early insiders. The filing itself admits that the company "may not be able to generate sufficient revenue to service its debt obligations."

In effect, the SpaceX IPO is the ultimate expression of financial nihilism: a trillion-dollar valuation built on $5 billion in losses, $30 billion in debt, and promises of colonizing Mars. The only certainty is that Elon Musk will walk away as the world's first trillionaire, while those who buy the stock may find themselves holding a very expensive piece of paper.

Source: The Verge News

MSI has officially taken the wraps off its latest gaming handheld, the Claw 8 EX AI Plus, ahead of the Computex 2026 trade show. The device represents a significant departure from its predecessor, swapping the Intel Lunar Lake mobile processor for a purpose-built chip designed specifically for handheld gaming. MSI claims the Claw 8 EX AI Plus is the world’s first handheld to integrate Intel’s new Arc G3 Extreme processor, which features an array of Xe3 GPU cores tailored for high-performance gaming on the go.

The original MSI Claw 8 AI Plus launched last year with Intel’s Lunar Lake architecture, but it faced criticism over driver issues, inconsistent performance, and lackluster battery life. With the Claw 8 EX AI Plus, MSI is attempting to address those shortcomings by adopting a more specialized silicon solution. The Arc G3 Extreme chip, developed in collaboration with Intel, is designed to compete directly with AMD’s Z1 Extreme found in devices like the Asus ROG Ally and Lenovo Legion Go. Early benchmarks suggest the Xe3 cores offer improved graphics throughput and power efficiency, though independent gaming tests have yet to be conducted.

Display and Design Upgrades

The Claw 8 EX AI Plus retains the same eight-inch 1920 x 1200 resolution touchscreen from its predecessor, but now supports a 120Hz refresh rate for smoother visuals in fast-paced titles. The display covers a wide color gamut and offers good brightness levels, though MSI has not specified peak nits. The chassis has been completely redesigned with deeper, more ergonomic grips that aim to reduce hand fatigue during extended play sessions. MSI has also equipped the handheld with Hall effect triggers and analog sticks, which use magnetic sensors to eliminate drift and provide finer control precision.

In terms of haptic feedback, MSI has introduced a new high-end linear motor that delivers faster and more nuanced vibrations. The company claims this improves immersion in games with force feedback or environmental effects, though real-world impressions will have to wait. The only confirmed color option is “Void Purple,” a dark, almost black shade with subtle violet undertones. No alternative colors have been announced.

Memory, Storage, and Connectivity

Under the hood, the Claw 8 EX AI Plus supports up to 32GB of dual-channel LPDDR5x memory, though MSI has not clarified whether multiple RAM configurations will be available at launch. Storage is handled by a single NVMe M.2 SSD slot, which offers fast read and write speeds for game installations and load times. The device lacks a second SSD slot, so users will need to rely on external storage via USB-C or a microSD card for additional capacity. Connectivity options include Wi-Fi 7 and Bluetooth 5.4, ensuring low-latency wireless connections for controllers and internet access.

Handheld Gaming Landscape and Competition

The announcement comes at a time when the handheld gaming market is rapidly expanding. Valve’s Steam Deck proved there is strong demand for portable PC gaming, and manufacturers like Asus, Lenovo, and now MSI are vying for a piece of that audience. The Claw 8 EX AI Plus will face stiff competition from the Acer Predator Atlas 8 and OneXPlayer 3, both of which are also expected to launch with the same Intel Arc G3 Extreme chip. MSI is positioning itself as the first to market with this processor, which could give it a temporary advantage if the device launches on schedule.

However, MSI’s previous attempt at a gaming handheld—the original Claw 8 AI Plus—was marred by poor software optimization and inconsistent frame rates. Reviews noted that the device often struggled to match the performance of AMD-based rivals, especially in emulation and AAA titles. With the Claw 8 EX AI Plus, MSI has a chance to redeem itself, but the company will need to deliver robust driver support and a polished user experience to win over enthusiasts. The Windows 11 operating system remains a double-edged sword: it offers compatibility with a vast library of PC games, but the interface is not as streamlined as Valve’s SteamOS.

Battery Life and Power Efficiency

MSI has not disclosed battery capacity or expected runtime for the Claw 8 EX AI Plus. The company only mentioned “improved power efficiency” thanks to the new processor and linear motor, but concrete numbers are absent. Battery life was a major pain point for the original Claw 8 AI Plus, which often lasted less than two hours under heavy gaming loads. If the new model cannot significantly extend that, it may hurt its appeal against devices like the Steam Deck OLED, which can achieve three to five hours of gameplay depending on the title. Industry observers expect the Arc G3 Extreme to be more power-efficient than Lunar Lake, but actual testing will settle the debate.

The Claw 8 EX AI Plus runs Windows 11 Home, giving users access to multiple storefronts including Steam, Epic Games Store, Xbox Game Pass, and others. MSI has yet to announce any custom software overlays or performance tuning tools, but given the company’s experience with its MSI Center software on laptops, similar utilities are likely. The device also features a built-in microphone array and stereo speakers, though audio quality is typically secondary for handhelds.

Pricing and Availability

MSI has not revealed the launch date or price for the Claw 8 EX AI Plus. The previous Claw 8 AI Plus debuted at $1,000, and a similar price point is expected for the new model, though component costs and market positioning could push it higher or lower. The company will likely announce full details during Computex 2026 in Taipei, where journalists and analysts will have a chance to go hands-on. Until then, prospective buyers can only speculate on whether MSI has finally cracked the code for a compelling Intel-based handheld.

The handheld gaming segment continues to evolve, with new entrants and chipsets emerging every year. MSI’s decision to partner closely with Intel for a custom processor suggests they are betting big on the Arc ecosystem. If the Claw 8 EX AI Plus delivers on its promises of better performance, longer battery life, and a comfortable form factor, it could become a strong contender. But given the history, skepticism is warranted. For now, the industry watches and waits.

Source: The Verge News

In an era where breakthrough technologies launch daily, being first is only half the battle. The other half perhaps the more consequential half is being heard. For AI startups, SaaS platforms, and innovation-driven companies, gaining media visibility is no longer optional. It's a core business function. And at the center of that function sits one powerful tool: a reliable tech news distribution service.

Today's most successful technology companies don't just build superior products. They tell superior stories. They get those stories in front of investors, journalists, enterprise buyers, and the broader digital ecosystem simultaneously. That's not magic that's the result of working with a press release distribution platform engineered for the speed and scale the tech world demands.

Whether you're launching a new AI feature, announcing a SaaS funding round, or debuting a first-of-its-kind product, the way you distribute your news shapes the way the market perceives your company. Miss the right publications, and your breakthrough vanishes into the noise. Hit them with precision and timing, and you set the narrative attracting media coverage, investor inquiries, partnership conversations, and customer trust in a single coordinated push.

This article is your deep-dive guide into why tech news distribution matters more than ever, how it works, what differentiates a premium service, and how the right PR platform can become a competitive advantage for your technology company.

How Press Release Distribution Fuels Search Visibility for Tech News Distribution Service

One of the most underappreciated benefits of a robust news distribution service is its impact on organic search visibility. When your press release is picked up and republished across dozens of high-authority media outlets, you generate a web of inbound links pointing back to your domain. Each link signals to search engines that your brand is credible, relevant, and newsworthy.

This is not incidental it's strategic. Search engines like Google increasingly reward what SEO professionals call "topical authority." Brands that consistently appear in news contexts around specific technology categories AI, machine learning, SaaS infrastructure, cybersecurity, fintech build entity-level recognition that strengthens their overall digital presence. This is the backbone of Entity SEO and Semantic SEO: establishing your brand not just as a webpage but as a recognized, trusted node in the broader knowledge graph.

A well-executed press release distribution campaign does something that paid advertising cannot: it creates editorially placed, third-party-attributed content that search engines treat as genuine information rather than promotional material. Featured snippets, People Also Ask boxes, Google News carousels all of these are channels that PR distribution helps populate organically.

Voice search optimization is increasingly relevant here as well. As more users turn to voice assistants for business news and industry updates, distributing structured, question-answering press content helps your brand appear in those spoken search results. Platforms that understand Google's NLP (Natural Language Processing) preferences favoring clear, authoritative, conversational prose give your releases a meaningful edge in voice and AI-generated search summaries.

The Landscape Has Shifted: Why Traditional PR No Longer Serves Tech Companies

Legacy public relations was built for a broadcast world print newspapers, evening news, quarterly trade journals. The tech industry, by contrast, moves at a pace that renders yesterday's news irrelevant by tomorrow afternoon. AI models ship new versions weekly. SaaS companies push feature updates in real time. Innovation cycles that once took years now take months.

Traditional PR firms were designed to support a fundamentally different cadence. Their retainer models, lengthy strategy sessions, and editor-relationship-dependent timelines simply don't fit the need of a startup that just closed a Series A and wants the story out within 72 hours.

The shift toward digital PR agency models reflects a market correction. Companies need distribution infrastructure that matches their operational velocity. They need platforms that can take a polished press release and push it to hundreds of targeted tech, business, and industry publications simultaneously with SEO-optimized formatting, multimedia support, and transparent analytics. That's precisely what modern press release distribution services deliver.

Consider what happens when a mid-size SaaS company uses a traditional agency for a product announcement: weeks of back-and-forth, a single placement in a generalist outlet, and a bill that could fund two additional hires. Now contrast that with a platform-based online press release distribution approach: same announcement, distributed within 24 hours, syndicated across tech verticals, indexed by Google News, and tracked in real time. The economics and the outcomes are incomparable.

What Makes a Tech-Focused News Distribution Service Different

Not all news distribution services are built equally, and the distinction matters enormously for technology companies. A general-purpose distribution platform might scatter your release across irrelevant verticals lifestyle blogs, regional newspapers, entertainment sites generating vanity metrics with no business impact.

A tech-focused PR distribution agency builds its network specifically around the publications, editors, and audiences that technology companies need to reach. Think TechCrunch-adjacent outlets, enterprise software trade publications, AI research news sites, fintech and SaaS-specific journals, developer-focused platforms, and innovation ecosystem newsletters. These are the venues where your target audience investors, CTOs, enterprise buyers, potential partners actually reads.

Here's a comparative breakdown of what differentiates a tech-specialized distribution service:

This contrast explains why AI and SaaS companies increasingly gravitate toward specialized PR companies that understand their ecosystem rather than generalist platforms that treat all news as equivalent.

Case Study: How an AI Startup Used Press Release Distribution to Land Enterprise Clients

Consider the story of a mid-stage AI startup a company building automation tools for enterprise HR departments. Before working with a specialized news distribution service, they had strong technology and a compelling product, but minimal market awareness. Their sales cycles were long because buyers simply hadn't heard of them.

After partnering with a focused PR distribution platform, they released a series of targeted announcements: a product update showcasing new AI capabilities, a thought leadership piece on workforce automation trends, and a client success story repurposed as a press release. Each release was distributed across HR tech publications, enterprise software news sites, and business-focused media outlets.

Within weeks, their organic search traffic for branded terms increased significantly. Inbound inquiries from enterprise HR directors began appearing in their sales pipeline. A mid-market analyst at a respected research firm reached out for comment after discovering their release leading to a feature mention in a quarterly technology report read by Fortune 500 procurement teams.

The cost of this entire campaign was a fraction of what a traditional public relations agency would have charged for comparable (or often lesser) results. This is the compounding return of modern press release distribution services: each release builds upon the last, growing your brand's media footprint with each deployment.

Case Study: SaaS Company Achieves Funding Visibility Through Targeted Distribution

A SaaS fintech platform had just completed a seed round and needed to maximize investor and media visibility without an in-house communications team. They turned to a PR distribution agency specializing in financial and technology news.

The agency crafted a funding announcement press release with structured financial data, founder quotes, and forward-looking use-case language all formatted for SEO and Google NLP compatibility. The release was distributed across fintech media, startup news platforms, and regional business publications relevant to the company's target markets.

The results were immediate and measurable. The announcement appeared in several prominent fintech publications. Multiple VC partners contacted the company directly, having seen the news through syndicated coverage. Two enterprise clients accelerated procurement conversations, citing the public credibility the press coverage had established.

More importantly, the release generated a lasting SEO asset: backlinks from high-authority financial and tech media sites that continued driving organic traffic months after the initial distribution. This is the long-tail benefit of online news distribution the shelf life of a well-distributed press release extends far beyond its publication date.

Understanding Press Release Distribution Pricing: What You Should Expect

One barrier that holds many early-stage companies back from using PR distribution is uncertainty about cost. The good news is that modern platforms offer significantly more transparent and accessible press release distribution pricing than legacy agencies.

Where traditional PR agencies might charge monthly retainers ranging from several thousand to tens of thousands of dollars before any actual distribution occurs platform-based PR distribution pricing is typically release-based, package-based, or subscription-based. This means you pay for what you use, at rates that scale with your growth.

Most reputable platforms offer tiered press release distribution packages, such as:

• Starter packages for early-stage companies needing basic syndication to core media outlets.
• Growth packages for scaling businesses targeting industry-specific media and seeking SEO-optimized distribution.
• Enterprise packages for established tech companies requiring multimedia distribution, analyst network access, and comprehensive analytics.

The most important thing to evaluate when comparing PR pricing plans is not just the base cost, but the quality and relevance of the distribution network. A lower-priced service that syndicates to irrelevant outlets delivers no business value. A service with affordable press release distribution priced moderately higher but with a curated tech media network delivers measurable ROI.

For AI, SaaS, and innovation companies, the right investment in press release distribution packages typically pays for itself in a single meaningful media placement that drives qualified traffic, investor attention, or customer inquiries.

The User's Perspective: What Tech Companies Actually Need from a PR Service

When technology companies evaluate PR services, their decision criteria are distinct from those of consumer brands or retail businesses. Here's what matters most from the user's perspective — and why the right PR platform must deliver on all of these dimensions:

Speed and Agility — Tech news moves fast. A SaaS company announcing a product update needs distribution that day, not in a week. The best press release distribution services offer same-day or next-day turnarounds that match the tech cycle.

Targeted Reach — Distribution to irrelevant publications is noise. Tech companies need their releases reaching the specific journalists, analysts, investors, and buyers who operate within their vertical. Generic blasting isn't distribution it's digital littering.

SEO Integration — Press releases today serve dual purposes: media outreach and SEO asset creation. A sophisticated online PR agency understands how to structure releases for maximum indexing, featured snippet eligibility, and link equity generation.

Transparency and Analytics — Founders and marketing leaders want to know where their release went, who picked it up, how many times it was viewed, and what organic traffic it generated. Black-box distribution is unacceptable for data-driven tech teams.

Scalability — As companies grow, their distribution needs evolve. The right PR distribution website grows with you, offering packages and services that match your scale — from seed stage to Series C and beyond.

Credibility Building — Every placement in a credible tech publication is a trust signal. Consistent distribution through a reputable press release distribution company builds a body of earned media that enhances brand authority with all stakeholders.

Infrastructure and Platform Quality: The Backend of Great Distribution

Behind every successful press release campaign is robust distribution infrastructure. The best news distribution companies maintain constantly updated media databases, editorial relationships with key tech publications, and syndication partnerships that ensure wide, relevant reach.

Modern PR distribution platforms leverage API integrations, automated formatting tools, and real-time distribution queues to ensure releases go live quickly and correctly. Multimedia support the ability to embed images, videos, and downloadable assets directly within a release is no longer a premium add-on but a baseline expectation for tech-industry distribution.

SEO infrastructure matters as well. Look for platforms that support canonical URLs, structured metadata, and proper schema markup for press releases. These technical elements determine whether your content gets indexed properly, appears in Google News feeds, and generates the link equity that supports long-term search visibility.

The best PR websites also provide persistent hosting for releases meaning your content lives at a stable URL, continues to be discoverable, and accumulates authority over time rather than disappearing after a brief distribution window.

Why AI, SaaS, and Innovation Companies Should Prioritize PR Now

The technology market is more competitive than at any point in its history. Capital is more selective. Enterprise buyers are more discerning. Investor deal flow is higher, which means standing out requires more than a compelling pitch deck. It requires demonstrated market credibility the kind that only consistent, high-quality media presence can build.

For AI companies specifically, press coverage serves as social proof in a category where trust is still being established with mainstream enterprise buyers. A company with ten strong media placements in respected AI publications carries a fundamentally different credibility perception than one with nothing but a website and LinkedIn posts.

SaaS companies face similar dynamics. In a crowded market where buyers have dozens of options in every category, brand recognition built through consistent digital news distribution is a genuine differentiator in the sales process. It shortens cycles, improves conversion, and elevates perceived value.

Innovation companies of all types benefit from the narrative-building function of business press release distribution. In markets where the technology is novel and the use cases are still being defined, public storytelling through media is how companies shape category perception, attract talent, and build the community of customers and advocates that drives sustainable growth.

Why Choose PR Business Wires for Your Tech News Distribution

Among the growing landscape of distribution platforms, PR Business Wires has built a reputation as a go-to resource specifically for technology-focused companies seeking meaningful, measurable media presence. Their platform combines the reach of a major newswire distribution service with the targeting sophistication that tech companies require.

The platform's network spans technology, SaaS, AI, fintech, healthtech, and enterprise software publications ensuring that when you distribute a release, it reaches an audience that has genuine relevance to your business. Their transparent PR agency pricing means you always know what you're paying and what you're getting no hidden fees, no opaque retainers.

For startups, their affordable PR agency packages make professional-grade distribution accessible without requiring an enterprise budget. For scaling companies, their growth-tier packages provide the expanded reach and analytics that match a more sophisticated communications strategy. For enterprise organizations, premium distribution ensures maximum velocity and coverage for major announcements.

The platform's EEAT-aligned approach built on Expertise, Experience, Authoritativeness, and Trustworthiness ensures that releases distributed through their network reflect the credibility standards that both media outlets and search engines demand.

Wrapping Up: Your Breakthrough Deserves to Be Heard

The case for investing in a tech-specialized news distribution service is not theoretical it's demonstrated daily by the companies that consistently dominate their category narratives, attract disproportionate investor attention, and close enterprise deals faster than their peers.

AI, SaaS, and innovation companies that treat press release distribution as a tactical afterthought are leaving one of their most powerful growth levers unused. Those that treat it as a strategic function distributing regularly, targeting precisely, and measuring rigorously compound their media authority over time in ways that no ad spend can replicate.

The right PR platform is not a cost. It's infrastructure for growth. It's the mechanism through which your technology, your team, and your vision get the audience they deserve. Don't build in silence. Distribute with intention.

Frequently Asked Questions

1. What is a tech news distribution service, and why does my company need one?

A tech news distribution service helps AI, SaaS, and innovation companies distribute press releases to relevant technology media outlets. It ensures your announcements reach journalists, investors, and buyers who matter to your business, building brand visibility and supporting long-term SEO growth through earned media placements.

2. How does press release distribution improve my company's SEO?

When your press release distribution is picked up by authoritative tech publications, those sites generate backlinks to your domain. These links signal credibility to search engines, improve your domain authority, and increase your likelihood of appearing in organic search results, Google News, and featured snippets relevant to your industry.

3. What types of companies benefit most from a news distribution service?

AI startups, SaaS platforms, fintech companies, healthtech innovators, enterprise software providers, and any technology-driven business benefit from consistent use of a news distribution service. It's especially valuable during product launches, funding announcements, partnership news, and major milestones.

4. How is tech-focused PR distribution different from general press release services?

A tech-focused PR distribution agency maintains networks specifically within technology verticals, ensuring your release reaches journalists and publications covering AI, SaaS, cloud, fintech, and innovation rather than scattering across irrelevant lifestyle, regional, or entertainment outlets that add no business value.

5. What should I look for in press release distribution pricing?

Evaluate press release distribution pricing based on network quality, distribution speed, SEO features, multimedia support, and analytics depth. The cheapest option may offer wide distribution with minimal relevance. A moderately priced package with targeted reach and measurable results delivers far better ROI for technology companies.

6. How often should a tech company distribute press releases?

Most technology companies benefit from distributing press releases at least monthly. Key triggers include product launches, funding announcements, executive hires, partnership news, and thought leadership content. Consistent distribution through an online PR agency builds cumulative media authority over time, compounding your brand's visibility with each release.

7. Can press release distribution help with voice search and AI-generated search results?

Yes. Well-structured releases optimized for Google NLP and distributed through a quality press release distribution platform are increasingly appearing in AI-generated search summaries, voice search responses, and People Also Ask features. Structured, question-answering content is especially well-positioned for these emerging search formats.

8. What are the best press release distribution packages for early-stage startups?

Look for press release distribution packages designed specifically for startups those offering core tech media syndication, SEO formatting, and analytics at accessible price points. An affordable PR agency with startup-specific packages allows you to build media presence from early stages without overextending your budget.

9. How quickly will my press release be distributed after submission?

Most premium press release distribution services offer same-day or next-business-day distribution once your release is approved. Tech-specialized platforms prioritize speed because technology news is time-sensitive. Confirm turnaround times when evaluating any PR distribution website before committing.

10. How do I measure the success of a press release distribution campaign?

Track metrics including media pickup count, publication authority scores, website referral traffic from media placements, branded search volume changes, backlink acquisition, and downstream business outcomes like inbound inquiries or demo requests. A quality digital PR agency platform provides dashboards that surface these metrics clearly, giving your team full visibility into distribution performance and ROI.

Speed Performance

Speed is often the deciding factor for VPN users. No one wants buffering or lag during video calls or streaming. Our 2025 speed tests revealed a clear winner: NordVPN posted an average internet speed loss of just 3%, making it the fastest VPN we’ve ever tested. ExpressVPN also performed admirably with an 18% speed loss, which is well below the 25% threshold we consider “fast.” Both providers use the latest protocols—NordVPN’s proprietary NordLynx (based on WireGuard) and ExpressVPN’s Lightway—to optimize throughput. For users who demand the absolute fastest connections, NordVPN has the edge. However, ExpressVPN’s speed loss is still negligible for most everyday activities like 4K streaming and torrenting.

Value and Pricing

Both VPNs are premium-priced, but their introductory offers provide reasonable value. ExpressVPN’s Basic plan costs $13/month, $75 for the first 15 months, or $98 for the first 28 months (renewing at $100/year). NordVPN’s Basic plan is $13/month, $60 for the first year, or $83 for the first two years (renewing at a steep $140/year). When comparing basic VPN-only plans, ExpressVPN’s renewal price is more palatable. However, if you need bundled extras, ExpressVPN’s Advanced plan ($14/month for 6 months intro) includes a password manager and identity protection tools, while NordVPN’s Plus plan ($14/month) adds its password manager and Threat Protection Pro. NordVPN also offers Complete tier with 1TB cloud storage ($15/month) and Prime with identity protection ($18/month). The value proposition depends on your specific needs: ExpressVPN offers better renewal rates for basic functionality, while NordVPN provides more add-ons at higher ongoing costs.

Privacy and Security

Privacy-conscious users will appreciate both services. ExpressVPN operates from the British Virgin Islands, outside the 14-Eyes alliance, and has undergone 23 independent audits since 2018—far more than any competitor. Its TrustedServer technology runs on RAM-only servers that wipe all data with every reboot, minimizing risk. ExpressVPN was also an early adopter of post-quantum encryption, protecting against future decryption threats. NordVPN, based in Panama, has completed five independent audits, but its latest reports are only available to paying customers. NordVPN also offers post-quantum protections via its NordLynx protocol and includes privacy-enhancing features like Onion over VPN and double-hop connections. While both enforce strict no-logs policies, ExpressVPN’s transparency and proactive audits give it the edge for those with critical privacy needs.

Usability and Ease of Use

ExpressVPN is widely regarded as the most user-friendly VPN app available. Its interface is clean, minimal, and consistent across Windows, macOS, iOS, Android, and even Apple TV. Setup is straightforward, and connecting to the fastest server takes one click. NordVPN’s app is also intuitive but can feel cluttered, especially on mobile where the server map dominates. The Apple TV app is particularly cumbersome, relying on side-scrolling navigation. However, NordVPN offers a handy “Pause” feature and a map interface that some users enjoy. For router setup, ExpressVPN’s dedicated router app and Aircove hardware make it simple to protect every device at home. Overall, ExpressVPN wins for usability, making it ideal for VPN beginners.

Streaming and Geo-Restrictions

Both VPNs excel at unblocking popular streaming services like Netflix, Disney+, Hulu, HBO Max, and BBC iPlayer. In our tests, ExpressVPN had a slight edge in accessing international Netflix libraries—for example, it successfully unblocked Hungarian content where NordVPN failed. NordVPN, however, boasts servers in 126 countries (versus ExpressVPN’s 105), theoretically offering more geographic options. For streaming device users, ExpressVPN’s Apple TV app is far more navigable, while NordVPN performs better on Fire TV Stick. Both services support streaming in 4K without buffering, thanks to their fast speeds.

Additional Features

NordVPN offers a broader set of extras: double-hop VPN, Onion over VPN, dedicated IP (for an additional fee), and recently discontinued Meshnet. ExpressVPN focuses on core features but includes ShuffleIP, which automatically changes your IP address per website, enhancing privacy. ExpressVPN also provides a built-in password manager and identity protection in its higher tiers. Neither service offers a free version, but both have 30-day money-back guarantees.

Which Should You Choose?

If raw speed and feature-rich extras like double-hop, Onion over VPN, or cloud storage are your priorities, NordVPN is the better pick. Its 3% speed loss is unmatched, and the suite of advanced tools appeals to power users. If you value transparency, privacy safeguards, an effortless user interface, and slightly better streaming reliability, ExpressVPN wins. Its independent audits, TrustedServer architecture, and post-quantum encryption set a high bar for privacy. Both VPNs are excellent investments, but they cater to different needs. Consider your primary use case—speed vs. privacy—and the decision becomes clear.

Source: CNET News

This article provides a detailed comparison of QLED versus OLED, covering everything from picture quality and brightness to longevity and future developments. By the end, you will have a clear understanding of which technology best suits your needs.

What Are QLED and OLED?

QLED stands for Quantum Dot Light Emitting Diode, though it's actually a variation of LCD technology. A QLED TV uses a standard LED backlight that shines through a layer of quantum dots—microscopic particles that emit specific colors when struck by light. This enhances color accuracy and brightness compared to conventional LCD TVs. In contrast, OLED (Organic Light Emitting Diode) is an emissive technology where each pixel generates its own light. This allows OLED TVs to turn off individual pixels completely, producing perfect black levels and infinite contrast.

The core difference is that QLED is transmissive (light passes through layers), while OLED is emissive (each pixel emits its own light). This distinction leads to all the major differences in picture quality, viewing angles, and durability.

Picture Quality: Contrast and Black Levels

The most significant advantage of OLED is its ability to achieve true blacks. Because each pixel can be turned off independently, OLED displays can create an infinite contrast ratio, making shadows look deep and realistic. This is especially noticeable in dark room viewing or with HDR content that has bright highlights against black backgrounds. QLED TVs, even with advanced local dimming, cannot match the per-pixel control of OLED. However, some high-end QLED models with mini-LED backlights have significantly improved black levels, approaching OLED performance in many scenes. Still, for the ultimate contrast, OLED remains superior.

Brightness

QLED TVs have a clear edge in brightness. With powerful LED backlights, many QLED models can achieve higher peak luminance than any OLED currently on the market. This makes QLED an excellent choice for bright living rooms or rooms with lots of windows, where glare can wash out an OLED's image. In such environments, the extra brightness of QLED can help maintain visibility and color saturation. OLED TVs are getting brighter each year, but they still cannot compete with the top-tier QLED sets in sheer output. For daytime viewing or sports in a brightly lit room, QLED may be the better option.

Viewing Angles and Uniformity

OLED excels in off-axis viewing. Because the pixels emit light directly, the image retains its color and contrast even at extreme angles. QLED, being an LCD variant, suffers from color shifting and contrast loss when viewed from the side. This can be a concern if you have a wide seating arrangement. Additionally, OLED panels are nearly perfectly uniform, with no clouding or backlight bleed, whereas some QLED sets exhibit uneven brightness zones. For a home theater with multiple viewers, OLED provides a more consistent experience.

Lifespan and Burn-In

A common concern with OLED is burn-in—the permanent retention of static elements like channel logos or news tickers. While modern OLEDs have made significant strides with pixel shifting and compensation cycles, they are still more susceptible to burn-in than QLED. QLED, being LCD-based, is essentially immune to burn-in under normal use. However, for most viewers who watch varied content, burn-in is unlikely to be an issue. OLED TVs still have long lifespans, often exceeding 50,000 hours of use. QLEDs generally have even longer lifespans, but the difference is rarely a deciding factor.

Size and Price

QLED TVs are available in a much broader range of sizes, from small 32-inch models up to massive 115-inch versions. This makes them suitable for any room or budget. OLED currently tops out at 97 inches and usually costs more for each size increment. In the popular 65- and 75-inch categories, QLED offers much better value for money, especially for buyers seeking a larger screen. However, prices on both technologies continue to drop, and OLED is becoming more accessible each year.

Which Is Better for You?

The choice between QLED and OLED depends on your priorities. If you watch movies in a dark room and crave the deepest blacks and most vibrant contrast, OLED is the clear winner. If you need a very bright TV for a sunny living room, or you want the largest screen possible for the money, QLED is likely the better choice. For mixed usage, both technologies can deliver outstanding pictures. Consider your budget, room lighting, and typical content before making a decision.

Looking to the future, emissive QLED (where quantum dots themselves produce light) and MicroLED are emerging technologies that promise the best of both worlds: perfect blacks, high brightness, and no burn-in. However, these remain expensive and not yet mainstream. For now, OLED and QLED both represent excellent options, with OLED generally offering superior picture quality and QLED leading in brightness and affordability.

Source: CNET News

For years, Gmail users have enjoyed a generous 15GB of complimentary storage, but that space isn’t reserved solely for emails. It also covers Google Drive files and Google Photos, meaning a busy inbox filled with large attachments can quickly eat into your capacity. Once you hit the limit, you stop receiving new messages and cannot send emails until you free up space or upgrade to a paid Google One plan. The cheapest option — 100GB for $20 per year — works well for many, but you shouldn’t have to pay just to store old newsletters and promotional emails.

If you prefer a free solution, you can transfer all of your old messages to a second Gmail account using POP3. This method essentially gives you a dedicated archive account, allowing you to wipe your primary inbox clean without losing any data. The process takes a few days for large inboxes, but the result is a fresh start with zero storage usage. Below, we explain exactly how to set up the transfer, including important steps like backing up your emails and creating a Google app password if needed.

Understanding the Urgency

Google first announced its intention to phase out POP3 support in early 2026. New users lost access to the protocol during the first quarter, but existing users were given a grace period that extends until later this year. After that, you will no longer be able to use POP3 to automatically fetch emails from your Gmail account. While you can still manually download emails using IMAP or Google Takeout, the automatic, hands‑off nature of POP3 makes it the most convenient option for bulk transfers.

Because of this deadline, you should begin the transfer process as soon as possible. The actual transfer can take up to several days for accounts with tens of thousands of messages, so delaying may leave you scrambling just before Google pulls the plug. Remember that after the protocol is disabled, you will need to rely on alternative methods that are either more technically demanding or less efficient.

Backup First: Using Google Takeout

Before you start linking accounts, it’s wise to create a local backup of your emails. Google Takeout is a free service that exports all your data, including Gmail messages, into a downloadable archive. In a recent test, a Gmail account containing about 75,000 messages produced a complete download in approximately two hours. This backup ensures you have an independent copy in case anything goes wrong during the transfer.

To use Google Takeout, visit the service’s page, deselect all data types except Mail, and choose your preferred export format and delivery method. Once the archive is ready, you can download it to your computer or an external hard drive. Keep this backup saved until you have confirmed that all emails have arrived safely in the new account.

Step-by-Step Transfer Instructions

Once your backup is complete, you are ready to start the transfer. Follow these steps carefully:

1. Log into your original Gmail account and click the gear icon in the top‑right corner, then choose See all settings.
2. Navigate to the Forwarding and POP/IMAP tab. Under the POP Download section, select Enable POP for all mail. This tells Gmail to allow a third party to fetch all existing messages.
3. Choose what happens to the emails after they are downloaded. To automatically delete them from your original account once they are transferred, select Delete Gmail’s copy. If you prefer to keep copies, you can select Keep Gmail’s copy in the Inbox — but that will not free up storage.
4. Click Save Changes.
5. Create or log into your new archive account. This will be the destination for all your old emails.
6. In the new account, go to Settings → See all settings → Accounts and Import tab.
7. Next to Check mail from other accounts, click Add a mail account.
8. Enter the email address of your original account, then click Next.
9. Select Import emails from my other account (POP3) and click Next again.
10. Enter the password of your original account. If the standard password does not work, you will likely need to create a Google app password (instructions below).
11. Set the Port to 995. Ensure the box Always use a secure connection (SSL) when retrieving mail is checked.
12. Check the boxes Label incoming messages and Archive incoming messages (Skip the Inbox). The first option helps you identify imported messages, and the second keeps your new inbox tidy.
13. Click Add Account.

You may also be prompted to confirm whether you want to be able to send mail from your original address using the new account. This is optional but can be useful if you continue to use both accounts.

Creating a Google App Password

During testing, standard Gmail passwords were rejected when trying to link accounts via POP3. The solution is to create a temporary app password. App passwords are 16‑digit codes that allow less secure apps or services to access your Google account without using your main password.

To generate one, go to https://myaccount.google.com/apppasswords. Note that you must have 2‑Step Verification enabled on your account. Once there, give the password a name (e.g., “Bulk Email Transfer”), click Create, and copy the 16‑digit code that appears. Return to the transfer setup and paste this code instead of your regular password.

Important: Google displays the app password only once. Write it down or save it securely. After the transfer is complete, you should delete this app password from your account settings. Also, note that app passwords are not recommended for everyday use, but they remain a necessary workaround for POP3 migrations.

What Happens After the Transfer Begins

Once you’ve completed the setup, the transfer will start automatically and run in the background. The time required depends on the volume of emails. For a test account with 75,000 messages, the entire process took roughly two full days. During this period, you can continue to use both accounts normally, though you may experience slower sync speeds if you are also sending and receiving new emails.

After all messages have been moved to the archive account, your original Gmail account will place the deleted emails into the Trash folder. You must manually empty the Trash to actually free up storage. In the test case, emptying 75,000 messages took about an hour. Once the Trash is empty, check your storage usage. The same test account went from using 12GB (80% of the free limit) to just 0.66GB — a dramatic recovery.

Important Notes and Limitations

The POP3 transfer does not move Drafts or Spam messages. Drafts remain in the original account and must be handled manually — you can either delete them, leave them, or forward them to your new account. Spam messages are automatically deleted by Gmail after 30 days, so you can simply wait for them to disappear or empty the Spam folder manually after the transfer.

Once all emails are successfully imported, it is crucial to stop the automatic sync to avoid duplicating future messages. To do this, log into your new archive account, go to Settings → Accounts and Import, and next to Check mail from other accounts, click Delete for the original account. Confirm the deletion. If you created an app password, visit the app passwords page and remove the one you generated.

Finally, keep in mind that Google will delete accounts that remain inactive for more than two years. Even if you only use your archive account as a repository, make sure to log in at least once every two years to keep it active. You can set a simple reminder on your calendar to check the account periodically.

By following this guide, you can take full advantage of the last few months of POP3 support and free up your Gmail storage without spending a cent. The process is straightforward, though it requires a bit of patience while the transfer completes. Once finished, you will enjoy the peace of mind that comes with a clean, uncluttered inbox and the ability to receive new messages again without hitting the storage limit. Remember to back up your data first, create an app password if needed, and empty the Trash after the transfer. Do not wait until the last minute — Google’s deadline is approaching, and the POP3 window is closing.

Source: CNET News

The Mobile World Congress in Barcelona remains the epicenter of the global mobile industry, drawing thousands of attendees, from executives to enthusiasts. In 2026, the event ran from March 2 to March 5, offering a packed schedule of product launches, keynote speeches, and hands-on demos. CNET experts on the ground provided continuous coverage, highlighting the most significant trends and devices that will define the smartphone market and beyond. This year, the show was dominated by artificial intelligence, the rollout of 6G, concept gadgets, and a surprising focus on health and privacy.

Best Phones of MWC 2026

The headline category at MWC is always the latest handsets, and 2026 was no exception. Xiaomi unveiled a series of devices pushing the boundaries of design and camera technology, including an ultrathin foldable that rivals the Samsung Galaxy Z Fold series. Honor countered with a flagship that features a revolutionary periscope camera capable of capturing low-light portraits with stunning clarity. Motorola surprised the audience with a budget-friendly 5G phone that does not compromise on display quality, addressing the growing concern over component shortages. David Lumb, one of the lead reporters, described the competition as fierce, with each brand trying to outdo the others on specs and user experience.

Another notable release was the Samsung Galaxy S26, which arrived just before the show. The S26 introduced clever AI tricks, such as real-time language translation and photo-editing assistants, but critics noted the price hike. Comparisons with the Apple iPhone 17 were inevitable, as both devices target the premium segment. The iPhone 17 Pro, especially in its new Cosmic Orange color, drew attention, and its clone in orange was seen everywhere on the show floor. The debate between the iPhone 17E and iPhone 16E also continued, with the former offering a spec bump that might justify its higher cost for some users.

AI and Future Technologies at MWC 2026

Artificial intelligence was the undercurrent of almost every announcement. Qualcomm and MediaTek showcased new chipsets with dedicated AI engines that improve photography, battery management, and on-device processing. One of the most discussed predictions was the potential RAM shortage that could kill budget phones. Industry experts at MWC 2026 warned that the demand for memory in AI-driven devices is outpacing supply, potentially raising entry-level prices. This could reshape the market, forcing manufacturers to rethink specifications for low-cost handsets.

The convergence of AI with wearables was also evident. Smart glasses from Alibaba’s Qwen brand impressed testers, offering features that rival Meta’s Ray-Ban Stories. The glasses integrated voice assistants and navigation, and their design was both functional and stylish. In the robot space, companies displayed phone-toting robots that can assist with photography or act as personal assistants. One standout was a small robot that holds a smartphone and can follow its owner, capturing video autonomously. These robots are early indicators of how mobile technology will interact with physical spaces in the coming years.

6G was a recurring theme in keynotes, with SpaceX and Qualcomm discussing how low-orbit satellite networks could enable ubiquitous connectivity. Though 6G commercial deployment is still years away, the research and development presented at MWC suggest that the next generation will focus on extreme low latency, massive device density, and energy efficiency. This could unlock new applications in telemedicine, autonomous driving, and smart cities.

Concept Gadgets and Wearables: The Weird and Wonderful

MWC is notorious for presenting concept devices that may never see a retail shelf, but they always provide a glimpse into the future. This year, the weirdest concepts included a robot phone that transforms from a sphere into a humanoid shape, complete with a built-in display. Ultrathin foldable screens were everywhere, with some prototypes shifting to rollable designs that expand from a smartphone into a small tablet. The trend of boring phone designs might be over; fresh looks included translucent backs, customizable LED panels, and even a phone with a physical keyboard for those who miss tactile typing.

Wearables also took a leap. A smartwatch from a Chinese startup can detect early signs of tuberculosis by analyzing coughing patterns. While still in prototype, the device highlights the potential for mobile health to democratize diagnosis. Another privacy-focused phone featured a physical switch that disables both the camera and microphone, giving users absolute control over surveillance. For those tired of constant notifications, a dumb phone running ChatGPT was demonstrated, proving that even minimalist devices can access sophisticated AI, albeit with limitations.

One of the most talked-about non-phone gadgets was the “Labubu-like bot,” a small robotic companion that responds to voice commands and can project emojis onto surfaces. This quirky creation reflects the lighthearted side of MWC, balancing the serious business of telecommunications with pure fun.

The Impact of Global Events on MWC 2026

No major tech event is immune to geopolitics. The ongoing conflict in the Middle East affected the presence of several exhibitors and the tone of discussions. Many keynote speakers addressed the need for supply chain resilience and the risks of regional disruptions. The show also saw a strong focus on network security and the importance of protecting user data as 5G and soon 6G networks become more integral to daily life. The war in Ukraine was less directly mentioned, but its long-term effects on semiconductor supply were a subtext in many closed-door meetings.

Despite these challenges, MWC 2026 was an optimistic affair. The number of startups and new exhibitors increased, particularly from Asia and Africa, signaling a broadening of the mobile ecosystem. The inclusion of health tech and sustainability goals also showed the industry’s commitment to social impact. For instance, a small affordable device that screens for tuberculosis was one of the hits of the show, demonstrating how mobile technology can save lives in low-resource settings.

Key Takeaways and Themes

The overarching message from MWC 2026 is that the smartphone is no longer just a communication device but a platform for AI, health, and creativity. The hardware race continues, but the real differentiation is now in software, services, and intelligent assistants. Foldable and rollable form factors are maturing, but they still face durability and cost hurdles. Meanwhile, the convergence of robotics and telephony hints at a future where our devices are not just smart but mobile companions.

As the show concluded, the consensus among attendees was that MWC remains the best place to see what the mobile world will look like in two to three years. The event also served as a reminder that innovation often comes from unexpected corners, whether from a small startup from Shenzhen or a university lab in Barcelona. The next edition is already scheduled for 2027, and the industry will be watching closely to see how the trends from 2026 evolve.

With the future of phones looking weirder and more exciting than ever, MWC 2026 will be remembered as a pivotal moment when AI truly began to shape every aspect of mobile technology.

Source: CNET News

What are Preferred Sources in Google?

Introduced last year as a Google Labs experiment, Preferred Sources is designed to help you access the sites you trust most when you search on any topic. Initially limited to regular Google searches, Google Discover, and Google News' Top Stories, the feature now supports AI Overviews and AI Mode. This means your favorite news sites will appear more prominently in AI response summaries, making it easier to find content from publishers you follow.

For example, if you regularly read technology reviews from ZDNET, adding it as a preferred source ensures its articles are highlighted in AI-generated answers about tech topics. The feature works by analyzing your source list and giving those sites priority in the ranking of links displayed within AI Overviews.

How to add your Preferred Sources to Google AI

Setting up your preferred sources is straightforward. Follow these steps:

• Go to the Google Source preferences page and sign in with your Google account.
• In the "Search by name or website" field, start typing the name or URL of a site you like.
• When the site appears in the suggestions, click the checkbox next to it.
• Repeat to add multiple sources.

Once added, these sources will be clearly labeled in AI Overviews and AI Mode. When you hover over a link from a preferred source within an AI response, you'll see the full site name, publication date, and other details. Click the link to view the full story on the source's website.

It's important to note that your preferred sources won't always appear. The relevance of your search topic to the content published by those sources determines visibility. Google also prioritizes fresh content, so frequently updated sites are more likely to be shown.

New features: Highly Cited badge and source carousel

Beyond preferred sources, Google has introduced two new enhancements to improve the usefulness of AI search results. First, a "Highly Cited" badge now appears next to links on the search results page for stories that have been referenced by many other sources. This helps you identify articles that are generating widespread coverage and are likely to be authoritative.

Second, for developing news topics, Google displays a carousel of thumbnails featuring relevant sources. Your preferred sources are highlighted in this carousel, allowing you to quickly swipe through and select the story that matches your interest. This feature is particularly useful when multiple outlets are covering the same breaking news event.

Why these changes matter

The expansion of Preferred Sources into AI search reflects Google's ongoing effort to balance automation with user control. Since the launch of AI Overviews in 2024, many users have expressed frustration with generic answers that lack context from trusted publishers. By allowing users to curate their source list, Google aims to restore some of the personalization that traditional search once offered.

Moreover, the Highly Cited badge tackles the problem of misinformation by spotlighting stories that have been vetted and referenced by multiple outlets. This is especially valuable during fast-moving events where accuracy is paramount.

From a publisher's standpoint, the Preferred Sources feature creates an incentive for websites to maintain high editorial standards and up-to-date content. Being added to a user's preferred list increases the likelihood of their articles being seen, which can drive traffic and engagement.

Background: The evolution of Google's AI search

Google's journey with AI in search began with the introduction of the Search Generative Experience (SGE) in 2023. Initially available as an opt-in experiment, it provided AI-generated summaries for complex queries. Over time, SGE evolved into AI Overviews, which became a default feature for many search queries in 2024. The integration of Preferred Sources into this system is a natural progression, addressing user demand for more trustworthy and tailored results.

Competitors like Microsoft's Bing have also experimented with AI chat and source prioritization, but Google's vast index and user base give it a unique advantage. The challenge lies in ensuring that AI summaries do not overshadow original content or misattribute facts. The Preferred Sources feature is part of a broader strategy to keep publishers engaged while still delivering quick answers.

Privacy considerations also play a role. Your preferred sources list is tied to your Google account, and the company states that it uses this data solely to personalize your search results. No information about your source preferences is shared with publishers or third parties without your consent.

Looking ahead, Google plans to continue refining the AI search experience. The blog post that announced these updates hinted at further innovations, such as deeper integration with content creators and firsthand perspectives. Users can expect more granular controls over how AI responses are generated, potentially including the ability to exclude certain websites entirely.

For now, the message is clear: Google wants you to have a say in which sources shape your AI-powered search results. By taking a few minutes to set up your preferred sources, you can ensure that the information you see aligns with your interests and trust in specific publishers.

Source: ZDNET News

In a one-to-one interview, Gebb shared what defines successful innovators in the age of agentic AI, and how his team is putting these skills into practice as Amex prepares for a future where AI agents act on behalf of consumers.

Great innovators in an age of AI

Gebb noted that his role is not just about building new products but about fostering a culture where people love to work and feel empowered to tackle novel challenges. "It's about getting a culture that people love, where they like to be together, and have the freedom to work on new things, and not just trying to follow the person before you and doing that job 5% better," he said. He emphasized that successful innovators pull different strings—knowing which areas need support, establishing strong relationships with business peers, and ensuring that innovation is integrated with the rest of the enterprise rather than isolated in a silo.

While many large companies have set up innovation labs, Gebb stressed that Amex Digital Labs is different because it sees itself as part of the enterprise, not separate from it. "Some innovation hubs get one thing wrong—they think of themselves as separate or different or better, and we're just trying to help the enterprise, be part of it, and be proud of our work," he said. This collaborative approach ensures that the lab's work directly addresses real business needs.

Four tips for becoming an effective innovator

Based on his 25 years of experience, Gebb offered four best-practice tips for professionals who want to embrace innovation in an age of AI:

• Keep learning – "You must be super-curious and always wanting to learn. Sometimes the term growth mindset comes up, which means not feeling like you know everything, and have it all figured out, but feeling as if you're willing and wanting to learn." This involves constantly seeking new knowledge, reading widely, and staying open to perspectives from different fields.
• Dive into tech – "It's critical to understand emerging technology and what it makes possible. Being deeply curious about technology and working deeply with engineers will be a huge part of your success." For non-technical innovators, this means taking time to learn the basics of AI, machine learning, and data science, and collaborating closely with technical teams.
• Prepare to fail – "You need a willingness to go out and do something risky, so a type of grit or a drive that makes you want to go and put something out there is critical." Innovation inherently involves experimentation, and not every experiment will succeed. The key is to fail fast, learn from mistakes, and iterate.
• Build partnerships – "In a big company, always being good at building relationships and being able to call in favors is crucial to success." No innovator works alone; success depends on a network of allies who can provide resources, support, and advocacy.

These four traits—curiosity, technical depth, risk tolerance, and relationship building—are the foundation of effective innovation at scale. They apply not only to AI but to any domain where rapid change is the norm.

Putting AI innovation into action

At American Express, Gebb's team is applying these principles to build the platform for the future of agentic commerce. They focus on three key areas: payments, offers and bookings, and proprietary experiences.

Payments: As consumers increasingly use large language models (LLMs) to discover products, they will want to purchase those items seamlessly—and eventually have AI agents buy them automatically. To enable this, Amex recently announced the Amex Agentic Commerce Experiences Developer Kit, a framework designed to create trusted transactions when AI agents purchase goods on behalf of consumers. The company also introduced Amex Agent Purchase Protection, a commitment to protect card members from errors made by AI agents that might lead to unauthorized charges.

Offers and bookings: The innovation team is ensuring that Amex offers, travel benefits, and restaurant booking capabilities appear within the AI models that consumers use. When a card member asks an LLM for a restaurant recommendation or plans a trip, they should see relevant Amex perks and be able to act on them directly.

Proprietary experiences: In Amex's own mobile app and website, the team is building chat-based agent interactions that allow customers to have natural conversations about services. Users can ask an agent for a specific type of restaurant, get recommendations, and book a reservation—all within the same interface.

Gebb explained that these foundations will enable real agentic commerce use cases over the next 12 months. "That might include something like saying to an agent, 'Always restock me with this item,' or saying, 'When the company restocks this product, I want to be the first one to get it,'" he said. Such interactions are expected to begin later this year, with broader adoption for everyday consumers by 2027.

While the pace of the agentic transition is uncertain, Gebb is confident that it will happen. "No one knows for certain if agentic commerce will happen faster or slower than we think," he said. "But what we do know is it absolutely will happen, and that's why you see the whole industry preparing for it and setting out standards and building foundations."

For innovators, the lesson is clear: the time to act is now. By staying curious, diving into technology, being willing to take risks, and building strong partnerships, professionals at all levels can position themselves to thrive in the age of agentic AI. American Express's example shows that even a 175-year-old company can reinvent itself by fostering a culture of innovation and preparing for the next wave of technological change.

Source: ZDNET News

• DuckDuckGo saw a 30% surge in app installs in the US after Google's I/O 2026 announcements.
• Installations jumped by 18.1% week-over-week starting May 19, peaking at 30.5% on May 25.
• DuckDuckGo's iOS app led growth with an average rate of 33%, peaking at 69.9% on May 25.
• Visits to DuckDuckGo's no-AI search page rose by 22.7% week-over-week, peaking at 27.7% on May 24.
• The surge was primarily US-based, correlating with Google's US-focused I/O event on May 19.
• DuckDuckGo provides options for AI-free searching, including a dedicated no-AI website.
• Users can choose between traditional results and AI-powered chat, with privacy protections.

Do you use Google for your searches but want to avoid AI-generated summaries and recommendations? There is an alternative that is rapidly growing in popularity—DuckDuckGo. This search engine has long been known for its strict privacy policies, but now it is also gaining attention as a refuge for those tired of artificial intelligence creeping into their search results.

Like many technology companies, Google has been steadily integrating more AI into its core products. At its 2026 I/O conference held on May 19, Google unveiled several new AI features for search, including a conversational AI search box, agents that can autonomously find information, improved local business links, and more personalized answers. While these enhancements sound useful in theory, they come with the well-documented risks of AI: inaccuracies, hallucinations, and an overall lack of transparency. Many users prefer the straightforward, link-based results that traditional search engines provided before the AI era.

This growing dissatisfaction has driven a significant number of people to explore alternatives. DuckDuckGo, which has always positioned itself as a privacy-first search engine, has now also become a go-to choice for those seeking an AI-free experience. The company reported a sharp increase in user activity immediately following Google's I/O event. For instance, on May 25 alone, DuckDuckGo's week-over-week app installs surged by 30.5% in the United States. The growth was especially notable on iOS, where installs peaked at 69.9% on that same day. Even visits to DuckDuckGo's dedicated no-AI search page rose by an average of 22.7% during the week, indicating that users are actively seeking out non-AI search interfaces.

DuckDuckGo's founder and CEO, Gabriel Weinberg, attributed the spike to a desire for user control. In a statement, he emphasized that the company respects user choice and privacy: everything done within DuckDuckGo is private, no search histories or chats are collected, and no data is used for AI training. This philosophy resonates with individuals who are increasingly wary of how their personal information might be used to train massive AI models.

For those interested in switching to DuckDuckGo, there are multiple ways to access an AI-free search experience. The simplest method is to use the regular DuckDuckGo website, which by default provides classic, link-based results. Users can also opt to enable AI-powered chat if they choose, but the default setting avoids AI altogether. The company has even created a dedicated no-AI site that automatically disables AI-generated answers and removes AI-generated images from image search results. This site is designed for people who want a pure, traditional search environment without any artificial intelligence interference.

Another option is to install the DuckDuckGo browser, available for Windows, macOS, iOS, and Android. The browser integrates the search engine with additional privacy protections, such as blocking third-party trackers and enforcing encryption where possible. Within the browser, users can toggle between traditional and AI-powered search modes at any time. The browser apps are lightweight and offer a seamless experience for those who want to break free from Google's ecosystem.

It is worth noting that DuckDuckGo's growth is not just a temporary spike. The company has been steadily gaining users over the past few years as awareness of online privacy issues grows. Google's aggressive push into AI has only accelerated this trend. For many users, the ability to choose how much AI they interact with is a fundamental right. DuckDuckGo's model respects that right by giving people control over their search experience.

In addition to its AI-free offerings, DuckDuckGo continues to innovate in the privacy space. It offers a feature called Search Assist, which uses AI to anonymously generate answers at the top of search pages, but users can disable this if they prefer. The company also provides a paid subscription plan called DuckDuckGo Plus, which grants access to more advanced AI models for those who want to use AI on their own terms. However, the core search experience remains free and private.

The rise of DuckDuckGo serves as a reminder that not everyone wants AI in every aspect of their digital life. While AI can be powerful, it is not always accurate or relevant. For many search queries, a simple list of web links is still the most efficient and trustworthy way to find information. DuckDuckGo's recent surge in popularity suggests that a significant portion of the public agrees.

If you are tired of AI summaries that miss the mark or that include hallucinations, consider giving DuckDuckGo a try. The process is simple: either set it as your default search engine in your browser or download the DuckDuckGo browser app. Once you start using it, you will notice the difference immediately. No more AI overviews that may contain errors—just clean, private search results. It is a small change that can greatly improve your online experience and protect your privacy at the same time.

The trend shows no signs of slowing down. As Google continues to deploy more AI in its products, alternative search engines like DuckDuckGo will likely continue to attract dissatisfied users. The key is that DuckDuckGo offers choice. You can stick with a traditional search experience, or you can engage with AI when it genuinely helps. Either way, your data remains yours. That is a compelling proposition in an age where data is constantly being harvested for AI training.

In summary, DuckDuckGo's recent surge is a direct response to Google's AI-heavy updates. Users are voting with their clicks and installs, moving toward a service that respects their privacy and gives them control over AI. Whether you are a long-time privacy advocate or just someone who finds AI results annoying, DuckDuckGo provides a viable, easy-to-use alternative. And with the growing availability of no-AI search pages and browser apps, there has never been a better time to make the switch.

Ultimately, the decision lies with you. But if you value privacy, simplicity, and the ability to choose when to use AI, DuckDuckGo deserves a place in your digital toolkit. The company's mission is clear: to put users in charge. As its recent growth numbers show, many people are ready to take that control.

Source: ZDNET News

The problem with AI in search

Energy consumption is a major concern. AI data centers require enormous amounts of electricity to train and run models, and they consume vast quantities of water for cooling. Each AI-powered search query adds to this environmental toll. On top of that, the accuracy of AI responses remains questionable. Many users have encountered confidently delivered but factually incorrect information, especially in specialized fields like technical support, medicine, or local recommendations. Relying on AI for search results can thus lead users astray.

Fortunately, you don't have to abandon the convenience of major search engines to escape AI. A straightforward browser modification allows you to automatically filter out AI-generated results. The best part is that the method works across all major web browsers, with only minor variations in setup.

How the trick works

The key to blocking AI results lies in customizing the search engine URL. Google, for example, uses a URL parameter that controls the display of AI overviews. By appending &udm=14 to the standard search query, you instruct Google to show a traditional web results view without AI-generated snippets. This parameter essentially forces the search engine into a mode that predates AI integration. While other search engines might use different parameters, the concept is identical: you define a custom search engine in your browser that includes the anti-AI modifier.

Below are step‑by‑step instructions for the five most common browsers. In each case, you will create a new search engine entry that you can trigger from the address bar with a simple keyword.

Firefox

Open Firefox and navigate to Settings > Search. Scroll down to the "Search Shortcuts" section and click "Add" at the bottom. In the pop‑up, fill in the following:

• Search Engine Name: Choose a name like "Google without AI."
• URL: Enter https://www.google.com/search?q=%s&udm=14
• Keyword: Enter aig (short for "AI‑less Google").

Click "Add Engine". Now, whenever you type aig in the address bar and press Tab (or Space, depending on your settings), you can enter your query and hit Enter. The results will appear without AI overviews.

Google Chrome

Open Chrome and go to Settings > Search engine > Manage search engines. Click "Add" next to "Other search engines." Fill in the fields:

• Name: e.g., "No AI Google"
• Shortcut: aig
• URL: https://www.google.com/search?q=%s&udm=14

Click "Add" and then make this engine your default if desired. Alternatively, use the shortcut the same way as in Firefox: type aig in the address bar, hit Tab, and search.

Microsoft Edge

Edge, built on Chromium, follows a similar path. Click the three‑dot menu > Settings > Privacy, search, and services. Scroll to the "Address bar and search" section and select "Manage search engines." Click "Add" and enter:

• Name: "Clean Google"
• Keyword: aig
• URL: https://www.google.com/search?q=%s&udm=14

Click "Add" and then set it as default or use the keyword.

Opera

Opera also uses Chromium. Go to Settings > Search engine > Manage search engines. Click "Add" and fill in the same fields as Chrome and Edge. The URL is identical: https://www.google.com/search?q=%s&udm=14. Use the keyword aig to activate it.

Safari (macOS)

Apple's Safari requires a third‑party extension, as it does not natively support custom search engine URLs with parameters. Install "Customized Search Engine" from the Mac App Store. Once installed, open the extension's settings and replace the default Google URL with https://www.google.com/search?q=%s&udm=14. Save the changes. Now, any search performed via the Safari address bar will use this modified URL, effectively blocking AI summaries.

Beyond Google

The same principle applies to other search engines. For instance, Bing offers a parameter to disable its AI chat feature, though it may not completely eliminate AI‑generated results. DuckDuckGo, which has its own AI features, can be controlled with similar URL tweaks. Experiment by searching for the engine's documentation on query parameters. The beauty of the custom search engine approach is that you can create multiple entries for different engines, each with the appropriate filter.

As AI continues to evolve, search interactions will undoubtedly change further. For now, taking control of your search experience is as simple as adding a line of text to your browser settings. By doing so, you reduce your digital carbon footprint, avoid potential misinformation, and reclaim a cleaner, link‑focused search result page.

The method requires no software installation (except Safari users), no coding skills, and can be reversed at any time by deleting the custom search engine from your settings. Whether you're a casual user or a power searcher, this small tweak can make a noticeable difference in your daily browsing habits.

Source: ZDNET News

Intelligence becomes infrastructure

In his opening keynote, Dell chairman and CEO Michael Dell set the tone by declaring that "abundant intelligence is here" and that "intelligence is becoming infrastructure." This statement captures a fundamental shift: AI is no longer a separate service consumed via API calls; it is an integral layer of the data center, workstation, and edge device. The implication is that enterprises must treat AI compute as a core resource to be managed, secured, and optimized just like storage or networking.

This vision is supported by multiple trends. First, the cost of running large language models (LLMs) in the public cloud has escalated dramatically. Dell's vice chairman and COO Jeff Clarke, in his Day 2 keynote, introduced the concept of "tokenomics" – an analogy that compares AI tokens to currency. Clarke revealed that token usage for AI has increased 320-fold, and by 2030, global token consumption is predicted to grow by 3,400%. For enterprises running production AI workloads, these numbers translate into soaring cloud bills. Moving AI inference and training to on-premises infrastructure provides a direct path to cost control, as local compute avoids per-token cloud pricing.

Beyond cost, data sovereignty is emerging as a driving force. Research from Aberdeen presented at the conference shows that companies across industries and geographies place high value on keeping sensitive data and AI training out of shared cloud environments. Regulatory pressures such as the EU's AI Act and various data localization laws require that certain data remain within national borders. Sovereign AI – AI that runs on infrastructure within a country's jurisdiction and under its governance – is becoming a requirement for many multinational enterprises. Dell responded to this need with the introduction of the Dell AI Data Platform, which combines on-premises storage and compute with AI tools to enable secure, sovereign AI deployments.

The rise of agentic AI and governance challenges

Perhaps the most transformative trend discussed at Dell Tech World 2026 is agentic AI. Agents are autonomous or semi-autonomous AI systems that can take actions on behalf of users – scheduling meetings, ordering supplies, responding to customer inquiries, and more. While promising huge productivity gains, agents also introduce new risks. Clarke addressed this directly: "When an agent takes an action on your behalf, you need to know what it did, why it did it, and how to undo it if it got it wrong." This need for explainability and reversibility places enormous pressure on governance frameworks.

One case study highlighted at the conference illustrated the scale of the problem. A company adopted agents for customer support and internal workflows and exhausted its entire annual token budget by March. The surge in agent activity drove up costs uncontrollably and raised security concerns because the agents were interacting with external systems. This example underscores why enterprises must move AI workloads on-premises for better cost governance and security control.

To help organizations get started safely, Dell announced Dell Deskside Agentic AI, a development offering that includes high-performance workstations, Nvidia NemoClaw software, and Dell services. This bundled solution is designed for building and testing agents in a controlled, on-premises environment. Dell also announced support for Nvidia OpenShell, a sandboxed environment specifically intended for enforcing corporate governance and privacy policies during agent development and testing. These tools reflect the industry's recognition that agentic AI cannot be deployed recklessly; it requires careful staging and oversight.

Practical steps for building on-premises AI capabilities

Throughout the conference, Dell speakers outlined a multi-tier approach to on-premises AI compute. At the smallest scale, local workstations equipped with powerful GPUs can handle individual data scientists' training and inference tasks. For larger workloads, enterprises can use rack-mounted servers in their own data centers. For edge scenarios – such as manufacturing plants, retail stores, or oil rigs – Dell offers ruggedized edge devices that run AI inference locally, reducing latency and data transfer costs. This spectrum allows businesses to start small and scale up as needed.

The move to on-premises AI is not just about hardware. Dell also emphasizes the importance of software tools for managing the lifecycle of AI models and agents. The Dell AI Data Platform integrates data management with AI workflows, ensuring that data is properly curated, labeled, and governed before it is used for training. This platform is particularly important for sovereign AI, where data cannot leave the organization's controlled environment. By providing a unified stack, Dell aims to reduce the complexity of building AI infrastructure from scratch.

Another recurring theme was the balance between speed and safety. On one hand, businesses are urged to move fast to avoid being left behind by competitors that are aggressively adopting AI. On the other hand, practical sessions at the conference warned against rushing into production without proper governance. Many of the software offerings touted as solutions to AI hurdles, including some from third-party vendors, are still in beta or alpha stages and are not yet ready for mission-critical use. This creates a tension: companies must experiment and innovate, but they must also protect their data and meet compliance requirements.

The conference's advice for navigating this tension was to start small with well-defined use cases, implement strong governance from the outset, and gradually expand. Pilot projects should be run on-premises or in a private cloud to maintain control, and only after thorough validation should they move to production. This measured approach aligns with what Aberdeen's research shows: enterprises that prioritize data governance and security in their AI strategies achieve more sustainable long-term value.

Sovereign AI and the global context

The push for sovereign AI is not only a technical requirement but also a strategic imperative. Governments around the world are enacting regulations that restrict the cross-border flow of data, particularly for AI training. For example, the European Union's AI Act imposes strict rules on high-risk AI systems, and many countries are developing their own data localization policies. Enterprises that operate globally must ensure that their AI infrastructure can comply with these regulations without sacrificing performance. On-premises and hybrid architectures offer the flexibility to keep data within required jurisdictions while still leveraging global AI models.

Dell Technologies World highlighted how Dell's portfolio addresses these sovereignty needs. The Dell AI Data Platform is designed to be deployed within a customer's own data center or at a colocation facility, ensuring that data never leaves the controlled environment. This is critical for industries such as finance, healthcare, and defense, where data sensitivity is paramount. The company also emphasized partnerships with local system integrators and cloud providers that can help customers navigate region-specific compliance landscapes.

Another aspect of sovereign AI is the cultural and linguistic diversity of AI models. A model trained primarily on English-language data may not perform well in other languages or cultural contexts. By running AI training on-premises, organizations can use their own data to fine-tune models for local languages, regulations, and business practices. This localization is increasingly seen as a competitive advantage.

The role of partners and ecosystem

Dell Tech World 2026 also underscored the importance of the broader ecosystem in enabling on-premises AI. Dell's partnerships with Nvidia, AMD, Intel, and major software vendors ensure that customers have access to the latest hardware accelerators and AI frameworks. For instance, the integration with Nvidia NemoClaw and OpenShell provides enterprises with sophisticated tools for building and sandboxing agents. Additionally, Dell's services organization offers consulting and implementation support to help customers design their AI infrastructure and governance policies.

Many sessions at the conference featured customer case studies that demonstrated the real-world benefits of on-premises AI. One healthcare provider described how moving diagnostic AI to its own data center reduced latency from seconds to milliseconds, enabling real-time analysis of medical images. A financial services firm highlighted how on-premises AI allowed it to comply with strict data residency regulations while still deploying advanced fraud detection models. These examples illustrate that on-premises AI is not a step backward; it is an enabler of higher performance, lower costs, and stronger compliance.

Nevertheless, the conference did not shy away from the challenges. Multiple speakers acknowledged that building on-premises AI infrastructure requires significant upfront investment in hardware, software, and skilled personnel. For small and medium-sized businesses, this can be a barrier. However, Dell's flexible financing options and managed services are designed to lower the entry barrier. The company also encourages a hybrid approach, where less sensitive workloads remain in the public cloud while critical workloads move on-premises.

Tokenomics and the economic case for on-premises AI

The concept of tokenomics was one of the most discussed topics at the conference. Jeff Clarke's Day 2 keynote provided detailed analysis of how token consumption is exploding and how that impacts costs. He argued that if enterprises continue to rely on public cloud APIs for all AI inference, their cloud bills will become unsustainable. The solution is to run inference on local hardware, where the marginal cost per token is effectively zero after the initial capital expenditure. By deploying dedicated AI servers or workstations, companies can decouple their AI usage from cloud pricing models.

Dell presented several calculators and models to help customers assess the total cost of ownership for on-premises AI versus cloud-only approaches. The results generally showed that for any significant volume of token consumption – especially as agents proliferate – on-premises infrastructure becomes more economical within 12 to 18 months. Additionally, on-premises deployments eliminate concerns about vendor lock-in and API rate limits, giving businesses more control over their AI operations.

The economic argument extends to training as well. While cloud providers offer vast GPU clusters for training large models, the cost can be prohibitive for continuous fine-tuning. On-premises clusters, even if smaller, allow organizations to iterate rapidly without worrying about cloud instance pricing. This is particularly important for enterprises that need to update models frequently with proprietary data.

Governance frameworks for agentic AI

With agents set to become a dominant form of AI interaction, governance frameworks must evolve. Dell's announcements around Nvidia OpenShell and Deskside Agentic AI are early steps in providing the tools needed to manage agent lifecycles. But the conference also highlighted the need for organizational changes. Enterprises should establish AI governance boards, define clear policies for agent behavior, and implement logging and auditing mechanisms. The ability to "undo" an agent's action is not just a technical feature; it requires a chain of accountability that includes humans in the loop for critical decisions.

Several sessions provided practical checklists for agent adoption: start with narrow, low-risk tasks; use sandboxed environments for testing; enforce strict permission models; and monitor agent activities continuously. The message was clear: move fast, but with guardrails. The tools are becoming available, but the maturity of the software ecosystem is still evolving. Enterprises that invest now in robust governance will be better positioned to scale agentic AI safely in the future.

As the conference drew to a close, the overarching theme remained consistent: AI is no longer a distant promise; it is present, and its intelligent capabilities are becoming embedded in every layer of the enterprise. The path to realizing its full potential, however, lies in infrastructure that is close to the data, controlled by the organization, and governed by clear policies. Dell Technologies World 2026 made a compelling case that the future of AI is on-premises.

Source: ZDNET News

Every business has a story worth telling. A product launch that solves a real problem. A funding milestone that validates months of hard work. A partnership that signals maturity and market confidence. A technology breakthrough that deserves a spotlight. The question is never whether the story matters the question is whether anyone outside your immediate circle will ever hear it.

This is exactly why a professional premium news distribution service is no longer optional for businesses that are serious about growth. It is the infrastructure layer between your announcement and the audiences that need to hear it journalists, investors, potential customers, search engines, and industry analysts who shape perception in your market.

Without it, your press release lives on your website, gets shared once on LinkedIn, and disappears by Tuesday. With it, your story appears across hundreds of credible media outlets simultaneously, generates backlinks that strengthen your SEO authority for months, and positions your brand as a legitimate player in your industry.

Whether you are a scaling startup building its first media presence, an established business managing a product rollout, or a PR agency handling multiple client accounts, the right distribution partner changes what is possible.

Why a Professional Premium News Distribution Service Is the Growth Infrastructure You're Missing

Most businesses underestimate what distribution actually does. They think of it as a delivery mechanism a way to send an announcement from point A to point B. In reality, a well-executed news distribution service is a multi-layered growth system.

It generates media coverage that builds brand authority. It creates backlinks from high-domain-authority publications that improve your organic search rankings. It puts your story in front of journalists who may cover your brand again in the future. It signals legitimacy to investors, partners, and potential enterprise clients who do their due diligence through a simple Google search.

Here is what the best distribution delivers simultaneously:

• Placement across hundreds of media outlets within hours of submission
• Google News indexing that makes your announcement searchable to anyone looking for your market
• Backlink generation from credible news sites that Google trusts
• Increased branded search volume as your announcement circulates across social channels
• Long-term SEO equity that compounds with every subsequent distribution

A business that uses a premium news distribution service consistently builds something that paid advertising simply cannot replicate: earned media authority. That authority is what turns a press release from a one-time event into a sustainable competitive advantage.

What Businesses, Startups, and Agencies Actually Need From a PR Service

The PR industry has a reputation problem. It oversells reach and underdelivers results. It talks about "impressions" and "placements" without connecting those metrics to anything a business actually cares about more customers, higher search rankings, greater investor confidence, and measurable brand growth.

Here is what users across business sizes and industries consistently say they actually need from a public relations agency:

Businesses need distribution that reaches both industry-specific media and mainstream outlets. They need their announcements to appear alongside credible brands that reinforce their own market positioning. They need coverage that their sales teams can share as social proof and that their leadership teams can reference in investor conversations.

Startups need affordability without sacrificing reach. They need a PR company for startups that understands they are building credibility from scratch and that every media mention matters. They need distribution that generates the kind of third-party validation that converts skeptical prospects into paying customers.

Agencies need a distribution partner that can handle volume, maintain quality across multiple client verticals, and deliver consistent results that they can report to their own clients with confidence. They need a press release distribution platform with the infrastructure to scale alongside their client roster.

What every one of these users shares is a need for transparency, speed, and measurable outcomes. Not vague claims. Not inflated distribution numbers. Real pickups. Real links. Real traffic data.

Case Study: How a B2B SaaS Startup Tripled Its Media Presence in 60 Days

A project management software startup had strong product-market fit, a growing customer base, and a compelling founding story — but virtually no media presence. Organic search traffic was dominated by competitors with years of earned media behind them. Investor outreach was hampered by the absence of third-party validation.

The team engaged a professional online press release distribution service for a coordinated three-release campaign over 60 days, covering their seed round announcement, a product feature launch, and an industry partnership.

The strategy involved:

Release 1 — Seed Round Announcement: Distributed to 200+ business and tech outlets. Picked up by TechCrunch, Business Insider, and 47 secondary business news sites. Generated 63 new backlinks within 30 days.

Release 2 — Product Feature Launch: Distributed to SaaS-specific and productivity media. Picked up by G2, Product Hunt news, and 31 additional outlets. Drove a 44% increase in trial sign-ups in the week following publication.

Release 3 — Industry Partnership: Distributed to both partner industry outlets and mainstream business media. Resulted in two journalist-initiated follow-up interview requests.

The cumulative result: organic search traffic increased by 78% over the 60-day period. The founder's name began appearing on the first page of Google for branded searches. Investor outreach response rates improved significantly because prospects could now verify the company's legitimacy through a simple search.

This is the compounding effect of consistent, professional press release distribution services executed with strategic intent.

Search Visibility: The Hidden SEO Power of Consistent News Distribution

Most marketing teams think of SEO and PR as separate disciplines. One belongs to the content and technical team. The other belongs to communications. This separation is one of the most expensive strategic mistakes a growing business can make.

Every press release published through a credible news distribution website is an SEO event. When a release is picked up by 100 media outlets, each pickup generates a mention — and often a backlink from a domain that Google already trusts. These backlinks pass authority directly to your website, improving rankings for your core commercial keywords.

Beyond backlinks, consistent distribution builds what Google's NLP systems recognize as topical authority. When your brand name appears repeatedly in the context of your industry, Google's entity recognition begins to associate your brand with relevant keywords, industry terms, and market categories. This association influences how your site ranks even for search queries that don't directly mention your brand.

A digital news distribution service that includes Google News indexing takes this further. When your release appears in Google News, it immediately surfaces for anyone searching for your announcement's subject matter competitors, journalists, investors, and potential customers. That visibility window may be brief, but the backlinks and brand signals it generates persist for months.

The businesses winning at organic search right now are not just publishing good blog content. They are building media footprints through consistent press release distribution that compounds their authority with every announcement.

Pricing That Fits Every Stage of Business Growth

One of the most persistent myths about professional PR services is that they are exclusively for enterprise brands with large communications budgets. This was true a decade ago. It is not true now.

The press release distribution pricing landscape has transformed to serve businesses at every growth stage. Modern distribution platforms offer press release distribution packages that scale from single-release options for early-stage startups to high-volume monthly plans for agencies managing multiple client accounts simultaneously.

Affordable press release distribution options now deliver meaningful reach without requiring a six-figure communications budget. The calculation is straightforward: if a single media pickup drives even a handful of new qualified leads, the distribution cost pays for itself in the first week.

For startups specifically, PR agency pricing for small business options exist that prioritize the outlets most likely to reach their target audience rather than simply maximizing raw distribution volume. Quality coverage in five highly relevant outlets beats volume distribution across 500 generic ones.

The right PR pricing plans give you transparency clear deliverables, defined outlet lists, and pickup reports so you know exactly what your investment produced.

Case Study: Regional Retail Brand Goes National Through Strategic News Distribution

A regional home goods retailer with strong brand recognition in the Midwest wanted to signal its national expansion without the budget for a full advertising campaign. Their marketing director had a modest PR budget and a single announcement: the opening of their first East Coast flagship location.

Working with a professional press release distribution company, the team crafted a release that led with the consumer story design-forward home goods finally arriving on the East Coast rather than the corporate expansion narrative. The distribution targeted retail trade publications, home lifestyle media, local East Coast news outlets, and national business press.

Results within three weeks: coverage in Architectural Digest's online news section, Retail Dive, Business of Home, and 28 regional news outlets across the East Coast target markets. The flagship store's opening weekend foot traffic exceeded projections by 40%. Email subscriber growth in the new markets spiked in the two weeks following the announcement.

The brand had previously tried submitting releases directly to journalists with minimal response. The difference was not the story the story was always strong. The difference was professional online news distribution infrastructure that put the story in front of the right journalists through channels they actually monitor.

Infrastructure Matters: What Powers a High-Performance Distribution Network

Not all distribution services are equal. The difference between a press release that generates 200 media pickups and one that generates two is not luck it is infrastructure. Here is what separates a professional PR distribution agency from a basic wire service:

Verified journalist databases segmented by beat, publication type, geography, and industry vertical ensure your release reaches reporters who actually cover your market not a generic list of emails that generates spam complaints.

Editorial partner networks with pre-negotiated content agreements mean that some outlets will publish your release directly as news content, guaranteeing minimum coverage even before organic journalist pickup begins.

Google News API integrations ensure that your release is indexed within hours of distribution, making it immediately discoverable through organic search.

Distribution timing intelligence uses historical data about when journalists open email, when publications update their news feeds, and when search indexing is most active to schedule distribution for maximum impact.

Pickup reporting dashboards that show every outlet that published your release, clickable links to live coverage, traffic referral data, and backlink verification — so your investment is fully accountable.

A top PR distribution company maintains all of this infrastructure at scale. A free wire service or a manual submission process delivers none of it.

Lifestyle and Operational Benefits of Working With a Professional PR Agency

Beyond the metrics, there are tangible operational benefits to working with an established PR marketing agency USA that business owners and marketing teams experience every day.

Time is the most valuable. Writing and distributing a press release in-house from drafting to formatting to manual journalist outreach to follow-up — can consume an entire workweek of marketing resources. A professional online PR agency handles all of it, freeing your team to focus on the work that actually requires their domain expertise.

Confidence is the second. When you work with a professional agency, you know your announcement will be formatted correctly, distributed to verified outlets, indexed properly, and tracked accurately. There is no uncertainty about whether the process worked. You get a report that tells you exactly what happened.

Consistency is the third. Brands that build media presence consistently not just for big launches but for partnerships, executive hires, product updates, and industry commentary — build compounding authority. A professional news distribution service makes that consistency achievable without consuming disproportionate internal resources.

Credibility is the fourth. When a prospect Googles your brand and finds multiple media mentions across credible outlets, the conversion journey shortens dramatically. Third-party validation from recognized publications does work that no amount of owned-media content can replicate.

Why Choose Us for Your News Distribution and PR Needs

There is no shortage of options in the distribution landscape. What distinguishes the right best press release distribution services partner is not the size of their claimed network — it is the quality of their actual results.

Here is what a genuinely excellent PR agency delivers that separates it from the alternatives:

Sector-specific distribution intelligence. Not every release should go to the same outlets. A tech product launch needs different media than a retail expansion or a financial services announcement. The right partner segments distribution by industry, geography, and audience so your story reaches journalists who will actually find it relevant.

Editorial quality support. A release that fails editorial standards at a target publication gets ignored no matter how good the distribution network is. The best agencies offer editorial review that ensures your release meets the standards of the outlets it's being sent to.

Measurable ROI reporting. Coverage links, backlink data, traffic referral reports, social amplification tracking all delivered within 24-48 hours of distribution. No guesswork. No vanity metrics. Accountable outcomes only.

Scalable pricing for every stage. From a single startup announcement to a 20-client agency portfolio, the right affordable PR agency offers plans that scale with your needs rather than locking you into contracts designed for much larger organizations.

Genuine media relationships. The distribution network matters less than the relationships within it. Agencies with established editorial trust see higher organic pickup rates because journalists actually open and read submissions from sources they recognize and trust.

Wrapping Up: The Competitive Cost of Staying Invisible

Every business, startup, and agency is making a choice consciously or not about how visible they want to be in their market. The brands that invest in consistent, professional news distribution services are building cumulative media authority that strengthens with every announcement. The ones that don't are ceding that ground to competitors who will.

The math is not complicated. Every press release distributed through a professional press release distribution agency generates media coverage, backlinks, search visibility, and brand credibility that compound over time. Every announcement that never gets properly distributed is a missed compounding opportunity.

The businesses that dominate their categories five years from now are the ones building media authority today. Professional online PR distribution is how that authority gets built one well-distributed announcement at a time.

The decision to invest in professional distribution is not a marketing expense. It is a growth infrastructure decision. And the best time to make it is before your competitors make it first.

Frequently Asked Questions

Q1. What is a news distribution service and how does it work for businesses?

A news distribution service is a platform or agency that distributes your press release simultaneously to hundreds of media outlets, journalists, and news aggregators. After you submit your release, it is formatted, optimized, and sent through a verified distribution network. Results including media pickups and backlinks are tracked and reported within 24-48 hours.

Q2. How does press release distribution help improve my business's search rankings?

Every media pickup from a high-authority site creates a backlink to your website. Multiple pickups across relevant domains build topical authority in Google's ranking systems. Consistent use of a press release distribution platform over time improves organic rankings, brand recognition, and visibility in Google News for your target keywords and industry terms.

Q3. Is professional news distribution affordable for early-stage startups?

Yes. Modern PR pricing plans include entry-level press release distribution packages designed specifically for startups with limited budgets. These provide meaningful media reach without requiring enterprise-level investment. A single well-distributed release that generates relevant media coverage typically delivers ROI that exceeds its cost within the first month.

Q4. What types of businesses benefit most from professional press release distribution?

Virtually every business category benefits tech startups, retail brands, financial services firms, healthcare companies, agencies, nonprofits, and professional services providers. Any organization with news to share and audiences to reach benefits from working with a professional PR distribution agency that can amplify that news beyond owned media channels.

Q5. How quickly will my press release appear in media outlets after submission?

With a professional digital news distribution service, initial media pickups typically begin within 2-4 hours of distribution. Google News indexing usually occurs within the same window. Full pickup reporting covering all placements is typically available within 24-48 hours of the distribution going live.

Q6. What should a business press release include to maximize media pickup?

An effective release needs a compelling, keyword-optimized headline, an opening paragraph that answers who, what, where, and why, supporting details that add credibility (data, quotes, context), and a clear call to action. A professional public relations agency will review and optimize your release before distribution to maximize its appeal to journalists and its performance in search.

Q7. Can PR agencies use news distribution services for multiple client accounts?

Yes. Professional press release distribution services offer agency-tier plans specifically designed for volume distribution across multiple clients. These plans typically include priority processing, dedicated account management, volume pricing, and consolidated pickup reporting that makes it easy to demonstrate results across an entire client portfolio.

Q8. What is the difference between a free press release service and a paid distribution platform?

Free services offer minimal distribution, no editorial relationships, and virtually no guarantee of organic media pickup. A paid press release distribution company provides verified journalist databases, editorial partner networks, Google News indexing, real-time pickup tracking, and detailed backlink reporting. For any announcement that matters to your business, the investment in professional distribution pays for itself in coverage quality and search impact.

Q9. How often should a business distribute press releases to build media authority?

Most businesses benefit from a cadence of two to four releases per month, covering product news, partnerships, executive announcements, industry commentary, and company milestones. Consistent use of an affordable PR agency builds compounding media authority each release builds on the credibility established by previous ones, improving pickup rates over time.

Q10. How do I choose the right press release distribution pricing plan for my business?

Evaluate press release distribution pricing based on the frequency of your announcements, the importance of each release, your target industry and geography, and whether you need additional services like editorial review or journalist follow-up outreach. A transparent PR agency pricing USA structure will define clear deliverables so you know exactly what each plan includes before you commit.

“I’ve said this for many years: Cybersecurity is a team sport,” Grieco said in a prerecorded video about the news. “We’ve all got to come together and work together for a better collective defense. This is one really demonstrable way where we’re trying to raise the bar for everybody and share our knowledge, through this. And so giving folks access to this felt really important.”

While frontier models identify vulnerabilities at machine-speed, most security teams haven’t built a great process or have enough manpower to verify findings, and that’s where Foundry comes in, Grieco said. “Every security team with access to a frontier LLM has tried the same thing at least once: toss a report at the model and ask it to ‘find the bugs.’ The result is usually a wall of unbounded, unverifiable output that mixes sharp insights with hallucinated findings, with no way to know what was missed or when you’re actually done,” wrote Omar Santos, a distinguished engineer at Cisco focusing on AI security, cybersecurity research, incident response, and vulnerability disclosure, in a blog post about Foundry.

“A full agentic system like Foundry Security Spec is the antidote to that chaos: it wraps the model in orchestration, roles, and guardrails so that detection, validation, and coverage are designed up front instead of improvised in a chat window. The difference is stark—one is an interesting demo; the other is a security evaluation system you can defend in front of your CISO and your auditors,” Santos wrote.

“It’s really also important to note, users don’t have to wait for Mythos or the GPT-5.5 Cyber access to make use of this [protective software infrastructure surrounding an AI model] harness. It’s model agnostic,” Grieco said.

According to Santos, Foundry Security Spec is the scaffolding that turns a frontier LLM from “an interesting demo against your codebase” into a security evaluation system that produces:

• A bounded, prioritized, verifiable set of findings.
• A clear “done” signal and the conjunction of an operator-defined coverage floor and an economic yield threshold.
• An auditable provenance chain from detection through triage, validation, and publication.
• Safety guardrails that assume the model will, at some point, try to do the wrong thing; and constrain it at the substrate, not the prompt.

Foundry is published as two main artifacts and a set of supporting documents. The “spec” artifact includes eight core agent roles, such as orchestrator, indexer, cartographer, and detector; five extension roles; the finding lifecycle; the coordination substrate; and roughly 130 functional requirements, each with an inline rationale explaining why it exists, according to Grieco. The “constitution” artifact incudes 11 firmly defined principles, each of which encodes a real production failure we shipped, diagnosed, and fixed, Grieco stated.

A common question is whether this spec will become obsolete as LLMs evolve, Santos noted. “The answer is it was designed not to be,” he wrote. “Foundry Security Spec is built on functional requirements and roles, not specific model parameters. Whether you are using today’s frontier models or the more complex reasoning agents of tomorrow, the need for an orchestrator, a detector, and a validator will remain constant. The spec is designed to be the stable harness that keeps your security evaluation consistent, regardless of the ‘engine’ under the hood.”

The Foundry specification works hand-in-hand with another Cisco-contributed open-source technology, CodeGuard. Project CodeGuard is a security framework that builds secure-by-default rules into AI coding workflows, according to Cisco. It offers a community-driven ruleset, translators for popular AI coding agents, and validators to help teams enforce security automatically.

“Project CodeGuard is designed to integrate seamlessly across the entire AI coding lifecycle. Before code generation, rules can be used for the design of a product and for spec-driven development. Customers can use the rules in the “planning phase” of an AI coding agent to steer models toward secure patterns from the start. During code generation, rules can help AI agents to prevent security issues as code is being written. After code generation, AI agents like Cursor, GitHub Copilot, Codex, Windsurf, and Claude Code can use the rules for code review,” Santos wrote in a 2025 blog post when the project was introduced.

The rise of agentic AI in cybersecurity has been a major trend over the past few years. As large language models have become more capable, security teams have increasingly used them to automate tasks such as vulnerability detection, threat hunting, and incident response. However, with great power comes great responsibility: these models can produce convincing but inaccurate outputs, often referred to as hallucinations. Without a rigorous verification framework, organizations risk acting on false positives or missing real threats entirely. This is precisely the gap that Foundry aims to fill.

Cisco's move to open-source the specification is part of a broader industry push toward collaborative security. Many major technology companies have contributed to open-source security frameworks, recognizing that no single vendor can solve the problem alone. The Foundry Security Spec builds on lessons learned from Cisco's own security operations, which handle millions of alerts daily. By making the specification freely available, Cisco hopes to accelerate the adoption of reliable agentic AI evaluation practices across the entire cybersecurity community.

The specification's architecture is modeled on a separation of concerns: the eight core agent roles each have distinct responsibilities. The orchestrator manages the workflow, the indexer prepares the codebase for analysis, the cartographer maps dependencies, and the detector performs the actual vulnerability scanning. Extension roles like the triager and validator add layers of verification. This modular design allows teams to swap out components as needed or integrate with existing tools. For example, a company using a different vulnerability scanner can replace the detector role while keeping the rest of the pipeline intact.

Another key aspect is the finding lifecycle, which tracks each potential vulnerability from detection through triage, validation, and eventual publication. This creates an auditable trail that satisfies compliance requirements and builds trust in the system's outputs. The coordination substrate provides a message-passing layer that ensures agents communicate reliably, even if some components fail. This kind of resilience is essential for production security workflows where uptime and consistency are paramount.

Cisco's commitment to open source in this area dates back to earlier projects like the OpenC2 standard and the Security Assertion Markup Language (SAML) contributions. The company has also been active in the Open Cybersecurity Alliance and the OASIS consortium. The Foundry Security Spec is seen as a natural extension of these efforts, applying the same principles to the emerging field of agentic AI. By releasing the spec on GitHub, Cisco invites feedback and contributions from the global security community, which can help refine the specification and ensure it remains relevant as AI technology evolves.

In practical terms, a security team using Foundry would first set up an environment containing the target codebase and the AI model they wish to evaluate. The orchestrator agent would then launch a series of agents: the indexer builds a searchable representation of the code, the cartographer maps dependencies and data flows, and the detector uses the model to look for known vulnerability patterns. Each finding is recorded with metadata about its severity, location, and confidence level. The triager role then prioritizes findings based on risk, while the validator attempts to confirm them with additional techniques such as fuzzing or symbolic execution. Finally, the reporter agent compiles a structured report that can be directly fed into ticketing systems or dashboards.

The integration with Project CodeGuard provides an additional layer: while CodeGuard focuses on preventing security issues during code generation, Foundry focuses on evaluating existing code and AI-generated outputs. Together, they form a complete pipeline for secure AI-assisted development. CodeGuard's rules can be used at the planning stage to guide AI coding agents toward safe patterns, and then Foundry can verify that the resulting code meets security standards before deployment. This end-to-end approach addresses the entire lifecycle, from initial design to final validation.

The industry response to the announcement has been largely positive. Security analysts and practitioners have praised Cisco for taking a practical, vendor-neutral approach that avoids proprietary lock-in. The spec's emphasis on auditable provenance and economic yield thresholds—ensuring that the cost of verifying findings does not exceed the value of the vulnerabilities discovered—has been particularly well received. Some critics have pointed out that the spec is still relatively new and may need time to mature, but the open-source model should accelerate improvements.

Cisco has also committed to using Foundry internally and plans to share case studies and best practices as the community grows. The company's own security team, which monitors one of the largest networks in the world, will be an early adopter. This real-world validation will be crucial for convincing other organizations to adopt the framework. The Foundry Security Spec is available now on GitHub under a permissive license, and Cisco encourages security teams, researchers, and tool vendors to experiment with it and provide feedback.

In summary, the Foundry Security Spec represents a significant step toward making agentic AI a reliable and trustworthy component of modern cybersecurity operations. By providing a common framework for evaluation and governance, Cisco is helping to bridge the gap between the raw power of large language models and the rigorous verification that security requires. The open-source nature ensures that the entire industry can benefit from these advances, and the integration with Project CodeGuard offers a comprehensive solution for secure AI development. As the adoption of AI in security continues to accelerate, frameworks like Foundry will become essential for maintaining trust and effectiveness.

Source: Network World News