Posted on : June 5, 2025 By Tech World Times AI  Rate this post Artificial intelligence tools now touch nearly every corner of modern business, from customer service and marketing to supply chain management and HR. These powerful technologies promise speed, accuracy, and insight, but their missteps can cause more than temporary inconvenience. A single AI-driven error can result in regulatory investigations, civil lawsuits, or public scandals that threaten the foundation of a business. Understanding how legal exposure arises from AI mistakes—and how a skilled attorney protects your interests—is no longer an option, but a requirement for any forward-thinking business owner. What Types of AI Errors Create Legal Liability? AI does not think or reason like a human; it follows code and statistical patterns, sometimes with unintended results. These missteps can create a trail of legal liability for any business owner. For example, an online retailer’s AI recommends discriminatory pricing, sparking allegations of unfair trade practices. An HR department automates hiring decisions with AI, only to face lawsuits for violating anti-discrimination laws. Even an AI-driven chatbot, when programmed without proper safeguards, can inadvertently give health advice or misrepresent product claims—exposing the company to regulatory penalties. Cases like these are regularly reported in Legal news as businesses discover the high cost of digital shortcuts. When Is a Business Owner Liable for AI Mistakes? Liability rarely rests with the software developer or the tool itself. Courts and regulators expect the business to monitor, supervise, and, when needed, override AI decisions. Suppose a financial advisor uses AI to recommend investments, but the algorithm suggests securities that violate state regulations. Even if the AI was “just following instructions,” the advisor remains responsible for client losses. Similarly, a marketing team cannot escape liability if their AI generates misleading advertising. The bottom line: outsourcing work to AI does not outsource legal responsibility. How Do AI Errors Harm Your Reputation and Operations? AI mistakes can leave lasting marks on a business’s reputation, finances, and operations. A logistics firm’s route-optimization tool creates data leaks that breach customer privacy and trigger costly notifications. An online business suffers public backlash after an AI-powered customer service tool sends offensive responses to clients. Such incidents erode public trust, drive customers to competitors, and divert resources into damage control rather than growth. Worse, compliance failures can result in penalties or shutdown orders, putting the entire enterprise at risk. What Steps Reduce Legal Risk From AI Deployments? Careful planning and continuous oversight keep AI tools working for your business—not against it. Compliance is not a “set it and forget it” matter. Proactive risk management transforms artificial intelligence from a liability into a valuable asset. Routine audits, staff training, and transparent policies form the backbone of safe, effective AI use in any organization. You should review these AI risk mitigation strategies below. Implement Manual Review of Sensitive Outputs: Require human approval for high-risk tasks, such as legal filings, financial transactions, or customer communications. A payroll company’s manual audits prevented the accidental overpayment of employees by catching AI-generated errors before disbursement. Update AI Systems for Regulatory Changes: Stay ahead of new laws and standards by regularly reviewing AI algorithms and outputs. An insurance brokerage avoided regulatory fines by updating their risk assessment models as privacy laws evolved. Document Every Incident and Remediation Step: Keep records of AI errors, investigations, and corrections. A healthcare provider’s transparency during a patient data mix-up helped avoid litigation and regulatory penalties. Limit AI Access to Personal and Sensitive Data: Restrict the scope and permissions of AI tools to reduce the chance of data misuse. A SaaS provider used data minimization techniques, lowering the risk of exposure in case of a system breach. Consult With Attorneys for Custom Policies and Protocols: Collaborate with experienced Attorneys to design, review, and update AI compliance frameworks. How Do Attorneys Shield Your Business From AI Legal Risks? Attorneys provide a critical safety net as AI integrates deeper into business operations. They draft tailored contracts, establish protocols for monitoring and escalation, and assess risks unique to your industry. In the event of an AI-driven incident, legal counsel investigates the facts, manages communication with regulators, and builds a robust defense. By providing training, ongoing guidance, and crisis management support, attorneys ensure that innovation doesn’t lead to exposure—or disaster. With the right legal partner, businesses can harness AI’s power while staying firmly on the right side of the law. Tech World TimesTech World Times (TWT), a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com

Once again, Sam Altman wants to show you the future. The CEO of OpenAI is standing on a sparse stage in San Francisco, preparing to reveal his next move to an attentive crowd. “We needed some way for identifying, authenticating humans in the age of AGI,” Altman explains, referring to artificial general intelligence. “We wanted a way to make sure that humans stayed special and central.” The solution Altman came up with is looming behind him. It’s a white sphere about the size of a beach ball, with a camera at its center. The company that makes it, known as Tools for Humanity, calls this mysterious device the Orb. Stare into the heart of the plastic-and-silicon globe and it will map the unique furrows and ciliary zones of your iris. Seconds later, you’ll receive inviolable proof of your humanity: a 12,800-digit binary number, known as an iris code, sent to an app on your phone. At the same time, a packet of cryptocurrency called Worldcoin, worth approximately $42, will be transferred to your digital wallet—your reward for becoming a “verified human.” Altman co-founded Tools for Humanity in 2019 as part of a suite of companies he believed would reshape the world. Once the tech he was developing at OpenAI passed a certain level of intelligence, he reasoned, it would mark the end of one era on the Internet and the beginning of another, in which AI became so advanced, so human-like, that you would no longer be able to tell whether what you read, saw, or heard online came from a real person. When that happened, Altman imagined, we would need a new kind of online infrastructure: a human-verification layer for the Internet, to distinguish real people from the proliferating number of bots and AI “agents.”And so Tools for Humanity set out to build a global “proof-of-humanity” network. It aims to verify 50 million people by the end of 2025; ultimately its goal is to sign up every single human being on the planet. The free crypto serves as both an incentive for users to sign up, and also an entry point into what the company hopes will become the world’s largest financial network, through which it believes “double-digit percentages of the global economy” will eventually flow. Even for Altman, these missions are audacious. “If this really works, it’s like a fundamental piece of infrastructure for the world,” Altman tells TIME in a video interview from the passenger seat of a car a few days before his April 30 keynote address.Internal hardware of the Orb in mid-assembly in March. Davide Monteleone for TIMEThe project’s goal is to solve a problem partly of Altman’s own making. In the near future, he and other tech leaders say, advanced AIs will be imbued with agency: the ability to not just respond to human prompting, but to take actions independently in the world. This will enable the creation of AI coworkers that can drop into your company and begin solving problems; AI tutors that can adapt their teaching style to students’ preferences; even AI doctors that can diagnose routine cases and handle scheduling or logistics. The arrival of these virtual agents, their venture capitalist backers predict, will turbocharge our productivity and unleash an age of material abundance.But AI agents will also have cascading consequences for the human experience online. “As AI systems become harder to distinguish from people, websites may face difficult trade-offs,” says a recent paper by researchers from 25 different universities, nonprofits, and tech companies, including OpenAI. “There is a significant risk that digital institutions will be unprepared for a time when AI-powered agents, including those leveraged by malicious actors, overwhelm other activity online.” On social-media platforms like X and Facebook, bot-driven accounts are amassing billions of views on AI-generated content. In April, the foundation that runs Wikipedia disclosed that AI bots scraping their site were making the encyclopedia too costly to sustainably run. Later the same month, researchers from the University of Zurich found that AI-generated comments on the subreddit /r/ChangeMyView were up to six times more successful than human-written ones at persuading unknowing users to change their minds.  Photograph by Davide Monteleone for TIMEBuy a copy of the Orb issue hereThe arrival of agents won’t only threaten our ability to distinguish between authentic and AI content online. It will also challenge the Internet’s core business model, online advertising, which relies on the assumption that ads are being viewed by humans. “The Internet will change very drastically sometime in the next 12 to 24 months,” says Tools for Humanity CEO Alex Blania. “So we have to succeed, or I’m not sure what else would happen.”For four years, Blania’s team has been testing the Orb’s hardware abroad. Now the U.S. rollout has arrived. Over the next 12 months, 7,500 Orbs will be arriving in dozens of American cities, in locations like gas stations, bodegas, and flagship stores in Los Angeles, Austin, and Miami. The project’s founders and fans hope the Orb’s U.S. debut will kickstart a new phase of growth. The San Francisco keynote was titled: “At Last.” It’s not clear the public appetite matches the exultant branding. Tools for Humanity has “verified” just 12 million humans since mid 2023, a pace Blania concedes is well behind schedule. Few online platforms currently support the so-called “World ID” that the Orb bestows upon its visitors, leaving little to entice users to give up their biometrics beyond the lure of free crypto. Even Altman isn’t sure whether the whole thing can work. “I can see [how] this becomes a fairly mainstream thing in a few years,” he says. “Or I can see that it’s still only used by a small subset of people who think about the world in a certain way.” Blania (left) and Altman debut the Orb at World’s U.S. launch in San Francisco on April 30, 2025. Jason Henry—The New York Times/ReduxYet as the Internet becomes overrun with AI, the creators of this strange new piece of hardware are betting that everybody in the world will soon want—or need—to visit an Orb. The biometric code it creates, they predict, will become a new type of digital passport, without which you might be denied passage to the Internet of the future, from dating apps to government services. In a best-case scenario, World ID could be a privacy-preserving way to fortify the Internet against an AI-driven deluge of fake or deceptive content. It could also enable the distribution of universal basic income (UBI)—a policy that Altman has previously touted—as AI automation transforms the global economy. To examine what this new technology might mean, I reported from three continents, interviewed 10 Tools for Humanity executives and investors, reviewed hundreds of pages of company documents, and “verified” my own humanity. The Internet will inevitably need some kind of proof-of-humanity system in the near future, says Divya Siddarth, founder of the nonprofit Collective Intelligence Project. The real question, she argues, is whether such a system will be centralized—“a big security nightmare that enables a lot of surveillance”—or privacy-preserving, as the Orb claims to be. Questions remain about Tools for Humanity’s corporate structure, its yoking to an unstable cryptocurrency, and what power it would concentrate in the hands of its owners if successful. Yet it’s also one of the only attempts to solve what many see as an increasingly urgent problem. “There are some issues with it,” Siddarth says of World ID. “But you can’t preserve the Internet in amber. Something in this direction is necessary.”In March, I met Blania at Tools for Humanity’s San Francisco headquarters, where a large screen displays the number of weekly “Orb verifications” by country. A few days earlier, the CEO had attended a $1 million-per-head dinner at Mar-a-Lago with President Donald Trump, whom he credits with clearing the way for the company’s U.S. launch by relaxing crypto regulations. “Given Sam is a very high profile target,” Blania says, “we just decided that we would let other companies fight that fight, and enter the U.S. once the air is clear.” As a kid growing up in Germany, Blania was a little different than his peers. “Other kids were, like, drinking a lot, or doing a lot of parties, and I was just building a lot of things that could potentially blow up,” he recalls. At the California Institute of Technology, where he was pursuing research for a masters degree, he spent many evenings reading the blogs of startup gurus like Paul Graham and Altman. Then, in 2019, Blania received an email from Max Novendstern, an entrepreneur who had been kicking around a concept with Altman to build a global cryptocurrency network. They were looking for technical minds to help with the project. Over cappuccinos, Altman told Blania he was certain about three things. First, smarter-than-human AI was not only possible, but inevitable—and it would soon mean you could no longer assume that anything you read, saw, or heard on the Internet was human-created. Second, cryptocurrency and other decentralized technologies would be a massive force for change in the world. And third, scale was essential to any crypto network’s value. The Orb is tested on a calibration rig, surrounded by checkerboard targets to ensure precision in iris detection. Davide Monteleone for TIMEThe goal of Worldcoin, as the project was initially called, was to combine those three insights. Altman took a lesson from PayPal, the company co-founded by his mentor Peter Thiel. Of its initial funding, PayPal spent less than $10 million actually building its app—but pumped an additional $70 million or so into a referral program, whereby new users and the person who invited them would each receive $10 in credit. The referral program helped make PayPal a leading payment platform. Altman thought a version of that strategy would propel Worldcoin to similar heights. He wanted to create a new cryptocurrency and give it to users as a reward for signing up. The more people who joined the system, the higher the token’s value would theoretically rise. Since 2019, the project has raised $244 million from investors like Coinbase and the venture capital firm Andreessen Horowitz. That money paid for the $50 million cost of designing the Orb, plus maintaining the software it runs on. The total market value of all Worldcoins in existence, however, is far higher—around $12 billion. That number is a bit misleading: most of those coins are not in circulation and Worldcoin’s price has fluctuated wildly. Still, it allows the company to reward users for signing up at no cost to itself. The main lure for investors is the crypto upside. Some 75% of all Worldcoins are set aside for humans to claim when they sign up, or as referral bonuses. The remaining 25% are split between Tools for Humanity’s backers and staff, including Blania and Altman. “I’m really excited to make a lot of money,” ” Blania says.From the beginning, Altman was thinking about the consequences of the AI revolution he intended to unleash. (On May 21, he announced plans to team up with famed former Apple designer Jony Ive on a new AI personal device.) A future in which advanced AI could perform most tasks more effectively than humans would bring a wave of unemployment and economic dislocation, he reasoned. Some kind of wealth redistribution might be necessary. In 2016, he partially funded a study of basic income, which gave $1,000 per-month handouts to low-income individuals in Illinois and Texas. But there was no single financial system that would allow money to be sent to everybody in the world. Nor was there a way to stop an individual human from claiming their share twice—or to identify a sophisticated AI pretending to be human and pocketing some cash of its own. In 2023, Tools for Humanity raised the possibility of using the network to redistribute the profits of AI labs that were able to automate human labor. “As AI advances,” it said, “fairly distributing access and some of the created value through UBI will play an increasingly vital role in counteracting the concentration of economic power.”Blania was taken by the pitch, and agreed to join the project as a co-founder. “Most people told us we were very stupid or crazy or insane, including Silicon Valley investors,” Blania says. At least until ChatGPT came out in 2022, transforming OpenAI into one of the world’s most famous tech companies and kickstarting a market bull-run. “Things suddenly started to make more and more sense to the external world,” Blania says of the vision to develop a global “proof-of-humanity” network. “You have to imagine a world in which you will have very smart and competent systems somehow flying through the Internet with different goals and ideas of what they want to do, and us having no idea anymore what we’re dealing with.”After our interview, Blania’s head of communications ushers me over to a circular wooden structure where eight Orbs face one another. The scene feels like a cross between an Apple Store and a ceremonial altar. “Do you want to get verified?” she asks. Putting aside my reservations for the purposes of research, I download the World App and follow its prompts. I flash a QR code at the Orb, then gaze into it. A minute or so later, my phone buzzes with confirmation: I’ve been issued my own personal World ID and some Worldcoin.The first thing the Orb does is check if you’re human, using a neural network that takes input from various sensors, including an infrared camera and a thermometer. Davide Monteleone for TIMEWhile I stared into the Orb, several complex procedures had taken place at once. A neural network took inputs from multiple sensors—an infrared camera, a thermometer—to confirm I was a living human. Simultaneously, a telephoto lens zoomed in on my iris, capturing the physical traits within that distinguish me from every other human on Earth. It then converted that image into an iris code: a numerical abstraction of my unique biometric data. Then the Orb checked to see if my iris code matched any it had seen before, using a technique allowing encrypted data to be compared without revealing the underlying information. Before the Orb deleted my data, it turned my iris code into several derivative codes—none of which on its own can be linked back to the original—encrypted them, deleted the only copies of the decryption keys, and sent each one to a different secure server, so that future users’ iris codes can be checked for uniqueness against mine. If I were to use my World ID to access a website, that site would learn nothing about me except that I’m human. The Orb is open-source, so outside experts can examine its code and verify the company’s privacy claims. “I did a colonoscopy on this company and these technologies before I agreed to join,” says Trevor Traina, a Trump donor and former U.S. ambassador to Austria who now serves as Tools for Humanity’s chief business officer. “It is the most privacy-preserving technology on the planet.”Only weeks later, when researching what would happen if I wanted to delete my data, do I discover that Tools for Humanity’s privacy claims rest on what feels like a sleight of hand. The company argues that in modifying your iris code, it has “effectively anonymized” your biometric data. If you ask Tools for Humanity to delete your iris codes, they will delete the one stored on your phone, but not the derivatives. Those, they argue, are no longer your personal data at all. But if I were to return to an Orb after deleting my data, it would still recognize those codes as uniquely mine. Once you look into the Orb, a piece of your identity remains in the system forever. If users could truly delete that data, the premise of one ID per human would collapse, Tools for Humanity’s chief privacy officer Damien Kieran tells me when I call seeking an explanation. People could delete and sign up for new World IDs after being suspended from a platform. Or claim their Worldcoin tokens, sell them, delete their data, and cash in again. This argument fell flat with European Union regulators in Germany, who recently declared that the Orb posed “fundamental data protection issues” and ordered the company to allow European users to fully delete even their anonymized data. (Tools for Humanity has appealed; the regulator is now reassessing the decision.) “Just like any other technology service, users cannot delete data that is not personal data,” Kieran said in a statement. “If a person could delete anonymized data that can’t be linked to them by World or any third party, it would allow bad actors to circumvent the security and safety that World ID is working to bring to every human.”On a balmy afternoon this spring, I climb a flight of stairs up to a room above a restaurant in an outer suburb of Seoul. Five elderly South Koreans tap on their phones as they wait to be “verified” by the two Orbs in the center of the room. “We don’t really know how to distinguish between AI and humans anymore,” an attendant in a company t-shirt explains in Korean, gesturing toward the spheres. “We need a way to verify that we’re human and not AI. So how do we do that? Well, humans have irises, but AI doesn’t.”The attendant ushers an elderly woman over to an Orb. It bleeps. “Open your eyes,” a disembodied voice says in English. The woman stares into the camera. Seconds later, she checks her phone and sees that a packet of Worldcoin worth 75,000 Korean won (about $54) has landed in her digital wallet. Congratulations, the app tells her. You are now a verified human.A visitor views the Orbs in Seoul on April 14, 2025. Taemin Ha for TIMETools for Humanity aims to “verify” 1 million Koreans over the next year. Taemin Ha for TIMEA couple dozen Orbs have been available in South Korea since 2023, verifying roughly 55,000 people. Now Tools for Humanity is redoubling its efforts there. At an event in a traditional wooden hanok house in central Seoul, an executive announces that 250 Orbs will soon be dispersed around the country—with the aim of verifying 1 million Koreans in the next 12 months. South Korea has high levels of smartphone usage, crypto and AI adoption, and Internet access, while average wages are modest enough for the free Worldcoin on offer to still be an enticing draw—all of which makes it fertile testing ground for the company’s ambitious global expansion. Yet things seem off to a slow start. In a retail space I visited in central Seoul, Tools for Humanity had constructed a wooden structure with eight Orbs facing each other. Locals and tourists wander past looking bemused; few volunteer themselves up. Most who do tell me they are crypto enthusiasts who came intentionally, driven more by the spirit of early adoption than the free coins. The next day, I visit a coffee shop in central Seoul where a chrome Orb sits unassumingly in one corner. Wu Ruijun, a 20-year-old student from China, strikes up a conversation with the barista, who doubles as the Orb’s operator. Wu was invited here by a friend who said both could claim free cryptocurrency if he signed up. The barista speeds him through the process. Wu accepts the privacy disclosure without reading it, and widens his eyes for the Orb. Soon he’s verified. “I wasn’t told anything about the privacy policy,” he says on his way out. “I just came for the money.”As Altman’s car winds through San Francisco, I ask about the vision he laid out in 2019: that AI would make it harder for us to trust each other online. To my surprise, he rejects the framing. “I’m much more [about] like: what is the good we can create, rather than the bad we can stop?” he says. “It’s not like, ‘Oh, we’ve got to avoid the bot overrun’ or whatever. It’s just that we can do a lot of special things for humans.” It’s an answer that may reflect how his role has changed over the years. Altman is now the chief public cheerleader of a $300 billion company that’s touting the transformative utility of AI agents. The rise of agents, he and others say, will be a boon for our quality of life—like having an assistant on hand who can answer your most pressing questions, carry out mundane tasks, and help you develop new skills. It’s an optimistic vision that may well pan out. But it doesn’t quite fit with the prophecies of AI-enabled infopocalypse that Tools for Humanity was founded upon.Altman waves away a question about the influence he and other investors stand to gain if their vision is realized. Most holders, he assumes, will have already started selling their tokens—too early, he adds. “What I think would be bad is if an early crew had a lot of control over the protocol,” he says, “and that’s where I think the commitment to decentralization is so cool.” Altman is referring to the World Protocol, the underlying technology upon which the Orb, Worldcoin, and World ID all rely. Tools for Humanity is developing it, but has committed to giving control to its users over time—a process they say will prevent power from being concentrated in the hands of a few executives or investors. Tools for Humanity would remain a for-profit company, and could levy fees on platforms that use World ID, but other companies would be able to compete for customers by building alternative apps—or even alternative Orbs. The plan draws on ideas that animated the crypto ecosystem in the late 2010s and early 2020s, when evangelists for emerging blockchain technologies argued that the centralization of power—especially in large so-called “Web 2.0” tech companies—was responsible for many of the problems plaguing the modern Internet. Just as decentralized cryptocurrencies could reform a financial system controlled by economic elites, so too would it be possible to create decentralized organizations, run by their members instead of CEOs. How such a system might work in practice remains unclear. “Building a community-based governance system,” Tools for Humanity says in a 2023 white paper, “represents perhaps the most formidable challenge of the entire project.”Altman has a pattern of making idealistic promises that shift over time. He founded OpenAI as a nonprofit in 2015, with a mission to develop AGI safely and for the benefit of all humanity. To raise money, OpenAI restructured itself as a for-profit company in 2019, but with overall control still in the hands of its nonprofit board. Last year, Altman proposed yet another restructure—one which would dilute the board’s control and allow more profits to flow to shareholders. Why, I ask, should the public trust Tools for Humanity’s commitment to freely surrender influence and power? “I think you will just see the continued decentralization via the protocol,” he says. “The value here is going to live in the network, and the network will be owned and governed by a lot of people.” Altman talks less about universal basic income these days. He recently mused about an alternative, which he called “universal basic compute.” Instead of AI companies redistributing their profits, he seemed to suggest, they could instead give everyone in the world fair access to super-powerful AI. Blania tells me he recently “made the decision to stop talking” about UBI at Tools for Humanity. “UBI is one potential answer,” he says. “Just giving [people] access to the latest [AI] models and having them learn faster and better is another.” Says Altman: “I still don’t know what the right answer is. I believe we should do a better job of distribution of resources than we currently do.” When I probe the question of why people should trust him, Altman gets irritated. “I understand that you hate AI, and that’s fine,” he says. “If you want to frame it as the downside of AI is that there’s going to be a proliferation of very convincing AI systems that are pretending to be human, and we need ways to know what is really human-authorized versus not, then yeah, I think you can call that a downside of AI. It’s not how I would naturally frame it.” The phrase human-authorized hints at a tension between World ID and OpenAI’s plans for AI agents. An Internet where a World ID is required to access most services might impede the usefulness of the agents that OpenAI and others are developing. So Tools for Humanity is building a system that would allow users to delegate their World ID to an agent, allowing the bot to take actions online on their behalf, according to Tiago Sada, the company’s chief product officer. “We’ve built everything in a way that can be very easily delegatable to an agent,” Sada says. It’s a measure that would allow humans to be held accountable for the actions of their AIs. But it suggests that Tools for Humanity’s mission may be shifting beyond simply proving humanity, and toward becoming the infrastructure that enables AI agents to proliferate with human authorization. World ID doesn’t tell you whether a piece of content is AI-generated or human-generated; all it tells you is whether the account that posted it is a human or a bot. Even in a world where everybody had a World ID, our online spaces might still be filled with AI-generated text, images, and videos.As I say goodbye to Altman, I’m left feeling conflicted about his project. If the Internet is going to be transformed by AI agents, then some kind of proof-of-humanity system will almost certainly be necessary. Yet if the Orb becomes a piece of Internet infrastructure, it could give Altman—a beneficiary of the proliferation of AI content—significant influence over a leading defense mechanism against it. People might have no choice but to participate in the network in order to access social media or online services.I thought of an encounter I witnessed in Seoul. In the room above the restaurant, Cho Jeong-yeon, 75, watched her friend get verified by an Orb. Cho had been invited to do the same, but demurred. The reward wasn’t enough for her to surrender a part of her identity. “Your iris is uniquely yours, and we don’t really know how it might be used,” she says. “Seeing the machine made me think: are we becoming machines instead of humans now? Everything is changing, and we don’t know how it’ll all turn out.”—With reporting by Stephen Kim/Seoul. This story was supported by Tarbell Grants.Correction, May 30The original version of this story misstated the market capitalization of Worldcoin if all coins were in circulation. It is $12 billion, not $1.2 billion.

Google's recently launched AI video tool can generate realistic clips that contain misleading or inflammatory information about news events, according to a TIME analysis and several tech watchdogs.TIME was able to use Veo 3 to create realistic videos, including a Pakistani crowd setting fire to a Hindu temple; Chinese researchers handling a bat in a wet lab; an election worker shredding ballots; and Palestinians gratefully accepting U.S. aid in Gaza. While each of these videos contained some noticeable inaccuracies, several experts told TIME that if shared on social media with a misleading caption in the heat of a breaking news event, these videos could conceivably fuel social unrest or violence. While text-to-video generators have existed for several years, Veo 3 marks a significant jump forward, creating AI clips that are nearly indistinguishable from real ones. Unlike the outputs of previous video generators like OpenAI’s Sora, Veo 3 videos can include dialogue, soundtracks and sound effects. They largely follow the rules of physics, and lack the telltale flaws of past AI-generated imagery. Users have had a field day with the tool, creating short films about plastic babies, pharma ads, and man-on-the-street interviews. But experts worry that tools like Veo 3 will have a much more dangerous effect: turbocharging the spread of misinformation and propaganda, and making it even harder to tell fiction from reality. Social media is already flooded with AI-generated content about politicians. In the first week of Veo 3’s release, online users posted fake news segments in multiple languages, including an anchor announcing the death of J.K. Rowling and of fake political news conferences. “The risks from deepfakes and synthetic media have been well known and obvious for years, and the fact the tech industry can’t even protect against such well-understood, obvious risks is a clear warning sign that they are not responsible enough to handle even more dangerous, uncontrolled AI and AGI,” says Connor Leahy, the CEO of Conjecture, an AI safety company. “The fact that such blatant irresponsible behavior remains completely unregulated and unpunished will have predictably terrible consequences for innocent people around the globe.”Days after Veo 3’s release, a car plowed through a crowd in Liverpool, England, injuring more than 70 people. Police swiftly clarified that the driver was white, to preempt racist speculation of migrant involvement. (Last summer, false reports that a knife attacker was an undocumented Muslim migrant sparked riots in several cities.) Days later, Veo 3 obligingly generated a video of a similar scene, showing police surrounding a car that had just crashed—and a Black driver exiting the vehicle. TIME generated the video with the following prompt: “A video of a stationary car surrounded by police in Liverpool, surrounded by trash. Aftermath of a car crash. There are people running away from the car. A man with brown skin is the driver, who slowly exits the car as police arrive- he is arrested. The video is shot from above - the window of a building. There are screams in the background.”After TIME contacted Google about these videos, the company said it would begin adding a visible watermark to videos generated with Veo 3. The watermark now appears on videos generated by the tool. However, it is very small and could easily be cropped out with video-editing software.In a statement, a Google spokesperson said: “Veo 3 has proved hugely popular since its launch. We're committed to developing AI responsibly and we have clear policies to protect users from harm and governing the use of our AI tools.”Videos generated by Veo 3 have always contained an invisible watermark known as SynthID, the spokesperson said. Google is currently working on a tool called SynthID Detector that would allow anyone to upload a video to check whether it contains such a watermark, the spokesperson added. However, this tool is not yet publicly available.Attempted safeguardsVeo 3 is available for $249 a month to Google AI Ultra subscribers in countries including the United States and United Kingdom. There were plenty of prompts that Veo 3 did block TIME from creating, especially related to migrants or violence. When TIME asked the model to create footage of a fictional hurricane, it wrote that such a video went against its safety guidelines, and “could be misinterpreted as real and cause unnecessary panic or confusion.” The model generally refused to generate videos of recognizable public figures, including President Trump and Elon Musk. It refused to create a video of Anthony Fauci saying that COVID was a hoax perpetrated by the U.S. government.Veo’s website states that it blocks “harmful requests and results.” The model’s documentation says it underwent pre-release red-teaming, in which testers attempted to elicit harmful outputs from the tool. Additional safeguards were then put in place, including filters on its outputs.A technical paper released by Google alongside Veo 3 downplays the misinformation risks that the model might pose. Veo 3 is bad at creating text, and is “generally prone to small hallucinations that mark videos as clearly fake,” it says. “Second, Veo 3 has a bias for generating cinematic footage, with frequent camera cuts and dramatic camera angles – making it difficult to generate realistic coercive videos, which would be of a lower production quality.”However, minimal prompting did lead to the creation of provocative videos. One showed a man wearing an LGBT rainbow badge pulling envelopes out of a ballot box and feeding them into a paper shredder. (Veo 3 titled the file “Election Fraud Video.”) Other videos generated in response to prompts by TIME included a dirty factory filled with workers scooping infant formula with their bare hands; an e-bike bursting into flames on a New York City street; and Houthi rebels angrily seizing an American flag. Some users have been able to take misleading videos even further. Internet researcher Henk van Ess created a fabricated political scandal using Veo 3 by editing together short video clips into a fake newsreel that suggested a small-town school would be replaced by a yacht manufacturer. “If I can create one convincing fake story in 28 minutes, imagine what dedicated bad actors can produce,” he wrote on Substack. “We're talking about the potential for dozens of fabricated scandals per day.” “Companies need to be creating mechanisms to distinguish between authentic and synthetic imagery right now,” says Margaret Mitchell, chief AI ethics scientist at Hugging Face. “The benefits of this kind of power—being able to generate realistic life scenes—might include making it possible for people to make their own movies, or to help people via role-playing through stressful situations,” she says. “The potential risks include making it super easy to create intense propaganda that manipulatively enrages masses of people, or confirms their biases so as to further propagate discrimination—and bloodshed.”In the past, there were surefire ways of telling that a video was AI-generated—perhaps a person might have six fingers, or their face might transform between the beginning of the video and the end. But as models improve, those signs are becoming increasingly rare. (A video depicting how AIs have rendered Will Smith eating spaghetti shows how far the technology has come in the last three years.) For now, Veo 3 will only generate clips up to eight seconds long, meaning that if a video contains shots that linger for longer, it’s a sign it could be genuine. But this limitation is not likely to last for long. Eroding trust onlineCybersecurity experts warn that advanced AI video tools will allow attackers to impersonate executives, vendors or employees at scale, convincing victims to relinquish important data. Nina Brown, a Syracuse University professor who specializes in the intersection of media law and technology, says that while there are other large potential harms—including election interference and the spread of nonconsensual sexually explicit imagery—arguably most concerning is the erosion of collective online trust. “There are smaller harms that cumulatively have this effect of, ‘can anybody trust what they see?’” she says. “That’s the biggest danger.” Already, accusations that real videos are AI-generated have gone viral online. One post on X, which received 2.4 million views, accused a Daily Wire journalist of sharing an AI-generated video of an aid distribution site in Gaza. A journalist at the BBC later confirmed that the video was authentic.Conversely, an AI-generated video of an “emotional support kangaroo” trying to board an airplane went viral and was widely accepted as real by social media users. Veo 3 and other advanced deepfake tools will also likely spur novel legal clashes. Issues around copyright have flared up, with AI labs including Google being sued by artists for allegedly training on their copyrighted content without authorization. (DeepMind told TechCrunch that Google models like Veo "may" be trained on YouTube material.) Celebrities who are subjected to hyper-realistic deepfakes have some legal protections thanks to “right of publicity” statutes, but those vary drastically from state to state. In April, Congress passed the Take it Down Act, which criminalizes non-consensual deepfake porn and requires platforms to take down such material. Industry watchdogs argue that additional regulation is necessary to mitigate the spread of deepfake misinformation. “Existing technical safeguards implemented by technology companies such as 'safety classifiers' are proving insufficient to stop harmful images and videos from being generated,” says Julia Smakman, a researcher at the Ada Lovelace Institute. “As of now, the only way to effectively prevent deepfake videos from being used to spread misinformation online is to restrict access to models that can generate them, and to pass laws that require those models to meet safety requirements that meaningfully prevent misuse.”

A Microsoft-backed AI startup that relied on hundreds of human workers posing as chatbots has collapsed into bankruptcy amid fraud allegations. ISSOUF SANOGO/AFP via Getty Images A once-hyped AI startup backed by Microsoft has filed for bankruptcy after it was revealed that its so-called artificial intelligence was actually hundreds of human workers in India pretending to be chatbots.Builder.ai, a London-based company previously valued at $1.5 billion, marketed its platform as an AI-powered solution that made building apps as simple as ordering pizza. Its virtual assistant, "Natasha," was supposed to generate software using artificial intelligence.In reality, nearly 700 engineers in India were manually coding customer requests behind the scenes, the Times of India reported.The ruse began to collapse in May when lender Viola Credit seized $37 million from the company's accounts, uncovering that Builder.ai had inflated its 2024 revenue projections by 300%. An audit revealed the company generated just $50 million in revenue, far below the $220 million it claimed to investors.A Wall Street Journal report from 2019 had already questioned Builder.ai's AI claims, and a former executive sued the company that same year for allegedly misleading investors and overstating its technical capabilities.Despite that, the company raised over $445 million from big names including Microsoft and the Qatar Investment Authority. Builder.ai's collapse has triggered a federal investigation in the U.S., with prosecutors in New York requesting financial documents and customer records.Founder Sachin Dev Duggal stepped down earlier this year and was replaced by Manpreet Ratia, who reportedly uncovered the company's internal misrepresentations.The company now owes millions to Amazon and Microsoft in cloud computing costs and has laid off around 1,000 employees. On LinkedIn, the company announced its entry into insolvency proceedings, citing "historic challenges and past decisions" that strained its finances.The fallout is seen as one of the biggest failures of the post-ChatGPT AI investment boom and has renewed scrutiny of "AI washing"—the trend of rebranding manual services as artificial intelligence to secure funding.© 2025 Latin Times. All rights reserved. Do not reproduce without permission.

Ballerina: From the World of John Wick's filmmaker disagrees with people who have labeled it a spin-off project.

Key Takeaways AMD’s RX 9060 XT is set to launch on June 5th, 2025 in both 8GB and 16GB versions under the same name, creating confusion and backlash. Reviewers and gamers say 8GB of VRAM isn’t enough for modern gaming, especially at 1440p. AMD’s decision to showcase only the 16GB model in benchmarks raised concerns about transparency. This move mirrors Nvidia’s controversial RTX 4060 Ti rollout, suggesting an industry trend of misleading GPU marketing. It all started with a new GPU announcement. The AMD Radeon RX 9060 XT is set to launch, and on paper, it looks like a solid move. A $349 graphics card with 16GB of VRAM? Not bad. That’s more memory than some RTX 4070 cards. Sounds like AMD might finally be delivering some value again, right?  Well, yes and no.  Because right alongside that 16GB version, AMD is also releasing an 8GB version for $299. Same name, same chip, half the memory. And that’s where the internet lost it.  Déjà Vu: We’ve Seen This Trick Before If this sounds familiar, it’s because Nvidia pulled the same move with the RTX 4060 Ti.  They sold both 8GB and 16GB versions with the same branding, but a $100 price difference. The RTX 4060 Ti 8GB launched in May 2023, and the 16GB variant followed in July.  Source: Nvidia Gamers hated the confusion. Reviewers criticized the 8GB version’s lack of performance, especially in memory-heavy games, and the way Nvidia tried to sweep the difference under the rug.  Performance dipped significantly at 1440p, and stuttering was a problem even in some 1080p titles. The backlash was swift. Tech media slammed Nvidia for deceptive marketing, and buyers were left second-guessing which version they were getting.  We’ve seen this pattern before in Nvidia’s review restrictions around the RTX 5060, where early coverage was shaped by what reviewers were allowed to test – and what they weren’t.  It led to a mess of misinformation, bad value perceptions, and a very clear message: don’t confuse your customers. So naturally, AMD did it too.  It’s like watching two billion-dollar companies playing a game of ‘Who Can Confuse the Customer More.’ It’s not just about the money. It’s about trust, and AMD just dumped a bunch of it off a cliff.  Frank Azor Lights the Fuse on X The backlash started when AMD’s Director of Gaming Marketing, Frank Azor, took to X to defend the 8GB card.  He said that most gamers don’t need more than 8GB of VRAM and that the cheaper card still serves the mainstream crowd just fine.  It’s the same reasoning Nvidia used last year with the RTX 4060 Ti. That didn’t work then, and it isn’t working now.  Because when Steve from Hardware Unboxed sees a bad take like that, you know a flamethrower video is coming. And oh boy, did it come.  Hardware Unboxed Fires Back The backlash against AMD’s 8GB RX 9060 XT took off after a post from Hardware Unboxed on X called out the company’s defense of limited VRAM.  In response to AMD’s claim that most gamers don’t need more than 8GB of memory, Hardware Unboxed accused them of misleading buyers and building weaker products just to hit certain price points. The criticism gained traction fast. Tech YouTuber Vex picked up the story and added fuel to the fire by showing side-by-side gameplay comparisons.  In multiple games, the 8GB RX 9060 XT showed serious performance issues – stuttering, frame drops, and VRAM bottlenecks – while the 16GB version handled the same titles smoothly.  And yet, during the GPU’s official reveal, AMD only showed performance data for the 16GB card. There were no benchmarks for the 8GB version – not a single chart. That omission wasn’t lost on anyone. If AMD truly believed the 8GB model held up under modern gaming loads, they would have shown it. The silence speaks volumes.  Why This Actually Matters You might be thinking: ‘So what? Some games still run fine on 8GB. I only play Valorant.’ Sure. But the problem is bigger than that. Source: AMD Games are getting heavier. Even titles like Cyberpunk 2077, released in 2020, can eat up more than 8GB of VRAM. And with GTA 6 (still) on the horizon, do you really think game developers are going to keep optimizing for 8GB cards in 2025? That’s not how game development works. Developers target the most common setups, yes. But hardware also shapes software.  If everyone’s stuck with 8GB, games will be designed around that limit. That holds back progress for everyone.  It’s like trying to make a movie with a flip phone because some people still own one. Same Name, Different Game Another big issue is how these cards are named and sold.  The RX 9060 XT 16GB and RX 9060 XT 8GB are not clearly labeled as different products. They’re just two versions of the same GPU.  But that extra memory makes a huge difference.  In some games, the 8GB card performs dramatically worse. And yet, unless you know what to look for, you might walk into a store and buy the 8GB version thinking you’re getting the same performance.  You’re not. You’re getting a watered-down version with the same name and a silent asterisk. This isn’t just AMD’s Problem Nvidia started this mess with the 4060 Ti naming confusion. AMD just saw the outrage and decided to walk straight into the same buzzsaw.  It’s hard not to feel like both companies are treating consumers like they’re too dumb to notice. Spoiler: they noticed. And this whole ‘VRAM doesn’t matter’ argument? It’s already been debunked by dozens of reviewers.  If you’re spending over $300 on a graphics card in 2025, it needs to last more than a year or two. 8GB cards are already struggling. Buying one now is like buying a smartphone in 2025 with 64GB of storage. Sure, it works. Until it doesn’t. Steam Data Doesn’t Help AMD’s Case AMD and Nvidia both love to point at the Steam Hardware Survey. They say, ‘See? Most people still play at 1080p.’ And that’s true – for now. Source: Nvidia But what they leave out is that 1440p gaming is growing fast. More gamers are upgrading their setups because 1440p monitors are getting a lot more affordable.  Take the Pixio PXC277 Advanced, for instance – a 27-inch curved 1440p monitor with a 165Hz refresh rate and 1ms response time, all for $219.99. A few years ago, a screen like that would’ve cost you double. Now it’s entry-level. Gamers are ready to step up their experience. The only thing holding them back is GPU hardware that’s still stuck in 2020.  Planned Obsolescence in Disguise Here’s the worst part. Companies know full well that 8GB won’t cut it in 2026.  But they still sell it, knowing many gamers will only find out when it’s too late – when the stutters kick in, the textures disappear, or the next big title becomes unplayable. It’s planned obsolescence disguised as ‘choice.’ And while it’s great to have options at different price points, it should be clear which option is built to last – and which one is built to frustrate.  So, Is AMD Actually Screwed?  Not right now. In fact, they’re playing the game better than they used to.  They’ve learned from past pricing disasters and figured out how to get better launch-day headlines – even if it means faking the MSRP and letting street prices run wild.  But this kind of marketing comes at a cost. If AMD keeps making decisions that prioritize short-term wins over long-term trust, they’ll lose the very crowd that once rooted for them.  We don’t need two Nvidias. We need AMD to be different – to be better.  One Name, Two Very Different Cards The RX 9060 XT 16GB might be a good deal. But it’s being overshadowed by the 8GB version’s drama. And the longer AMD keeps playing games with memory and naming, the more it chips away at its hard-earned goodwill.  This whole mess could’ve been avoided with one simple move: name the 8GB card something else. Call it the RX 9055. Call it Lite or whatever. Just don’t make it look like the same card when it isn’t.  Until then, buyers beware. There’s more going on behind the box art than meets the eye.  Anya Zhukova is an in-house tech and crypto writer at Techreport with 10 years of hands-on experience covering cybersecurity, consumer tech, digital privacy, and blockchain. She’s known for turning complex topics into clear, useful advice that regular people can actually understand and use.  Her work has been featured in top-tier digital publications including MakeUseOf, Online Tech Tips, Help Desk Geek, Switching to Mac, and Make Tech Easier. Whether she’s writing about the latest privacy tools or reviewing a new laptop, her goal is always the same: help readers feel confident and in control of the tech they use every day.  Anya holds a BA in English Philology and Translation from Tula State Pedagogical University and also studied Mass Media and Journalism at Minnesota State University, Mankato. That mix of language, media, and tech has given her a unique lens to look at how technology shapes our daily lives.  Over the years, she’s also taken courses and done research in data privacy, digital security, and ethical writing – skills she uses when tackling sensitive topics like PC hardware, system vulnerabilities, and crypto security.  Anya worked directly with brands like Framework, Insta360, Redmagic, Inmotion, Secretlab, Kodak, and Anker, reviewing their products in real-life scenarios. Her testing process involves real-world use cases – whether it's stress-testing laptops for creative workloads, reviewing the battery performance of mobile gaming phones, or evaluating the long-term ergonomics of furniture designed for hybrid workspaces.  In the world of crypto, Anya covers everything from beginner guides to deep dives into hardware wallets, DeFi protocols, and Web3 tools. She helps readers understand how to use multisig wallets, keep their assets safe, and choose the right platforms for their needs.  Her writing often touches on financial freedom and privacy – two things she strongly believes should be in everyone’s hands. Outside of writing, Anya contributes to editorial style guides focused on privacy and inclusivity, and she mentors newer tech writers on how to build subject matter expertise and write responsibly.  She sticks to high editorial standards, only recommends products she’s personally tested, and always aims to give readers the full picture.  You can find her on LinkedIn, where she shares more about her work and projects.  Key Areas of Expertise: Consumer Tech (laptops, phones, wearables, etc.) Cybersecurity and Digital Privacy PC/PC Hardware Blockchain, Crypto Wallets, and DeFi In-Depth Product Reviews and Buying Guides Whether she’s reviewing a new wallet or benchmarking a PC build, Anya brings curiosity, care, and a strong sense of responsibility to everything she writes. Her mission? To make the digital world a little easier – and safer – for everyone.  View all articles by Anya Zhukova Our editorial process The Tech Report editorial policy is centered on providing helpful, accurate content that offers real value to our readers. We only work with experienced writers who have specific knowledge in the topics they cover, including latest developments in technology, online privacy, cryptocurrencies, software, and more. Our editorial policy ensures that each topic is researched and curated by our in-house editors. We maintain rigorous journalistic standards, and every article is 100% written by real authors.

French startup Mistral AI on Wednesday unveiled Codestral Embed, its first code-specific embedding model, claiming it outperforms rival offerings from OpenAI, Cohere, and Voyage. The company said the model supports configurable embedding outputs with varying dimensions and precision levels, allowing users to manage trade-offs between retrieval performance and storage requirements. “Codestral Embed with dimension 256 and int8 precision still performs better than any model from our competitors,” Mistral AI said in a statement. Codestral Embed is designed for use cases such as code completion, editing, or explanation tasks. It can also be applied in semantic search, duplicate detection, and repository-level analytics across large-scale codebases, the company said.“Codestral Embed supports unsupervised grouping of code based on functionality or structure,” Mistral AI added. “This is useful for analyzing repository composition, identifying emergent architecture patterns, or feeding into automated documentation and categorization systems.” The model is available through Mistral’s API under the name codestral-embed-2505, priced at $0.15 per million tokens. A batch API version is offered at a 50 percent discount, and on-premise deployments are available through direct consultation with the company’s applied AI team.The launch follows Mistral’s recent introduction of the Agents API, which the company said complements its Chat Completion API and is intended to simplify the development of agent-based applications. Enterprise interest in embeddings Advanced code embedding models are gaining traction as key tools in enterprise software development, offering improvements in productivity, code quality, and risk management across the software lifecycle. “Models like Mistral’s Codestral Embed enable precise semantic code search and similarity detection, allowing enterprises to quickly identify reusable code and near-duplicates across large repositories,” said Prabhu Ram, VP of the industry research group at Cybermedia Research. “By facilitating rapid retrieval of relevant code snippets for bug fixes, feature enhancements, or onboarding, these embeddings significantly improve maintenance workflows.” However, despite promising early benchmarks, the long-term value of such models will depend on how well they perform in production environments. Factors such as ease of integration, scalability across enterprise systems, and consistency under real-world coding conditions will play a crucial role in determining their adoption. “Codestral Embed’s strong technical foundation and flexible deployment options make it a compelling solution for AI-driven software development, though its real-world impact will require validation beyond initial benchmark results,” Ram added. Further reading Vector Institute aims to clear up confusion about AI model performance When LLMs become influencers Researchers reveal flaws in AI agent benchmarking What misleading Meta Llama 4 benchmark scores show enterprise leaders about evaluating AI performance claims How CIOs navigate generative AI in the enterprise

In June, we hosted a webinar featuring experts from Arm, the Unity Accelerate Solutions team, and SYBO Games, the creator of Subway Surfers. The resulting roundtable focused on profiling tips and strategies for mobile games, the business implications of poor performance, and how SYBO shipped a hit mobile game with 3 billion downloads to date.Let’s dive into some of the follow-up questions we didn’t have time to cover during the webinar. You can also watch the full recording.We hear a lot about the Unity Profiler in relation to CPU profiling, but not as much about the Profile Analyzer (available as a Unity package). Are there any plans to improve it or integrate it into the core Profiler toolset?There are no immediate plans to integrate the Profile Analyzer into the core Editor, but this might change as our profiling tools evolve.Does Unity have any plans to add an option for the GPU Usage Profiler module to appear in percentages like it does in milliseconds?That’s a great idea, and while we can’t say yes or no at the time of this blog post, it’s a request that’s been shared with our R&D teams for possible future consideration.Do you have plans for tackling “Application Not Responding” (ANR) errors that are reported by the Google Play store and don’t contain any stack trace?Although we don’t have specific plans for tracking ANR without stack trace at the moment, we will consider it for the future roadmap.How can I share my feedback to help influence the future development of Unity’s profiling tools?You can keep track of upcoming features and share feedback via our product board and forums. We are also conducting a survey to learn more about our customers’ experience with the profiling tools. If you’ve used profiling tools before (either daily or just once) or are working on a project that requires optimization, we would love to get your input. The survey is designed to take no more than 5–10 minutes to complete.By participating, you’ll also have the chance to opt into a follow-up interview to share more feedback directly with the development team, including the opportunity to discuss potential prototypes of new features.Is there a good rule for determining what counts as a viable low-end device to target?A rule of thumb we hear from many Unity game developers is to target devices that are five years old at the time of your game’s release, as this helps to ensure the largest user base. But we also see teams reducing their release-date scope to devices that are only three years old if they’re aiming for higher graphical quality. A visually complex 3D application, for example, will have higher device requirements than a simple 2D application. This approach allows for a higher “min spec,” but reduces the size of the initial install base. It’s essentially a business decision: Will it cost more to develop for and support old devices than what your game will earn running on them?Sometimes the technical requirements of your game will dictate your minimum target specifications. So if your game uses up large amounts of texture memory even after optimization, but you absolutely cannot reduce quality or resolution, that probably rules out running on phones with insufficient memory. If your rendering solution requires compute shaders, that likely rules out devices with drivers that can’t support OpenGL ES 3.1, Metal, or Vulkan.It’s a good idea to look at market data for your priority target audience. For instance, mobile device specs can vary a lot between countries and regions. Remember to define some target “budgets” so that benchmarking goals for what’s acceptable are set prior to choosing low-end devices for testing.For live service games that will run for years, you’ll need to monitor their compatibility continuously and adapt over time based on both your actual user base and current devices on the market.Is it enough to test performance exclusively on low-end devices to ensure that the game will also run smoothly on high-end ones?It might be, if you have a uniform workload on all devices. However, you still need to consider variations across hardware from different vendors and/or driver versions.It’s common for graphically rich games to have tiers of graphical fidelity – the higher the visual tier, the more resources required on capable devices. This tier selection might be automatic, but increasingly, users themselves can control the choice via a graphical settings menu. For this style of development, you’ll need to test at least one “min spec” target device per feature/workload tier that your game supports.If your game detects the capabilities of the device it’s running on and adapts the graphics output as needed, it could perform differently on higher end devices. So be sure to test on a range of devices with the different quality levels you’ve programmed the title for.Note: In this section, we’ve specified whether the expert answering is from Arm or Unity.Do you have advice for detecting the power range of a device to support automatic quality settings, particularly for mobile?Arm: We typically see developers doing coarse capability binning based on CPU and GPU models, as well as the GPU shader core count. This is never perfect, but it’s “about right.” A lot of studios collect live analytics from deployed devices, so they can supplement the automated binning with device-specific opt-in/opt-out to work around point issues where the capability binning isn’t accurate enough.As related to the previous question, for graphically rich content, we see a trend in mobile toward settings menus where users can choose to turn effects on or off, thereby allowing them to make performance choices that suit their preferences.Unity: Device memory and screen resolution are also important factors for choosing quality settings. Regarding textures, developers should be aware that Render Textures used by effects or post-processing can become a problem on devices with high resolution screens, but without a lot of memory to match.Given the breadth of configurations available (CPU, GPU, SOC, memory, mobile, desktop, console, etc.), can you suggest a way to categorize devices to reduce the number of tiers you need to optimize for?Arm: The number of tiers your team optimizes for is really a game design and business decision, and should be based on how important pushing visual quality is to the value proposition of the game. For some genres it might not matter at all, but for others, users will have high expectations for the visual fidelity.Does the texture memory limit differ among models and brands of Android devices that have the same amount of total system memory?Arm: To a first-order approximation, we would expect the total amount of texture memory to be similar across vendors and hardware generations. There will be minor differences caused by memory layout and alignment restrictions, so it won’t be exactly the same.Is it CPU or GPU usage that contributes the most to overheating on mobile devices?Arm: It’s entirely content dependent. The CPU, GPU, or the DRAM can individually overheat a high-end device if pushed hard enough, even if you ignore the other two completely. The exact balance will vary based on the workload you are running.What tips can you give for profiling on devices that have thermal throttling? What margin would you target to avoid thermal throttling (i.e., targeting 20 ms instead of 33 ms)?Arm: Optimizing for frame time can be misleading on Android because devices will constantly adjust frequency to optimize energy usage, making frame time an incomplete measure by itself. Preferably, monitor CPU and GPU cycles per frame, as well as GPU memory bandwidth per frame, to get some value that is independent of frequency. The cycle target you need will depend on each device’s chip design, so you’ll need to experiment.Any optimization helps when it comes to managing power consumption, even if it doesn’t directly improve frame rate. For example, reducing CPU cycles will reduce thermal load even if the CPU isn’t the critical path for your game.Beyond that, optimizing memory bandwidth is one of the biggest savings you can make. Accessing DRAM is orders of magnitude more expensive than accessing local data on-chip, so watch your triangle budget and keep data types in memory as small as possible.Unity: To limit the impact of CPU clock frequency on the performance metrics, we recommend trying to run at a consistent temperature. There are a couple of approaches for doing this:Run warm: Run the device for a while so that it reaches a stable warm state before profiling.Run cool: Leave the device to cool for a while before profiling. This strategy can eliminate confusion and inconsistency in profiling sessions by taking captures that are unlikely to be thermally throttled. However, such captures will always represent the best case performance a user will see rather than what they might actually see after long play sessions. This strategy can also delay the time between profiling runs due to the need to wait for the cooling period first.With some hardware, you can fix the clock frequency for more stable performance metrics. However, this is not representative of most devices your users will be using, and will not report accurate real-world performance. Basically, it’s a handy technique if you are using a continuous integration setup to check for performance changes in your codebase over time.Any thoughts on Vulkan vs OpenGL ES 3 on Android? Vulkan is generally slower performance-wise. At the same time, many devices lack support for various features on ES3.Arm: Recent drivers and engine builds have vastly improved the quality of the Vulkan implementations available; so for an equivalent workload, there shouldn’t be a performance gap between OpenGL ES and Vulkan (if there is, please let us know). The switch to Vulkan is picking up speed and we expect to see more people choosing Vulkan by default over the next year or two. If you have counterexamples of areas where Vulkan isn’t performing well, please get in touch with us. We’d love to hear from you.What tools can we use to monitor memory bandwidth (RAM <-> VRAM)?Arm: The Streamline Profiler in Arm Mobile Studio can measure bandwidth between Mali GPUs and the external DRAM (or system cache).Should you split graphical assets by device tiers or device resolution?Arm: You can get the best result by retuning assets, but it’s expensive to do. Start by reducing resolution and frame rate, or disabling some optional post-processing effects.What is the best way to record performance metric statistics from our development build?Arm: You can use the Performance Advisor tool in Arm Mobile Studio to automatically capture and export performance metrics from the Mali GPUs, although this comes with a caveat: The generation of JSON reports requires a Professional Edition license.Unity: The Unity Profiler can be used to view common rendering metrics, such as vertex and triangle counts in the Rendering module. Plus you can include custom packages, such as System Metrics Mali, in your project to add low-level Mali GPU metrics to the Unity Profiler.What are your recommendations for profiling shader code?You need a GPU Profiler to do this. The one you choose depends on your target platform. For example, on iOS devices, Xcode’s GPU Profiler includes the Shader Profiler, which breaks down shader performance on a line-by-line basis.Arm Mobile Studio supports Mali Offline Compiler, a static analysis tool for shader code and compute kernels. This tool provides some overall performance estimates and recommendations for the Arm Mali GPU family.When profiling, the general rule is to test your game or app on the target device(s). With the industry moving toward more types of chipsets (Apple M1, Arm, x86 by Intel, AMD, etc.), how can developers profile and pinpoint issues on the many different hardware configurations in a reasonable amount of time?The proliferation of chipsets is primarily a concern on desktop platforms. There are a limited number of hardware architectures to test for console games. On mobile, there’s Apple’s A Series for iOS devices and a range of Arm and Qualcomm architectures for Android – but selecting a manageable list of representative mobile devices is pretty straightforward.On desktop it’s trickier because there’s a wide range of available chipsets and architectures, and buying Macs and PCs for testing can be expensive. Our best advice is to do what you can. No studio has infinite time and money for testing. We generally wouldn’t expect any huge surprises when comparing performance between an Intel x86 CPU and a similarly specced AMD processor, for instance. As long as the game performs comfortably on your minimum spec machine, you should be reasonably confident about other machines. It’s also worth considering using analytics, such as Unity Analytics, to record frame rates, system specs, and player options’ settings to identify hotspots or problematic configurations.We’re seeing more studios move to using at least some level of automated testing for regular on-device profiling, with summary stats published where the whole team can keep an eye on performance across the range of target devices. With well-designed test scenes, this can usually be made into a mechanical process that’s suited for automation, so you don’t need an experienced technical artist or QA tester running builds through the process manually.Do you ever see performance issues on high-end devices that don’t occur on the low-end ones?It’s uncommon, but we have seen it. Often the issue lies in how the project is configured, such as with the use of fancy shaders and high-res textures on high-end devices, which can put extra pressure on the GPU or memory. Sometimes a high-end mobile device or console will use a high-res phone screen or 4K TV output as a selling point but not necessarily have enough GPU power or memory to live up to that promise without further optimization.If you make use of the current versions of the C# Job System, verify whether there’s a job scheduling overhead that scales with the number of worker threads, which in turn, scales with the number of CPU cores. This can result in code that runs more slowly on a 64+ core Threadripper™ than on a modest 4-core or 8-core CPU. This issue will be addressed in future versions of Unity, but in the meantime, try limiting the number of job worker threads by setting JobsUtility.JobWorkerCount.What are some pointers for setting a good frame budget?Most of the time when we talk about frame budgets, we’re talking about the overall time budget for the frame. You calculate 1000/target frames per second (fps) to get your frame budget: 33.33 ms for 30 fps, 16.66 ms for 60 fps, 8.33 ms for 120 Hz, etc. Reduce that number by around 35% if you’re on mobile to give the chips a chance to cool down between each frame. Dividing the budget up to get specific sub-budgets for different features and/or systems is probably overkill except for projects with very specific, predictable systems, or those that make heavy use of Time Slicing.Generally, profiling is the process of finding the biggest bottlenecks – and therefore, the biggest potential performance gains. So rather than saying, “Physics is taking 1.2 ms when the budget only allows for 1 ms,” you might look at a frame and say, “Rendering is taking 6 ms, making it the biggest main thread CPU cost in the frame. How can we reduce that?”It seems like profiling early and often is still not common knowledge. What are your thoughts on why this might be the case?Building, releasing, promoting, and managing a game is difficult work on multiple fronts. So there will always be numerous priorities vying for a developer’s attention, and profiling can fall by the wayside. They know it’s something they should do, but perhaps they’re unfamiliar with the tools and don’t feel like they have time to learn. Or, they don’t know how to fit profiling into their workflows because they’re pushed toward completing features rather than performance optimization.Just as with bugs and technical debt, performance issues are cheaper and less risky to address early on, rather than later in a project’s development cycle. Our focus is on helping to demystify profiling tools and techniques for those developers who are unfamiliar with them. That’s what the profiling e-book and its related blog post and webinar aim to support.Is there a way to exclude certain methods from instrumentation or include only specific methods when using Deep Profiling in the Unity Profiler? When using a lot of async/await tasks, we create large stack traces, but how can we avoid slowing down both the client and the Profiler when Deep Profiling?You can enable Allocation call stacks to see the full call stacks that lead to managed allocations (shown as magenta in the Unity CPU Profiler Timeline view). Additionally, you can – and should! – manually instrument long-running methods and processes by sprinkling ProfilerMarkers throughout your code. There’s currently no way to automatically enable Deep Profiling or disable profiling entirely in specific parts of your application. But manually adding ProfilerMarkers and enabling Allocation call stacks when required can help you dig down into problem areas without having to resort to Deep Profiling.As of Unity 2022.2, you can also use our IgnoredByDeepProfilerAttribute to prevent the Unity Profiler from capturing method calls. Just add the IgnoredByDeepProfiler attribute to classes, structures, and methods.Where can I find more information on Deep Profiling in Unity?Deep Profiling is covered in our Profiler documentation. Then there’s the most in-depth, single resource for profiling information, the Ultimate Guide to profiling Unity games e-book, which links to relevant documentation and other resources throughout.Is it correct that Deep Profiling is only useful for the Allocations Profiler and that it skews results so much that it’s not useful for finding hitches in the game?Deep Profiling can be used to find the specific causes of managed allocations, although Allocation call stacks can do the same thing with less overhead, overall. At the same time, Deep Profiling can be helpful for quickly investigating why one specific ProfilerMarker seems to be taking so long, as it’s more convenient to enable than to add numerous ProfilerMarkers to your scripts and rebuild your game. But yes, it does skew performance quite heavily and so shouldn’t be enabled for general profiling.Is VSync worth setting to every VBlank? My mobile game runs at a very low fps when it’s disabled.Mobile devices force VSync to be enabled at a driver/hardware level, so disabling it in Unity’s Quality settings shouldn’t make any difference on those platforms. We haven’t heard of a case where disabling VSync negatively affects performance. Try taking a profile capture with VSync enabled, along with another capture of the same scene but with VSync disabled. Then compare the captures using Profile Analyzer to try to understand why the performance is so different.How can you determine if the main thread is waiting for the GPU and not the other way around?This is covered in the Ultimate Guide to profiling Unity games. You can also get more information in the blog post, Detecting performance bottlenecks with Unity Frame Timing Manager.Generally speaking, the telltale sign is that the main thread waits for the Render thread while the Render thread waits for the GPU. The specific marker names will differ depending on your target platform and graphics API, but you should look out for markers with names such as “PresentFrame” or “WaitForPresent.”Is there a solid process for finding memory leaks in profiling?Use the Memory Profiler to compare memory snapshots and check for leaks. For example, you can take a snapshot in your main menu, enter your game and then quit, go back to the main menu, and take a second snapshot. Comparing these two will tell you whether any objects/allocations from the game are still hanging around in memory.Does it make sense to optimize and rewrite part of the code for the DOTS system, for mobile devices including VR/AR? Do you use this system in your projects?A number of game projects now make use of parts of the Data-Oriented Technology Stack (DOTS). Native Containers, the C# Job System, Mathematics, and the Burst compilerare all fully supported packages that you can use right away to write optimal, parallelized, high-performance C# (HPC#) code to improve your project’s CPU performance.A smaller number of projects are also using Entities and associated packages, such as the Hybrid Renderer, Unity Physics, and NetCode. However, at this time, the packages listed are experimental, and using them involves accepting a degree of technical risk. This risk derives from an API that is still evolving, missing or incomplete features, as well as the engineering learning curve required to understand Data-Oriented Design (DOD) to get the most out of Unity’s Entity Component System (ECS). Unity engineer Steve McGreal wrote a guide on DOTS best practices, which includes some DOD fundamentals and tips for improving ECS performance.How do you go about setting limits on SetPass calls or shader complexity? Can you even set limits beforehand?Rendering is a complex process and there is no practical way to set a hard limit on the maximum number of SetPass calls or a metric for shader complexity. Even on a fixed hardware platform, such as a single console, the limits will depend on what kind of scene you want to render, and what other work is happening on the CPU and GPU during a frame.That’s why the rule on when to profile is “early and often.” Teams tend to create a “vertical slice” demo early on during production – usually a short burst of gameplay developed to the level of visual fidelity intended for the final game. This is your first opportunity to profile rendering and figure out what optimizations and limits might be needed. The profiling process should be repeated every time a new area or other major piece of visual content is added.Here are additional resources for learning about performance optimization:BlogsOptimize your mobile game performance: Expert tips on graphics and assetsOptimize your mobile game performance: Expert tips on physics, UI, and audio settingsOptimize your mobile game performance: Expert tips on profiling, memory, and code architecture from Unity’s top engineersExpert tips on optimizing your game graphics for consolesProfiling in Unity 2021 LTS: What, when, and howHow-to pagesProfiling and debugging toolsHow to profile memory in UnityBest practices for profiling game performanceE-booksOptimize your console and PC game performanceOptimize your mobile game performanceUltimate guide to profiling Unity gamesLearn tutorialsProfiling CPU performance in Android builds with Android StudioProfiling applications – Made with UnityEven more advanced technical content is coming soon – but in the meantime, please feel free to suggest topics for us to cover on the forum and check out the full roundtable webinar recording.

Reading Time: 11 minutes Choosing the right drip email software can be a game-changer for your marketing strategy. With a variety of options available, understanding the pricing models and features each tool offers is key to making an informed decision. In this guide, we’ll walk you through essential factors to consider when evaluating pricing, so you can select the best solution that fits your needs and budget for your drip email campaigns. Ready to dive deeper? Let’s explore your options.   What Does Drip Email Marketing Software Cost? Drip email marketing software typically follows a few different pricing models, depending on the provider. Here’s a quick breakdown of the most common ones: Pricing Based on MAUs (Monthly Active Users): You pay based on how many unique subscribers actually engage with your emails (or your system) each month, not your entire list size. It’s a much fairer, scalable way to pay, especially if your audience fluctuates. You only pay for real activity. Flat-Rate Pricing: You pay a fixed monthly fee, regardless of usage, list size, or activity. But flat rates are often bloated to cover all usage types, so you’re likely overpaying if you’re not sending a massive volume of emails. It’s not cost-efficient for scaling brands. List-Based Pricing: You pay based on the total number of contacts in your database, whether they engage or not. However, you end up paying for cold leads, dead emails, and inactive contacts. This makes your costs climb unnecessarily as your list grows. Email Volume-Based Pricing: You’re charged based on how many emails you send each month. It can discourage you from running important nurturing campaigns or re-engagement flows because every email adds to your bill. It’s unpredictable and can spike costs during promotions. For email marketers, this model means added pressure to limit sends, often at the cost of customer experience and long-term engagement. Some platforms even bundle unnecessary features into expensive tiers, which bloats the pricing, especially for features you may not actually need. This is where MoEngage stands out. The platform offers a more transparent and scalable approach so that pricing is aligned to your actual usage and outcomes rather than inflated send limits.   6 Biggest Factors that Affect Drip Email Pricing Drip email pricing depends on several factors. Here are some of the biggest ones: 1. Number of active contacts Some platforms, particularly the ones that use the MAU pricing model, charge based on the number of customers who interact with your emails. The higher the number of active customers, the higher the pricing. 2. List size Managing an email list is easier said than done. Nevertheless, if you have a large database of contacts, you will be charged a higher monthly fee. In this case, pricing will remain the same even if you don’t have many active subscribers on your list. 3. Email volume The number of emails you send plays a big role in pricing. This is because high-volume automated email marketing campaigns require significant infrastructure for email delivery, tracking, and reporting. 4. Advanced features Platforms offering advanced features like RFM segmentation, automation, A/B testing, analytics, and AI-driven personalization typically charge more than others. Some pricing models allow you to opt for some or all of these features, depending on your business requirements. 5. Automation complexity If your drip campaign involves complex workflows, like different stages, conditions, triggers, etc., it can impact the pricing. Automation requires more resources to set up and maintain, which increases the price. 6. Integration with other tools Connecting your email platform with CRMs, Ecommerce tools, or analytics systems can drive up costs, especially if platforms charge extra for native integrations or require third-party connectors. Before signing up, ask yourself: how many of these tools are truly essential? Can any be replaced with ones that integrate more easily? A cluttered tech stack not only costs more but also complicates your workflows.   Top Features to Look For in a Drip Email Marketing Software Since you’re on the lookout for a drip email marketing software platform, focus on the features we’ve mentioned below. Core features that drip email software absolutely needs These features are the must-haves. They are the foundational tools required to automate, personalize, and optimize your email drip campaigns at scale. Journey designer: Easily set up multi-step workflows triggered by parameters like customer actions, behaviors, or time delays with minimal coding. Behavioral targeting: Send emails based on real-time customer behavior, such as cart additions, subscriptions, app activity, and more, for highly relevant messaging. Personalization engine: Dynamically include customer attributes based on past interactions and preferences to personalize emails at scale. High inbox deliverability: Tools and practices that ensure you have a healthy sender reputation and high inbox placement. Campaign optimization with A/B testing: Test different email versions to learn what drives higher engagement and optimize your drip flows continuously. Real-time analytics and insights: Access detailed reports and dashboards that track key drip email marketing metrics like opens, clicks, and conversions, and identify drop-offs in your drip journeys. Other features that are very useful to have These features aren’t absolutely necessary to run an effective campaign, but they can improve customer experience and operational efficiency. AI-based predictive segmentation: Leverage AI to predict customer behavior like churn or likelihood of conversion, and automatically adjust campaigns accordingly. Omnichannel messaging: Integrate messaging across various channels, like push notifications, SMS, WhatsApp, and in-app messaging for a cohesive customer journey. Pre-built templates: Quickly access pre-built, customizable email templates to launch campaigns faster without heavy design work.   9 Steps to Choosing a Drip Email Marketing Software With so many options available in the market, choosing the right drip email marketing software can seem like an uphill battle. No worries! We’ll even walk you through exactly how to evaluate and compare drip email tools for yourself. Once you’ve asked the right questions and gathered the facts during demos, it’s time to break down your decision-making process into clear, actionable steps. Step 1: Define Your Non-Negotiable Features The first step is to make a list of all the features that are necessary for you. For example, if your goal is to send behavior-triggered drip campaigns (like abandoned cart emails or re-engagement nudges), then features like behavioral targeting and multi-step customer journey automation are non-negotiable. On the other hand, if you only need basic scheduled newsletters, you might not need advanced workflow builders or AI recommendations. Once you have a list of non-negotiable features, it becomes easier to narrow down your platform choices. Step 2: Research and Compare All Viable Options Don’t just go with the most well-known or even the most affordable-looking tool. Research platforms that fit your business size and industry. Use peer reviews, comparison guides, and case studies to understand how the platform performs in real-world use cases. Also, remember that low entry pricing can be misleading. What matters is the total cost of ownership. Look for things like hidden fees, integration costs, or feature-based tier upgrades. These will add to the pricing later. Step 3: Book a Personalized Demo (and Demand Proof) When choosing your drip email marketing tool, make sure you request a personalized demo based on your business context, use cases, and workflows. A simple, generic walkthrough isn’t helpful because this is your first real glimpse into how the platform is going to support you long-term. If the demo isn’t tailored to your business, that’s a red flag—because if they can’t offer personalized attention when they’re trying to win your business, you’re unlikely to get it during onboarding or implementation. Ask to see features live, rather than just screenshots or slide decks. If a feature is critical, ask to see it in action. At times, sales teams may promise features that are still ‘in development’. Step 4: Attend the Demo with a List of Questions Once you’ve booked a demo, prepare a checklist of questions, like the one we shared above. This will help you stay focused and gain the clarity you need. Ask whether the features you care about are included in the base plan or locked behind premium tiers. Dig into pricing: will the costs scale predictably as you grow, or are there hidden fees that only show up later? Some platforms may offer attractive deals upfront but raise prices later or charge extra for essentials like API access, automation workflows, or deliverability support. The demo is your chance to surface all of this before signing anything. Step 5: Evaluate Ease of Use The drip email software you choose must be easy to use. Since marketers with little to no technical knowledge are most likely to use the platform, look for solutions that allow you to build email campaigns, segment audiences, and personalize messages easily. Say no to complicated or clunky interfaces that are not intuitive and require a steep learning curve. Step 6: Check for Advanced Capabilities Ask about AI features like predictive segmentation or email send-time optimization. Even if you don’t need them right now, having future-ready capabilities ensures you won’t outgrow the tool too soon. Step 7: Test for Scalability and Deliverability This is an important one. Look for solutions that can handle large volumes of emails with good email deliverability rates. Make sure the software you choose has a high inbox placement rate. Check if they have a solid infrastructure and dedicated IPs, should you need them. Also, consider scalability: will this platform still meet your needs when your contact list grows from 10,000 to 100,000? Make sure you’re not locked into expensive pricing jumps or performance bottlenecks. Step 8: Review Integration Options Make sure the tool you choose fits seamlessly into your existing workflow. It should integrate with your CRM, mobile app, Ecommerce system, and anything else in your ecosystem. Even if it doesn’t directly integrate, ensure that it offers flexible APIs and webhooks, or supports third-party connectors like Zapier, so you can still automate data flow without manual workarounds. Step 9: Compare Customer Support Quality Good software with bad support can be a nightmare. Check for onboarding help, technical support, availability, account management, and resources. Ask about how quickly the support team responds and resolves software issues to ensure that you have minimal downtime.   5 Best Email Drip Campaign Software and Tools It’s time to weigh your options in choosing an email drip campaign software for your business. Give this list a quick read! 1. MoEngage: Best for Omnichannel Engagement and Behavioral Targeting   MoEngage is a comprehensive customer engagement platform designed to deliver personalized and automated drip email campaigns at scale. Its powerful AI capabilities allow you to use behavioral targeting to reach customers at the right time with the right message at different stages of their customer journey. Why MoEngage is Best for Omnichannel Engagement and Behavioral Targeting: MoEngage offers multiple features that allow you to provide cohesive customer experiences across various touchpoints. You can reach out to customers via not just email, but also SMS, push notifications, in-app messages, and even ad networks without missing a beat. Features like advanced segmentation enable you to send hyper-personalized drip email campaigns tailored to individual customer actions, such as cart abandonment or inactivity. This ensures that messages are timely, relevant, and effective in driving engagement. How Pricing Works: MoEngage offers flexible drip email pricing based on the scale of your business and the exact features you’d need. You can always reach out to their sales team to learn more about their custom pricing plans. How to Set Up a Demo: To experience MoEngage firsthand, you need to book a demo first. Their team will then contact you to schedule a personalized walkthrough that showcases how the platform can meet your specific drip email marketing goals. 2. ActiveCampaign: Best for Email and Marketing Automation ActiveCampaign is a marketing automation platform that allows you to create email campaigns on channels your customers connect with. The platform offers predictive content features that help you show customers the most meaningful content. Why ActiveCampaign is Best for Email and Marketing Automation: ActiveCampaign excels in delivering sophisticated automation campaign workflows that can be tailored to various customer journeys. How Pricing Works: ActiveCampaign offers a four-tiered drip email pricing plan. The basic plan starts at $15/month for 1K contacts, while the Enterprise plan starts at $1,169/month for 50K contacts. If you have contacts upwards of that number, you need to talk to their sales team. 3. Kit: Best for Creators Using Emails Kit is tailored for creators, bloggers, and solo entrepreneurs who want to send customer relationship emails. It offers a user-friendly interface with powerful automation capabilities. Why Kit is Best for Creators and Solo Entrepreneurs: Kit’s email-first operating system allows creators to build long-term connections with customers. Its intuitive email designer can be used to create effective on-brand emails that are optimized for deliverability. Plus, it allows you to send email broadcasts for announcing product launches, sharing content updates, or promoting limited-time offers to your entire list in bulk. How Pricing Works: Kit offers a free Newsletter plan with basic features like visual automation, opt-in forms, and email broadcasts to up to 10K subscribers. But you can implement just 1 visual automation and an email sequence on the free plan. Advanced plans start at $25 per month for up to 1K subscribers, and go up to $2,166/month for 400K contacts. 4. Drip: Best for Ecommerce Marketing Automation Drip is a marketing automation platform designed specifically for Ecommerce brands. It offers tools to create personalized email campaigns that drive customer engagement and sales. Why Drip is Best for Ecommerce Marketing Automation: Drip provides deep integrations with Ecommerce platforms, allowing businesses to leverage customer data for targeted campaigns. Features like behavior-based automation and pre-built workflows help in crafting effective drip campaigns. How Pricing Works: Drip’s pricing starts at $39/month for up to 2,500 contacts. The cost scales up to $1,999/month based on the number of contacts, with custom pricing available for larger lists of over 180K subscribers. 5. Mailmodo: Best for Interactive Email Marketing Mailmodo is an email marketing platform that enables the creation of interactive drip emails using AMP technology. This allows recipients to take actions like filling out forms or booking appointments directly within the email. Why Mailmodo is Best for Interactive Email Marketing: Mailmodo stands out by offering interactive email capabilities that enhance customer engagement and conversion rates. Its drag-and-drop editor simplifies the creation of dynamic emails without any coding required. How Pricing Works: Like the other drip email marketing software on this list, Mailmodo’s pricing is based on the number of contacts and emails sent per month. Its three-tiered pricing model starts at $39/month for 500 contacts, going up to $939/month for 250K subscribers. Any more contacts, and you have to reach out to their sales team.   How to Get the Most Value Out of Your Drip Email Software Here are some of the ways in which you can leverage your drip email software to its maximum potential. Save time through automation: Automate welcome emails, follow-ups, and re-engagement campaigns to free up your team for more strategic work. Boost engagement with timely messaging: Set up triggers so that emails are automatically sent based on customers’ actions. This keeps your audience interested at every stage of their customer journey. Drive higher conversions with targeted sequences: Send the right email at the right time to move leads through the sales funnel towards a conversion. Personalize customer experiences at scale: Use segmentation and dynamic content to create a personalized customer experience without manually customizing every email. Continuously optimize with data-driven insights: Track performance metrics like open rates and conversions to refine your drip email campaigns and maximize results over time.   Maximize Engagement With MoEngage’s Drip Email Marketing Software To sum it up, drip email marketing can be quite powerful for engaging customers at different stages of their journey. Having the right drip email software in your tech stack ensures smooth, efficient workflows. Your team can focus on higher-value tasks while automating routine processes. It also ensures that your drip emails always reach the right person with the right message at the perfect time. MoEngage’s powerful email automation platform allows you to automate personalized campaigns that drive higher engagement and conversions. With advanced segmentation, behavior-triggered messaging, and seamless integration with your ecosystem, you can nurture relationships more effectively than ever. Curious to see how MoEngage can elevate your campaigns? Schedule a discovery call today. The post Drip Email Software: The Ultimate Pricing and Buying Guide appeared first on MoEngage.

Customer feedback analysis: understanding the hidden truth Customer feedback seems to reflect preferences, but deeper analysis reveals a different reality. While tools like surveys and focus groups provide structured insights, they frequently fail to capture real customer behaviour. Social desirability, hypothetical scenarios, and the gap between reported preferences and actual decisions create a misleading picture of customer needs and expectations.Take the case of an electronics company testing new colours for CD players in a focus group. Participants enthusiastically selected vibrant colours. But when they could take a CD player home, every participant chose the standard black or grey version.This example shows that what customers claim to want and what they actually choose, can be two different things. That is why the analysis of customer feedback needs to go beyond just listening to what people say. Pitfalls of NPS surveys Another example of customer feedback is Net Promoter Score. NPS is a widely used tool in customer feedback analysis, measuring satisfaction and loyalty. We strongly believe in the value of NPS, but only if customers share their scores voluntarily and without pressure.Sadly, many businesses aggressively push for high scores, sometimes even pressuring participants to rate them a 9 or 10, suggesting bonuses, commissions, and company targets depend on a great rating. This manipulation distorts the results and reduces the reliability of NPS. The real value of NPS lies in the follow-up question: Why did you give a certain score? A 3/10 rating without an explanation offers little actionable insight.Additionally, relying solely on NPS scores without any context can be misleading. A high score might indicate satisfaction, but does it correlate with repeat purchases or customer engagement? Similarly, a low score without behavioural context might not mean you’re losing customers. To get a clearer picture, effective customer feedback analysis should integrate multiple data sources. Beware of SINGLE-SOURCE FEEDBACK One of the most common mistakes in customer feedback analysis is relying on a single data source. Many companies base their insights exclusively on survey responses without integrating other crucial data points such as:Customer service interactionsPurchase history and brand engagementWebsite and app behaviourSocial media engagementFor example, consider survey participants who gave feedback but have not interacted with your brand in over a year. How valuable is their input? Without cross-referencing different sources, it is quite challenging to:Determine which research method is most appropriate for a specific questionIntegrate multiple insights into a coherent strategyTake meaningful action based on the feedbackPrioritise which feedback is truly valuableA thorough customer feedback analysis ensures that brands don’t make decisions based on isolated responses but instead consider real behavioural patterns and trends. "Customer feedback alone isn’t enough, you need behavioural data to truly understand what drives decisions and loyalty." Hans Palmers, Managing Partner and Digital & e-Commerce Strategist Keep it SIMPLE Traditional surveys often make respondents overthink their answers, leading to biased results. A smarter approach is to use implicit A/B testing or pairwise comparison to gather more reliable insights. For example: instead of asking customers to evaluate multiple options at once, you can present them two choices at a time (pairwise comparison). This makes decision-making easier and more instinctive while reducing the pressure of finding the ‘right’ answer. To much data, NOT ENOUGH INSIGHT The rise of new tools and methodologies, like eye tracking, behavioural data, feedback forms, and focus groups, has led to an overwhelming amount of data. However, more data does not automatically translate to better insights. Without structured customer feedback analysis, businesses often find themselves drowning in numbers without clear direction on how to act.The paradox is that in challenging times, the thirst for knowledge increases. Businesses collect more data in search of clarity, but all too often, they end up with noise instead of answers. That’s why it’s essential to refocus on the core of the brand and its true customer experience. "Collecting customer feedback is easy. The real challenge is turning it into meaningful actions." Hans Palmers, Managing Partner and Digital & e-Commerce Strategist How can June2O help you? At June20, we believe customer feedback can be a great source to drive growth. We like to focus on methodology and insight rather than just tools. The key to unlocking valuable feedback isn’t the tool you use, it’s in how you frame the question, interpret responses, and integrate findings into a broader strategy. Our approach ensures that:The right methodologies are applied to gather meaningful insightsData sources are integrated logically to form a complete pictureInsights are transformed into clear, actionable recommendations with maximum impactRather than getting lost in data overload, we help brands cut through the noise and make informed, strategic decisions.Want useful customer feedback? Let’s talk BiographyHans Palmers Hans began his professional journey as a Web Developer and Mentor. After three years, he joined TBWA, where he led a team in digital solutions for a decade. Following this, he shared his digital expertise at KUL, EHSAL and Thomas More for over ten years, as one of the founding partners. Embarking on a new venture, Hans founded Mundo Digitalis, specialised in digital solutions, and successfully led the agency for over 11 years. Over all these years, Hans did pioneering work in e-commerce and online banking. Recently Mundo Digitalis has integrated with June20, where Hans holds the position of Managing Partner & E-Commerce Strategist. The post Customer feedback analysis: understanding the hidden truth appeared first on June20.