Finance

We’ve all celebrated that first sale, only to watch customers vanish. Our experts reveal what keeps them coming back this week. We’ve all been there. Celebrating that first sale, only to watch the customer disappear into the void of endless shopping options. So what’s the secret sauce that keeps them coming back? We posed this million-dollar question to our panel of retail and marketing experts to weigh in on the age-old challenge of turning one-time buyers into repeat customers. Their answers might surprise you in this week’s edition of Let’s Talk. Let’s Talk! More Let’s Talk episodes Contribute to Dynamic Business ✍ Ginger Kidd, VP Marketing and Communications APAC, Sinch Ginger Kidd, VP Marketing and Communications APAC, Sinch “In a crowded marketplace, building customer loyalty is crucial, especially as 72% of consumers expect brands to recognise them as individuals. The key is a personalised, omnichannel marketing approach that combines personalisation with message repetition. “Our five top non-negotiables to ensure that buyers turn into repeat customers are: Map the Customer Journey: Understand how customers interact with your brand and identify key touchpoints for engagement. Personalise every interaction: Use data to tailor follow-ups, offers, and recommendations to individual preferences and behaviours. Adopt an omnichannel approach: Ensure a seamless, consistent experience across email, SMS, social messaging, and more – letting customers choose how they interact. Automate where you can: Implement chatbots or automated tools where budget allows for instant, 24/7 support, freeing staff for more complex queries. Measure and optimise: Track key metrics, for example response time and customer satisfaction, to continually refine your strategy. “By embracing this approach, SMEs can move beyond transactional relationships to create memorable customer experiences that drive loyalty. Consistent, personalised engagement not only keeps your brand top-of-mind, but also turns satisfied customers into enthusiastic advocates, fueling repeat business and sustainable success.” Alison Morris, SVP/GM International, Worldpay for Platforms Alison Morris, SVP/GM International, Worldpay for Platforms “Turning one-time buyers into repeat customers is one of the biggest challenges and opportunities for small businesses. We see a significant momentum when SMEs focus on reducing friction at checkout, offering flexible payment options, and using smart tools to better understand customer behaviour. “According to Worldpay’s Payments performance report, 34% of customers abandon a digital transaction if their preferred payment option is not available. For SMEs, this means that it is essential to offer a variety of payment options to meet customer expectations, from cards to BNPL, digital wallets, which account for almost 20% of Australian in-store spend. Another method growing in popularity is PayTo, which enables real-time account-to-account transfers. “Subscription-based models are another powerful way to drive repeat purchases, but convenience is key. More than two in three (69%) consumers say it is their top priority when it comes to subscriptions, and 76% say it is important that their payment details are linked across all devices. A seamless and secure payment process helps avoid disruptions and builds trust, which in turn drives retention and repeat revenue. “Ultimately, when payments are simple, secure and aligned with customer habits, small businesses are far more likely to create loyal, returning customers.” Bede Hackney, Head of ANZ at Zoom Bede Hackney, Head of ANZ at Zoom “Turning a one-time buyer into a loyal advocate starts with a standout experience – not just during the sale, but through the entire customer journey. “With a unified communications platform, SMEs can bring together video, chat, phone, docs, and more into one easy-to-use workspace shared across teams. Information is retained from one interaction to the next, so customers don’t have to go through the annoying process of providing it all again every time they speak to someone new. “You can focus on what the customer wants right now, rather than re-gathering information you already have. It becomes easier for customers to move between the team members and functions that best meet their needs. Done right, it all feels like one conversation, making it far more possible to meet customer needs and upsell them to new products. “Using AI tools like Zoom Revenue Accelerator can also automatically take the highlights and pain-points of individual customer conversations and capture them as coachable moments for your entire service team. “Personalising customer interactions with the help of AI tools makes them feel less like anonymous transactions and more like a relationship with someone your business knows and values. It’s key to keeping them coming back.” Kim Owen Jones, GM – Customer Acquisition, MYOB Kim Owen Jones, GM – Customer Acquisition, MYOB “Turning a one-time buyer into a loyal customer is one of the most valuable, cost-effective strategies a SME can pursue. Beyond great service, consistent follow-up, personalised experiences and the right tools are required to ensure a customer is loyal for life. “The June 2025 Edition of the MYOB Bi-Annual Business Monitor reveals only 15% of SMEs plan to increase investment in customer retention over the next year, while 11% will boost investment in IT systems and processes. Businesses which invest in both will be the ones that build loyalty and long-term value. “Start with the post-purchase experience. A thank you email or a follow-up message to check how things went shows customers they’re more than a transaction. Use tools like CRMs and automation software to track purchase history and behaviour, then tailor offers and communications accordingly. Loyalty programs, early-access offers, or educational content all deepen the connection. “Customers return to businesses that make them feel remembered, respected and rewarded. You don’t need a big budget to win repeat business, just a smart approach and the right tech.” Shaun Broughton, Managing Director, APAC and Japan at Shopify Shaun Broughton, Managing Director, APAC and Japan at Shopify “Consistency and convenience are key to turning a one-off purchase into repeat business. That means making it easy for customers to reorder, offering targeted promotions, and creating personalised shopping experiences that build stronger relationships and keep customers coming back. “This becomes far more effective when your checkout, email, and point-of-sale data are unified. By consolidating analytics, you

You can’t fuel AI with outdated pipes. Without the right data infrastructure, even the smartest AI is just guessing. You know the old saying that you can lead a horse to water, but not make it drink?  The same sort of logic can be applied to AI adoption by modern businesses: You can roll out AI systems, but you can’t force them to use the data they need to operate effectively.  In fact, without a modern data infrastructure, you can’t feed relevant data into AI systems very well at all — hence why challenges related to data infrastructure modernization are among the top barriers to successful AI adoption for companies across a range of industries, according to a recent Indicium survey about data infrastructure and AI adoption.  Keep reading for a dive into what the survey reveals about the role of data in AI rollouts, along with guidance on how businesses can solve obstacles related to data infrastructure as part of their AI strategies.  The inextricable link between AI, data and data infrastructure  Data has long been important for businesses. But in the age of AI, it has become absolutely critical.  The reason why is simple: Without data, AI tools and services can do very little. AI can’t identify relevant trends and patterns, summarize information or generate novel content without being able to parse large amounts of information.  To be clear, we’re not talking here primarily about the generic data used to build AI models, which are usually pre-trained on vast amounts of publicly available information. In the context of enterprise AI adoption, the most important type of data is information that is specific to individual businesses. The ability to feed this type of data into AI solutions is what makes these tools capable of delivering unique business insights, accelerating business processes and so on. Without access to proprietary business data, AI tools can only answer generic questions, not meet the unique challenges faced by a particular organization.  Ensuring that businesses can connect proprietary data to AI systems is where data infrastructure comes in. Data infrastructure consists of the tools and technology that an organization uses to store, process and manage its data. Maintaining an efficient, scalable data infrastructure — and one capable of accommodating all types of data, including structured as well as unstructured data sources — is absolutely crucial for ensuring that AI tools and applications can connect to the data they need to operate.  How outdated data infrastructure hinders AI adoption  Unfortunately, the data infrastructures that many businesses have built over the past decade or two were designed for the pre-AI age, and they fall short when it comes to powering AI tools and services.  Conventional data platforms are typically slower to develop, and they lack robust built-in data governance and quality features. What’s more, traditional solutions are often designed only to support structured data, making it challenging to feed other types of information — like documents and images — into AI systems. And they may involve multiple disparate parts, impeding efforts to move data quickly and cost-effectively between the various places where it is stored and into the AI tools that need it.  The Indicium survey findings reflect the inadequacy of traditional data platforms for the AI era. Asked how prepared they are to use data in conjunction with AI apps and systems, nearly half of respondents reported moderate-to-low levels of confidence.  What’s more, the survey found that preparing data for use with AI tools and apps is the number one reason why businesses are pursuing data modernization projects — highlighting the priority that organizations place on being able to transform data using a methodology that provides scalability and closes the gap between the business and its technology. Other goals, like reducing storage costs, improving data security and speeding up processes, were much less likely than AI to be the driving force behind data modernization today.  Bringing data infrastructure up to speed with AI  What, specifically, are businesses actually doing to address data infrastructure challenges? The survey provides some clear insights.  Common strategies included migrating from on-prem to cloud-based data platforms, a step taken by 80.9 percent of companies that have pursued data modernization projects. Deploying modern data warehouses, such as Snowflake, Databricks, Redshift and BigQuery, is also a widespread data modernization tactic, embraced by 53.9 percent of survey respondents.  It’s important to note, however, that simply deploying modern data platforms is only one step in data modernization. Equally important is establishing a firm data management methodology and accompanying organizational culture that defines how to meet data governance, quality and scalability needs with assistance from modern tools. Simply moving to newer solutions does not automatically modernize data management processes.  Notably, implementing data platforms that specifically target AI-centric data management, such as Vertex and SageMaker, was a less common practice, with only 29.1 percent of companies reporting the use of solutions like this. This is likely because, rather than investing in AI platforms alone, businesses are opting for holistic data modernization strategies that can help not just with integrating data into AI-powered applications and tools, but also with enhancing data governance, security and scalability across the board, not just in the context of AI.  For the vast majority of businesses, investments like these paid off. Asked whether data modernization projects had left them in a better position to use data with AI tools and applications, 95.1 percent of organizations said they had.  Data modernization as the key to AI success  The core takeaway is clear: Building better data infrastructure is an essential step in taking full advantage of AI technology. A business can deploy all of the AI tools and services it wants. But without a modern data platform and management methodology capable of ensuring governance, quality and speed, it’s unlikely to achieve much value.  The good news is that, for organizations whose data infrastructures are currently out of date, coming up to speed with the AI era is far from impossible. It just requires making

Manufacturing’s under fire — AI could be the lifeline, but only if leaders can outrun chaos, outsmart cyber threats and win over a wary workforce. Manufacturing is under pressure from all sides, from tariffs to recession worries to extreme competition. But right here, right now, leaders across the industry are rising to the occasion and investigating every advantage technology offers. I’ve seen it firsthand — both through data and in between the trendlines. My colleagues at Rockwell Automation set aside time each year to survey thousands of manufacturing professionals about their experiences with and uses of smart technology. What’s working? What isn’t? Which internal and external factors are motivating their changes? It’s a process I’m proud to support, and I always look forward to comparing this quantitative data against the qualitative data I’ve gained through decades of conversations in the field. I worked as an industry consultant helping customers apply solutions to solve problems in my earlier career. Now as a business unit leader, I talk often with leaders looking to the future and making sure we are aligned. This almost always results in discussions about what future trends are likely, how manufacturing will evolve and how we can jointly make the best business decisions possible to be prepared and reduce risks. When I reviewed the data for our 10th survey, I saw an industry caught between a constellation of rocks and hard places. People alone cannot match the hour-by-hour volatility of current economic conditions or keep up with the cybersecurity arms race that leaves supply chains vulnerable, and 81% confirmed that these pressures (internal and external) are accelerating their digital transformation timelines. This makes sense. Manufacturers need to fill gaps. However, they also need to push to beat their competition to the AI use cases that will generate current and future value — whether that’s mass adoption of physical AI on the factory floor or pragmatic quality control. And surrounding it all, an industry-wide resistance to change.  Manufacturing needs AI — But they’re still figuring out where and how Manufacturing leaders are almost unanimously adopting AI — our survey this year found that 95% of respondents are turning to the technology. This doesn’t surprise me based on what I’ve seen firsthand, but I was excited to see established use cases from our research last year turning into best practices. Notably, AI-powered quality control is changing manufacturing. Nearly half of the respondents (48%) plan to deploy this use case. In the field, I see the impact human error can have on quality control, especially in situations like our current trade conditions. Manufacturers now must quickly adjust where and when things are made, and that means new processes and people will come into play. That introduces opportunity for human error, leading to lower quality, so it is important to apply these AI use cases in conjunction with flexible automation solutions to ensure quality is maintained. Our survey’s respondents also highlighted cybersecurity as a key AI use case, as manufacturing companies accounted for 21% of all ransomware attacks in 2024 — only inflation and economic growth ranked as more concerning risks among our survey’s respondents. As bad actors adopt more sophisticated tactics to deploy cyberattacks, manufacturers are realizing that they can’t have people “watching” the system for bad actors. It is just too much and too complex. They are relying more on AI to do that for them and catch things quicker. In fact, nearly half of our survey respondents indicated they plan to use AI/ML for cybersecurity over the next year. We’re even seeing industry leaders pivoting from reactive to proactive. They’re proactively planning improvements in system hardening, patching and monitoring, and tying into current risk levels. This philosophy shift is especially noticeable in end-of-life (EOL) migrations. Historically, manufacturing EOL policy has been “since it is running, don’t touch it…” That resulted in old systems with out-of-date or obsolete parts in the critical system. Manufacturers are realizing this now puts them at risk. So, to get ahead of it, they need to be more proactive and work updates into their plans, so they don’t get to that bad end state and have risk. But this takes broad company support and a willingness to change the old mindset here. There’s more to do, but the impacts are already clear: faster detection, smarter patch prioritization and better recovery planning. But that’s just the start of AI’s utility in the industry. Robotics is the next major chapter in the manufacturing industry’s AI transformation. As technologists debate the final form of physical AI, manufacturers are exploring the practical uses for these technologies now. In my work, I’ve seen manufacturers experimenting with fleets of autonomous transporters taking literal tons of goods from point A to point B, and sparing people from more dangerous tasks in production. And we’re starting to see that borne out in the data — 37% of respondents in this year’s survey indicated their organizations plan to experiment with robotics in the next 12 months.  IT leaders in manufacturing need to recalibrate the time it takes to establish “best practice” and act quickly to keep pace with the rate of AI. So, on the one hand, that means rapid adoption and openness to piloting experimental technology. To be an early adopter, though, you need a supportive workforce. Championing transformation with a workforce resistant to change The speed of change is catching up with our manufacturing workforce. 30% of our respondents this year identified resistance to change as their top leadership challenge — more than any other option we offered. This factor didn’t even make our top three in 2024.  The industry’s continued breakneck innovation speed (23% of respondents said they lack the technology they need to outpace competition in the next year) means manufacturers are unlikely to overcome this challenge without ensuring employees directly feel the benefits. Whether it’s major adjustments — like automating dangerous tasks and retraining an employee to supervise the process — or something as simple as a chat

CIO Gary Brantley discusses building AI capabilities across every aspect of the league’s operations. The National Football League is embracing AI across every aspect of its operations, from enhancing fan engagement to revolutionizing on-field measurements. Here, CIO Gary Brantley shares how the organization deploys AI to improve accuracy, speed up game play, personalize fan experiences, and streamline operations — all while building the governance structure needed to scale these innovations responsibly across the league and its 32 clubs. How is the NFL using AI to improve the fan experience? OnePass is a free mobile app that allows fans access to NFL events, including Combine, Draft, the Super Bowl and international games. OnePass has many functions, from managing your tickets to allowing fans to sign up for activities. Then there’s Ask Vince, a gen-AI powered agent that helps fans plan for the event in real time — like where to go and what time to get there — and explains policies like what you can take inside the stadium. How is AI impacting action on the field? One key area of advancement is our use of optical tracking technology. Traditionally, the league has relied on chain systems to measure field position. Now with the integration of AI and computer vision, we’re leveraging optical tracking to transform the process. Speed and precision are also top priorities for the NFL. During the 2025 season, six 8K cameras will serve as primary tools to determine whether an offense has achieved a first down. This system enables the NFL to deliver fast precision measurements that reduce interruptions to the game, and provide more information to the teams, league officials, and fans. And working closely with our broadcast partners and game presentation producers, we’ll showcase these results using animations and graphics across both television broadcasts and stadium displays. AI assist leads us to a faster and more seamless game. Any other compelling use-cases? We’re using AI in marketing campaign orchestration. Over the last two years, traffic through our digital channels has increased more than 300% just by attaching AI to the engine. AI also helps our creative work and shortens the time to orchestrate content from weeks to days. For example, let’s look at trading cards. Producing a card takes a lot of quality assurance, which has always been a manual process. Every year there are new players in new positions, and the right content about that player needs to go on those cards. This includes the right photo with the right uniform and the right action shot appropriate to the player’s position on the field. Today, AI sifts through that data and increases the speed of producing the cards. The globalization and localization efforts we’re making are also affected by AI. That means we can adapt our creative approach to the international market with automated language translation. If we’re marketing to our 35 million fans in Brazil, it’s important the creative approach resonates with them, and that we reflect their interests, language, and some of their favorite players. AI lets us quickly gather the right data to understand the cultural aspects of our markets. How have you changed your governance to generate and deploy these AI solutions? We started with AI ethical guidelines, but not just for the NFL; we pushed the policy out to the clubs, as well. We then identified leaders across the organization who will help us expand the guidelines across our enterprise. For this, we created an AI steering committee with legal, marketing, security, technology, data and analytics, international, commercial sponsorship, and business development. [ Learn how CIOs are addressing the ethics of implementing AI and how you can establish an effective AI GRC framework ] We thought a lot about how to level the governance structure and decided to put the AI steering committee at the EVP and SVP level, and our working groups beneath it. We felt the best way to move quickly was to get the strategy and vision people, the EVPs and SVPs, out of the way. The working groups are led by VPs and cover football technology, marketing, policy and legal, and sponsorships. Those groups meet weekly on strategy, and the chairs report to the AI steering committee. This structure gives us the ability to connect the overarching strategy with individual work streams and keep everyone connected. We’re also able to push more decision making to the VP level. A big part of AI leadership is storytelling. How do you tell the story of AI and the NFL? I describe it the same way for fans, partners, and players. AI is an extension of your current capabilities. Think of it as a superpower. If you’re very good at tackling today, AI can enhance the angles you’re taking. If you’re a fast runner, AI can give you instant feedback to show you where you can improve. And that’s just on the field. AI can help you break down the playbook for your comprehension style. Some people are visual learners, others like reading, and some people are listeners. Regardless of your style, AI will help you learn the plays. It’s an extension of how good you already are. What have you been doing to prep your tech stack for AI? The key to architecture is an integrated business. Years ago, many CIOs experienced shadow IT, with businesses buying their own technology, and creating a fragmented architecture. This era of shadow IT made it harder to use AI, because with AI, you need a technology stack that connects the business in the right ways. At the NFL, about two years ago, we started bringing all IT systems and resources back into IT, and we established an architectural review board. By bringing historically siloed areas together, we’re now making decisions at the digital core, the architectural base of our organization. Today, all parts of the business are linked. NFL Network, NFL Films, cyber, data and analytics, and MarTech. Our leaders know we can no longer make decisions that are

Decision-makers have access to more information than ever. But digital leaders must set expectations about the right pace, and place, to exploit disparate data sources to achieve winning results. AI is a boon for data analysis. Professionals can automate routine tasks, such as data processing and anomaly detection, while complicated mathematical equations can run in almost real-time. As a result, they have access to information faster than ever before, and the traditional decision-making cycle has been reduced from weeks to seconds with AI-driven insights, leading Gartner to predict that 50% of business decisions will be augmented or automated by AI agents by 2027. Such access is great, but it also creates expectations about the pace of decision-making, and increases the risk of overlooking key things along the way. It’s something that’s crossed Satpal Chana’s mind. “All this technology puts insight into the decision makers’ hands way sooner than they’ve ever had it before, which leads to an interesting discussion because now, they feel like they need to think about things,” says the deputy director for data analytics at Visit Britain, an organization that uses Databricks tech and AI to analyze sentiment, travel trends, and UK tourism. “There’s a lot of information, but is there too much of it?” Jakob Rissmann, principal product owner of data services at transport specialist FlixBus, is another digital leader who recognizes the scale of the issue. He refers to a recent conversation with one of his firm’s data scientists, who talked about the challenges of trusting outputs, before posing a rhetorical question about the exploitation of AI. “He asked if we should do it less and I thought about it, and replied, ‘I never thought about that concept before,’” he says. “Usually the direction is the other way around, and it’s all about how we can use AI more.” Regardless of quantity of use, evidence so far certainly points to ever-growing investments in AI. Gartner also recently predicted that global gen AI spend is expected to increase over 76% this year to reach $644 billion, indicative of an imminent time when AI becomes even more integral to business operations and consumer products than it already is. However, a decision to further invest in technology is no guarantee of success. While Carruthers and Jackson’s annual Data Maturity Index found that 68% of digital leaders believe the technology in their organizations either overwhelmingly or mostly supported data use, close to a third of CDOs, in contrast, said it hindered data use. The consultancy’s CEO Caroline Carruthers says investing in AI is simply a starting point. “Most business leaders I talk to can see the connection between AI and data,” she says. “They’re excited by these developments, but they also recognize their business needs to feed these technologies the right stuff. There’s this dawning recognition that other issues such as data governance and management are important.” Setting expectations for AI exploitation One of the most important things to get right is education. While companies continue to invest in AI, many of the most popular tools, like ChatGPT and Gemini, can be tested for free by employees at home. Amit Patel, CDO for wholesale banking at Truist, who spoke on a media panel at the recent Snowflake Summit in San Francisco, says digital leaders are likely to encounter users who’ve witnessed the rapid pace of change outside work and feel frustrated about the slower pace of enterprise transformation. “The ease of being able to use these large language models in your personal life has influenced the perception of how quickly people think they should be able to deploy models in a business setting,” he said at the event. “I think there’s an education process that says you can’t just turn AI on and point it at a database or an application and generate answers tomorrow.” Patel says he regularly encounters employees who expect AI tools to be accessible and available. However, IT departments exist for a reason: to ensure new technologies are adopted safely and securely. He adds that CIOs must work with business partners to establish expectations and ensure employees understand that any use of AI tools is compliant with business policies and industry rules and regulations. FlixBus’ Rissmann also recognizes the challenges of implementing new technologies in an enterprise setting, and the importance of education. He suggests introducing AI into a business should involve a process that’s well-rehearsed with every innovation. “First, it’s overwhelming, and then we, as a society, learn how to use it the right way, but also know where it can be dangerous,” he says. “That process takes place through education, by using these technologies and understanding them. People should make their own informed decisions about where they want to use AI and where they don’t.” The message that emerges from discussions with digital leaders is that exploiting insight that’s generated through AI involves nuance. Employees are eager to use tools that can generate quick answers to intractable questions. But the role of CIOs in this balancing act is to help ensure AI-generated insights are applied in the most appropriate places to create maximum business value. That’s an approach being taken by Antony Hausdoerfer, group CIO at UK’s auto breakdown specialist The AA. He’s driving a digital transformation program, using key partners such as Ericsson and the company’s car-health assistant Vixa, to deliver data-enabled services. Hausdoerfer’s experiences lead him to suggest CIOs must take a targeted approach.                                                                                “I don’t think you should fear AI, because the technology is probably going to give you more insight than you’ve ever had before,” he says. “But success is related to how you take that information and turn it into a meaningful decision, so you’re coherent. That process is about how data contributes to that kind of decision-making, as opposed to just constantly going from one thing to the next.” Taking a strategic attitude to insight This targeted approach will be the key to success for CIOs who want to help their businesses turn insight into groundbreaking decisions. HPE

You don’t need to move to Silicon Valley to cash in on the boom for IT talent. These 10 US cities are among the fastest growing tech hubs, according to CompTIA. Net tech employment in the US reached just over 9.6 million in 2024 and is projected to grow 2.5% to 9.9 million by the end of 2025, according to the State of the Tech Workforce 2025 report from CompTIA. The economic impact of the tech industry also continues to grow, accounting for an estimated 8.6% of direct economic value, or around $2 trillion. In its report, CompTIA evaluated the net tech employment of every metro area in America, combining different data points to paint a more comprehensive picture of the growing tech workforce. While the foundation of the tech industry starts with tech professionals working in technical positions, CompTIA also points to the importance of business professionals employed by technology companies. Just as IT professionals are vital to non-tech companies, non-tech business professionals also make up 38% of net tech employment, and are vital to the industry. Plus, CompTIA ranked each metro area based on four quartiles for cost of living and tech wage premiums, with the first quartile being the most favorable rating. Based off that data, CompTIA identified these top 10 metro areas that have experienced tech job growth, and are predicted to see even more growth through the rest of the year. Las Vegas Las Vegas ranked in the third quartile for cost of living and tech wage premiums. In addition to tech, industries driving tech hiring in Las Vegas include management and holding companies; professional, scientific, and technical services; and the public sector. Net tech employment in Last Vegas is projected to grow 4.7% and currently makes up just over 3% of the overall workforce, with an economic impact of $7.2 billion in 2024. Some of the top tech companies in Las Vegas include Tapestry, Cox Enterprises, PwC, DraftKings, and Take-Two Interactive Software. Median tech wage: $91,848 Percent higher than national median: 106% Austin Austin ranked in the top quartile for cost of living and second for tech wage premium. In addition to tech, the top industries driving tech hiring include professional, scientific, and technical services; public sector; and finance and insurance. Net tech employment in Austin is projected to grow 4.4% and currently makes up just over 13% of the overall workforce, with an economic impact of $51.2 billion in 2024. Some of the top tech companies in Austin include Apple, Tesla, Google, Dell, Amazon, Samsung, AlertMedia, BAE Systems, and General Motors. Median tech wage: $118,888 Percent higher than national median: 126% Nashville Nashville ranks in the first quartile for cost of living and the third for tech wage premiums. In addition to tech, industries driving tech hiring include professional, scientific, and technical services; management and holding companies; and finance and insurance. Net tech employment in Nashville is expected to grow 4.3% and currently makes up just over 5% of the overall workforce, with economic impact of $12.3 billion in 2024. Some of the top tech companies in Nashville include Toast, PwC, SharkNinja, and Motorola Solutions. Median tech wage: $100,856 Percent higher than national median: 104% Charleston Charleston ranked in the second quartile for cost of living and for tech wage premiums. In addition to tech, industries driving tech hiring include the public sector; professional, scientific, and technical services; and manufacturing. Net tech employment in Charleston is expected to grow 3.9% for the rest of the year and currently makes up 4.5% of the overall workforce, with an economic impact of $4 billion in 2024. Some of the top tech companies in Charleston include Red Hat, Workiva, GoodUnited, and Avoxi. Median tech wage: $101,350 Percent higher than national median: 112% Salt Lake City Salt Lake City ranked in the third quartile for cost of living and for tech wage premiums. In addition to tech, industries driving tech hiring include professional, scientific, and technical services; finance and insurance; and the public sector. Net tech employment in SLC is expected to grow 3.8% and currently makes up 8.5% of the overall workforce, with an economic impact of $13 billion in 2024. Some of the top tech companies include Cash App, Square, Block, Discover, PwC, and Motorola Solutions. Median tech wage: $109,762 Percent higher than national median: 112% Dallas Dallas ranked in the second quartile for cost of living and first for tech wage premium. In addition to tech, industries driving tech hiring include finance and insurance; professional, scientific, and technical services; and administrative services. Net tech employment in Dallas is expected to grow 3.7% and currently makes up nearly 9% of the overall workforce, with an economic impact of $85.3 billion in 2024. Some of the top tech companies in Dallas include Texas Instruments, AT&T, Capital One, Cisco, Microsoft, ServiceNow, and Snap Inc. Median tech wage:  $119,586 Percent higher than national median: 137% Denver Denver ranked in the third quartile for cost of living and tech wage premium. In addition to tech, the industries driving tech hiring include professional, scientific, and technical services; finance and insurance; and management and holding companies. Net tech employment in Denver is expected to grow 3.2% and makes up just over 9% of the overall workforce, with an economic impact of $37 billion in 2024. Some of the top tech companies in Denver include BAE Systems, Inc., Square, Monday.com, Duda, Inc., Slack and Salesforce. Median tech wage:  $123,282 Percent higher than national median: 103% Seattle Seattle ranked in the fourth quartile for cost of living and first for tech wage premiums. In addition to tech, industries driving tech hiring include management and holding companies; professional, scientific, and technical services; and manufacturing. Net tech employment in Seattle is expected to grow 3.1% and makes up just over 12.4% of the overall workforce, with an economic impact of $151.4 billion in 2024. Some of the top tech companies in Seattle include Amazon, Microsoft, Google, Apple, and ServiceNow. Median tech wage: $152,466 Percent

Long-time Bitcoin critic Peter Schiff surprisingly endorsed BTC over Ethereum in a viral tweet, citing chart analysis rather than personal conviction. Ethereum has surged 66% in the past month to US$3,732, reaching its highest 2025 level. US Ethereum ETFs recorded massive inflows of over US$2.1 billion last week, while Bitcoin ETFs experienced net outflows of US$131 million yesterday. Nearly 519,000 ETH worth US$1.92 billion sits in Ethereum’s validator exit queue, indicating profit-taking behaviour following the cryptocurrency’s 160% rally since April. These are strange times when Bitcoin sceptics suddenly endorse the largest crypto and recommend allocating to it. Gold bug Peter Schiff even sent out a tweet read more than two million times, in which he claims that Bitcoin is a better investment than Ethereum, saying, “selling Ether and buying Bitcoin with the proceeds is a better trade than holding Ether”. Ether is back near the upper end of its trading range again. If you own any, this is a great time to sell. As much as it pains me to say, selling Ether and buying Bitcoin with the proceeds is a better trade than holding Ether. — Peter Schiff (@PeterSchiff) July 21, 2025 When pressed by a commentator on why Schiff favours BTC over ETH, the gold advocate replied that it isn’t a matter of conviction but simply what the charts show, adding that Ethereum faces more competition than Bitcoin: I think based on the way the tokens are hyped, Ether has more acknowledged competition for the narrative of its use case. Peter Schiff It’s an interesting turn, because for years Schiff dismissed Bitcoin as a tulip bubble. He often questioned its value as “digital gold” and derided it outright – so seeing him now almost endorse BTC is striking. Related: JPMorgan Eyes Crypto‑Backed Loans Despite Dimon’s “Fraud” Past Remarks ETH ETFs Hit Record Numbers Nevertheless, he’s probably wrong to dismiss Ethereum. The second-largest coin has just seen a phenomenal run-up towards US$4k (AU$6k). ETH has rallied 66% over the past month and currently trades at US$3,732 (AU$5,683), its highest level in 2025 so far. Ethereum’s price has gone up 66% in the past month, source: CoinMarketCap This surge coincides with record inflows into US spot Ethereum exchange‑traded funds (ETFs): over US$2.1 billion (AU$3.2 billion) was added last week, and yesterday alone saw more than US$500 million (AU$760 million) flow in, even as Bitcoin ETFs recorded US$131 million (AU$200 million) in net outflows. According to some analysts, Ethereum is only just getting started: Bitwise CIO Matt Hougan suggested that the ETH trend remains bullish for now. Validators Take Profits Meanwhile, Ethereum’s “exit queue” for validators hit its highest level in over a year on Tuesday, with nearly 519,000 ETH (≈US$1.92 billion or AU$2.92 billion) waiting to leave the network. This backlog stems from proof‑of‑stake (PoS) limits on how quickly stakers can exit – and reflects profit‑taking after ETH’s rally since April. Ethereum’s validator queue, source: validatorqueue.com As Figment co‑founder Andy Cronk notes, price spikes typically trigger both retail and institutional unstaking to lock in gains  – and occasionally coincide with large players shifting custodians or wallet infrastructure. Related: SpaceX Reactivates Bitcoin Holdings After Three Years, Consolidating $153M in BTC Read More

Over the past month LetsBonk has surged in popularity and now has 64% of the total Solana memecoin launchpad market share, as measured by tokens launched. Over the same time, the former leading launchpad, Pump.fun, has seen its market share plummet from 90% to just 24%. Pump.fun’s recently launched utility token, PUMP, has also been struggling recently, recording a new all-time low yesterday driven by its loss of market share to LetsBonk and uncertainty around the token’s airdrop. There’s a new leader in the Solana memecoin launchpad space and it goes by the name LetsBonk. In the past few weeks LetsBonk has exploded in popularity among Solana degens, decisively taking the mantle as the leading Solana launchpad away from Pump.fun, for now at least. LetsBonk surged from just 5% of the market a month ago to around 64% today, as measured by tokens launched. Over the same period Pump.fun’s market share has collapsed from 90% to just 24%. Last week saw approximately 150,000 tokens launched on LetsBonk, an increase of 14% over the previous week. On July 18 alone the platform saw a daily record of 26,600 tokens created. Tokens launched on Solana memecoin launchpads over the past month. Source: The Block   The number of tokens graduating — that is, tokens that attract enough capital to be listed on mainstream Solana decentralised exchanges such as Raydium — has also surged on LetsBonk and now far outstrips Pump.fun.  As of July 18, LetsBonk had 79% market share of graduating tokens, compared to Pump.fun’s 18%. On July 16, LetsBonk saw a record 282 tokens graduate, a 20% increase over its previous daily record. Unsurprisingly, this surge in LetsBonk’s popularity has also seen it surpass Pump.fun’s trading volume — recording around US$179 million (AU$272.8m) in daily volume on July 18, compared to Pump.fun US$52 million (AU$79m). For context, a month ago LetsBonk was averaging under US$10 million (AU$15m) in daily volume, while Pump.fun was well over US$100 million (AU$152m). Daily volume on Solana memecoin launchpads over the past month. Source: The Block   This surge in volume has translated into a big increase in LetsBonk’s trading fee revenue, with the platform generating over US$8 million (AU$12m) in fees in just the past week, around twice the fee revenue Pump.fun generated. Related: Solana Memecoin Platform Pump.fun Faces X Suspensions Amid Regulatory Speculation Pump.fun Token Plummets In Price Following Launch The bad news for Pump.fun extends to its recently launched utility token PUMP. Following PUMP’s launch last week, which saw all the available tokens sell out in just 12 minutes, the token has struggled. In the days following the launch, PUMP’s price surged 72% to a high of US$0.006878, driven largely by token buy-backs from the PUMP team.  Since then though, the buy-backs have slowed and the token price has tanked. PUMP is now down 45% from its all-time high and after plummeting 15% on Tuesday, it hit an all-time low of US$0.003602 — less than what it sold for during its initial coin offering (ICO). The price drop seems to be related to Pump.fun’s significant loss of market share to LetsBonk, but there are also concerns in the community that there’s still no announced date for PUMP to be airdropped to early supporters. Related: Pump.fun Presale Dump: Nearly 60% of PUMP Buyers Have Already Offloaded Tokens, BitMEX Finds In the official announcement launching PUMP, which was posted to X two weeks ago, the PUMP team said the airdrop was “coming soon”. Since then, though, the communication around the airdrop has been vague and cryptic. Read More

Topline General Motors posted a $3 billion second-quarter profit—down $1.1 billion primarily because of tariffs imposed by the Trump administration, the company said in its earnings call on Tuesday, a day after Stellantis (Jeep, Fiat, Chrysler) blamed steep losses on tariffs. The American auto manufacturer’s net income fell by 35%, the company said in its

Topline Stephen Colbert addressed the cancellation of ‘The Late Show’ in his monologue Monday night, where he repeatedly attacked President Donald Trump and vowed the “gloves are off,” for the next 10 months while he is still on air, as several celebrities and fellow late-night show hosts appeared at the Ed Sullivan Theater to show