Hacker are claiming to have breached Cisco and stolen data, with the company saying it is investigating the claims.
According to BleepingComputer, bad actors have been trying to sell data purportedly stolen from Cisco via online forums. The hackers making the claims are the well-known “IntelBroker,” working along with “EnergyWeaponUser and “zjj.”
Tune in as we dive into hackers’ claims of breaching Cisco!
“Compromised data: Github projects, Gitlab Projects, SonarQube projects, Source code, hard coded credentials, Certificates, Customer SRCs, Cisco Confidential Documents, Jira tickets, API tokens, AWS Private buckets, Cisco Technology SRCs, Docker Builds, Azure Storage buckets, Private & Public keys, SSL Certificates, Cisco Premium Products & More!,” reads the post on one hacking forum.
Cisco says it is aware of the hackers’ claims and it is investigating their validity.
“Cisco is aware of reports that an actor is alleging to have gained access to certain Cisco-related files,” a Cisco spokesperson told BleepingComputer.
“We have launched an investigation to assess this claim, and our investigation is ongoing.”
The hackers say they breached Cisco on October 6 and provided samples of the purported stolen data, although they did not provide details on how the hack was carried out.
Tesla has lifted the curtain on a series of major revelations about its upcoming robotaxi, providing a glimpse into its strategic efforts to revolutionize autonomous transport. The recent Tesla event left industry experts and analysts buzzing as Tesla executives discussed everything from the intricacies of the robotaxi’s engineering to its potential market impact.
Jeff Lutz, a seasoned supply chain executive, offered key insights on the Brighter with Herbert YouTube channel, noting that Elon Musk’s recent moves have been “mind-boggling” in their scope and ambition.
Revealing the Cyber Cab
One of the centerpieces of Tesla’s announcement was the unveiling of what insiders are now calling the “Cyber Cab,” a two-seater robotaxi designed to change how people experience urban transport. According to Lutz, this new prototype is more than just a concept vehicle; it represents the future of mass-produced autonomous transport. “They had twenty of these out there, and it wasn’t just for show. We got in them and experienced what Tesla has been working towards,” said Lutz, pointing to the impressive scope of the demonstration.
The design of the Cyber Cab is an embodiment of Tesla’s emphasis on efficiency and purpose-driven features. Lutz highlighted that every element, from the doors to the seats, has a reason behind it. “This isn’t just about aesthetics,” he said, emphasizing the utilitarian nature of Tesla’s choices. “Tesla has always been about pushing boundaries, and here it’s about engineering for functionality and cost-efficiency. This is what will make scaling these vehicles feasible.”
Autonomous Driving & Hardware 3
One of the major questions surrounding the Cyber Cab was whether it would run on Tesla’s existing autonomous technology. During the event, Kim Java interviewed Tesla executives Franz von Holzhausen and Lars Moravy, who confirmed that the Cyber Cab leverages Tesla’s Full Self-Driving (FSD) software, and even older vehicles equipped with Hardware 3 could be made ready for robotaxi duties. “Yes, it’s good to go even with Hardware 3,” Moravy stated, dispelling doubts about the capability of earlier models. This revelation points to Tesla’s broader approach of making its entire fleet adaptable, a strategy that promises to extend the operational life and usefulness of its older cars.
Lutz elaborated on this point, mentioning how Tesla’s steer-by-wire technology is playing a critical role in these advancements. “Tesla is using the Cybertruck as a platform to refine steer-by-wire technology, which is crucial for scaling the Cyber Cab,” Lutz explained. “They’ve figured out how to get it right with the Cybertruck, and now they can scale that to the robotaxi. It’s all about building on existing innovations to move faster.”
Why No Steering Wheel?
Another key feature of the Cyber Cab is the option to remove the steering wheel, an idea that seems counterintuitive but makes perfect sense in Tesla’s grand strategy. “Tesla wants to build a super set that leaves the factory in one way and then adapts to customer needs,” said Lutz. “If a customer needs a steering wheel, it can be added. If they don’t, it stays out, which ultimately reduces manufacturing complexity. Tesla’s objective here is clear—reduce changeover and maintain production efficiency.”
The robotaxi is a two-seater, which some analysts have speculated is part of a plan to eventually evolve the vehicle into a $25,000 mass-market car. Lutz commented, “It makes perfect sense. This is Tesla testing the waters. If they can do this with a robotaxi, then scaling down to a compact model, including options like a steering wheel, is entirely possible.”
Elon Musk’s Long-Term Vision
Lutz believes that Musk’s ambitious vision for Tesla’s robotaxi goes well beyond what other companies have managed so far. “You see companies showing off flashy prototypes that ultimately fail to scale economically. Tesla starts with production solutions—they design for scale from day one. That’s the difference,” Lutz emphasized.
He also reflected on the significant improvement in AI and hardware integration since Tesla’s AI Day in 2022. “I was there, sitting in the third row. In just 24 months, they’ve gone from a single humanoid walking on stage to an entire army of Tesla Bots interacting and serving drinks,” Lutz said. “The rapid progress they’ve made—both with autonomous cars and robotics—is something no one else in the market has come close to achieving.”
The First Robotaxi Customers: Salesforce?
One of the unexpected revelations during the event was the potential for Salesforce to become one of the first major customers for Tesla Bots. Mark Benioff, CEO of Salesforce, was in attendance and appeared impressed with the capabilities of Tesla’s humanoid robots. Benioff hinted at a future collaboration, posting about using these robots to power Salesforce’s “agent force,” essentially putting Tesla Bots in customer service roles in various sectors.
“Mark knows partnerships,” said Lutz. “He was there for a reason—to see if Tesla’s vision could integrate into Salesforce’s ecosystem. The Bots’ ability to serve drinks and interact with people shows they’re beyond just a concept. It’s a functioning reality. If Salesforce signs on, it’s the beginning of a massive new market for Tesla.”
No $25,000 Compact Car—Yet
Despite speculation, Tesla did not unveil a new $25,000 compact car during the event, and Lutz wasn’t surprised. “Tesla is not going to introduce a lower-cost vehicle until they have the right timing to manage existing inventory and avoid any disruptions in their current supply chain,” he said. “It’s about avoiding what’s called ‘excess and obsolescence’ (E&O). Introducing a new model too soon would create a financial liability in the form of unsold inventory. Tesla is playing the long game, and they’re not going to risk their balance sheet just to satisfy market rumors.”
Lutz pointed out that Tesla’s approach to reducing the cost of the Cyber Cab—from the simple, non-motorized seats to the single-pane side windows—is all part of making autonomous vehicles economically viable. “Elon Musk said it would cost around $30,000, and that likely includes FSD,” Lutz added. “The economics of this vehicle are focused on hitting the price point needed for mass adoption, but not at the expense of Tesla’s production efficiency.”
Scale and Simplicity: The Tesla Advantage
At its core, Tesla’s robotaxi is built for scale. The company has thought through every aspect of the vehicle—from wireless charging to optimized seat designs that facilitate faster cleaning—all with the goal of reducing cost and increasing operational efficiency. “Tesla’s vision isn’t just about putting an autonomous car on the road,” Lutz said. “It’s about an integrated system, including charging, maintenance, and even cleaning—all designed to scale.”
According to Lutz, the absence of a charge port and the move towards wireless charging is a bold step. “Five years from now, wireless charging will be the norm, and Tesla is already building for that future. The reduction in complexity and cost from removing traditional charging components is significant.”
The Bigger Picture: Partnerships and the Future of Tesla
Lutz believes the event served as more than a tech showcase—it was a signal to potential partners that Tesla’s solutions are ready for real-world applications. “In 2022, the event was about recruitment, about showing off Tesla’s engineering prowess,” Lutz explained. “This year, it was about partnerships. There were CEOs and corporate executives everywhere—people who could see firsthand the potential of these robots and autonomous cars to transform their own operations.”
“What people don’t realize is that Tesla isn’t just selling cars anymore,” Lutz continued. “They’re selling solutions—FSD, Optimus, software, and now potentially, robots. The sum of Tesla’s parts is becoming far greater than the whole, and no one’s properly modeling this yet. Tesla is about to fundamentally change the game, and this event was just the beginning.”
Tesla’s robotaxi project is more than just a new model of transport—it’s the embodiment of Elon Musk’s vision for scalable, autonomous solutions. As Tesla navigates partnerships, prepares to scale its production, and explores new applications for its AI and robotics, it’s clear that the company is playing at a different level. Jeff Lutz summed it up well: “Tesla’s not just a car company anymore—it’s a technology company building the future.” As Tesla brings the Cyber Cab closer to reality, it signals the dawn of a new era in transport, manufacturing, and human-machine collaboration, with the potential to disrupt industries far beyond the automotive sector.
The battle over content usage in the era of generative AI continues, with the New York Times taking direct aim at the AI-powered search startup, Perplexity. On Tuesday, per to a report in the Wall Street Journal, the Times issued a cease-and-desist notice, demanding that the Bezos-backed company stop accessing and utilizing its content for AI-generated summaries. According to the letter, reviewed by The Wall Street Journal, Perplexity has allegedly violated the newspaper’s rights under copyright law.
Perplexity, which launched two years ago, has positioned itself as an emerging challenger to search giants like Google, offering users AI-generated summaries with selected sources and links. Despite the demand from the New York Times, Perplexity CEO Aravind Srinivas stated, “We are very much interested in working with every single publisher, including the New York Times. We have no interest in being anyone’s antagonist here.”
Tune in for the New York Times vs. Perplexity AI clash!
The Stakes for Publishers
The clash between Perplexity and the Times is not an isolated incident. Generative AI technologies are reshaping the landscape for media and content-driven industries, prompting publishers to recalibrate their strategies in the face of rapid advancements. News outlets, long reliant on advertising and subscription revenue, see both promise and peril in AI. The technology’s ability to analyze data and create content at scale offers efficiency, but it also introduces new risks of misuse and content theft.
The Times has been proactive in protecting its content, and this isn’t the first time it has taken legal action to curb AI firms from exploiting its journalism. The publisher has also filed a lawsuit against OpenAI, the creator of ChatGPT, for alleged copyright infringement. “Perplexity and its business partners have been unjustly enriched by using, without authorization, The Times’s expressive, carefully written and researched, and edited journalism without a license,” the Times wrote in its notice to Perplexity.
The Current Lawsuit Against OpenAI
The New York Times’ legal action against OpenAI further highlights the intensifying struggle between publishers and AI companies over content rights. The lawsuit, filed late last year, accuses OpenAI of using millions of the Times’ articles without permission to train its language models, including ChatGPT. The Times claims that OpenAI’s actions constitute copyright infringement, as its chatbot generates summaries and responses based on the expressive content of the Times’ journalism.
OpenAI, for its part, has denied any wrongdoing, arguing that the data used in training ChatGPT falls under fair use, a defense often invoked by AI companies. The company also contends that some of the Times’ tests in support of the lawsuit were specifically designed to provoke outputs resembling original articles, which OpenAI claims were not representative of typical chatbot responses.
The implications of this lawsuit extend beyond just OpenAI and the Times. If successful, it could set a precedent for how AI companies can legally access and use publisher content, shaping the future of generative AI models and the scope of fair use. This legal battle underscores the broader concerns that media companies have about AI scraping and summarizing their work without appropriate compensation or licensing agreements.
The lawsuit against OpenAI shares many parallels with the current demands made to Perplexity, as both companies have been accused of unauthorized use of copyrighted content. As the generative AI landscape evolves, the outcomes of these legal actions could have significant ramifications for the boundaries between content ownership and technological innovation.
Perplexity’s Response
Perplexity has reportedly assured the Times in the past that it would stop using crawling technology that circumvents website restrictions, but the Times asserts that the company’s assurances have not been honored. The Times asked Perplexity to provide detailed information on how it has been accessing the publisher’s website despite the Times’s preventative measures.
In response, Srinivas emphasized that Perplexity “isn’t ignoring the Times’s efforts to block crawling of its site.” He added that the company plans to address the issues raised in the legal notice by the October 30 deadline. Perplexity has previously struck a handful of deals with publishers, though media companies have described the startup’s terms as less favorable compared to the lucrative licensing agreements that others, like OpenAI, have offered.
Perplexity’s Challenge to Google
Perplexity is backed by Jeff Bezos, and while the company is still a small player compared to Google, it has ambitious plans. In September, Perplexity reported processing 340 million searches, a tiny fraction of Google’s volume but still indicative of growing interest. Perplexity plans to introduce ads under its AI-generated responses later this month, with the company pledging to share up to 25% of the ad revenue with publishing partners whose content it utilizes.
The use of AI-generated search summaries is becoming an increasingly sensitive issue, as traditional publishers worry that users who find information from AI summaries may no longer click through to the full articles. Perplexity is sending some traffic to publishers’ sites, but the volume is still relatively small. According to data from digital measurement firm Similarweb, referrals from Perplexity to the Times’s website increased eightfold over the year ending in August 2024, but they remain a fraction of the traffic driven by Google.
These grievances are part of a larger conversation within the media industry regarding the balance between AI innovation and intellectual property protection. Some publishers have opted to sign licensing deals with AI companies—OpenAI has agreements with media organizations such as News Corp (the parent of The Wall Street Journal), Dotdash Meredith, and Politico owner Axel Springer—that compensate them for the use of their content.
The Complex Dynamics of AI Content Usage
The Times and other publishers have long taken steps to block AI firms from scraping their content without permission. One of the key measures used is the inclusion of specific code in websites that indicates their content should not be scraped, but enforcement remains a challenge. As Perplexity and similar startups continue to gain traction, media companies face the ongoing task of safeguarding their content.
While Perplexity is attempting to carve out its own niche in the competitive search market, the startup is walking a fine line. Its current valuation stands at approximately $1 billion, following a new funding deal earlier this year. Most of its revenue currently comes from a subscription offering priced at $20 per month, which provides users access to more advanced AI capabilities. However, monetizing its AI-generated search through ads—and sharing that revenue with publishers—is a crucial part of its strategy going forward.
A Legal Landscape in Flux
The ongoing disputes between Perplexity, the New York Times, and other publishers highlight the unsettled nature of the legal framework surrounding generative AI. While Perplexity has positioned itself as willing to collaborate with publishers, the path to mutually beneficial agreements is far from straightforward. As Srinivas put it, “We are not interested in being anyone’s antagonist here.” Nevertheless, the tensions around content scraping and copyright issues suggest that the broader fight over content usage by AI is only beginning.
Publishers are finding themselves in a challenging position—embracing technological advancements while safeguarding their core assets. As more media companies weigh legal actions, partnerships, or licensing deals, the industry is grappling with how best to coexist with generative AI firms in a way that preserves both innovation and the value of journalistic content.
The next few months may prove pivotal as Perplexity responds to the Times’s cease-and-desist notice and as other publishers decide whether to follow a similar course. The questions raised by the use of AI in news search—including how to protect original content and fairly compensate creators—remain unresolved, and how these issues play out could define the future relationship between media and artificial intelligence.
Data-driven decision-making has emerged as a critical factor for achieving business success. For Chief Marketing Officers (CMOs) and other enterprise-level executives, understanding and implementing data-driven marketing strategies is not merely an option but a necessity to drive growth and competitive advantage. “Data-Driven Marketing,” a seminal work in the field, explores how leveraging data can transform marketing efforts from intuition-based approaches to precision-targeted strategies.
The Imperative of Data-Driven Decision-Making
In today’s digital economy, the ability to harness data effectively is paramount. As Michael Brenner, CEO of Marketing Insider Group, asserts, “Marketing without data is like driving with your eyes closed. You might get somewhere, but it won’t be the destination you intended.” This sentiment underscores the pivotal role that data plays in shaping marketing strategies. Marketers who rely on data are better equipped to understand and predict consumer behavior, optimize campaigns, and ultimately enhance their return on investment (ROI).
The systematic approach to data-driven marketing begins with the collection and analysis of data. By examining customer behavior, market trends, and competitive dynamics, executives can tailor their strategies to meet the specific needs and preferences of their target audience. According to Kristina Jaramillo, a B2B marketing strategist, “Data gives marketers the ability to personalize their outreach and engage with customers on a more meaningful level, which is essential for driving conversions.”
Metrics: The Cornerstone of Effective Marketing
One of the critical aspects of data-driven marketing is the use of metrics to guide decision-making. Metrics serve as the benchmarks against which marketing performance is measured and optimized. The “10/90 rule” highlights the importance of investing in talent over tools, suggesting that 10% of the marketing budget should be allocated to technology and 90% to skilled professionals who can interpret and act on the data. This approach emphasizes the need for expertise in data analysis to ensure that insights are actionable and aligned with business goals.
Josh Collins, VP of Marketing at Clearbit, notes, “The right metrics can reveal powerful insights about customer behavior and campaign effectiveness. However, without the right talent to analyze and apply these insights, even the most sophisticated tools are of limited value.” Metrics such as customer lifetime value (CLV), customer acquisition cost (CAC), and conversion rates are essential for assessing the effectiveness of marketing initiatives and making data-driven adjustments.
Aligning Marketing with Business Objectives
Effective data-driven marketing requires alignment with broader business objectives. Marketing should not operate in isolation but should be integrated with other departments, such as sales and finance, to ensure that all efforts contribute to the company’s overall goals. Data provides a comprehensive view of how marketing activities impact the bottom line, enabling executives to make informed decisions that drive business growth.
As Shama Hyder, CEO of Zen Media, puts it, “Data-driven marketing isn’t just about numbers; it’s about aligning those numbers with strategic business goals. When marketing initiatives are synchronized with the company’s objectives, the results are far more impactful.” This alignment ensures that marketing efforts are not only effective but also strategically relevant.
Practical Applications and Real-World Examples
Implementing a data-driven marketing strategy involves more than just theoretical knowledge; it requires practical application and real-world insights. Successful case studies illustrate how companies have effectively utilized data to achieve their marketing objectives. For example, Netflix’s data-driven approach to content recommendations has been a key factor in its ability to attract and retain subscribers, demonstrating the power of data in enhancing customer experience and driving engagement.
Similarly, Amazon’s use of data to personalize product recommendations has set a benchmark for e-commerce platforms. As Marc Kiven, Co-founder of Zeta Global, observes, “Amazon’s ability to use data for personalization has revolutionized the retail industry. Their approach serves as a model for how data can be leveraged to create highly relevant customer experiences.”
Overcoming Challenges in Data-Driven Marketing
While the benefits of data-driven marketing are substantial, implementing such strategies comes with its own set of challenges. Issues such as data overload, misinterpretation of data, and difficulties in integrating data across platforms can hinder the effectiveness of marketing efforts. Addressing these challenges requires a clear strategy, the right tools, and a skilled team capable of navigating the complexities of data analysis.
As Dave Frankland, Principal Analyst at Forrester Research, points out, “The key to overcoming data challenges lies in having a robust data management strategy and ensuring that the team is equipped with the skills needed to turn data into actionable insights.” Solutions to these challenges involve streamlining data processes, investing in advanced analytics tools, and fostering a culture of data-driven decision-making within the organization.
The Future of Data-Driven Marketing
Marketing is rapidly evolving, driven by unprecedented advancements in data analytics and technology. For enterprise-level executives, particularly Chief Marketing Officers (CMOs), understanding and leveraging these developments is not just advantageous but essential for staying competitive. As we look ahead, several key trends and emerging technologies are poised to reshape the data-driven marketing domain.
Personalization Through AI and Machine Learning
Artificial intelligence (AI) and machine learning are at the forefront of transforming data-driven marketing. These technologies enable a level of personalization that was previously unattainable. “AI is not just a tool for automation; it’s the cornerstone of personalized marketing strategies,” says Annalise Richards, a leading AI strategist at IBM. “The ability to analyze vast datasets and predict customer behavior allows companies to tailor their marketing efforts with unparalleled precision.”
Machine learning algorithms are increasingly sophisticated, offering deeper insights into customer preferences and behaviors. For instance, predictive analytics can forecast future buying behaviors based on historical data, allowing marketers to create highly targeted campaigns. “AI-driven personalization can significantly enhance customer engagement by delivering content that resonates with individual preferences,” notes Sam Levine, Chief Data Scientist at Salesforce. “This not only improves customer satisfaction but also drives higher conversion rates.”
Enhanced Data Privacy and Security
With the rise of data-driven marketing comes the critical need for enhanced data privacy and security. The implementation of regulations such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) underscores the importance of safeguarding consumer information. “Data privacy is a fundamental concern that cannot be overlooked,” emphasizes Laura Mitchell, Chief Compliance Officer at Oracle. “As marketers harness the power of data, they must also prioritize robust security measures to protect against breaches and misuse.”
Transparency and consent are becoming increasingly important. Companies are investing in technologies that allow consumers to control their data preferences and understand how their information is being used. “Building trust with customers involves not only safeguarding their data but also being transparent about its use,” says Emily Carter, Director of Data Ethics at Microsoft. “A commitment to data integrity and security can enhance brand reputation and foster stronger customer relationships.”
Integration of Multi-Channel Data
The integration of data across multiple channels is another significant trend shaping the future of marketing. With consumers interacting with brands through various touchpoints—social media, email, mobile apps, and websites—integrating data from these diverse sources provides a holistic view of customer interactions. “A unified data strategy is essential for creating a seamless customer experience,” states Michael Wong, VP of Marketing Technology at Adobe. “By consolidating data from different channels, marketers can gain a comprehensive understanding of customer journeys and optimize their strategies accordingly.”
Omni-channel marketing, supported by integrated data platforms, enables brands to deliver consistent and personalized experiences across all touchpoints. “The goal is to provide a cohesive brand experience regardless of where the customer engages with the company,” explains Olivia Grant, Chief Marketing Officer at HubSpot. “A unified approach ensures that marketing messages are relevant and timely, enhancing customer engagement and loyalty.”
The Role of Real-Time Data
Real-time data analysis is becoming increasingly vital for effective marketing. The ability to access and act on data in real-time allows marketers to respond swiftly to emerging trends and shifts in consumer behavior. “Real-time analytics empowers marketers to make informed decisions and adjust strategies on the fly,” says Jason Lee, Chief Analytics Officer at Nielsen. “This agility is crucial in a fast-paced digital environment where customer expectations are constantly evolving.”
Real-time data facilitates dynamic adjustments to marketing campaigns, ensuring that content is relevant and engaging. “Immediate insights allow for rapid optimization of campaigns, enhancing their effectiveness and maximizing ROI,” notes Karen Lee, Head of Digital Marketing at Google. “This capability is especially valuable in competitive markets where staying ahead of the curve is essential.”
Future Trends in Data Visualization
Data visualization continues to advance, providing marketers with innovative ways to interpret and present complex datasets. Emerging tools and techniques are making it easier to translate data into actionable insights through intuitive visual representations. “Effective data visualization is key to making complex data comprehensible and actionable,” highlights David Chen, Lead Data Visualization Specialist at Tableau. “Interactive dashboards and advanced graphics enable marketers to spot trends, identify patterns, and make data-driven decisions with greater clarity.”
The evolution of data visualization tools is enabling more sophisticated analysis and reporting. “As visualization technology evolves, so too does the ability to uncover deeper insights from data,” adds Sophia Robinson, Director of Business Intelligence at SAS. “These advancements are transforming how marketers interpret and leverage data, ultimately driving more effective strategies.”
Mastering Market Intelligence
As data-driven marketing continues to evolve, CMOs and marketing leaders must stay abreast of these advancements to maintain a competitive edge. Embracing AI and machine learning for personalization, prioritizing data privacy and security, integrating multi-channel data, leveraging real-time analytics, and advancing data visualization are all critical components of a forward-thinking marketing strategy. By understanding and implementing these trends, executives can unlock new opportunities for growth and drive superior marketing outcomes.
The future of data-driven marketing is not just about keeping up with trends but leading the way in how data is harnessed to create impactful and resonant marketing strategies.
Looking ahead, data-driven marketing is poised to become even more integral to business success. As technology continues to advance, the capabilities of data analytics will expand, offering new opportunities for marketers to refine their strategies and drive growth. Embracing data as a fundamental aspect of marketing will enable organizations to stay competitive in an increasingly data-centric world.
Mastering market intelligence through data-driven marketing is essential for CMOs and other enterprise-level executives seeking to enhance their marketing strategies and achieve superior outcomes. By understanding and applying data effectively, businesses can transform their marketing efforts, align them with broader business objectives, and navigate the challenges of the digital age with greater agility. Data-driven marketing is not just a trend but a fundamental shift in how marketing should be approached, offering powerful insights and opportunities for those who embrace its potential.
In a bold, unified message to European policymakers, Meta, Spotify, Ericsson, and other prominent business leaders issued an open letter warning that the European Union’s fragmented regulatory environment is stifling AI innovation and putting the region at risk of falling behind in the global AI race. The letter, coordinated by Meta and signed by more than two dozen CEOs and technology leaders, underscores the growing concern that Europe’s inconsistent regulatory decisions are hampering its ability to compete with the United States, China, and other regions that are more aggressively embracing artificial intelligence.
“Europe has become less competitive and less innovative compared to other regions and now risks falling further behind in the AI era due to inconsistent regulatory decision-making,” the letter states. This plea echoes the sentiments of Meta CEO Mark Zuckerberg and Spotify CEO Daniel Ek, who recently co-authored a similar letter calling for Europe to embrace open-source AI to remain competitive on the global stage.
Listen to our conversation on Meta’s EU warning. Will Europe miss out on the AI boom?
Fragmented Regulation Stifling Innovation
At the core of the open letter is a stark critique of the EU’s regulatory framework, particularly the uneven application of the General Data Protection Regulation (GDPR). While GDPR was designed to harmonize data protection across Europe, business leaders argue that inconsistent interpretations and unpredictable enforcement are creating a barrier to AI development. The inability of European regulators to reach consensus on how AI should use data is, according to these leaders, hindering the continent’s AI innovation.
“Meta has been told to delay training its models on content shared publicly by adults on Facebook and Instagram—not because any law has been violated but because regulators haven’t agreed on how to proceed,” Zuckerberg and Ek wrote in a previous letter, underscoring the frustration with regulatory ambiguity. This delay, they warn, prevents European AI models from being trained on European data, effectively ensuring that the continent’s AI development lags behind its global competitors.
In the most recent letter, the signatories emphasize the growing urgency, warning that without clear, harmonized regulations, Europe risks missing out on the massive economic potential AI promises. “Research estimates that Generative AI could increase global GDP by 10 percent over the coming decade, and EU citizens shouldn’t be denied that growth,” the letter stresses.
The Role of Open-Source AI in Europe’s Future
A significant portion of the letter focuses on the critical importance of open-source AI models—AI technologies that are freely available for developers to build upon and modify. These open models, the letter argues, offer a way for Europe to level the playing field and reclaim its technological edge by enabling small businesses, researchers, and public institutions to harness AI’s transformative potential.
Zuckerberg and Ek have previously highlighted the role of open-source AI in driving innovation, pointing out that it democratizes access to cutting-edge technology and helps institutions maintain control over their data. “The internet largely runs on open-source technologies, and so do most leading tech companies,” the two CEOs wrote. “We believe the next generation of ideas and startups will be built with open-source AI because it lets developers incorporate the latest innovations at low cost and gives institutions more control over their data.”
The letter emphasizes that Europe is particularly well-positioned to capitalize on open-source AI, noting that the region has more open-source developers than the United States. However, without regulatory clarity and support, Europe risks losing its advantage in this critical area. “Fragmented regulation is holding back developers and preventing Europe from realizing its full potential in AI,” the letter warns.
The Economic Stakes
The business leaders who signed the open letter argue that AI presents an unparalleled opportunity to boost productivity, drive scientific research, and add hundreds of billions of euros to the European economy. However, they caution that the current regulatory environment is deterring investment and innovation, both of which are critical to capturing these benefits.
The stakes are particularly high when it comes to multimodal AI models—advanced systems that can process text, images, and speech simultaneously. These models represent the next leap in AI capabilities and could have a profound impact on industries from healthcare to education. However, without access to the latest models, European businesses and researchers will be left using outdated technology. “These concerns aren’t theoretical,” Zuckerberg and Ek warned in their earlier letter. “Given the current regulatory uncertainty, Meta won’t be able to release upcoming models like Llama multimodal… European organizations won’t be able to get access to the latest open-source technology.”
The letter goes on to argue that Europe’s regulatory environment is not just limiting AI development but actively reducing the continent’s competitiveness. “Laws designed to increase European sovereignty and competitiveness are achieving the opposite,” it says, pointing out that many of Europe’s brightest AI talents are leaving the continent for regions with more supportive regulatory frameworks.
A Call for Harmonization and Clarity
Both the open letter and the previous statement by Zuckerberg and Ek emphasize the need for regulatory simplification and harmonization. Europe’s complex and inconsistent regulations, they argue, are creating a hostile environment for AI development and threatening the region’s ability to compete globally. “Europe should be simplifying and harmonizing regulations by leveraging the benefits of a single yet diverse market,” the executives argue, pointing to the widening gap between the number of homegrown European tech leaders and those emerging from the U.S. and Asia.
The letter concludes by urging EU policymakers to take decisive action and create a regulatory framework that fosters innovation while ensuring privacy and security. “With the right regulatory environment, combined with the right ambition and some of the world’s top AI talent, the EU would have a real chance of leading the next generation of tech innovation,” the letter states.
The Race Against Time
As AI continues to evolve at breakneck speed, Europe faces a critical decision: adopt a regulatory framework that supports innovation and fosters growth, or risk being left behind in the global AI race. “On its current course, Europe will miss this once-in-a-generation opportunity,” Zuckerberg and Ek warned. “Because the one thing Europe doesn’t have, unless it wants to risk falling further behind, is time.”
For now, the message from Europe’s business leaders is clear: the continent’s fragmented regulatory environment is holding back AI innovation, and without urgent reforms, Europe risks missing out on the transformative potential of artificial intelligence. The time to act, they argue, is now.
Yum! Brands, home to renowned restaurant chains such as Taco Bell, Pizza Hut, KFC, and Habit Burger, is the world’s largest restaurant company. With a presence in 155 countries and more than 50,000 locations, managing and optimizing customer data is critical to their ability to serve millions of customers each day. At the helm of this data-driven revolution is Cameron Davies, Chief Data Officer (CDO) at Yum! Brands, who has been instrumental in transforming the company’s approach to data. In a recent conversation, Davies shared a deep dive into the company’s evolving customer data strategy, emphasizing the role of first-party data, the importance of technology partnerships, and how the company is positioning itself for the future.
The Strategic Role of Data in Global Operations
Yum! Brands’ data strategy is not just about marketing or customer engagement—it underpins the entire operational infrastructure of the business. Davies explains, “We look at data from three fundamental perspectives: easy operations, easy experiences, and easy intelligence. The goal is to use data to simplify and improve everything from supply chain management to customer interactions.”
The concept of “easy operations” is critical for a global company like Yum! Brands. Davies elaborates that his team is responsible for helping restaurant operators make data-driven decisions on a day-to-day basis. “We’re leveraging AI and machine learning to determine everything from how much food we should order, to how fast we should cook it, and even how much we should cook in real-time,” he says. This granular use of data has significant implications for reducing waste, improving service speed, and ensuring consistency across thousands of locations worldwide.
The “easy experiences” pillar focuses on how Yum! Brands can use data to improve customer journeys. “Are we remembering your preferences? Are we getting you relevant offers that resonate with your tastes? Are we ensuring that these experiences translate when you move from a digital platform to a physical restaurant?” asks Davies. He stresses that data is at the core of creating seamless, omnichannel experiences for customers, especially as consumer expectations for personalization continue to rise. For Davies, this is where data becomes truly transformational, enabling the company to deliver on the promise of a more connected, convenient, and personalized dining experience.
“Data is not just about operational efficiencies; it’s about enhancing the experience for both the customer and our employees in the restaurants,” says Davies. “The intelligence we derive from our data allows us to anticipate needs, personalize offers, and ultimately, build deeper relationships with our customers.”
First-Party Data: Unlocking a Valuable Resource
Yum! Brands’ journey into data transformation began with a realization about the value of its first-party data. “When you start asking yourself, ‘How much first-party data do we actually have?’ you go in, look, and say, ‘Holy smokes!’” Davies recalls. The amount of customer data across the company’s four major brands—Taco Bell, Pizza Hut, KFC, and Habit Burger—was staggering. This data was not just vast but also unique in its potential to deliver actionable insights.
“Think about it,” says Davies, “when someone orders a pizza, they’re willing to give us a lot of personal information if it means getting their pizza delivered on time and hot. There’s a natural value exchange in our business that is not as prevalent in other industries.” This direct interaction with customers has allowed Yum! Brands to accumulate a treasure trove of data, from purchase histories to location preferences, all of which can be used to enhance customer experiences.
Yet, having access to first-party data is only part of the equation. The challenge, as Davies points out, lies in effectively using that data. “From an operations perspective, we’ve been doing pretty well. But from a forward-thinking, one-to-one digital marketing perspective, we realized that we were not leveraging our data as effectively as we could be,” he admits. This gap between data collection and data activation spurred Yum! Brands to embark on a journey of transformation, focused on optimizing the way it uses customer data to drive personalized marketing and operational efficiencies.
Choosing the Right Customer Data Platform (CDP)
For any enterprise-level organization, choosing the right Customer Data Platform (CDP) is a pivotal decision. It was no different for Yum! Brands, which undertook a rigorous process to select a partner that could meet its unique requirements. “When we started looking for a CDP, it wasn’t just about commercials or functionality. It was about finding a partner who could go on this journey with us,” says Davies. The right CDP partner, according to Davies, is not just a vendor but an extension of the organization’s data strategy, one that can adapt and grow alongside the business.
This philosophy of partnership led Yum! Brands to select Treasure Data as its CDP provider. “There were a lot of good companies out there, but we were looking for something more than just a product. We wanted a partner who understood the complexities of working in a franchisee environment and who could collaborate with us in a meaningful way,” Davies notes. The ability to work closely with franchisees is crucial for Yum! Brands, as the company operates on a decentralized model where individual franchisees often have different needs and challenges. “At Yum! Brands, we like to use the term ‘taking people with you,’ because we can’t just dictate solutions from the top down. We have to bring our franchisees along on the journey,” says Davies.
This approach to collaboration was essential in the decision-making process. Davies emphasizes that the partnership with Treasure Data has allowed Yum! Brands to maintain flexibility while pursuing its long-term goals. “We’ve had to flex, but that’s what a journey is all about—it’s never a straight line. We need partners who are willing to adapt as we move forward,” he explains. This adaptability is particularly important in an environment as dynamic as the restaurant industry, where consumer behaviors can shift rapidly, and operational demands can vary widely by region.
Navigating the Complexities of a Global Franchise
One of the most unique aspects of Yum! Brands’ data strategy is its global franchise model, which introduces an additional layer of complexity when it comes to data integration and utilization. “Operating in a franchisee environment is fundamentally different from a corporate-owned model,” says Davies. “You don’t just implement changes overnight. You have to bring your franchisees along on the journey, helping them see the value of the new data tools and platforms.”
For Davies and his team, this means constant collaboration, both internally and with external partners like Treasure Data. “We call it ‘taking people with you’ because it’s about moving everyone in the same direction. I can’t tell a franchisee to do something—they have to want to do it themselves,” he explains. This collaborative approach has been essential in aligning the company’s broader data strategy with the needs and priorities of individual franchisees.
Davies notes that one of the keys to making this model work is clear communication and flexibility. “It’s not about dictating a solution; it’s about listening, adjusting, and making sure that the strategy we’re implementing works for everyone,” he says. This decentralized approach to data management allows Yum! Brands to be both agile and responsive, ensuring that its data strategy is adaptable to the unique challenges of each market and franchise.
A Data-Driven Transformation
As Yum! Brands continues to build out its customer data strategy, Davies is optimistic about the future. “We’ve come a long way, but there’s still so much potential to unlock,” he says. The company’s focus on first-party data, combined with its commitment to collaboration and innovation, positions it as a leader in the restaurant industry’s digital transformation. “We’ve got some really good data,” says Davies. “Now it’s about using it effectively to deliver on our customer promise and to create better, more personalized experiences for each of our customers.”
For Chief Data Officers at enterprise organizations, Yum! Brands’ journey offers valuable lessons in how to approach data transformation at scale. From the importance of choosing the right technology partners to navigating the complexities of a franchise model, Yum! Brands is demonstrating how a thoughtful, data-driven strategy can drive both operational efficiencies and enhanced customer experiences.
As Davies puts it, “This isn’t just about technology; it’s about leadership. It’s about taking people with you, understanding their needs, and building a strategy that works for everyone.” For Yum! Brands, the journey has only just begun, but with a clear focus on collaboration and customer experience, the company is well-positioned to continue leading the way in the evolving world of data-driven business.
For many, going to a college or another post-secondary institution is the next step. However, in Kentucky, as many as 1.4 million adults have no postsecondary credential. Over 928,000 adults, or around 66%, have never enrolled in college. The other 33% have some college credits, but never attained any credential or degree. But why are postsecondary credentials so important?
The Rise of Adult Learning
By 2031, over 60% of jobs in Kentucky will require postsecondary education. This is compared to only 55% of Kentucky adults who currently hold a postsecondary credential. Aside from mere job selection, the lack of postsecondary graduates heavily influences the number of households who receive a livable wage. More than 30% of Kentucky households have incomes less than $35,000. Compare that to households with one college graduate receiving a staggering $40,000 more in earnings compared to non-graduate households.
Despite these benefits, college graduates in both public Kentucky universities and the Kentucky Community and Technical College System (KCTCS) have seen a regression in undergraduate students. Overall, there is around a staggering 40% decline through the entire post-secondary system across the decade from 2014 to 2024. The KCTCS specifically saw the sharpest decrease in learners from slightly above 76,000 to just below 50,000, or a 34% decrease in just 10 years. The drop in enrollment isn’t exclusive to the adult learning students, but for the post-secondary education systems in general. Public universities and the KCTCS have seen enrollment rates plummet by tens of thousands of students year after year. But what is driving this rapid decrease in post-secondary enrollment despite the potentially lucrative benefits?
For many, prior commitments prevent college enrollment. Around 48% of adult learners have children, and the limited childcare availability is a major obstacle. Between 2019 and 2021, nearly 16,000 childcare centers closed nationwide. Simultaneously, the average cost of childcare rose to $6,411 per year, or $534 monthly. Aside from childcare, adult workers must cover not only the cost of college, but family expenses and other bills. 58% of full-time undergraduates need to work, and 79% of part-time undergraduates are working adult learners.
However, there are other obstacles to adults in post-secondary education. Many require assistance with coursework in addition to the normal classes due to the years spent out of the classroom. Gateway courses have low success rates in both the public university and KCTCS systems. Adult learners in the KCTCS score less than 25% on English and Math, and learners in the public university system scored 15% on average in both English and Math.
Conclusion
When the financial and academic hindrances combine, they can make adults as much as 4 times less likely to complete postsecondary education. So how can we boost adult learner success in their post-secondary journey? Providing financial assistance and academic support is the best way to help adult learners. Scholarships like the Pell Grant enable learners to finance the costs of college, but also any excess aid covers childcare and other family expenses. This excess aid also provides them with more time to spend studying rather than working.
(@thejefflutz) 