Tools & Platforms
What AI Policy Can Learn From Cyber: Design for Threats, Not in Spite of Them
If you want to understand why regulatory guardrails can supercharge, not stifle, technological innovation, don’t look to theory. Look to cybersecurity. The field is, by definition, mission-critical: cybersecurity keeps our technical infrastructure resilient, protects financial institutions, and allows both individuals and businesses to leverage the internet safely. Cybersecurity methods must evolve quickly, or our critical infrastructure could be at risk.
For decades, cybersecurity has been a proving ground for innovation despite many constraints. It has faced decentralized architectures, hostile threat actors, a fragmented policy landscape, and sprawling systems beyond any one entity’s control. As with many technical tools before, the challenges didn’t paralyze progress — rather, they drove people to invent new technologies and methods. And the innovations that emerged, like zero trust architecture, weren’t built in spite of policy pressure and hard constraints. They were built because of them.
While many are hailing the recent decision to strike the 10-year moratorium on state AI laws from the Senate’s budget bill as a step in the right direction, it’s far from the end of the debate. The instinct to preempt state action remains strong in Republican-controlled Washington, often cloaked as a desire to avoid a “patchwork” of regulation. But that patchwork, messy as it may be, is often where the real progress begins. Good policy doesn’t just keep bad tech in check. It makes better tech possible. And the antidote to a counterproductive patchwork is a federal baseline that sets a clear and consistent standard.
As we navigate a perception in Washington that guardrails stifle innovation, we should ask: What did we learn from cybersecurity? The answer should be obvious. Innovation didn’t die because of oversight. It flourished under it.
Cybersecurity is a case study in how innovation thrives not in regulatory vacuums but in thoughtfully constrained, collaborative ecosystems. Cybersecurity policies, such as the California Consumer Privacy Act, were written because policymakers, practitioners, community advocates, consumers, and businesses all acknowledged what was at stake. They recognized that as we as a society became increasingly reliant on technology, we needed guardrails to direct the development of tools.
Consider the golden child of modern cybersecurity: zero trust architecture. In the old model of network security, anyone who got inside a computer system’s digital boundary was assumed to be trustworthy. That model crumbled under the weight of cloud computing, remote work, and global supply chains, which prompted attackers to find new ways in using stolen passwords, mistakes in cloud setups, or hidden malware in software updates. Engineers could no longer control the perimeter, because the perimeter didn’t exist —the line between “inside” and “outside” the organization was gone. They could no longer control or fully see the systems they were building.
This innovation of architecture that assumes every access request is from an untrusted source until authenticated and authorized didn’t happen in spite of this constraint — it happened because of it. Zero trust wasn’t just a workaround — it became a superior design model for a decentralized world, showing that well-designed, collaboratively crafted, technology and context-informed policy constraints are prompts rather than barriers. They force engineers to think differently, more creatively, and more rigorously.
Most importantly, zero trust didn’t ignore or evade constraints. It embraced them. It turned limitations into design principles. It was shaped by guidance from the National Institute of Standards & Technology (NIST), spurred by government procurement policies, and supported by industry standards. It thrived in a policy ecosystem that demanded better answers and gave engineers a new blueprint to build against.
This is exactly the kind of policy-technology feedback loop we need in AI.
The narrative that constraints kill innovation is both lazy and false. In cybersecurity, we’ve seen the opposite. Federal mandates like the Federal Information Security Modernization Act (FISMA), which forced agencies to map their systems, rate data risks, and monitor security continuously, and state-level laws like California’s data breach notification statute created the pressure and incentives that moved security from afterthought to design priority. The private sector didn’t flee from these requirements. It evolved to meet them and, in many cases, to exceed them.
Yes, federal leadership matters. But the idea that states must sit on the sidelines for a decade while Washington catches up is both strategically naïve and historically unsupported. States have long been the laboratories of democratic governance, including in cyber. Think of California’s Consumer Privacy Act (CCPA), which forced companies nationwide to reckon with data rights. Or New York’s Department of Financial Services (DFS) cybersecurity regulations, which set a new bar for financial sector accountability.
These state-led efforts haven’t derailed innovation. They clarified expectations, set policy floors (not ceilings), and showed that governance can be iterative, flexible, and innovation-enhancing.
The same is true for AI. We’re already seeing AI systems shape hiring, housing, healthcare, and more often with opaque logic, little accountability, and disproportionate harm to the most vulnerable. Waiting a decade for federal consensus wouldn’t preserve innovation. It would preserve incumbency and inequity.
The irony is that the people who build AI, like their cybersecurity peers, are more than capable of innovating within meaningful boundaries. We’ve both worked alongside engineers and product leaders in government and industry who rise to meet constraints as creative challenges. They want clear rules, not endless ambiguity. They want the chance to build secure, equitable, high-performing systems — not just fast ones.
The real risk isn’t that smart policy will stifle the next breakthrough. The real risk is that our failure to govern in real time will lock in systems that are flawed by design and unfit for purpose.
Cybersecurity found its footing by designing for uncertainty and codifying best practices into adaptable standards. AI can do the same if we stop pretending that the absence of rules is a virtue.
We don’t need ten years of silence. We need active, iterative, multilevel governance that gives engineers something worthy to build toward. The future of AI won’t be defined by what it can do in a vacuum. It will be defined by what we choose to ask of it.
AI dishes up savings and smiles at KFC and Pizza Hut
Last updated:
Edited By
Mackenzie Ferguson
AI Tools Researcher & Implementation Consultant
Yum China, the operator of popular fast-food franchises like KFC and Pizza Hut, is diving into the AI world to enhance efficiency and profitability. The company is leveraging AI technology to optimize everything from supply chain processes to in-store operations. As a result, customers can expect faster service and more personalized experiences. This tech rollout represents a significant move towards incorporating cutting-edge technology into everyday business operations.
Background and Context
Yum China, the operator of well-known fast-food chains such as KFC and Pizza Hut, is leveraging artificial intelligence to enhance efficiency and drive profitability in its operations. By incorporating AI technologies, Yum China aims to streamline processes and optimise various aspects of its business strategies. This move not only highlights the company’s commitment to innovation but also its adaptability in an ever-evolving business landscape. For more details on this initiative, you can check the original source here.
In a rapidly changing market, such technological advancements are indispensable for businesses aiming to stay competitive. Yum China’s integration of AI is a strategic move to not only increase operational efficiency but also enhance customer experience, allowing the company to better respond to consumer needs and preferences. This adoption of AI showcases a growing trend among major corporations to harness technology for maintaining relevance and achieving business goals in a digital age.
The initiative by Yum China to embrace AI technologies is also reflective of the broader shift within the restaurant industry towards automation and data-driven decision-making. As companies look to streamline operations and improve margins, artificial intelligence offers a pathway to achieve these objectives. This transformation is crucial for building resilience against market fluctuations and for ensuring long-term sustainability of business models.
Summary of the Article
Yum China, the operator of fast-food chains KFC and Pizza Hut, is increasingly integrating artificial intelligence (AI) into its operations as part of a strategy to enhance efficiency and profitability. The adoption of AI technologies by Yum China is a significant move in the restaurant industry, aiming to streamline processes and improve customer service dynamics. By leveraging AI, the company can not only predict customer preferences more accurately but also manage supply chains more effectively, ensure food quality, and potentially increase sales figures. This strategic embrace of AI underscores Yum China’s commitment to staying ahead in a competitive market landscape where technological adaptation is crucial for business success.
Experts suggest that Yum China’s focus on AI could set a precedent for other major players in the fast-food industry. The integration of technology in food service can lead to more personalized dining experiences, as AI systems are well-equipped to handle and interpret large sets of data related to consumer preferences. This technological shift is especially relevant given the fast-paced nature of consumer markets today, where adaptability can lead to significant competitive advantages. The proactive use of AI could also address labor challenges by shifting tedious and repetitive tasks to machines, thereby allowing human employees to focus on more value-added services.
Public reactions to Yum China’s AI initiatives are largely positive, with consumers expressing interest in faster service and more customized meal options. However, there are also discussions regarding potential job losses due to automation. This has sparked debates on how the balance between AI integration and employment opportunities can be maintained. The future implications of such technological integration suggest that other industries may follow suit, adopting AI not only to improve efficiency but also to innovate in customer service practices—creating a ripple effect throughout the economy.
Related Events
The recent initiatives undertaken by Yum China, the operator of KFC and Pizza Hut, in embracing AI technologies have sparked a series of related events across the business landscape in China. As highlighted in their recent strategies, the integration of AI is not merely about enhancing operational efficiency but also about revolutionizing customer experience. This shift is setting a precedent for other major players in the fast-food industry, encouraging them to explore similar technological advancements.
In response to Yum China’s adoption of AI, various technology firms in China are collaborating with fast-food chains to offer AI solutions tailored to the food and beverage sector. This burgeoning collaboration marks a significant trend in tech-driven partnerships aimed at bringing innovation to everyday consumer experiences. Such alliances are fostering a new era where technology and gastronomy intersect to redefine dining experiences.
Furthermore, this movement is influencing policy discussions at a governmental level, where the focus is increasingly on supporting AI development across different industries. The Chinese government’s enthusiasm for AI as a tool for modernization and efficiency is further emphasized by such corporate moves, thereby reinforcing national goals for technological advancement and self-reliance.
The ripple effects of Yum China’s AI integration are also evident in academic circles, where institutions are emphasizing AI research geared towards practical applications in commercial settings. This academic interest not only fuels future innovations but also ensures a steady supply of skilled professionals ready to meet the demands of a tech-driven economy. In essence, Yum China’s AI strategies are not just operational choices but are contributing to wider societal and economic shifts.
Expert Opinions
In the rapidly evolving landscape of the restaurant industry, particularly in China, expert opinions highlight significant opportunities for leveraging technology to enhance operational efficiency and profitability. Yum China, the operator behind fast-food giants KFC and Pizza Hut, is at the forefront of this transformation. As noted by industry analysts, the company’s strategic integration of AI solutions not only streamlines operations but also personalizes customer experiences. This move is seen as a response to the competitive market pressures and a shift towards more digital-savvy consumer preferences.
Experts have praised Yum China’s innovative approach, emphasizing that the use of AI technology could serve as a blueprint for global franchises aiming to modernize their operations. The company’s application of AI goes beyond mere efficiency. It enables a deeper understanding of consumer behavior, allowing for more targeted marketing strategies and adaptive supply chain management. Industry leaders believe that Yum China’s model could set new standards in the fast-food industry, potentially reshaping how global chains operate. More insights into this transformation can be found at the South China Morning Post.
Public Reactions
The integration of AI by Yum China, the operator of KFC and Pizza Hut in China, has sparked varied public reactions. Many customers have expressed excitement about the increased efficiency and improved service that AI can bring to their dining experience. Some diners appreciate the novelty and technological advancement, which they believe could streamline operations and enhance their overall experience at these popular food chains.
However, not all reactions have been positive. Some consumers have voiced concerns about privacy and data security, as AI systems often require extensive data collection to function effectively. These customers are wary of how their information might be used or shared and are calling for clearer policies and assurances from Yum China regarding data protection.
Moreover, there is a segment of the public that is apprehensive about the potential impact of AI on employment. With AI taking on tasks traditionally handled by human workers, concerns about job displacement have arisen, leading to discussions on how Yum China plans to balance technology integration with human resource management. This sentiment is shared by many globally, reflecting a broader anxiety about the rise of automation in various industries.
Overall, while the use of AI in Yum China’s operations presents exciting opportunities for innovation and growth, it also highlights significant issues that resonate with a global audience. For an in-depth look at Yum China’s AI strategy and public reaction, the South China Morning Post provides more insights here.
Future Implications
The integration of artificial intelligence (AI) into business operations is increasingly transforming industries across the globe. Yum China, the operator of fast-food giants like KFC and Pizza Hut, is a prime example of this trend. By leveraging AI to streamline their processes, they are setting a precedent for other companies to follow. This move is expected to significantly enhance their operational efficiency and profitability, as highlighted in a detailed article by the South China Morning Post.
Looking ahead, the adoption of AI by Yum China could have broader implications for the fast-food industry both in China and globally. As other companies observe Yum China’s successful integration of AI technologies, there may be a ripple effect, prompting more industry players to invest in AI solutions to remain competitive. This could lead to a revolution in customer service, supply chain management, and even menu personalization, driven by AI-driven insights.
Moreover, the shift towards AI can potentially reshape employment dynamics within the sector. While automation may reduce certain manual roles, it also opens up new opportunities for tech-savvy professionals who can develop, manage, and optimize these AI systems. This transformation necessitates a recalibration of workforce skills and continued education for employees to adapt to a tech-driven environment, as noted in discussions surrounding similar advancements.
Hangzhou, the picturesque capital of Zhejiang Province, is quickly emerging as a key pillar in China’s artificial intelligence (AI) revolution. Once known primarily for its cultural heritage and as the headquarters of e-commerce giant Alibaba, the city is now transforming into a powerful AI hub, driven by visionary government policies, a dynamic startup ecosystem, cutting-edge academic institutions, and high levels of private and public investment. Its rapid evolution exemplifies China’s broader strategy to lead the global race in artificial intelligence.
Government Initiatives and Strategic Policy Support
A major driver behind Hangzhou’s AI rise is the strong backing of the Chinese government, both at national and provincial levels. The “Hangzhou AI Industry Chain High-Quality Development Action Plan” has set bold objectives: certifying more than 2,000 new high-tech enterprises, launching over 300 large-scale technological projects, and injecting an impressive 300 billion RMB (approx. US$40 billion) into innovation annually. This funding supports AI research, development of cutting-edge applications, infrastructure, and talent cultivation.
Further cementing Hangzhou’s AI ambitions is the revitalization of “Project Eagle,” a policy initiative that allocates 15% of industrial development funds to future industries, with AI being a priority. These initiatives are not only helping to establish Hangzhou as a hub of AI innovation but are also attracting domestic and international investors eager to tap into this growth.
The Rise of the “Six Little Dragons”
One of the most notable signs of Hangzhou’s AI success story is the emergence of six pioneering startups, collectively referred to as the “Six Little Dragons.” These companies represent the city’s growing diversity and sophistication in AI application:
DeepSeek – Known for its work in natural language processing and large language models.
Game Science – A game development firm leveraging AI in next-gen interactive experiences.
Unitree Robotics – Specializes in agile AI-powered robots for various industrial and consumer applications.
DEEP Robotics – Develops quadruped robots capable of complex navigation and movement, often used for security and research.
BrainCo – Focuses on brain-computer interface (BCI) technologies that merge neuroscience and machine learning.
Manycore Tech – A hardware and software AI solutions provider with strengths in chip design and high-performance computing.
These companies are not only rapidly scaling within China but are also attracting international attention for their technological advancements and commercialization potential. Their presence underscores Hangzhou’s strength in fostering both technical excellence and business scalability.
Academic Foundations and Skilled Talent Pipeline
Hangzhou’s AI ecosystem is further bolstered by a solid academic foundation. Zhejiang University, one of China’s top-tier institutions, plays a critical role in producing AI talent and thought leadership. The university houses cutting-edge research labs and has established partnerships with top tech firms for collaborative innovation.
Graduates from Zhejiang University and other local institutions often go on to found startups or take leadership roles in the AI industry. The close connection between academia and industry ensures a continuous exchange of ideas, innovation, and expertise, which is essential for sustained growth in emerging technologies like AI.
In addition, Hangzhou has invested in AI-focused education and vocational training programs to ensure that its workforce remains competitive. This comprehensive talent strategy allows the city to meet the growing demand for data scientists, machine learning engineers, and AI researchers.
Industry Collaboration and Corporate Investments
Beyond startups and academia, major corporate players are betting big on Hangzhou’s AI future. Most notably, Alibaba, headquartered in the city, has been at the forefront of this transformation. Under the leadership of Eddie Wu, the company has pledged to deepen its involvement in generative AI and has launched internal initiatives aimed at developing new AI products and services.
In parallel, Alibaba has worked to attract foreign capital to Hangzhou’s AI sector, especially in connection with the Six Little Dragons. Following Jack Ma’s involvement in a high-level business symposium with President Xi Jinping, Alibaba’s influence in shaping Hangzhou’s AI roadmap has only increased.
Other corporations and venture capital firms are also taking notice. Investment funds are flowing into AI development zones, incubators, and innovation labs across Hangzhou, helping to establish a robust support system for tech entrepreneurship and research.
Infrastructure, Challenges, and Long-Term Outlook
Despite these promising developments, Hangzhou faces several challenges that come with rapid growth. Talent retention remains a concern, as other Chinese cities like Beijing and Shenzhen compete for the same AI professionals. Furthermore, as AI technology demands powerful computing infrastructure, continued upgrades in data centers, power grids, and 5G connectivity are essential.
Additionally, navigating regulatory uncertainty and ensuring responsible AI development will be key for Hangzhou to maintain sustainable growth. The city must also remain agile in adapting to global shifts, including trade policies, technology standards, and geopolitical tensions that may impact international partnerships and supply chains.
Nonetheless, the city’s proactive governance, talent pool, and innovative momentum offer strong indicators that Hangzhou is well-positioned to become a global AI innovation hub. As China continues to push its national AI ambitions, Hangzhou stands out as a leading example of how a regional city can emerge as a technological powerhouse through visionary planning, strong public-private partnerships, and relentless innovation.
The data industry is on the verge of a drastic transformation.
The market is consolidating. And if the deal flow in the past two months is any indicator — with Databricks buying Neon for $1 billion and Salesforce snapping up cloud management firm Informatica for $8 billion — momentum is building for more.
The acquired companies may range in size, age, and focus area within the data stack, but they all have one thing in common. These companies are being bought in hopes the acquired technology will be the missing piece needed to get enterprises to adopt AI.
On the surface level, this strategy makes sense.
The success of AI companies, and AI applications, is determined by access to quality underlying data. Without it, there simply isn’t value — a belief shared by enterprise VCs. In a TechCrunch survey conducted in December 2024, enterprise VCs said data quality was a key factor to make AI startups stand out and succeed. And while some of these companies involved in these deals aren’t startups, the sentiment still stands.
Gaurav Dhillon, the former co-founder and CEO of Informatica, and current chairman and CEO at data integration company SnapLogic, echoed this in a recent interview with TechCrunch.
“There is a complete reset in how data is managed and flows around the enterprise,” Dhillon said. “If people want to seize the AI imperative, they have to redo their data platforms in a very big way. And this is where I believe you’re seeing all these data acquisitions, because this is the foundation to have a sound AI strategy.”
But is this strategy of snapping up companies built before a post-ChatGPT world the way to increase enterprise AI adoption in today’s rapidly innovating market? That’s unclear. Dhillon has doubts too.
“Nobody was born in AI; that’s only three years old,” Dhillon said, referring to the current post-ChatGPT AI market. “For a larger company, to provide AI innovations to re-imagine the enterprise, the agentic enterprise in particular, it’s going to need a lot of retooling to make it happen.”
Fragmented data landscape
The data industry has grown into a sprawling and fragmented web over the past decade — which makes it ripe for consolidation. All it needed was a catalyst. From 2020 through 2024 alone, more than $300 billion was invested into data startups across more than 24,000 deals, according to PitchBook data.
The data industry wasn’t immune to the trends seen in other industries like SaaS where the venture swell of the last decade resulted in numerous startups getting funded by venture capitalists that only targeted one specific area or were in some cases built around a single feature.
The current industry standard of bundling together a bunch of different data management solutions, each with its own specific focus, doesn’t work when you want AI to crawl around your data to find answers or build applications.
It makes sense that larger companies are looking to snap up startups that can plug into and fill existing gaps in their data stack. A perfect example of this trend is Fivetran’s recent acquisition of Census in May — which yes, was done in the name of AI.
Fivetran helps companies move their data from a variety of sources into cloud databases. For the first 13 years of its business, it didn’t allow customers to move this data back out of said databases, which is exactly what Census offers. This means prior to this acquisition, Fivetran customers needed to work with a second company to create an end-to-end solution.
To be clear, this isn’t meant to cast shade on Fivetran. At the time of the deal, George Fraser, the co-founder and CEO of Fivetran, told TechCrunch that while moving data in and out of these warehouses seems like two sides of the same coin, it’s not that simple; the company even tried and abandoned an in-house solution to this problem.
“Technically speaking, if you look at the code underneath [these] services, they’re actually pretty different,” Fraser said at the time. “You have to solve a pretty different set of problems in order to do this.”
This situation helps illustrate how the data market has transformed in the last decade. For Sanjeev Mohan, a former Gartner analyst who now runs SanjMo, his own data trend advisory firm, these types of scenarios are a big driver of the current wave of consolidation.
“This consolidation is being driven by customers being fed up with a multitude of products that are incompatible,” Mohan said. “We live in a very interesting world where there are a lot of different data storage solutions, you can do open source, they can go to Kafka, but the one area where we have failed is metadata. Dozens of these products are capturing some metadata but to do their job, it’s an overlap.”
Good for startups
The broader market plays a role here too, Mohan said. Data startups are struggling to raise capital, Mohan said, and an exit is better than having to wind down or load up on debt. For the acquirers, adding features gives them better pricing leverage and an edge against their peers.
“If Salesforce or Google isn’t acquiring these companies, then their competitors likely are,” Derek Hernandez, a senior emerging tech analyst at PitchBook, told TechCrunch. “The best solutions are being acquired currently. Even if you have an award-winning solution, I don’t know that the outlook for staying private ultimately wins over going to a larger [acquirer].”
This trend brings big benefits to the startups getting acquired. The venture market is starving for exits and the current quiet period for IPOs doesn’t leave them a lot of opportunities. Getting acquired not only provides that exit, but in many cases gives these founding teams room to keep building.
Mohan agreed and added that many data startups are feeling the pains of the current market regarding exits and the slow recovery of venture funding.
“At this point in time, acquisition has been a much more favorable exit strategy for them,” Hernandez said. “So I think, kind of both sides are very incentivized to get to the finish line on these. And I think Informatica is a good example of that, where even with a bit of a haircut from where Salesforce was talking to them last year, it’s still, you know, was the best solution, according to their board.”
What happens next
But the doubt still remains if this acquisition strategy will achieve the buyers’ goals.
As Dhillon pointed out, the database companies being acquired weren’t necessarily built to easily work with the rapidly-changing AI market. Plus, if the company with the best data wins the AI world, will it make sense for data and AI companies to be separate entities?
“I think a lot of the value is in merging the major AI players with the data management companies,” Hernandez said. “I don’t know that a standalone data management company is particularly incentivized to remain so and, kind of like, play a third party between enterprises and AI solutions.”
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