Welcome to the new episode of the TNW Podcast — the show where we discuss the latest developments in the European technology ecosystem and feature interviews with some of the most interesting people in the industry. In today’s special episode, we’re happy to present an interview with Phill Robinson, founder of Boardwave — a networking platform for founders and CEOs working in the European software industry. The conversation — recorded by our senior editor Linnea Ahlgren — focuses around Europe’s pathway to success. What are we doing right and wrong, compared to the other continents? What’s the future of the European software industry like? And what does AI have to do with it? Calling all Scaleup founders! Join the Soonicorn Summit on November 28 in Amsterdam. Meet with the leaders of Picnic, Miro, Carbon Equity and more during this exclusive event dedicated to Scaleup Founders! Here are the links for this episode: The white paper: “How the UK & Europe can lead the global software industry by 2034” Music and sound engineering for this podcast are by Sound Pulse. Feel free to email us with any questions, suggestions, and opinions at [email protected]. source

Stop for a moment and pay attention to the things around you. The clothes you’re wearing, the device you’re using, what you’re sitting on, the building you’re in. What are they made of?  The simple answer is “stuff from nature” — woods, metals, rocks, oils, and plants refined into things like furniture, batteries, bricks, plastics, and clothes.  In 2017, humanity’s total material footprint — which refers to the total amount of raw materials we extract to fuel our economies — was 92 billion tonnes. The UN predicts this will more than double by 2060 without a change to the current patterns of consumption.  Our insatiable appetite for more stuff threatens to exceed Earth’s limits. True sustainability demands shifting to a circular economy that reuses resources, cuts waste, and restores nature. Slashing carbon is just one piece of the puzzle.  Overeating Calling all Scaleup founders! Join the Soonicorn Summit on November 28 in Amsterdam. Meet with the leaders of Picnic, Miro, Carbon Equity and more during this exclusive event dedicated to Scaleup Founders! For one second, imagine the global economy as a giant belching monster. The beast likes to dine on vast quantities of minerals, metals, biomass, and fossil fuels. It devours these materials to fuel its growth — to build things like roads, buildings, and cars. What the monster doesn’t use becomes waste. Some of this waste comes in the form of farts and burps — aka greenhouse gas emissions.  That metaphor was a bit of fun, but the problem of overconsumption is very serious. The material footprint of the average person stands at about 12 to 13 tonnes per year, almost triple what some scientists estimate to be the sustainable limit.   While we often talk about carbon footprint, the impact of our material consumption — resource depletion, habitat destruction, and pollution — is just as critical. And ironically, decarbonisation efforts could even be fuelling the problem. The race to net zero is driving demand for everything from EVs and solar panels to semiconductors and batteries. All of these climate technologies require heaps of materials, including vast amounts of rare earths like lithium, cobalt, and nickel. In the current economic system, this means more mining, exacerbating ecological degradation and inequality, particularly in many of the world’s poorest countries.   Cutting down In her groundbreaking book Doughnut Economics, Kate Raworth presents a vision for a world where humanity operates within a “safe and just space for all.” The outer ring of the doughnut represents the planetary boundaries — the ecological limits we cannot exceed without damaging Earth’s life-support systems. The inner ring represents the social foundation, the minimum standards for human well-being. The goal is to live in the “doughnut’s sweet spot,” where everyone’s needs are met within planetary boundaries.  In our rush to green the global economy, we are running the risk of breaching Earth’s outer ring. Cutting carbon emissions is critical, but we must ensure that this transition doesn’t exacerbate existing environmental and social crises.     Reaching Raworth’s “sweet spot” means breaking with our linear take-make-waste consumption habits and making them circular. In a circular economy, products are designed to be reused. When they are no longer useful, instead of becoming waste, they then become a resource for something new, just like in nature. Afterall, “waste” is just a resource in the wrong place. Doing more with less The first step toward a circular economy is ramping up recycling. Currently, the bulk of the critical materials used in clean technologies, as well as the products themselves, aren’t recycled. However, rising costs and skyrocketing demand for everything from lithium to steel is opening up a burgeoning new market for recycled materials. One company cashing in on this boom is German startup Cylib, which recently broke ground on its first industrial-scale battery recycling plant. The factory is expected to process 60,000 EV batteries a year once operational, scheduled for 2026. This would make it the largest such facility in Europe.  However, recycling should only be a final resort. We need to prioritise sharing, maintaining, reusing, redistributing, refurbishing, and remanufacturing. Keeping stuff in circulation for as long as possible will dramatically reduce the need to mine new resources.  Take the example of BikeFlip, recently founded by five students from Utrecht University. The startup refurbishes abandoned and neglected kids’ bikes in the Netherlands and then offers them on a subscription model for a fixed monthly fee, including the maintenance and repair of bikes. When the child outgrows the bicycle, the customer chooses a new one and returns the old one, so BikeFlip can deliver it to another customer. Transforming waste into new, better products is called upcycling, and it’s a pillar of circularity that companies like Papershell are turning into profits. The Swedish startup sources craft paper made from waste lignin and cellulose from the timber industry and turns it into “wood metal” — an ultra-strong, fire-resistant material that can replace aluminium, fibreglass, and plastics. Papershell is even experimenting with using mycelium to break down the high-tech wood once it reaches the end of its life — returning it to the soil and fostering biodiversity.   Other ventures are working on reusing entire buildings. Buildings consume about 30% of all the natural resources we extract from the environment, so there’s a big push to make them more circular.  Berlin-based startup Concular has developed software that captures and stores information about the components of a building — from bricks and beams to tiny screws. In this way, when a building reaches its end-of-life, its pieces can be broken up, advertised on a marketplace, and sold for use in the construction of new structures.  Last year, Dutch architecture firm MVRDV completed Matrix One — a six-storey, energy-efficient, office and laboratory block constructed using over 120,000 reusable components. Almost everything, from the doors and windows to ceilings and furniture, is fully detachable and reusable. Even the floors are made from prefabricated concrete slabs with no fixed connections — they can simply be unscrewed and removed.  To return

From Innovation to Infiltration: Safeguarding Against the Hidden Dangers in Your Software Ecosystem source

If software supply chains consisted solely of open source code, securing them would be easy. Effective tools and methodologies exist for discovering and remediating software supply chain security risks that arise from open source components. But supply chains also can, and typically do, contain closed-source code derived from third-party sources. Securing this part of the supply chain often proves much more challenging because software supply chain security tools often don’t focus on closed-source code. When you add software-as-a-service (SaaS) apps to the mix, securing the supply chain becomes even more complicated. These challenges can be solved, but they require a more extensive approach to software supply chain security than some businesses take. Here’s why securing open source alone is not enough and how organizations can do better. The three categories of software supply chains Broadly speaking, three types of software can exist within a software supply chain (which means the set of third-party software resources a business uses): Open source libraries, modules, and other code that developers integrate into applications they build or leverage as dependencies Closed-source products from external vendors that businesses deploy and manage on their infrastructure SaaS applications that are developed, hosted, and managed by an external vendor but used by the business Most organizations use a combination of these types of software resources. For example, IDC found that 66.7% of businesses identified open source as “critical” or “important” to their organizations (IDC’s Five Open Source Software Themes to Embrace, November 2023). At the same time, approximately one-third of businesses depend on SaaS applications to power core business functions, a figure that IDC expects to rise over time (IDC’s SaaSPath 2023 Executive Summary: Examining the SaaS Buyer’s Journey, June 2023). Other business functions are likely addressed using third-party closed-source applications that organizations operate themselves, since few companies build business software totally from scratch. The awkward role of closed-source code in supply chain security Despite the diverse types of software that exist in most supply chains, software supply chain security tools and strategies have tended to focus largely on risks associated with open source. For example, a key category of software supply chain security tooling is software composition analysis (SCA). Typically, SCA solutions work by scanning applications to identify components that resemble open source code, then flagging any such components that are subject to known security vulnerabilities. This approach doesn’t work well for uncovering flaws linked to closed-source code because that code is secret. As a result, SCA scanners cannot typically identify vulnerable closed-source components unless they have access to private code repositories and vulnerability databases — which is very rarely the case. Likewise, creating a software bill of materials (SBOM), which tracks third-party software components and dependency, has become a best practice for securing the software supply chain. But SBOMs are designed mainly to catalog open source software. Documenting closed-source components using SBOMs may be technically possible, but many of the assumptions made by SBOM formatting standards don’t make sense in the context of closed-source code. As a result, few SBOMs track closed-source components. In short, third-party closed-source code fits awkwardly, at best, within modern approaches to software supply chain security. Given the lack of effective tools and tracking methods, it is easy for businesses to neglect this part of their supply chains. Why closed-source and SaaS apps must be tracked Such oversights can result in enormous risks. Some of the highest-profile software supply chain attacks to date — like the SolarWinds breach — have involved closed-source code, not open source products. When incidents like these occur, businesses that fail to track which third-party closed-source apps they use risk not knowing that they are vulnerable or being able to confirm that patches have been installed. On balance, it’s worth noting that the risk surrounding closed-source security vulnerabilities are a bit different from open source flaws. With closed-source apps, it’s more likely that a vendor will automatically install a patch — although this doesn’t necessarily happen. In addition, because open source vulnerabilities are publicly documented, and sometimes accompanied by exploit code, it’s relatively easy for threat actors to take advantage of them. Exploiting vulnerabilities in closed-source code can be more challenging because information about them is often not as readily available. But just because closed-source apps are somewhat less ripe for attack in certain respects doesn’t mean businesses can simply ignore non-open source software when securing their supply chains. The fallout of an attack against closed-source applications can be quite serious, as many businesses that were using SolarWinds circa 2020 know. Integrating closed source into software supply chain security Given the lack of software supply chain security tools designed for closed-source apps, how can businesses gain visibility into these resources and their potential security risks? Part of the answer is to use enterprise architecture (EA) tools to document which applications the business has deployed. In this context, these tools can serve a purpose akin to SBOMs for open source. Using SBOMs to track SaaS applications would also be a wise practice. The idea has been proposed, but is not currently in widespread use. More generally, IT and cybersecurity leaders should make clear that managing third-party closed-source code within the context of supply chain security is just as much of a priority as protecting open source components. The process is complicated and not as straightforward as finding and fixing open source vulnerabilities, but it’s an imperative for businesses that want to make a comprehensive commitment to software supply chain security. Learn more about IDC’s research for technology leaders. International Data Corporation (IDC) is the premier global provider of market intelligence, advisory services, and events for the technology markets. IDC is a wholly owned subsidiary of International Data Group (IDG Inc.), the world’s leading tech media, data, and marketing services company. Recently voted Analyst Firm of the Year for the third consecutive time, IDC’s Technology Leader Solutions provide you with expert guidance backed by our industry-leading research and advisory services, robust leadership and development programs, and best-in-class benchmarking and

By Christopher Cole ( October 25, 2024, 4:57 PM EDT) — The Federal Communications Commission’s approval of radio station chain Audacy’s recent ownership change mirrored the way it handled similar media deals in past years, the FCC’s chief told critics alleging it fast-tracked the Audacy plan to benefit Democratic donor George Soros…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Meta Platforms has created smaller versions of its Llama artificial intelligence models that can run on smartphones and tablets, opening new possibilities for AI beyond data centers. The company announced compressed versions of its Llama 3.2 1B and 3B models today that run up to four times faster while using less than half the memory of earlier versions. These smaller models perform nearly as well as their larger counterparts, according to Meta’s testing. The advancement uses a compression technique called quantization, which simplifies the mathematical calculations that power AI models. Meta combined two methods: Quantization-Aware Training with LoRA adaptors (QLoRA) to maintain accuracy, and SpinQuant to improve portability. This technical achievement solves a key problem: running advanced AI without massive computing power. Until now, sophisticated AI models required data centers and specialized hardware. Tests on OnePlus 12 Android phones showed the compressed models were 56% smaller and used 41% less memory while processing text more than twice as fast. The models can handle texts up to 8,000 characters, enough for most mobile apps. Meta’s compressed AI models (SpinQuant and QLoRA) show dramatic improvements in speed and efficiency compared to standard versions when tested on Android phones. The smaller models run up to four times faster while using half the memory. (Credit: Meta) Tech giants race to define AI’s mobile future Meta’s release intensifies a strategic battle among tech giants to control how AI runs on mobile devices. While Google and Apple take careful, controlled approaches to mobile AI — keeping it tightly integrated with their operating systems — Meta’s strategy is markedly different. By open-sourcing these compressed models and partnering with chip makers Qualcomm and MediaTek, Meta bypasses traditional platform gatekeepers. Developers can build AI applications without waiting for Google’s Android updates or Apple’s iOS features. This move echoes the early days of mobile apps, when open platforms dramatically accelerated innovation. The partnerships with Qualcomm and MediaTek are particularly significant. These companies power most of the world’s Android phones, including devices in emerging markets where Meta sees growth potential. By optimizing its models for these widely-used processors, Meta ensures its AI can run efficiently on phones across different price points — not just premium devices. The decision to distribute through both Meta’s Llama website and Hugging Face, the increasingly influential AI model hub, shows Meta’s commitment to reaching developers where they already work. This dual distribution strategy could help Meta’s compressed models become the de facto standard for mobile AI development, much as TensorFlow and PyTorch became standards for machine learning. The future of AI in your pocket Meta’s announcement today points to a larger shift in artificial intelligence: the move from centralized to personal computing. While cloud-based AI will continue to handle complex tasks, these new models suggest a future where phones can process sensitive information privately and quickly. The timing is significant. Tech companies face mounting pressure over data collection and AI transparency. Meta’s approach — making these tools open and running them directly on phones — addresses both concerns. Your phone, not a distant server, could soon handle tasks like document summarization, text analysis, and creative writing. This mirrors other pivotal shifts in computing. Just as processing power moved from mainframes to personal computers, and computing moved from desktops to smartphones, AI appears ready for its own transition to personal devices. Meta’s bet is that developers will embrace this change, creating applications that blend the convenience of mobile apps with the intelligence of AI. Success isn’t guaranteed. These models still need powerful phones to run well. Developers must weigh the benefits of privacy against the raw power of cloud computing. And Meta’s competitors, particularly Apple and Google, have their own visions for AI’s future on phones. But one thing is clear: AI is breaking free from the data center, one phone at a time. source

By Ivan Moreno ( October 25, 2024, 7:57 PM EDT) — As Shira Perlmutter completes four years as the register of copyrights and director of the U.S. Copyright Office, she told attorneys Friday the agency dealt “with just a dizzying array of issues” — with artificial intelligence technology being ever present — but the Copyright Claims Board was one of her top accomplishments…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

Our 2025 predictions for software development come on the heels of a year that saw nearly every software tooling vendor incorporate (or plan to incorporate) a generative (genAI) “copilot” capability into their tools. And there’s good reason for them to do so, as Forrester’s most recent Developer Survey indicates that 49% of developers are expecting to use or are already using a genAI assistant in the coding phase of software development. Forrester refers to genAI coding assistants as TuringBots, and we feel that genAI will become a pervasive force through all phases of software delivery, not just coding. But AI assistants are only the beginning of the genAI software development revolution. In our report, The Rise Of Application Generation Platforms, we explain how genAI will change the very definition of what software development is over the next 10 years. For the near term in 2025 we’ve kept our predictions real and as actionable as possible so that leaders can be prepared for the genAI reality that awaits them. In the area of software development for 2025, we predict that: At least one organization will try to replace 50% of its developers with AI and fail. In Forrester’s Developer Survey, 2024, developers indicated that they spend about 24% of their time coding; the remaining time is spent doing designs, writing tests, fixing bugs, and meeting with stakeholders. So even though we expect developer productivity to improve with the usage of genAI coding assistants, it’s easy to see that developers are doing a lot more than just writing code. That’s why leaders need to take a step back from the hype and consider what’s really going on at the developer’s desk to see how realistic the hype is. Even with copilots, developers are the human in the loop making sure that the coding suggestions are correct, that the code actually performs the intended operations, and that when bad suggestions come back (and they will), the developer is there to make sure they don’t get placed into production. Don’t let genAI reality bite you, and set expectations accordingly. Fifty percent of enterprises will abandon individual best-of-breed tools for DevOps platforms. Platforms continue to gain on best-of-breed tools. Platform engineering is all the rage with users, and selling platforms are all the rage with software tools vendors. In fact, you can look at any of the once-niche markets for software development tools and see that many of the vendors that were once best of breed have been merging and acquiring each other for years. And it’s not just continuous integration and continuous delivery; it’s application portfolio management all the way down the line to application performance management. No one is satisfied with a slice of the pie; they want the whole pie. Rust will enter the top 10 of the TIOBE Index while C and C++ drop in rank. This year, we believe (or perhaps, really hope) that organizations will finally factor risk into their choice of programming language. With talk of genAI, who has time for worrying about programming language selection? Yet security-minded software engineers realize that the selection of programming language is a relevant and core concern. Until recently, getting everyone on board with this concern has been difficult. That is, until the White House issued a memo on the importance of using memory-safe languages. Forrester views this as a necessary “kick in the pants” for software delivery leaders to wake up and get moving in the right direction. Read our full Predictions 2025: Software Development report to get more detail about each of these predictions and read additional predictions. Set up a Forrester guidance session to discuss these predictions or plan out your 2025 software development strategy. If you aren’t yet a client, you can download our complimentary Predictions guide, which covers more of our top technology and security predictions for 2025. Get additional complimentary resources, including webinars, on the Predictions 2025 hub. source