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If you want to understand how even modern American cities became hostile to human life, dont start with the political conspiracies; look at the way city planners and road engineers calculate success. Every day, public agencies across the country greenlight projects that cost millions of dollars, destroy neighborhoods, and ultimately kill peopleall in the name of saving drivers a few seconds. This is standard operating procedure, justified by a single, dangerous metric: vehicular delay. In transportation bureaucratese, its called Level of Service (LOS). Think of it as a report card with grades A to F describing how freely cars move. But this grade has nothing to do with safety, quality of life, economic productivity, or human flourishing. Its entirely about how long a vehicle waits at an intersection or slows down during rush hour. The built environment is shaped around that metric. {"blockType":"creator-network-promo","data":{"mediaUrl":"","headline":"Urbanism Speakeasy","description":"Join Andy Boenau as he explores ideas that the infrastructure status quo would rather keep quiet. To learn more, visit urbanismspeakeasy.com.","substackDomain":"https:\/\/www.urbanismspeakeasy.com\/","colorTheme":"salmon","redirectUrl":""}} Take a look at this table that status quo planners and engineers use to measure an intersections performance. [Image: courtesy of the author] Experts give a failing grade to an intersection where people wait a little over a minute before going about their business. Taxpayers are forced to chip in for road expansion projects that cost hundreds of millions of dollars to buildprojects justified by the impatient nature of drivers. It gets worse. The intersections are graded during the busiest hour of the busiest day of a week. If the experts were honest about their analysis, theyd tell you the following: During the busiest hour of the day, the average driver waits 30 seconds at the stop sign. We give that a grade of D. The other 23 hours of the day dont matter. The average person attending a public hearing has been trained since early childhood that A is good and F is bad. So even if they dont like the idea of the local government seizing their front yard in order to widen a street to improve LOS, normies assume road expansions are for the greater good. Such is the treacherous nature of LOS. But wait, it gets even worse. Fortune-telling replaces advanced education and engineering judgment as the experts responsible for designing transportation systems use manuals that are the equivalent of Magic 8 Balls to ask if we need more space for car traffic decades into the future. The answer, of course, is always Signs point to yes. Again, if they were honest, the forecasting analysis would be described like this: The transportation department is guessing that 20 years from now, the average driver might have to wait an entire minute at the stop sign. We give that a grade of F. The other 23 hours of the day wont matter. With the prophecy of more traffic in hand, engineers not only design todays streets to earn good grades on the pseudoscience report card, they design for a future they cant possibly predict. With infrastructure designed for high-volume, high-speed, low-delay motor vehicles, anyone wanting to walk or ride a bike is put into a lethal game of Frogger. When a city does create bus and bicycle infrastructure to shift trips from vehicles to other modes, the traffic report cards dont reflect the fact that people have options. Its solely focused on cars. Theres no redeeming quality to LOS. This obsession with delay has had disastrous effects. When your only metric is vehicle throughput, you end up designing highways through neighborhoods, not places worth living in. A traffic engineer will tell you its more efficient to eliminate street parking and narrow sidewalks to make room for a dedicated right-turn lane. The spreadsheet says so. But that same design makes it harder to cross the street, harder to linger, harder to be a human being. The math checks out, the morality doesnt. A status quo success story is when a road expansion allows a driver to get home 18 seconds sooner but makes it impossible for a child to safely bike to the library. Every single intersection is analyzed and graded based on seconds of delay for drivers. So every single intersection needs more lanes to pump as much car traffic as quickly as possible through an intersection. Anything else (moms pushing strollers, grandparents with canes, kids on bicycles) interferes with LOS. Here are three important questions for experts to ponder: Is slow-moving car traffic ever safer than fast-moving traffic? Do we have any obligation to provide safe and convenient access for people when they arent inside cars? What are the economic downsides of wider, faster streets in the central business district? When planners and engineers truly wrestle with those questions, they can choose to remain a conformist who ignores the damage of traffic metrics, or become an outlier in the industry and make a positive impact that might be felt for generations to come. Things can get better in the end. {"blockType":"creator-network-promo","data":{"mediaUrl":"","headline":"Urbanism Speakeasy","description":"Join Andy Boenau as he explores ideas that the infrastructure status quo would rather keep quiet. To learn more, visit urbanismspeakeasy.com.","substackDomain":"https:\/\/www.urbanismspeakeasy.com\/","colorTheme":"salmon","redirectUrl":""}}
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I work in the data center industry, where were known for our digital-ready, adaptive infrastructure. Yet one of our most valuable products is actually the leaders we create. Developing leaders is critical for every growing company. For us, its an urgent priority. Demand for AI and high-powered computing means were expanding almost 30% annually. In just two years, weve grown from under 200 employees in the U.S. to around 900 across five countries.But as vital as leadership development is, it often gets overlooked. Just four out of 10 executives say their company has high-quality leadership, while 45% of managers dont think their organization is doing enough to develop senior talent.Turning your company into a leadership development engine requires looking at tomorrow through a talent lens. Its not just about hiring great peopleits about building a pipeline of leaders who can step up, inspire teams, and represent the business at its best. That means promoting from within, bringing in fresh perspectives, and upskilling existing leaders to be ready for whats next. Even for companies that arent on a rapid growth trajectory, our experience offers some lessons worth considering. Here are three things any business can do to develop its leaders. 1. Identify potential and create the roadmap To start, you need a clear leadership philosophy. Ours is simple: Grow people, grow the business. We see leaders as those who take initiative, elevate others, and deliver results without needing to be micromanaged. The next step: Create a leadership roadmap by figuring out which roles you need today and tomorrow. This isnt just about identifying people but also pinpointing business needs. Who on your team can be developed to meet those objectives? What roles call for a new hire? Who will need replacing? With an aging workforce threatening a talent shortage, succession planning is increasingly important for future-proofing. Its also crucial to balance internal promotions with new blood. When I became CEO, I could have recreated the C-suite from my previous company. Instead, we built a culture rooted in our unique business needsrecruiting leaders from a variety of organizations and developing existing talent. Last quarter alone, we promoted four executives within the company to new roles. Im also a firm believer that A players should hire A players. That demands letting go of fears around being replaced and bringing on people who help raise everybodys game. Finally, one of the most powerful things an organization can do is treat leadership as a behavior, not a goal. Give people the chance to lead projects, influence peers, and solve hard problems before they ever manage a team. It builds confidence, surfaces potential, and helps people grow into leaders long before their title says so. 2. Train and develop your leaders Identifying a future leader is just the beginning. The real work lies in helping them develop. General Electrics Leadership Development Institute once set the standard here, especially during the Jack Welch era. IBMs offerings include online leadership development programs that earn participants a certificate from a top business school. While some companies prefer a one-size-fits-all approach, we break down leadership development into three cohorts. One is for team members who have never led before. The next is for midlevel managers, covering topics like having tough conversations, big-picture thinking and leading rather than managing. For high-potential employees (chosen by the C-suite), we offer a Leadership Excellence program designed to accelerate those who can move the business forward. One-on-one training is also essential. Through our mentorship program, we pair top leadership candidates with senior executives. We also have promising leaders shadow more senior team members, especially if they might end up succeeding that person. Such efforts pay off. One study found that the average ROI for every dollar spent on leadership development is $7. Besides a revenue boost, those benefits include savings from higher employee retention and lower recruiting costs. 3. Support the leaders you have Leaders need autonomy to do what they do best, but that freedom hinges on support from their peers. We recently brought the entire executive team together for an offsite. Such meetings are a chance to align on priorities, share ideas, talk about what is and isnt working, and brainstorm how to overcome obstacles. Having that peer network to lean on helps set leaders up for success with their teams. Burnout is also a major problem. Younger people are especially vulnerable, with 75% of leaders under age 35 saying they feel used up at the end of each day. To prevent that, we provide executive coaching, settle on a realistic scope for leaders duties and encourage setting boundaries. Avoiding burnout also means normalizing vulnerability and urging leaders to tell us if theyre at capacity. The worst thing that can happen is someone quitting because they didnt have bandwidth. Especially when we can help them, whether thats by hiring or bringing in staff from elsewhere in the business. Challenges and opportunities Developing the next generation of leaders has its stumbling blocks. One hurdle is that many young professionals are reluctant to lead. More than half of Gen Z employees dont want to be middle managers, and roughly 70% would prefer to advance as individual contributors. Were tackling this challenge with a robust internship program that gives new grads exposure to multiple career paths, including leadership, so they can make an informed decision about whats right for them. AI adds another layer of complexity. On the one hand, I see it becoming a powerful development tool, offering leaders real-time feedback, personalized learning journeys and data-backed insights into team dynamics. On the other hand, AI is forcing leaders to start thinking about how it will transform the workforce and impact their teams. But no matter what changes AI brings, it cant replace the human element of leadership. For leaders at any successful business in any industry, qualities like empathy, judgment, and presence cant be outsourced. If anything, AI frees up more time for leaders to focus on their most important job: bringing out the best in the people around them. Andrew Schaap is CEO of Aligned Data Centers.
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Today, most AI is being built on blind faith inside of black boxes. It requires users to have on unquestioning belief in something neither transparent nor understandable. The industry is moving at warp speed, employing deep learning to tackle every problem, training on datasets that few people can trace, and hoping no one gets sued. The most popular AI models are developed behind closed doors, with unclear documentation, vague licensing, and limited visibility into the provenance of training data. Its a messwe all know itand it’s only going to get messier if we dont take a different approach. This train now, apologize later mindset is unsustainable. It undermines trust, heightens legal risk, and slows meaningful innovation. We dont need more hype. We need systems where ethical design is foundational. The only way we will get there is by adopting the true spirit of open source and making the underlying code, model parameters, and training data available for anyone to use, study, modify, and distribute. Increasing transparency in AI model development will foster innovation and lay a stronger foundation for civic discourse around AI policy and ethics. Open source transparency empowers users Bias is a technical inevitability in the architecture of current large learning models (LLMs). To some extent, the entire process of training is nothing but computing the billions of micro-biases that align with the contents of the training dataset. If we want to align AI with human values, instead of fixating on the red herring of bias, we must have transparency around training. The source datasets, fine-tuning prompts and responses, and evaluation metrics will reveal precisely the values and assumptions of the engineers who create the AI model. Consider a high school English teacher using an AI tool to summarize Shakespeare for literary discussion guides. If the AI developer sanitizes the Bard for modern sensibilities, filtering out language they personally deem inappropriate or controversial, they’re not just tweaking outputthey’re rewriting history. It is impossible to make an AI system tailored for every single user. Attempting to do so has led the recent backlash against ChatGPT for being too sycophantic. Values cannot be unilaterally determined at a low technical level, and certainly not by just a few AI engineers. Instead, AI developers should provide transparency into their systems so that users, communities, and governments can make informed decisions about how best to align the AI with societal values. Open source will foster AI innovation Research firm Forrester has stated that open source can help firms accelerate AI initiatives, reduce costs, and increase architectural openness, ultimately leading to a more dynamic, inclusive tech ecosystem. AI models consist of more than just software code. In fact, most models’ code is very similar. What uniquely differentiates them are the input datasets and the training regimen. Thus, an intellectually honest application of the concept of “open source” to AI requires disclosure of the training regimen as well as the model source code. The open-source software movement has always been about more than just its tech ingredients. Its about how people come together to form distributed communities of innovation and collective stewardship. The Python programming languagea foundation for modern AIis a great example. Python evolved from a simple scripting language into a rich ecosystem that forms the backbone of modern data processing and AI. It did this through countless contributions from researchers, developers, and innovatorsnot corporate mandates. Open source gives everyone permission to innovate, without installing any single company as gatekeeper. This same spirit of open innovation continues today, with tools like Lumen AI, which democratizes advanced AI capabilities, allowing teams to transform data through natural language without requiring deep technical expertise. The AI systems we’re building are too consequential to stay hidden behind closed doors and too complex to govern without collaboration. However, we will need more than open code if we want AI to be trustworthy. We need open dialogue among the enterprises, maintainers, and communities these tools serve because transparency without ongoing conversation risks becoming mere performance. Real trust emerges when those building the technology actively engage with those deploying it and those whose lives it affects, creating feedback loops that ensure AI systems remain aligned with evolving human values and societal needs. Open source AI is inevitable and necessary for trust Previous technology revolutions like personal computers and the Internet started with a few proprietary vendors but ultimately succeeded based on open protocols and massively democratized innovation. This benefited both users and for-profit corporations, although the latter often fought to keep things proprietary for as long as possible. Corporations even tried to give away closed technologies “for free,” under the mistaken impression that cost is the primary driver of open source adoption. A similar dynamic is happening today. There are many free AI models available, but users are left to wrestle with questions of ethics and alignment around these black-boxed, opaque models. For societies to trust AI technology, transparency is not optional. These powerful systems are too consequential to stay hidden behind closed doors, and the innovation space around them will ultimately prove too complex to be governed by a few centralized actors. If proprietary companies insist on opacity, then it falls upon the open source community to create the alternative. AI technology can and will follow the same commoditization trajectory as previous technologies. Despite all the hyperbolic press about artificial general intelligence, there is a simple, profound truth about LLMs: The algorithm to turn a digitized corpus can be turned into a thought-machine is straightforward, and freely available. Anyone can do this, given compute time. There are very few secrets in AI today. Open communities of innovation can be built around the foundational elements of modern AI: the source code, the computing infrastructure, and, most importantly, the data. It falls upon us, as practitioners, to insist on open approaches to AI, and to not be distracted by merely “free” facsimiles. Peter Wang is chief AI and innovation officer at Anaconda.
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