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When Austin, Josh, and I started Civic Roundtable in 2022, we never thought we’d be an AI company. We had worked in and around government for about a decade, seeing that public servants doing critical work were underserved by technology. We saw opportunities to make it better. In the public sector, the stakes are high: If an agencys technology fails, real people dont get the health services they need, disaster recovery efforts get delayed, and communities lose access to services they rely on. But we also know the right technology can empower public servants to have a bigger impact. Fast forward three years, and AI is everywhere. Chatbots abound and AI widgets appear in new applications daily. But rather than integrating AI for the sake of AI, wed like to suggest a different path: Start listening. Weve spent the last year on the road asking public servants, If technology could free up an hour of your time every day, how would your work change? Weve toured state agencies in California and Texas and met with county officials from Oregon to New Jersey. Weve run brainstorming sessions with public health officials, spent hours learning alongside election administrators, and strategized with emergency management professionals. The result? Some clear ideas about how AI might actually help the public servants behind the healthy functioning of our communities. So for anyone eager to put AI tools to work in support of the public sector, please steal these three lessons, free of charge. 1. AI must address an actual need Starting with AI capabilities puts the cart before the horse. Public servants know their own needs. Some processes are not the result of some imagined inefficiency, but arise from intentional, legally mandated processes. Similarly, certain government functions require human judgment for ethical or democratic reasons. This is a good thing. AI built for public servants should reflect the reality that specific agency workflows differ based on their departments function. For example, officials administering elections have different duties than those implementing programs to provide relief from extreme heat, and these require different technology functionality. Still, useful AI need not limit itself to specific departmental use cases. In our conversations with public servants, we heard again and again that they see clear value in finding information faster. Another area where AI can empower public servants is for repetitive, format-driven tasks, like budget analyses, stakeholder mapping, and memo drafting. A granular understanding of, and empathy with the public servants who best understand this work, means the difference between another AI tool and technology that meets public servants where they are, to help them do more. 2. AI must be reliably accurate One federal official confessed to us, Ive heard that AI is very good at lying. We cant have that. Hes right. AI deployed in government agencies needs to be reliably accurate. Even outside of government, people worry about hallucinations, like when Googles AI confidently told a user to use glue to keep the cheese on pizza. When the stakes are higherpublic health, emergency management, homelessness responseirrelevant or inaccurate information is a deal breaker. One technique to minimize errors, particularly well-suited to government tools, is intentionally limiting the content underpinning AI responses. This means restricting AI tools to exclusively reference resources that are vetted and approved by the government officials themselves. A second approach to enhance trustworthiness is ensuring that responses come with cited sources. When its clear where information is coming from, and easy for public servants to validate those sources, government officials can stand on the firm ground of actual policies, documentation, and their own data without worrying about trusting what AI says. Its okay if a purpose-built government AI tool cant tell you what Taylor Swifts most-streamed single is but can provide state agencies exceptionally precise answers about the resources, points of contact, and proper processes they need to execute their mission. 3. AI must be easy to deploy Deploying AI within a government agency can be a complex effort requiring significant work and IT expertise. AI tools that can specifically query an organization’s own data (known as RAG for retrieval-augmented generation), are a powerful way to increase the accuracy and relevance of AI outputs. But implementing RAG LLMs demands substantial technical competency and careful coordination with information technology teams. Most government agencies, especially those on a state, county, and local level, don’t have teams of developers waiting to integrate a custom API or put in work to configure a new platform. They need solutions that work from day one with minimal implementation overhead. Government workers are sophisticated users with complex needs, but they don’t have the luxury of a complex implementation. Technology that serves them well is ready to work immediately, with sensible defaults and clear documentation. Let public servants point the way These practical requirements underscore something deeper we learned on the road: You can’t build for government if you don’t listen to government workers and understand that they’re working toward a mission, trying to make a difference, and striving to have an impact. Sometimes this looks like visible acts of heroism, such as responding to natural disasters. Sometimes this work is largely invisible, like ensuring adequate distribution and funding for medical care and services in areas most in need. This should inspire technologists. Its profoundly rewarding to build tools that help people navigate complex systems to get the help they need, or that make it easier for dedicated public servants to do their jobs well. Every efficiency gain translates to faster disaster response, better benefit processing, or improved community services. The public sector deserves technology that’s built for the mission, not retrofitted from consumer applications. When we take the time to understand that missionand the real requirements that come with itwe can build tools that don’t just work, but actually make government work better for everyone. Madeleine Smith is cofounder and CEO of Civic Roundtable.
Category:
E-Commerce
Elon Musks AI company xAI sued Apple and ChatGPT maker OpenAI in federal court on Monday, alleging they conspired to freeze out competition in both the smartphone and AI chatbot markets. But xAIs lawyers face an uphill battle in proving that Apples defaulting to ChatGPT on iOS harms consumers. Apple has struggled to add its own advanced AI featuresincluding a chatbotto its operating systems for the iPhone, iPad, and Mac. At its June 2024 Worldwide Developers Conference (WWDC), Apple instead announced an exclusive agreement with OpenAI, making ChatGPT the default chatbot option on its devices. Musk and xAI argue that the deal hurts rivals, including xAIs Grok chatbot. Working in tandem, Defendants Apple and OpenAI have locked up markets to maintain their monopolies and prevent innovators like X and xAI from competing, reads the lawsuit, which was filed in the U.S. District Court for the Northern District of Texas. Plaintiffs bring this suit to stop Defendants from perpetrating their anticompetitive scheme and to recover billions in damages. Currently, iPhone users who want Grok must download its stand-alone app. Musk and xAI contend that Apple should be compelled to let users easily select any chatbot. While Apple has spoken about integrating other models such as Googles Gemini and Anthropics Claude, it has not acted on those talks so far. The suit raises antitrust issues, but recent precedent makes such cases hard to win. Courts typically judge them based on consumer benefitsuch as product quality and costrather than on harm to competition alone. (By one estimate, ChatGPT accounted for more than 80% of AI chatbot visits as of July.) Its also consistent with what antitrust laws are about, says Sarah Kreps, director of the Tech Policy Institute at Cornell University. Just because ChatGPT has 80% of the market doesnt mean that thats antitrust. The law doesnt want to punish companies for products that consumers want. OpenAI was first to market with a large language model-driven chatbot in late 2022, with ChatGPT hitting 100 million users in just two months. For many, ChatGPT became synonymous with AI chatbot well before its Apple deal. Kreps notes that xAIs lawyers must show the Apple-OpenAI partnership meaningfully raised prices for consumers, reduced innovation, or blocked consumer choice, which is a high bar to cross. The court may also question xAIs standing, since it is a direct competitor. The lawsuit also accuses Apple of favoring OpenAI in its App Store. “If not for its exclusive deal with OpenAI, Apple would have no reason to refrain from more prominently featuring the X app and the Grok app,” it claims. Musk previously threatened to sue Apple, saying it was impossible for any AI company besides OpenAI to reach #1 in the App Store. The new filing continues Musks long-running feud with OpenAI and CEO Sam Altman. Musk, who co-founded OpenAI in 2015 and left its board in 2018, sued the company last year for pursuing profit despite its nonprofit structure. A recent filing revealed Musk tried to buy OpenAIs core assets earlier this year, even approaching Meta CEO Mark Zuckerberg about a joint bid. The venue of xAI’s lawsuit also matters quite a bit here. Companies often file in the Northern District of Texas, where cases are frequently assigned to a single judge, allowing for judge shopping. That court falls under the conservative Fifth Circuit Court of Appeals, which can reduce the risk of unfavorable outcomes in antitrust cases. It also has a record of producing rulings that quickly rise to the Supreme Court. Politics could also influence the outcome. The cases visibility could lead the Trump administration to intervene, with President Donald Trump potentially siding with Musk (though the two men certainly have their differences) on Truth Social and calling Apples deal a monopoly. That, in turn, could prompt the Federal Trade Commission or Justice Department to investigate or file a brief urging Apple to stop favoring ChatGPT. But Kreps warns that the administrations stance is unpredictable. At one time, Trump courted tech industry players in part by promising minimal regulation, but then he’s also stoked populist resentment toward “West Coast elites” who run “liberal” tech companies. Given Trumps split loyalties and his fraught relationship with Musk, the safer political move might be to stay out of it. But whether Trump follows that advice is another matter.
Category:
E-Commerce
Turning an idea into working software has never been easier, faster, or more affordable. Thanks to a new class of AI-assisted tools, a movement known as vibe coding is reshaping how software gets built. If you’re not familiar with the term, vibe coding refers to the use of natural language tools that dramatically lower the technical barrier to software development. You describe what you wantliterally, just say itand the AI writes the code, building your app before your eyes. From there, you can give feedback, tweak results, and refine the product through simple, back-and-forth interaction. This shift empowers solopreneurs and business owners to focus on outcomes without getting bogged down in engineering. More importantly, it removes the steep cost and complexity once required just to get a prototype off the ground. Heres why vibe coding could be a game-changer for founders and small to midsize businessesand where its limits still lie. The upside: What vibe coding makes possible Build MVPs in hours, not weeks Before vibe coding, even testing an idea required weeks of planning and a sizable budget. Today, you can build a working minimum viable product in an afternoon. That means faster feedback loops, quicker iteration, and smarter go/no-go decisions. Move faster, pivot smarter With vibe coding, you can quickly test ideas in the market, pivot based on customer feedback, and invest engineering resources only in what gains traction. Its a more agile, lower-risk way to innovate. Empower internal teams Need a custom reporting dashboard? An onboarding tool? A resource scheduler? With vibe coding, nontechnical teams can create light apps that solve internal pain pointsno dev team required. Level the playing field for founders The broader impact? More ideas get a shot. More small businesses can take root. More diverse voices can be heard. For investors, it means concepts can be vetted earlier and with more clarityleading to smarter funding bets and higher odds of success. The limits: What vibe coding cant (yet) replace Of course, the obvious question arises: If a tool that once required a five-figure budget can now be built in an afternoon, are software professionals getting coded out of a job? The short answer: no. What vibe coding changes is when and why you bring in professional developersnot whether you need them. It democratizes access and speeds up early exploration. But there are important limits that still require experienced teams. Scalability becomes a bottleneck Vibe-coded apps are great for prototyping. But when you need scalehandling real users, integrating APIs, managing payments, maintaining uptimeyoull hit limits fast. For growth and complexity, youll need real engineers. Quality control still requires human judgment AI doesnt understand your edge cases. It doesnt anticipate compliance requirements or subtle business rules. Production-grade software needs thoughtful architecture, thorough testing, and rigorous review. Thats human territory. Security isnt baked in The moment you handle user dataeven something as basic as an emailyou step into legal and ethical territory. Most vibe-coded apps arent designed with secure auth flows, proper data storage, or access controls. Thats not a criticismits just not their purpose. Public-facing tools need professional-grade security. Bottom line: Vibe coding changes the game, but it doesnt replace the players. It simply lets more people step onto the field. A real-world example: My mom, a wedding venue, and an afternoon app My parents own a wedding venue, which theyve built over the years on a picturesque Midwest ranch. Their propertythe forest chapel, the refurbished historic barnits phenomenal. At the same time, this is a competitive industry, and they are constantly looking for new ways to add appeal: They built a stone bridge to a charming island they call Yonder; they raised a miniature donkey named Mojito who wears flowers and saddles beer for the reception. Its all good, but when budget is a primary concern, its unclear whether these added features will result in more yesses. My moms been doing this for a while. She knows that the primary concerns for couples are cost, sure, logistics, and the million little details and decisions that define both. I assured her that this is the sort of problem software can solve. So, the last time she visited, we vibe-coded a wedding planning app together on Lovable. It allows couples to mix, match, visualize, and budget, all on a single, simple interface. Its fun, it gives them transparency, and it delivers ease. Its making a difference for her business already, and it took us a single afternoon. A few key also trues from my moms example: Seeing a possible software solution and how it would work required mea career UX designer and professional software architect. The tool is private, used during venue toursnot something publicly hosted or scaled. The app uses static data, like a visual calculator. If we needed real-time bookings, payments, or user accounts, wed need proper backend infrastructureand a professional team. This is where vibe coding shines: rapid ideas, small use cases, single-user workflows. Its not the whole productit’s the first spark. Final thought: Get codingeven if you dont get coding I live in San Francisco, where it feels like everyone has an app idea. But I suspect millions of people across the country have equally brilliant conceptsideas that could improve their communities, businesses, or lives. Now, nothing stands in your way. If software shapes our worldand it increasingly doesthen its crucial that people from all walks of life help shape software. That starts by building. Spend an afternoon in Lovable, Replit, Bubble, or any other vibe-coding tool. If youre not sure which to use, describe your idea to ChatGPT or Perplexity and ask which platform fits best. Your first version doesnt need to be perfect. But it does need to exist. I cant wait to see where your experimentation leads. Lindsey Witmer Collins is CEO and founder of WLCM App Studio and Scribbly Inc.
Category:
E-Commerce
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