Picture this: You&#x27;re sitting in a conference room, halfway through a vendor pitch. The demo looks solid, and pricing fits nicely under budget. The timeline seems reasonable too. Everyone’s nodding along.You’re literally minutes away from saying yes.Then someone from your finance team walks in. They see the deck and frown. A few minutes later, they shoot you a message on Slack: “Actually, I threw together a version of this last week. Took me 2 hours in Cursor. Wanna take a look?”Wait… what?This <a href="https://venturebeat.com/ai/why-most-enterprise-ai-coding-pilots-underperform-hint-its-not-the-model">person doesn&#x27;t code</a>. You know for a fact they&#x27;ve never written a line of JavaScript in their entire life. But here they are, showing you a working prototype on their laptop that does... pretty much exactly what the vendor pitched. Sure, it&#x27;s got some rough edges, but it works. And it didn’t cost six figures. Just two hours of their time.Suddenly, the assumptions you walked in with — about how <a href="https://venturebeat.com/ai/why-ai-coding-agents-arent-production-ready-brittle-context-windows-broken">software is developed</a>, who makes it and how decisions are made around it — all start coming apart at the seams.<h2>The old framework</h2>For decades, every growing company asked the same question: Should we build this ourselves, or should we buy it?And, for decades, the answer was pretty straightforward: Build if it&#x27;s core to your business; buy if it isn’t.The logic made sense, because building was expensive and meant borrowing time from overworked engineers, writing specs, planning sprints, managing infrastructure and bracing yourself for a long tail of maintenance. Buying was faster. Safer. You paid for the support and the peace of mind.But something fundamental has changed: AI has made building accessible to everyone. What used to take weeks now takes hours, and what used to require fluency in a programming language now requires fluency in plain English.When the cost and complexity of building collapse this dramatically, the old framework goes down with them. It’s not build versus buy anymore. It’s something stranger that we haven&#x27;t quite found the right words for.<h2>When the market doesn’t know what you need (yet)</h2>My company never planned to build so many of the tools we use. We just had to build because the things we needed didn’t exist. And, through that process, we developed this visceral understanding of what we actually wanted, what was useful and what it could or couldn&#x27;t do. Not what vendor decks told us we needed or what analyst reports said we should want, but what actually moved the needle in our business.We figured out which problems were worth solving, which ones weren’t, <a href="https://venturebeat.com/ai/how-to-avoid-becoming-an-ai-first-company-with-zero-real-ai-usage">where AI created real leverage</a> and where it was just noise. And only then, once we had that hard-earned clarity, did we start buying.By that point, we knew exactly what we were looking for and could tell the difference between substance and marketing in about five minutes. We asked questions that made vendors nervous because we&#x27;d already built some rudimentary version of what they were selling.<h2>When anyone can build in minutes</h2>Last week, someone on our CX team noticed some customer feedback about a bug in Slack. Just a minor customer complaint, nothing major. In another company, this would’ve kicked off a support ticket and they’d have waited for someone else to handle it, but that’s not what happened here. They opened Cursor, described the change and let AI write the fix. Then they submitted a pull request that engineering reviewed and merged.Just 15 minutes after that complaint popped up in Slack, the fix was live in production.The person who did this isn’t technical in the slightest. I doubt they could tell you the difference between Python and JavaScript, but they solved the problem anyway. And that’s the point.AI has gotten so good at cranking out relatively simple code that it handles 80% of the problems that used to require a sprint planning meeting and two weeks of engineering time. It’s erasing the boundary between technical and non-technical. Work that used to be bottlenecked by engineering is now being done by the people closest to the problem.This is happening right now in companies that are actually paying attention.<h2>The inversion that’s happening</h2>Here&#x27;s where it gets fascinating for finance leaders, because AI has actually flipped the entire strategic logic of the build versus buy decision on its head.The old model went something like:<ol><li>Define the need.</li><li>Decide whether to build or buy.</li></ol>But defining the need took forever and required deep technical expertise, or you&#x27;d burn through money through trial-and-error vendor implementations. You&#x27;d sit through countless demos, trying to picture whether this actually solved your problem. Then you’d negotiate, implement, move all your data and workflows to the new tool and six months and six figures later discover whether (or not) <a href="https://venturebeat.com/ai/6-proven-lessons-from-the-ai-projects-that-broke-before-they-scaled">you were actually right</a>.Now, the whole sequence gets turned around:<ol><li>Build something lightweight with AI.</li><li>Use it to understand what you actually need.</li><li>Then decide whether to buy (and you&#x27;ll know exactly why).</li></ol>This approach lets you run controlled experiments. You figure out whether the problem even matters. You discover which features deliver value and which just look good in demos. Then you go shopping. Instead of letting some external vendor sell you on what the need is, you get to figure out whether you even have that need in the first place.Think about how many software purchases you&#x27;ve made that, in hindsight, solved problems you didn&#x27;t actually have. How many times have you been three months into an implementation and thought, “Hang on, is this actually helping us, or are we just trying to justify what we spent?”Now, when you do buy, the question becomes “Does this solve the problem better than what we already proved we can build?”That one reframe changes the entire conversation. Now you show up to vendor calls informed. You ask sharper questions, and negotiate from a place of strength. Most importantly, you avoid the most expensive mistake in enterprise software, which is solving a problem you never really had.<h2>The trap you need to avoid</h2>As this new capability emerges, I’m watching companies sprint in the wrong direction. They know they need to be AI native, so they go on a shopping spree. They look for AI-powered tools, filling their stack with products that have GPT integrations, chatbot UIs or “AI” slapped onto the marketing site. They think they’re transforming, but they’re not.Remember what physicist Richard Feynman called cargo cult science? After World War II, islanders in the South Pacific built fake airstrips and control towers, mimicking what they&#x27;d seen during the war, hoping planes full of cargo would return. They had all the outward forms of an airport: Towers, headsets, even people miming flight controllers. But no planes landed, because the form wasn’t the function.That’s exactly what’s happening with AI transformation in boardrooms everywhere. Leaders are buying AI tools without asking if they meaningfully change how work gets done, who they empower or what processes they unlock.They’ve built the airstrip, but the planes aren’t showing up.And the whole market&#x27;s basically set up to make you fall into this trap. Everything gets branded as AI now, but nobody seems to care what these products actually do. Every SaaS product has bolted on a chatbot or an auto-complete feature and slapped an AI label on it, and the label has lost all meaning. It’s just a checkbox vendors figure they need to tick, regardless of whether it creates actual value for customers.<h2>The finance team’s new superpower</h2>This is the part that gets me excited about what finance teams can do now. You don’t have to guess anymore. You don’t have to bet six figures on a sales deck. You can test things, and you can actually learn something before you spend.Here&#x27;s what I mean: If you’re evaluating vendor management software, prototype the core workflow with AI tools. Figure out whether you’re solving a tooling problem or a process problem. Figure out whether you need software at all.This doesn’t mean you’ll build everything internally — of course not. Most of the time, you’ll still end up buying, and that&#x27;s totally fine, because enterprise tools exist for good reasons (scale, support, security, and maintenance). But now you’ll buy with your eyes wide open.You’ll know what “good” looks like. You’ll show up to demos already understanding the edge cases, and know in about 5 minutes whether they actually get your specific problem. You’ll implement faster. You&#x27;ll negotiate better because you&#x27;re not completely dependent on the vendor&#x27;s solution. And you’ll choose it because it&#x27;s genuinely better than what you could build yourself.You&#x27;ll have already mapped out the shape of what you need, and you&#x27;ll just be looking for the best version of it.<h2>The new paradigm</h2>For years, the mantra was: Build or buy.Now, it’s more elegant and way smarter: Build to learn what to buy.And it&#x27;s not some future state. This is already happening. Right now, somewhere, a customer rep is using AI to fix a product issue they spotted minutes ago. Somewhere else, a finance team is prototyping their own analytical tools because they&#x27;ve realized they can iterate faster than they can write up requirements for engineering. Somewhere, a team is realizing that the boundary between technical and non-technical was always more cultural than fundamental.The companies that embrace this shift will move faster and spend smarter. They’ll know their operations more deeply than any vendor ever could. They&#x27;ll make fewer expensive mistakes, and buy better tools because they actually understand what makes tools good.The companies that stick to the old playbook will keep sitting through vendor pitches, nodding along at budget-friendly proposals. They’ll debate timelines, and keep mistaking professional decks for actual solutions.Until someone on their own team pops open their laptop, says, “I built a version of this last night. Want to check it out?,” and shows them something they built in two hours that does 80% of what they’re about to pay six figures for.And, just like that, the rules change for good.Siqi Chen is co-founder and CEO of Runway. Read more from our <a href="https://venturebeat.com/datadecisionmakers">guest writers</a>. Or, consider submitting a post of your own! See our <a href="https://venturebeat.com/guest-posts">guidelines here</a>.

تشير التغيرات الأخيرة في المشهد التكنولوجي إلى أن النقاش التقليدي حول بناء أو شراء حلول البرمجيات أصبح عتيقًا، ويرجع ذلك إلى حد كبير إلى التقدم في الذكاء الاصطناعي. مثال بارز هو أحد أعضاء فريق المالية الذي، دون خبرة سابقة في البرمجة، طور نموذجًا أوليًا يعمل في ساعتين فقط باستخدام Cursor، مما يدل على إمكانية الوصول إلى أدوات الذكاء الاصطناعي لتطوير سريع.

Un reciente cambio en el panorama tecnológico indica que el debate tradicional entre construir o comprar soluciones de software está quedando obsoleto, en gran parte debido a los avances en IA. Un ejemplo notable es un miembro del equipo financiero que, sin experiencia previa en codificación, desarrolló un prototipo funcional en solo dos horas utilizando Cursor, demostrando la accesibilidad de las herramientas de IA para un desarrollo rápido.

Un récent changement dans le paysage technologique indique que le débat traditionnel entre la construction et l'achat de solutions logicielles devient obsolète, en grande partie en raison des avancées de l'IA. Un exemple notable est celui d'un membre de l'équipe financière qui, sans expérience préalable en codage, a développé un prototype fonctionnel en seulement deux heures en utilisant Cursor, démontrant ainsi l'accessibilité des outils d'IA pour un développement rapide.

A recent shift in the technology landscape indicates that the traditional debate of building versus buying software solutions is becoming obsolete, largely due to advancements in AI. A notable example is a finance team member who, without prior coding experience, developed a working prototype in just two hours using Cursor, demonstrating the accessibility of AI tools for rapid development.

Build vs buy is dead — AI just killed it

<A HREF="https://venturebeat.com/ai/bolmos-architecture-unlocks-efficient-byte-level-lm-training-without"><IMG VSPACE="4" HSPACE="4" BORDER="0" ALIGN="RIGHT" SRC="http://www.techmeme.com/251215/i42.jpg"></A>
<A HREF="http://www.techmeme.com/251215/p42#a251215p42" TITLE="Techmeme permalink"><IMG WIDTH=11 HEIGHT=12 SRC="http://www.techmeme.com/img/pml.png" STYLE="border:none;padding:0;margin:0;"></A> Emilia David / <A HREF="https://venturebeat.com/">VentureBeat</A>: 
<A HREF="https://venturebeat.com/ai/bolmos-architecture-unlocks-efficient-byte-level-lm-training-without">Allen Institute for AI launches Bolmo 7B and Bolmo 1B, claiming they are &ldquo;the first fully open byte-level language models&rdquo;, built on its Olmo 3 models</A>&nbsp; &mdash;&nbsp; Enterprises that want tokenizer-free multilingual models are increasingly turning to byte-level language models to reduce brittleness in noisy or low-resource text.

أطلق معهد ألين للذكاء الاصطناعي نموذج Bolmo 7B وBolmo 1B، والذي يدعي أنهما أول نماذج لغة على مستوى البايت مفتوحة بالكامل، مبنية على نماذج Olmo 3 الخاصة به. تهدف هذه النماذج إلى توفير قدرات متعددة اللغات بدون الحاجة إلى أدوات تقسيم الكلمات، مما يلبي الحاجة إلى معالجة لغوية أكثر قوة في بيئات نصية صاخبة أو ذات موارد منخفضة.

El Instituto Allen para la IA ha lanzado Bolmo 7B y Bolmo 1B, que afirma ser los primeros modelos de lenguaje de nivel byte completamente abiertos, construidos sobre sus modelos Olmo 3. Estos modelos buscan proporcionar capacidades multilingües sin tokenización, abordando la necesidad de un procesamiento del lenguaje más robusto en entornos textuales ruidosos o de bajos recursos.

L'Institut Allen pour l'IA a lancé Bolmo 7B et Bolmo 1B, qu'il affirme être les premiers modèles de langage byte-level entièrement ouverts, construits sur ses modèles Olmo 3. Ces modèles visent à fournir des capacités multilingues sans tokenizer, répondant au besoin de traitements linguistiques plus robustes dans des environnements textuels bruyants ou à faibles ressources.

The Allen Institute for AI has launched Bolmo 7B and Bolmo 1B, which it claims are the first fully open byte-level language models, built on its Olmo 3 models. These models aim to provide tokenizer-free multilingual capabilities, addressing the need for more robust language processing in noisy or low-resource text environments.

Allen Institute for AI launches Bolmo 7B and Bolmo 1B, claiming they are "the first fully open byte-level language models", built on its Olmo 3 models (Emilia David/VentureBeat)

Dhyey Mavani is accelerating generative AI at LinkedIn and is a guest author at VentureBeat.Gen AI in software engineering has moved well beyond autocomplete. The emerging frontier is <a href="https://venturebeat.com/ai/why-ai-coding-agents-arent-production-ready-brittle-context-windows-broken">agentic coding</a>: AI systems capable of planning changes, executing them across multiple steps and iterating based on feedback. Yet despite the excitement around “AI agents that code,” most enterprise deployments underperform. The limiting factor is no longer the model. It’s context: The structure, history and intent surrounding the code being changed. In other words, enterprises are now facing a systems design problem: They have not yet engineered the environment these agents operate in.<h2>The shift from assistance to agency</h2>The past year has seen a rapid evolution from assistive coding tools to agentic workflows. Research has begun to formalize what agentic behavior means in practice: The ability to reason across design, testing, execution and validation rather than generate isolated snippets. Work such as <a href="https://arxiv.org/abs/2503.14269?utm_source=chatgpt.com">dynamic action re-sampling</a> shows that allowing agents to branch, reconsider and revise their own decisions significantly improves outcomes in large, interdependent codebases. At the platform level, providers like GitHub are now building dedicated agent orchestration environments, such as <a href="https://venturebeat.com/ai/githubs-agent-hq-aims-to-solve-enterprises-biggest-ai-coding-problem-too?utm_source=chatgpt.com">Copilot Agent and Agent HQ</a>, to support multi-agent collaboration inside real enterprise pipelines.But early field results tell a cautionary story. When organizations introduce agentic tools without addressing workflow and environment, productivity can decline. A randomized control study this year showed that developers who used AI assistance in unchanged workflows completed tasks more slowly, largely due to verification, rework and confusion around intent. The lesson is straightforward: Autonomy without orchestration rarely yields efficiency.<h2>Why context engineering is the real unlock</h2>In every unsuccessful deployment I’ve observed, the failure stemmed from context. When agents lack a structured understanding of a codebase, specifically its relevant modules, dependency graph, test harness, architectural conventions and change history. They often generate output that appears correct but is disconnected from reality. Too much information overwhelms the agent; too little forces it to guess. The goal is not to feed the model more tokens. The goal is to determine what should be visible to the agent, when and in what form.The teams seeing meaningful gains treat context as an engineering surface. They create tooling to snapshot, compact and version the <a href="https://venturebeat.com/ai/ontology-is-the-real-guardrail-how-to-stop-ai-agents-from-misunderstanding">agent’s working memory</a>: What is persisted across turns, what is discarded, what is summarized and what is linked instead of inlined. They design deliberation steps rather than prompting sessions. They make the specification a first-class artifact, something reviewable, testable and owned, not a transient chat history. This shift aligns with a broader trend some researchers describe as “specs becoming the new source of truth.”<h2>Workflow must change alongside tooling</h2>But context alone isn’t enough. Enterprises must re-architect the workflows around these agents. As <a href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/one-year-of-agentic-ai-six-lessons-from-the-people-doing-the-work?utm_source=chatgpt.com">McKinsey’s 2025 report “One Year of Agentic AI”</a> noted, productivity gains arise not from layering AI onto existing processes but from rethinking the process itself. When teams simply drop an agent into an unaltered workflow, they invite friction: Engineers spend more time verifying AI-written code than they would have spent writing it themselves. The agents can only amplify what’s already structured: Well-tested, modular codebases with clear ownership and documentation. Without those foundations, autonomy becomes chaos.Security and governance, too, demand a shift in mindset. AI-generated code introduces new forms of risk: Unvetted dependencies, subtle license violations and undocumented modules that escape peer review. Mature teams are beginning to integrate agentic activity directly into their <a href="https://venturebeat.com/ai/why-observable-ai-is-the-missing-sre-layer-enterprises-need-for-reliable">CI/CD pipelines</a>, treating agents as autonomous contributors whose work must pass the same static analysis, audit logging and approval gates as any human developer. GitHub’s own documentation highlights this trajectory, positioning Copilot Agents not as replacements for engineers but as orchestrated participants in secure, reviewable workflows. The goal isn’t to let an AI “write everything,” but to ensure that when it acts, it does so inside defined guardrails.<h2>What enterprise decision-makers should focus on now</h2>For technical leaders, the path forward starts with readiness rather than hype. Monoliths with sparse tests rarely yield net gains; agents thrive where tests are authoritative and can drive iterative refinement. This is exactly the loop <a href="https://www.anthropic.com/research/building-effective-agents?utm_source=chatgpt.com">Anthropic</a> calls out for coding agents. Pilots in tightly scoped domains (test generation, legacy modernization, isolated refactors); treat each deployment as an experiment with explicit metrics (defect escape rate, PR cycle time, change failure rate, security findings burned down). As your usage grows, treat agents as data infrastructure: Every plan, context snapshot, action log and test run is data that composes into a searchable memory of engineering intent, and a durable competitive advantage.Under the hood, agentic coding is less a tooling problem than a data problem. Every context snapshot, test iteration and code revision becomes a form of structured data that must be stored, indexed and reused. As these agents proliferate, enterprises will find themselves managing an entirely new data layer: One that captures not just what was built, but how it was reasoned about. This shift turns engineering logs into a knowledge graph of intent, decision-making and validation. In time, the organizations that can search and replay this contextual memory will outpace those who still treat code as static text.The coming year will likely determine whether agentic coding becomes a cornerstone of enterprise development or another inflated promise. The difference will hinge on context engineering: How intelligently teams design the informational substrate their agents rely on. The winners will be those who see autonomy not as magic, but as an extension of disciplined systems design:Clear workflows, measurable feedback, and rigorous governance.<h2>Bottom line</h2>Platforms are converging on orchestration and guardrails, and research keeps improving context control at inference time. The winners over the next 12 to 24 months won’t be the teams with the flashiest model; they’ll be the ones that engineer context as an asset and treat workflow as the product. Do that, and autonomy compounds. Skip it, and the review queue does.Context + agent = leverage. Skip the first half, and the rest collapses.Dhyey Mavani is accelerating generative AI at LinkedIn.Read more from our <a href="https://venturebeat.com/datadecisionmakers">guest writers</a>. Or, consider submitting a post of your own! See our <a href="https://venturebeat.com/guest-posts">guidelines here</a>.

يشير ذي مافاني، قائد الذكاء الاصطناعي التوليدي في لينكد إن، إلى أن العديد من تجارب برمجة الذكاء الاصطناعي في المؤسسات تعاني من ضعف الأداء، ويرجع ذلك أساسًا إلى نقص السياق بدلاً من نماذج الذكاء الاصطناعي نفسها. يشمل هذا السياق الهيكل والتاريخ والنوايا المحيطة بالتغييرات في الشيفرة، مما يشير إلى تحدٍ كبير في تصميم الأنظمة للمؤسسات.

Dhyey Mavani, un líder en IA generativa en LinkedIn, destaca que muchos pilotos de codificación de IA empresarial están bajo rendimiento, principalmente debido a un contexto inadecuado en lugar de los modelos de IA en sí. Este contexto incluye la estructura, historia e intención en torno a los cambios de código, lo que indica un desafío significativo de diseño de sistemas para las empresas.

Dhyey Mavani, un leader en IA générative chez LinkedIn, souligne que de nombreux projets pilotes d'IA de codage en entreprise sous-performent, principalement en raison d'un contexte inadéquat plutôt que des modèles d'IA eux-mêmes. Ce contexte inclut la structure, l'historique et l'intention entourant les changements de code, indiquant un défi de conception de systèmes significatif pour les entreprises.

Dhyey Mavani, a leader in generative AI at LinkedIn, highlights that many enterprise AI coding pilots are underperforming, primarily due to inadequate context rather than the AI models themselves. This context includes the structure, history, and intent surrounding the code changes, indicating a significant systems design challenge for enterprises.

Build vs buy is dead — AI just killed it

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