思想领袖
软件克隆 — 付费软件真的结束了吗？
在 Google 上的首选来源中添加 Unite.AI
使用AI工具克隆大众付费软件(最显著的是Adobe)的开发者最近的头条新闻引发了好坏参半的反响
但对于拥有原始软件的技术公司来说
ABBYY的网络安全架构，业务，以及AI Security的高级主管Matt Wood调查了这一日益严重的关注，并让他承担AI驱动软件开发的风险和机会。
1996年，Fatboy Slim从1960年代很少有人听到的吉他里夫的唱片上，建造了“Rockafeller Skank”。 化学兄弟将被遗忘的放克唱片循环成充满体育场歌普罗迪基对警报器进行了采样，胶片对话，和模糊的阴道进入一些东西，听起来在它之前什么都没有。 取样不仅降低了制作音乐的成本。 它改变了谁必须做它，它迫使整个行业问一个更难的问题：什么是真正算原创的？
大赦国际强迫企业今天提出同样的问题，但利害关系不是图表。 它们是业务成果、合规风险以及客户在企业作出的每一项决定中的信任。
复制是便宜的。 透视不是。
大型语言模型使remix现有知识变得非常容易：起草合同条款，总结报告，从任何它们能够获取的文本中产生一个合理答案。 这种能力具有真正的价值。 但复制与发现并不相同，将两者混为一谈是许多企业AI倡议悄悄停顿的地方。
区别很重要，因为基因系统非常擅长产生一些看起来已经完成的东西。 它们能够以非凡的流利来模仿语气，结构和信心。 他们自己不能做的是，保证他们生产的东西以正确的证据为基础，与正确的过程相关联，或由正确的判断决定。
这种紧张关系并非企业所独有。 在创造性工作中也可以看到这一点。 2024年的一项"科学进步"研究发现，AI援助提高了个人故事的感知质量和创造力，尤其是对于创作较少的作家而言，同时也使得总体的故事集更加相似。 换言之，产出在个人一级有所改善，但在集体一级趋于一致。 更多的生产并不一定意味着更多的独创性。 它往往意味着更多相同的想法，表达得更顺利。
这就是微型企业的风险。 当每个人都能获得相同的通用工具时，竞争优势并不来自产生更多的文本、更多的摘要或更多的建议。 它来自了解什么是重要，什么是信任，什么是值得采取行动。
商业问题不是一代。 这是进入现实。
大多数企业没有语言短缺。 他们缺乏方便、可靠的环境。
一个公司实际知道的东西通常分散在合同，发票，登机表，声称，电子邮件，报告，扫描的PDF，以及将这些文件从一个团队传送到另一个团队的工作流程之间。 财务上的付款例外、入职中的合规问题、或业务中的服务延误，往往开始成为半结构化、格式不统一、与系统领导人断绝联系从而作出决定的生命。
这就是为什么如此多的企业AI项目在演示中感到令人印象深刻，可能会在生产中失败。 模型可能有能力，但上下文是局部的。 它只能说明它能够达到什么目的，而且在许多组织中，它是真实情况的一个狭小部分。
这不仅仅是理论上的弱点。 它影响到决定的质量。 如果一个模型概括了一项合同，却无法查阅有关的修改，那么它可能听起来既清晰又不完整。 如果它分析应付账款问题，而不在发票格式和供应商的例外情况中引起注意，它可以提出整洁但无用的建议。 如果在关键信息仍然被扫描的形式或零散的工作流程困住时帮助客户登机，则可以加速错误的结论。
结果不是洞察力。 这是自信的猜测。
为什么模仿比理解更快
这就是音乐采样类比变得不止是一个比喻的地方。
取样使音乐制作更快，更便宜，更方便使用。 但制作人不是那些只复制碎片的人。 他们是那些有品味和纪律来改造他们的人。 任何人都可以提高节奏；不是每个人都可以围绕节奏制造新的声音。
大赦国际正在软件和知识工作方面形成类似的鸿沟。 模仿的屏障急剧下降。 复制接口、复制产出、模仿工作流程和大致专门知识比以往任何时候都容易。 软件的全部类别越来越有可能在表面一级再生产。
这并不意味着付费软件已经结束，正如开发商布兰登·托马斯所宣布的，他为Adobe创造了免费的替代品。 这意味着软件的价值正在转移。
当模仿变得便宜时，价值会转移到别处：信任、融合、判断，以及产生在现实世界条件下维持的成果的能力。 问题不再在于人工智能系统是否能够产生类似于专家工作的答案。 问题在于它是否能够可靠地在背景上这样做，并且有足够的透明度，在利害攸关时人们会相信它。
克隆输出容易产生。 可靠的结果不是。
隐藏的精华是专业知识
因此，在AI克隆时代，专门知识变得更加重要，而不是更少。
多年来，许多数字工具的价值来自于专门能力稀缺。 如果软件可以编码一个进程，使一个任务标准化，或简化以前困难的工作流程，客户将支付访问该包装的专业知识的费用。 Generative AI解析了这个模型，因为它现在可以低成本地模仿许多明显的专门知识特征。
但模仿只捕捉到外层。
真正的专门知识是知道哪些例外很重要，哪些是模糊的，哪些来源是权威的，以及当一个系统的答案应该受到质疑而不是被接受时。 从文件中提取案文和理解该案文是否足以支持企业的决定是不同的。 这是总结政策与承认将总结错误理解为业务后果之间的区别。
这也是许多关于AI和独创性的讨论被误导的地方。 商业上的原创性很少涉及新颖性本身。 这是关于看到别人错过的东西：一种隐藏在过程噪音中的风险，一种被零散的信息掩埋的模式，一种被断开的系统遮掩的机会。 这种独创性并不取决于雄辩的产生，而取决于准确的解释。
文档不是整个故事，但往往是故事开始的地方
这里的文件很重要，因为它们仍然是企业现实储存但不能充分使用的最常见地点之一。
这并不能使“AI文件”成为故事的英雄。 它只是使文件理解将操作信息转化为AI系统能够负责地工作的更大学科的一部分。 如果各组织希望遗传模型产生更好的结果，它们就需要更好地获得这些模型所依赖的证据。 在许多情况下，这意味着提取、验证和结构化信息，否则这些信息就会被困在表格、档案和分散的交割中。
通常，这种能力并不在于将某一类产品推广到另一类产品。 这是关于缩小企业所知道的东西和它的AI系统能够实际看到的东西之间的距离。
这种区别很重要。 最强的企业AI战略并非仅建立在一代之上。 它们建立在可用数据、流程意识和人类监督相结合的基础上。 例如麻省理工学院的NANDA项目(Project NANDA)近期的企业研究认为，AI采用的最大障碍往往不是孤立的模型质量，而是工作流程整合不良，以及对工具如何真正使用缺乏信任。
听起来挺熟悉的企业不会通过生产最合成的输出而取得胜利。 他们通过从他们能够捍卫的信息中更快和更一致地作出更好的决定来赢得胜利。
下一轮判决
取样并没有贬低音乐的价值。 这改变了原创性的意思。
一旦原材料更容易再利用，溢价就转向选择、安排和意图。 判决比获得判决更有价值。 企业AI也有同样的情况。 随着软件克隆变得更加容易，持久的优势不会来自拥有能够模仿胜任工作的模型。 它将来自了解如何将AI与正确的背景联系起来，适当约束它，并在人类的创造力和领域判断仍然最重要的地方应用它。
这是对话中许多“AI取代软件”论点漏掉的部分。 如果每个工具都可以在接口级别复制，那么真正的护城河不是接口。 它是其背后的深刻理解：工作流程逻辑、例外处理、可信赖的数据、机构知识和将产出转化为决策所需的人的专门知识。
付费软件真的结束了吗？只要我们相信软件的价值只是可见的一层。
对于软件的商品特性，压力会加大。 一些产品会被克隆。 一些类别会压缩。 某些功能一旦获得溢价，就将成为桌面赌注。 但是，持久的工具和公司将帮助各组织从模仿走向理解。
因此AI的采样时刻很重要。 它提醒我们，降低复制成本并不能消除对原创性的需求。 它只是改变原创生活的地方。
在音乐中，它从原材料的表演转向转化的工艺。
在企业AI中，它正在从产生可信的产出转向有纪律地使用可靠的信息、专家判断和创造性的解决问题。
复次轻重。 洞察是非无。 这就是为什么AI的下一阶段将不属于那些只有智能声音的系统。 它将属于那些知道如何使已经知道的东西能够充分用来发现真正新的东西的组织。
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
Thought Leaders
Software Cloning – Is Paid Software Really Over?
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Recent headlines of developers using AI tools to clone popular paid software – most notably Adobe – has sparked mixed reactions – from accusations of theft to welcoming free open-source tools.
But what does it mean for the tech companies who own the original software – or those who pay costly subscriptions to use it?
Matt Wood, Senior Director of Cyber Security Architecture, Operations, and AI Security at ABBYY, has investigated this growing concern, and gives his take on the risks and opportunities of AI-driven software development.
In 1996, Fatboy Slim built “Rockafeller Skank” from a guitar riff lifted from a 1960s record that few people had heard in decades. The Chemical Brothers looped breakbeats from forgotten funk records into stadium-filling anthems. The Prodigy sampled sirens, film dialogue, and obscure vinyl into something that sounded like nothing before it. Sampling did not just lower the cost of making music. It changed who got to make it, and it forced the entire industry to ask a harder question: what actually counts as original?
AI is forcing enterprises to ask the same question today, except the stakes are not chart positions. They are operational outcomes, compliance exposure, and the trust customers place in every decision a business makes.
Replication Is Cheap. Insight Is Not.
Large language models have made it remarkably easy to remix existing knowledge: draft a contract clause, summarize a report, generate a plausible-sounding answer from whatever text they can access. That capability has real value. But replication is not the same as discovery, and conflating the two is where many enterprise AI initiatives quietly stall.
The distinction matters because generative systems are very good at producing something that looks finished. They can imitate tone, structure, and confidence with extraordinary fluency. What they cannot do on their own is guarantee that what they produce is grounded in the right evidence, connected to the right process, or shaped by the right judgment.
That tension is not unique to business. It is visible in creative work too. A 2024 Science Advances study found that AI assistance improved the perceived quality and creativity of individual stories, especially for less creative writers, while also making the overall set of stories more similar to one another. In other words, the output improved at the individual level but converged at the collective level. More production did not necessarily mean more originality. It often meant more of the same ideas, expressed more smoothly.
That is the enterprise risk in miniature. When everyone has access to the same general-purpose tools, the competitive advantage does not come from generating more text, more summaries, or more recommendations. It comes from knowing what matters, what to trust, and what deserves action.
The Business Problem Is Not Generation. It Is Access to Reality.
Most businesses do not suffer from a shortage of language. They suffer from a shortage of accessible, reliable context.
What a company actually knows is usually scattered across contracts, invoices, onboarding forms, claims, emails, reports, scanned PDFs, and the workflows that shuttle those documents from one team to another. A payment exception in finance, a compliance issue in onboarding, or a service delay in operations often begins life as something half-structured, inconsistently formatted, and disconnected from the systems leaders rely on to make decisions.
That is why so many enterprise AI projects feel impressive in demos and can fail in production. The model may be capable, but the context is partial. It can only reason over what it can reach, and in many organizations that is a narrow slice of the real picture.
This is not just a theoretical weakness. It affects the quality of decisions. If a model summarizes a contract without access to the relevant amendments, it may sound clear while being incomplete. If it analyzes accounts payable issues without visibility into invoice exceptions across formats and suppliers, it may produce recommendations that are neat but not useful. If it helps with customer onboarding while key information remains trapped in scanned forms or fragmented workflows, it can accelerate the wrong conclusion.
The result is not insight. It is confident-sounding guesswork.
Why Imitation Scales Faster Than Understanding
This is where the music sampling analogy becomes more than a metaphor.
Sampling made music production faster, cheaper, and more accessible. But the producers who lasted were not the ones who merely copied fragments. They were the ones with the taste and discipline to transform them. Anyone could lift a beat; not everyone could build a new sound around it.
AI is creating a similar divide in software and knowledge work. The barrier to imitation has fallen dramatically. It is easier than ever to clone interfaces, reproduce outputs, mimic workflows, and approximate expertise. Entire categories of software increasingly risk becoming reproducible at the surface level.
That does not mean paid software is over, as pronounced by developer Brandon Thomas who has created free alternatives for Adobe. It means the value of software is shifting.
When imitation becomes cheap, value moves elsewhere: to trust, integration, judgment, and the ability to produce outcomes that hold up under real-world conditions. The question is no longer whether an AI system can produce an answer that resembles expert work. The question is whether it can do so reliably, in context, and with enough transparency that people will trust it when the stakes are high.
A cloned output is easy to produce. A dependable outcome is not.
The Hidden Premium Is Expertise
This is why expertise becomes more important, not less, in an age of AI cloning.
For years, many digital tools derived their value from making specialized capabilities scarce. If software could encode a process, standardize a task, or simplify a previously difficult workflow, customers would pay for access to that packaged expertise. Generative AI unsettles that model because it can now imitate many visible features of expertise at low cost.
But imitation only captures the outer layer.
Real expertise is knowing which exception matters, which ambiguity cannot be ignored, which source is authoritative, and when a system’s answer should be challenged rather than accepted. It is the difference between extracting text from a document and understanding whether that text is sufficient to support a business decision. It is the difference between summarizing policy and recognizing the operational consequences of getting that summary wrong.
This is also where many discussions of AI and originality go astray. Originality in business is rarely about novelty for its own sake. It is about seeing something others miss: a risk hidden in process noise, a pattern buried in fragmented information, an opportunity obscured by disconnected systems. That kind of originality depends less on eloquent generation than on accurate interpretation.
Documents Are Not the Whole Story, but They Are Often Where the Story Starts
Documents matter here because they remain one of the most common places where enterprise reality is stored but not fully usable.
That does not make “Document AI” the hero of the story. It simply makes document understanding one part of the larger discipline of turning operational information into something AI systems can work with responsibly. If organizations want better outcomes from generative models, they need better access to the evidence those models rely on. In many cases, that means extracting, validating, and structuring information that would otherwise remain trapped in forms, files, and fragmented handoffs.
Used well, that kind of capability is not about promoting one product category over another. It is about reducing the distance between what the business knows and what its AI systems can actually see.
That distinction matters. The strongest enterprise AI strategies are not built on generation alone. They are built on the combination of usable data, process awareness, and human oversight. Recent enterprise research from MIT’s Project NANDA, for example, argues that the biggest barriers to AI adoption are often not model quality in isolation but poor workflow integration and a lack of trust in how tools behave in real use.
That should sound familiar. Businesses do not win by producing the most synthetic output. They win by making better decisions, faster and more consistently, from information they can defend.
The Next Premium Is Judgment
Sampling did not devalue music. It changed what originality meant.
Once the raw materials became easier to reuse, the premium shifted to selection, arrangement, and intent. Judgment became more valuable than access. The same thing is happening now in enterprise AI. As software cloning gets easier, the durable advantage will not come from having a model that can imitate competent work. It will come from knowing how to connect AI to the right context, constrain it appropriately, and apply it where human creativity and domain judgment still matter most.
That is the part of the conversation many “AI replaces software” arguments miss. If every tool can be copied at the interface level, then the real moat is not the interface. It is the depth of understanding behind it: the workflow logic, the exception handling, the trusted data, the institutional knowledge, and the human expertise required to turn outputs into decisions.
So, is paid software really over? Only if we believed software’s value was ever just the visible layer.
For the commodity features of software, pressure will intensify. Some products will be cloned. Some categories will compress. Some functionality that once commanded a premium will become table stakes. But the tools and companies that endure will be the ones that help organizations move from imitation to understanding.
That is why AI’s sampling moment matters. It reminds us that lowering the cost of reproduction does not eliminate the need for originality. It simply changes where originality lives.
In music, it moved from the performance of raw material to the craft of transformation.
In enterprise AI, it is moving from the generation of plausible outputs to the disciplined use of trustworthy information, expert judgment, and creative problem-solving.
Replication is cheap. Insight is not. And that is why the next phase of AI will not belong to the systems that merely sound intelligent. It will belong to the organizations that know how to make what they already know usable enough to discover something genuinely new.
Auto-fetched and translated by AI Atlas. All rights belong to the original author.