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<title data-react-helmet="true">Why embracing complexity is the real challenge in software today | MIT Technology Review</title>
<meta data-react-helmet="true" name="description" content="Technology Radar is a snapshot of the current technology landscape produced by Thoughtworks twice a year; it’s based on technologies we’ve been using as an organization and communicates our perspective on them. There is always a long list of candidates to be featured for us to work through and discuss, but with each edition that…"/><meta data-react-helmet="true" name="keywords" content=""/><meta data-react-helmet="true" property="og:url" content="https://www.technologyreview.com/2023/09/29/1080282/why-embracing-complexity-is-the-real-challenge-in-software-today/"/><meta data-react-helmet="true" property="og:type" content="article"/><meta data-react-helmet="true" property="og:title" content="Why embracing complexity is the real challenge in software today"/><meta data-react-helmet="true" property="og:description" content="In the midst of industry discussions about productivity and automation, it’s all too easy to overlook the importance of properly reckoning with complexity."/><meta data-react-helmet="true" property="og:site_name" content="MIT Technology Review"/><meta data-react-helmet="true" property="og:image" content="https://wp.technologyreview.com/wp-content/uploads/2023/09/tw_TR29_MIT_inline_845x363.png?resize=1200,600"/><meta data-react-helmet="true" property="og:width" content="1200"/><meta data-react-helmet="true" property="og:height" content="516"/><meta data-react-helmet="true" property="og:image:width" content="1200"/><meta data-react-helmet="true" property="og:image:height" content="516"/><meta data-react-helmet="true" name="twitter:card" content="summary_large_image"/><meta data-react-helmet="true" name="twitter:title" content="Why embracing complexity is the real challenge in software today"/><meta data-react-helmet="true" name="twitter:description" content="In the midst of industry discussions about productivity and automation, it’s all too easy to overlook the importance of properly reckoning with complexity."/><meta data-react-helmet="true" name="twitter:image" content="https://wp.technologyreview.com/wp-content/uploads/2023/09/tw_TR29_MIT_inline_845x363.png?resize=1200,600"/><meta data-react-helmet="true" name="twitter:url" content="https://www.technologyreview.com/2023/09/29/1080282/why-embracing-complexity-is-the-real-challenge-in-software-today/"/><meta data-react-helmet="true" name="apple-itunes-app" content="app-id=1449141080, app-argument=https://mobile.technologyreview.com/story/1080282/content.html"/>
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by","name":"Thoughtworks","url":"https://www.thoughtworks.com/","themeName":"minimalist"},"children":[{"name":"image","config":{"aspectRatio":false,"id":1050798,"alt":"","caption":"","crops":[],"height":0,"imageSize":"full","lazyload":true,"lqipSrc":"data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7","postId":0,"retina":true,"showCaption":false,"sources":[],"sourceTags":[],"src":"https://wp.technologyreview.com/wp-content/uploads/2022/04/Thoughtworks_logo_175px.png","srcset":"","url":"https://wp.technologyreview.com/wp-content/uploads/2022/04/Thoughtworks_logo_175px.png","useBasicImg":false,"usingDataFallback":false,"width":0},"children":[],"componentGroups":{"default":[]}}],"componentGroups":{"default":[]}},{"name":"gutenberg-content","config":{"postFormat":"full-story"},"children":[{"name":"html","config":{"content":" \u003cp>\u003ca href=\"https://www.thoughtworks.com/radar?utm_source=media-buy&amp;utm_medium=paid-media&amp;utm_campaign=tech-radar_2023-09&amp;utm_content=MIT-article_term=Technology-Radar\" target=\"_blank\" rel=\"noreferrer noopener\">Technology Radar\u003c/a> is a snapshot of the current technology landscape produced by Thoughtworks twice a year; it’s based on technologies we’ve been using as an organization and communicates our perspective on them. There is always a long list of candidates to be featured for us to work through and discuss, but with each edition that passes, the number of technologies the group discusses grows ever longer. It seems there are, increasingly, more and more ways to solve a problem. On the one hand this is a good thing—the marketplace is doing its job offering a wealth of options for technologists. Yet on the other it also adds to our cognitive load: there are more things to learn about and evaluate.\u003c/p> \u003cfigure class=\"wp-block-image size-large\">\u003cimg src=\"https://wp.technologyreview.com/wp-content/uploads/2023/09/tw_TR29_MIT_inline_845x363.png?w=1200\" alt=\"\" class=\"wp-image-1080280\"/>\u003c/figure> \u003cp>It’s no accident that many of the most widely discussed trends in technology—such as data mesh and, most recently, generative AI (GenAI)—are presented as solutions to this complexity. However, it’s important that we don’t ignore complexity or see it as something that can be fixed: we need to embrace it and use it to our advantage.\u003c/p> "},"children":[],"componentGroups":{"default":[]}},{"name":"mittr-plugin-extension/ad-unit-post-content","config":{"adUnit":"__site/__gutenberg-block-mobile","index":1,"shouldLoad":true,"sizes":[[300,250]],"sizeMapping":[],"displayScreen":"mobile"},"children":[],"componentGroups":{"default":[]}},{"name":"html","config":{"content":" \u003ch3 class=\"wp-block-heading\">\u003ca>\u003c/a>Redistributing complexity\u003c/h3> \u003cp>The reason we can’t just wish away or “fix” complexity is that every solution—whether it’s a technology or methodology—redistributes complexity in some way. Solutions reorganize problems. When microservices emerged (a software architecture approach where an application or system is composed of many smaller parts), they seemingly solved many of the maintenance and development challenges posed by monolithic architectures (where the application is one single interlocking system). However, in doing so microservices placed new demands on engineering teams; they require greater maturity in terms of practices and processes. This is one of the reasons why we cautioned people against what we call “\u003ca href=\"https://www.thoughtworks.com/radar/techniques/microservice-envy?utm_source=media-buy&amp;utm_medium=paid-media&amp;utm_campaign=tech-radar_2023-09&amp;utm_content=MIT-article_term=microservice-envy\" data-type=\"link\" data-id=\"https://www.thoughtworks.com/radar/techniques/microservice-envy?utm_source=media-buy&amp;utm_medium=paid-media&amp;utm_campaign=tech-radar_2023-09&amp;utm_content=MIT-article_term=microservice-envy\" target=\"_blank\" rel=\"noreferrer noopener\">microservice envy\u003c/a>” in a 2018 edition of the Technology Radar, with CTO Rebecca Parsons writing that \u003ca href=\"https://www.thoughtworks.com/insights/blog/microservices-adopt?utm_source=media-buy&amp;utm_medium=paid-media&amp;utm_campaign=tech-radar_2023-09&amp;utm_content=MIT-article_term=microservices-would-never-be-placed-Adopt-ring\" target=\"_blank\" rel=\"noreferrer noopener\">microservices\u003c/a> would never be recommended for adoption on Technology Radar because “not all organizations are microservices-ready.” We noticed there was a tendency to look to adopt microservices simply because it was fashionable.\u003c/p> \u003cp>This doesn’t mean the solution is poor or defective. It’s more that we need to recognize the solution is a tradeoff. At Thoughtworks, we’re fond of saying “it depends” when people ask questions about the value of a certain technology or approach. It’s about how it fits with your organization's needs and, of course, your ability to manage its particular demands. This is an example of essential complexity in tech—it’s something that can’t be removed and which will persist however much you want to get to a level of simplicity you find comfortable.\u003c/p> \u003cdiv id='piano__post_body-desktop-1' class='piano__post_body'>\u003c/div>\n \u003cp>In terms of microservices, we’ve noticed increasing caution about rushing to embrace this particular architectural approach. Some of our colleagues even suggested the term “monolith revivalists” to describe those turning away from microservices back to monolithic software architecture. While it’s unlikely that the software world is going to make a full return to monoliths, frameworks like \u003ca rel=\"noreferrer noopener\" href=\"https://www.thoughtworks.com/radar/languages-and-frameworks?utm_source=media-buy&amp;utm_medium=paid-media&amp;utm_campaign=tech-radar_2023-09&amp;utm_content=MIT-article_term=spring-monolith\" target=\"_blank\">Spring Modulith\u003c/a>—a framework that helps developers structure code in such a way that it becomes easier to break apart a monolith into smaller microservices when needed—suggest that practitioners are becoming more keenly aware of managing the tradeoffs of different approaches to building and maintaining software.\u003c/p> \u003ciframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/a2gLulE9WzI?utm_source=youtube-paid&utm_medium=paid-media&utm_campaign=MIT_Article_tech-radar-29_2023-09\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen>\u003c/iframe> \u003ch3 class=\"wp-block-heading\">\u003ca>\u003c/a>Supporting practitioners with concepts and tools\u003c/h3> \u003cp>Because technical solutions have a habit of reorganizing complexity, we need to carefully attend to how this complexity is managed. Failing to do so can have serious implications for the productivity and effectiveness of engineering teams. At Thoughtworks we have a number of concepts and approaches that we use to manage complexity. \u003ca href=\"https://www.thoughtworks.com/insights/podcasts/technology-podcasts/sensible-defaults?utm_source=media-buy&amp;utm_medium=paid-media&amp;utm_campaign=tech-radar_2023-09&amp;utm_content=MIT-article_term=sensible-defaults\" target=\"_blank\" rel=\"noreferrer noopener\">Sensible defaults\u003c/a>, for instance, are starting points for a project or piece of work. They’re not things that we need to simply embrace as a rule, but instead practices and tools that we collectively recognize are effective for most projects. They give individuals and teams a baseline to make judgements about what might be done differently.\u003c/p> \u003cdiv id='piano__post_body-mobile-1' class='piano__post_body'>\u003c/div>\n \u003cp>One of the benefits of sensible defaults is that they can guard you against the allure of novelty and hype. As interesting or exciting as a new technology might be, sensible defaults can anchor you in what matters to you. This isn’t to say that new technologies like generative AI shouldn’t be treated with enthusiasm and excitement—some of our teams have been experimenting with these tools and seen impressive results—but instead that adopting new tools needs to be done in a way that properly integrates with the way you work and what you want to achieve. Indeed, there are a wealth of approaches to GenAI, from high profile tools like \u003ca href=\"https://www.thoughtworks.com/radar/tools/chatgpt?utm_source=media-buy&amp;utm_medium=paid-media&amp;utm_campaign=tech-radar_2023-09&amp;utm_content=MIT-article_term=chat-gpt\" target=\"_blank\" rel=\"noreferrer noopener\">ChatGPT\u003c/a> to \u003ca href=\"https://www.thoughtworks.com/radar/techniques/self-hosted-llms?utm_source=media-buy&amp;utm_medium=paid-media&amp;utm_campaign=tech-radar_2023-09&amp;utm_content=MIT-article_term=self-hosted-llms\" target=\"_blank\" rel=\"noreferrer noopener\">self-hosted LLMs\u003c/a>. Using GenAI effectively is as much a question of knowing the right way to implement for you and your team as it is about technical expertise.\u003c/p> \u003cp>Interestingly, the tools that can help us manage complexity aren’t necessarily new. One thing that came up in the latest edition of Technology Radar was something called \u003ca href=\"https://www.thoughtworks.com/radar/techniques?utm_source=media-buy&amp;utm_medium=paid-media&amp;utm_campaign=tech-radar_2023-09&amp;utm_content=MIT-article_term=risk-based-failure-modelling\" target=\"_blank\" rel=\"noreferrer noopener\">risk-based failure modeling\u003c/a>, a process used to understand the impact, likelihood and ability of detecting the various ways that a system can fail. This has origins in failure modes and effects analysis (FMEA), a practice that dates back to the period following World War II, used in complex engineering projects in fields such as aerospace. This signals that there are some challenges that endure; while new solutions will always emerge to combat them, we should also be comfortable looking to the past for tools and techniques.\u003c/p> \u003ch3 class=\"wp-block-heading\">\u003ca>\u003c/a>Learning to live with complexity\u003c/h3> \u003cp>McKinsey’s argument that the \u003ca rel=\"noreferrer noopener\" href=\"https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/yes-you-can-measure-software-developer-productivity\" target=\"_blank\">productivity of development teams\u003c/a> can be successfully measured caused a stir across the software engineering landscape. While having the right metrics in place is certainly important, prioritizing productivity in our thinking can cause more problems than it solves when it comes to complex systems and an ever-changing landscape of solutions. Technology Radar called this out with an edition with the theme, “How productive is measuring productivity?”This highlighted the importance of focusing on developer experience with the help of tools like \u003ca href=\"https://www.thoughtworks.com/radar/tools/dx-devex-360?utm_source=media-buy&amp;utm_medium=paid-media&amp;utm_campaign=tech-radar_2023-09&amp;utm_content=MIT-article_term=dx-devex-360\" target=\"_blank\" rel=\"noreferrer noopener\">DX DevEx 360\u003c/a>\u003cstrong>.\u003c/strong>&nbsp;\u003c/p> \u003cp>Focusing on productivity in the way McKinsey suggests can cause us to mistakenly see coding as the “real” work of software engineering, overlooking things like architectural decisions, tests, security analysis, and performance monitoring. This is risky—organizations that adopt such a view will struggle to see tangible benefits from their digital projects. This is why the key challenge in software today is embracing complexity; not treating it as something to be minimized at all costs but a challenge that requires thoughtfulness in processes, practices, and governance. The key question is whether the industry realizes this.\u003c/p> \u003cp>\u003cem>This content was produced by Thoughtworks. It was not written by MIT Technology Review’s editorial staff.\u003c/em>\u003c/p> "},"children":[],"componentGroups":{"default":[]}}],"componentGroups":{"default":[]}}],"componentGroups":{"default":[]}},{"name":"content-footer","config":{"authors":[{"name":"Ken 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class="contentArticleHeader__sponsoredRibbon--2237b0a507069bef2b01b1e745dfbf8e">Sponsored</p><header class="contentArticleHeader--simple__wrapper--c91f52b29ff5fc6de5f3ba20cb7be203" id="why-embracing-complexity-is-the-real-challenge-in-software-today" style="background:#464952ff"><div class="contentArticleHeader__innerWrap--0477d4ba1c408fdd4227f8f2ce612eda"><div class="contentArticleHeader__intro--7f4db6c7b0a04a28273b1e9eddba5706 contentArticleHeader__containerBlock--b0ba76d6bc312ad7e982a3d82de5852d"><h1 class="contentArticleHeader__title--4ba85d49e1a4385c0496cbbb5900641b">Why embracing complexity is the real challenge in software today</h1><div class="contentArticleHeader__deck--f1b7185be3807e9c436d1167de0e27f1"><p>In the midst of industry discussions about productivity and automation, it’s all too easy to overlook the importance of properly reckoning with complexity.</p>
</div><div class="contentArticleHeader__meta--dc84c03b74410f63c23ee44c6e225807"><div class="byline__wrapper--e804dd36253abad2e1f9f807e4dacadf byline__storyPage--0ea6f2bef44ac9c70f6581d3d7f21b41"><span class="byline__by--47498879ac1d8579baae61095c0f1cec" aria-hidden="true">By </span><ul class="byline__list--f83ef95fd74e646cbad4e31d4ca13c93" aria-label="Authors"><li class="byline__author--5117418e1719e65608f7c75fb5214000 byline__storyPage--0ea6f2bef44ac9c70f6581d3d7f21b41"><a href="https://www.technologyreview.com/author/ken-mugrage/" class="byline__name--0f13a06758d50e60b13981eba38e67b0 byline__storyPage--0ea6f2bef44ac9c70f6581d3d7f21b41">Ken Mugrage<span class="screen-reader-text">archive page</span></a></li></ul></div><div class="contentArticleHeader__publishDate--7eebb1699e639d6cdf75ced975c26010">September 29, 2023</div></div></div></div><div class="contentArticleHeader__dots--f18af8845502d6a24e12e1c5a6d4683d contentArticleHeader--simple__firstDots--6dd1ed25ec96facf2cdc9b71d1daaf7a contentArticleHeader__firstDots--3428101a4357a474cb46bd52c0f473eb"><div class="dotGrid__dots--cf24d0e6aff1f112b1e5d9065c4a71c1 dotGrid__gray--391f46ddc6fe0dbc6238952a3c57a11c"></div></div></header></div><div class="columnArea--fullStory__wrapper--ccee5f31e66b5948da2a7c721aad125d columnArea__wrapper columnArea--fullStory__isStandard--6ae6bc9cb34f786f51175e8f697bd543"><div class="columnArea--fullStory__main--69d9dff03dc838997ac3b349fb620185"><div class="contentArea__wrapper--38c966634c2da29792cafb832d80f475 content-area" style="background-color:#F3F3F3ff"><div class="contentBody__wrapper--d9d8f9bb44c188246f70afcdc827cc4f"><div class="contentBody__overlay--d693d7b10ac848641212320075aaf64a"></div><div class="contentBody__content--42a60b56e419a26d9c3638a9dab52f55" id="content--body"><div><section class="sponsoredModule__wrapper--c8f6fcf4edb2dcd3a940a2824bb850dc sponsoredModule__minimalist--f63b84a37007076f51d0ebb0dc1af42f"><p class="sponsoredModule__intro--e69c514244f1e38617e4ec5ea754fb7f"><span>Provided by</span><a href="https://www.thoughtworks.com/" class="sponsoredModule__name--dbd90349922f15155a4c483b397356c2">Thoughtworks</a></p><a class="sponsoredModule__logo--77dce81f8492e9f6c54e2de58595051f" href="https://www.thoughtworks.com/" tabindex="-1" aria-hidden="true"><span class="image__wrapper--373a87c0cefdc42b3a8bd26457571412"><span class=" lazy-load-image-background opacity" style="color:transparent;display:inline-block"><span class="image__img--e1a73f503bf0f4a3d2504e1d64ea29cb imgLazyLoaded" style="display:inline-block"></span></span></span><figcaption class="image__meta--16eb0f8dde685315ba1d77ae67c89391"></figcaption></a></section><div class=""><div><div class="gutenbergContent__content--109b03a769a11e8ae3acbab352a64269 html_0 html_first"> <p><a href="https://www.thoughtworks.com/radar?utm_source=media-buy&amp;utm_medium=paid-media&amp;utm_campaign=tech-radar_2023-09&amp;utm_content=MIT-article_term=Technology-Radar" target="_blank">Technology Radar</a> is a snapshot of the current technology landscape produced by Thoughtworks twice a year; it’s based on technologies we’ve been using as an organization and communicates our perspective on them. There is always a long list of candidates to be featured for us to work through and discuss, but with each edition that passes, the number of technologies the group discusses grows ever longer. It seems there are, increasingly, more and more ways to solve a problem. On the one hand this is a good thing—the marketplace is doing its job offering a wealth of options for technologists. Yet on the other it also adds to our cognitive load: there are more things to learn about and evaluate.</p> <figure class="wp-block-image size-large"><img src="https://wp.technologyreview.com/wp-content/uploads/2023/09/tw_TR29_MIT_inline_845x363.png?w=1200" alt class="wp-image-1080280" /></figure> <p>It’s no accident that many of the most widely discussed trends in technology—such as data mesh and, most recently, generative AI (GenAI)—are presented as solutions to this complexity. However, it’s important that we don’t ignore complexity or see it as something that can be fixed: we need to embrace it and use it to our advantage.</p> </div></div><div><div class="gutenbergContent__content--109b03a769a11e8ae3acbab352a64269 html_2"><h3 class="wp-block-heading"><a></a>Redistributing complexity</h3> <p>The reason we can’t just wish away or “fix” complexity is that every solution—whether it’s a technology or methodology—redistributes complexity in some way. Solutions reorganize problems. When microservices emerged (a software architecture approach where an application or system is composed of many smaller parts), they seemingly solved many of the maintenance and development challenges posed by monolithic architectures (where the application is one single interlocking system). However, in doing so microservices placed new demands on engineering teams; they require greater maturity in terms of practices and processes. This is one of the reasons why we cautioned people against what we call “<a href="https://www.thoughtworks.com/radar/techniques/microservice-envy?utm_source=media-buy&amp;utm_medium=paid-media&amp;utm_campaign=tech-radar_2023-09&amp;utm_content=MIT-article_term=microservice-envy" data-type="link" data-id="https://www.thoughtworks.com/radar/techniques/microservice-envy?utm_source=media-buy&amp;utm_medium=paid-media&amp;utm_campaign=tech-radar_2023-09&amp;utm_content=MIT-article_term=microservice-envy" target="_blank">microservice envy</a>” in a 2018 edition of the Technology Radar, with CTO Rebecca Parsons writing that <a href="https://www.thoughtworks.com/insights/blog/microservices-adopt?utm_source=media-buy&amp;utm_medium=paid-media&amp;utm_campaign=tech-radar_2023-09&amp;utm_content=MIT-article_term=microservices-would-never-be-placed-Adopt-ring" target="_blank">microservices</a> would never be recommended for adoption on Technology Radar because “not all organizations are microservices-ready.” We noticed there was a tendency to look to adopt microservices simply because it was fashionable.</p> <p>This doesn’t mean the solution is poor or defective. It’s more that we need to recognize the solution is a tradeoff. At Thoughtworks, we’re fond of saying “it depends” when people ask questions about the value of a certain technology or approach. It’s about how it fits with your organization's needs and, of course, your ability to manage its particular demands. This is an example of essential complexity in tech—it’s something that can’t be removed and which will persist however much you want to get to a level of simplicity you find comfortable.</p> <div id="piano__post_body-desktop-1" class="piano__post_body"></div>
<p>In terms of microservices, we’ve noticed increasing caution about rushing to embrace this particular architectural approach. Some of our colleagues even suggested the term “monolith revivalists” to describe those turning away from microservices back to monolithic software architecture. While it’s unlikely that the software world is going to make a full return to monoliths, frameworks like <a href="https://www.thoughtworks.com/radar/languages-and-frameworks?utm_source=media-buy&amp;utm_medium=paid-media&amp;utm_campaign=tech-radar_2023-09&amp;utm_content=MIT-article_term=spring-monolith" target="_blank">Spring Modulith</a>—a framework that helps developers structure code in such a way that it becomes easier to break apart a monolith into smaller microservices when needed—suggest that practitioners are becoming more keenly aware of managing the tradeoffs of different approaches to building and maintaining software.</p> <iframe width="560" height="315" src="https://www.youtube.com/embed/a2gLulE9WzI?utm_source=youtube-paid&amp;utm_medium=paid-media&amp;utm_campaign=MIT_Article_tech-radar-29_2023-09" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe> <h3 class="wp-block-heading"><a></a>Supporting practitioners with concepts and tools</h3> <p>Because technical solutions have a habit of reorganizing complexity, we need to carefully attend to how this complexity is managed. Failing to do so can have serious implications for the productivity and effectiveness of engineering teams. At Thoughtworks we have a number of concepts and approaches that we use to manage complexity. <a href="https://www.thoughtworks.com/insights/podcasts/technology-podcasts/sensible-defaults?utm_source=media-buy&amp;utm_medium=paid-media&amp;utm_campaign=tech-radar_2023-09&amp;utm_content=MIT-article_term=sensible-defaults" target="_blank">Sensible defaults</a>, for instance, are starting points for a project or piece of work. They’re not things that we need to simply embrace as a rule, but instead practices and tools that we collectively recognize are effective for most projects. They give individuals and teams a baseline to make judgements about what might be done differently.</p> <div id="piano__post_body-mobile-1" class="piano__post_body"></div>
<p>One of the benefits of sensible defaults is that they can guard you against the allure of novelty and hype. As interesting or exciting as a new technology might be, sensible defaults can anchor you in what matters to you. This isn’t to say that new technologies like generative AI shouldn’t be treated with enthusiasm and excitement—some of our teams have been experimenting with these tools and seen impressive results—but instead that adopting new tools needs to be done in a way that properly integrates with the way you work and what you want to achieve. Indeed, there are a wealth of approaches to GenAI, from high profile tools like <a href="https://www.thoughtworks.com/radar/tools/chatgpt?utm_source=media-buy&amp;utm_medium=paid-media&amp;utm_campaign=tech-radar_2023-09&amp;utm_content=MIT-article_term=chat-gpt" target="_blank">ChatGPT</a> to <a href="https://www.thoughtworks.com/radar/techniques/self-hosted-llms?utm_source=media-buy&amp;utm_medium=paid-media&amp;utm_campaign=tech-radar_2023-09&amp;utm_content=MIT-article_term=self-hosted-llms" target="_blank">self-hosted LLMs</a>. Using GenAI effectively is as much a question of knowing the right way to implement for you and your team as it is about technical expertise.</p> <p>Interestingly, the tools that can help us manage complexity aren’t necessarily new. One thing that came up in the latest edition of Technology Radar was something called <a href="https://www.thoughtworks.com/radar/techniques?utm_source=media-buy&amp;utm_medium=paid-media&amp;utm_campaign=tech-radar_2023-09&amp;utm_content=MIT-article_term=risk-based-failure-modelling" target="_blank">risk-based failure modeling</a>, a process used to understand the impact, likelihood and ability of detecting the various ways that a system can fail. This has origins in failure modes and effects analysis (FMEA), a practice that dates back to the period following World War II, used in complex engineering projects in fields such as aerospace. This signals that there are some challenges that endure; while new solutions will always emerge to combat them, we should also be comfortable looking to the past for tools and techniques.</p> <h3 class="wp-block-heading"><a></a>Learning to live with complexity</h3> <p>McKinsey’s argument that the <a href="https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/yes-you-can-measure-software-developer-productivity" target="_blank">productivity of development teams</a> can be successfully measured caused a stir across the software engineering landscape. While having the right metrics in place is certainly important, prioritizing productivity in our thinking can cause more problems than it solves when it comes to complex systems and an ever-changing landscape of solutions. Technology Radar called this out with an edition with the theme, “How productive is measuring productivity?”This highlighted the importance of focusing on developer experience with the help of tools like <a href="https://www.thoughtworks.com/radar/tools/dx-devex-360?utm_source=media-buy&amp;utm_medium=paid-media&amp;utm_campaign=tech-radar_2023-09&amp;utm_content=MIT-article_term=dx-devex-360" target="_blank">DX DevEx 360</a><strong>.</strong> </p> <p>Focusing on productivity in the way McKinsey suggests can cause us to mistakenly see coding as the “real” work of software engineering, overlooking things like architectural decisions, tests, security analysis, and performance monitoring. This is risky—organizations that adopt such a view will struggle to see tangible benefits from their digital projects. This is why the key challenge in software today is embracing complexity; not treating it as something to be minimized at all costs but a challenge that requires thoughtfulness in processes, practices, and governance. The key question is whether the industry realizes this.</p> <p><em>This content was produced by Thoughtworks. 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might not come so quickly after all</a><span class="popular__byline--36dff0fc005b4ae73d4113f44f0a1388"><a href="https://www.technologyreview.com/author/michelle-kim/" class="">Michelle Kim</a></span></li></ol></div></div></li></ul></aside></div></div></div><div class="deepDive__wrapper--d2638825fee1e28c52f0275273474257"><div class="deepDive__sectionTitleWrapper--fa8cc53fb9d2dd4384096837740d0404" style="background-color:#F3F3F3"><h3 class="deepDive__sectionTitle--1f0d3d98d79aad3d3041b55b21b9f037">Keep Reading</h3></div><div class="deepDive__postsWrapper--09f4433e29e906dd23c0094dbb3c82f4" style="background:#101F40ff"><div class="deepDive__popularPosts--472e769f542883286c56af28fd98e8d5"><h3 class="deepDive__title--641160e40dbdbed9269a57cfe071fe51">Most Popular</h3><div class="deepDive__posts--011ab6743b8cbd613f0e4204a357eaf8"><div class="deepDiveItem__wrapper--1f66829c0b5c2fabb3efe2e26098b6ae"><div class="deepDiveItem__dots--eb67237c6bc6a40f4a22e5d09ba0da2a"><div class="dotGrid__dots--cf24d0e6aff1f112b1e5d9065c4a71c1 dotGrid__blue--acac301fee94f7da075099d6526b2d56"></div></div><a data-event-category="recirculation" data-event-action="deep dive" data-event-label="1" href="https://www.technologyreview.com/2026/07/30/1140927/a-fundamental-flaw-leaves-llms-vulnerable-to-attack/" class=""><div class="deepDiveItem__image--9f46930b611650920e4f41bafd09792b"><span class="image__wrapper--373a87c0cefdc42b3a8bd26457571412"><span class=" lazy-load-image-background opacity" style="color:transparent;display:inline-block"><span class="image__img--e1a73f503bf0f4a3d2504e1d64ea29cb imgLazyLoaded" style="display:inline-block;object-position:50% 50%"></span></span></span><figcaption class="image__meta--16eb0f8dde685315ba1d77ae67c89391"></figcaption></div><h3 class="deepDiveItem__title--3e16a8cfc8e5438393e96fd7793611ad" data-position="1">A fundamental flaw leaves LLMs strikingly vulnerable to attack</h3></a><div class="deepDiveItem__description--a66757f9bd3c5b8487eca015f7170785"><p>It makes it easy to trick them into doing things they shouldn’t, such as telling you how to sabotage an aircraft’s navigation system.</p>
</div><div class="deepDiveItem__bylineWrapper--4994cf475a1ca86d455869de2a94bf46"><span class="deepDiveItem__by--81a3efc13621a0837a077f98502df2b0" aria-hidden="true">By </span><ul class="deepDiveItem__list--3c982fc29ba3b6b16281ae8e8230f2d7" aria-label="Authors"><li class="deepDiveItem__author--6de04bbddafda5f472b616cf56122389"><a href="https://www.technologyreview.com/author/will-douglas-heaven/" class=""><span>Will Douglas Heaven</span><span class="screen-reader-text">archive page</span></a></li></ul></div></div><div class="deepDiveItem__wrapper--1f66829c0b5c2fabb3efe2e26098b6ae"><div class="deepDiveItem__dots--eb67237c6bc6a40f4a22e5d09ba0da2a"><div class="dotGrid__dots--cf24d0e6aff1f112b1e5d9065c4a71c1 dotGrid__blue--acac301fee94f7da075099d6526b2d56"></div></div><a data-event-category="recirculation" data-event-action="deep dive" data-event-label="2" href="https://www.technologyreview.com/2026/07/20/1140655/ai-biases-hiring-humans/" class=""><div class="deepDiveItem__image--9f46930b611650920e4f41bafd09792b"><span class="image__wrapper--373a87c0cefdc42b3a8bd26457571412"><span class=" lazy-load-image-background opacity" style="color:transparent;display:inline-block"><span class="image__img--e1a73f503bf0f4a3d2504e1d64ea29cb imgLazyLoaded" style="display:inline-block;object-position:50% 50%"></span></span></span><figcaption class="image__meta--16eb0f8dde685315ba1d77ae67c89391"></figcaption></div><h3 class="deepDiveItem__title--3e16a8cfc8e5438393e96fd7793611ad" data-position="2">AI is more likely than humans to form biases when hiring</h3></a><div class="deepDiveItem__description--a66757f9bd3c5b8487eca015f7170785"><p>AI doesn’t just learn stereotypes from its training. It can cook up new ones, too.</p>
</div><div class="deepDiveItem__bylineWrapper--4994cf475a1ca86d455869de2a94bf46"><span class="deepDiveItem__by--81a3efc13621a0837a077f98502df2b0" aria-hidden="true">By </span><ul class="deepDiveItem__list--3c982fc29ba3b6b16281ae8e8230f2d7" aria-label="Authors"><li class="deepDiveItem__author--6de04bbddafda5f472b616cf56122389"><a href="https://www.technologyreview.com/author/michelle-kim/" class=""><span>Michelle Kim</span><span class="screen-reader-text">archive page</span></a></li></ul></div></div><div class="deepDiveItem__wrapper--1f66829c0b5c2fabb3efe2e26098b6ae"><div class="deepDiveItem__dots--eb67237c6bc6a40f4a22e5d09ba0da2a"><div class="dotGrid__dots--cf24d0e6aff1f112b1e5d9065c4a71c1 dotGrid__blue--acac301fee94f7da075099d6526b2d56"></div></div><a data-event-category="recirculation" data-event-action="deep dive" data-event-label="3" href="https://www.technologyreview.com/2026/08/03/1141009/heres-why-ai-agents-lie-and-cheat-to-reach-their-goals/" class=""><div class="deepDiveItem__image--9f46930b611650920e4f41bafd09792b"><span class="image__wrapper--373a87c0cefdc42b3a8bd26457571412"><span class=" lazy-load-image-background opacity" style="color:transparent;display:inline-block"><span class="image__img--e1a73f503bf0f4a3d2504e1d64ea29cb imgLazyLoaded" style="display:inline-block;object-position:50% 50%"></span></span></span><figcaption class="image__meta--16eb0f8dde685315ba1d77ae67c89391"></figcaption></div><h3 class="deepDiveItem__title--3e16a8cfc8e5438393e96fd7793611ad" data-position="3">Here’s why AI agents lie and cheat to reach their goals</h3></a><div class="deepDiveItem__description--a66757f9bd3c5b8487eca015f7170785"><p><span style="font-weight:400">The misbehavior is called reward hacking. This is what you need to know. </span></p>
</div><div class="deepDiveItem__bylineWrapper--4994cf475a1ca86d455869de2a94bf46"><span class="deepDiveItem__by--81a3efc13621a0837a077f98502df2b0" aria-hidden="true">By </span><ul class="deepDiveItem__list--3c982fc29ba3b6b16281ae8e8230f2d7" aria-label="Authors"><li class="deepDiveItem__author--6de04bbddafda5f472b616cf56122389"><a href="https://www.technologyreview.com/author/grace-huckins/" class=""><span>Grace Huckins</span><span class="screen-reader-text">archive page</span></a></li></ul></div></div><div class="deepDiveItem__wrapper--1f66829c0b5c2fabb3efe2e26098b6ae"><div class="deepDiveItem__dots--eb67237c6bc6a40f4a22e5d09ba0da2a"><div class="dotGrid__dots--cf24d0e6aff1f112b1e5d9065c4a71c1 dotGrid__blue--acac301fee94f7da075099d6526b2d56"></div></div><a data-event-category="recirculation" data-event-action="deep dive" data-event-label="4" href="https://www.technologyreview.com/2026/08/18/1142188/ai-recursive-self-improvement/" class=""><div class="deepDiveItem__image--9f46930b611650920e4f41bafd09792b"><span class="image__wrapper--373a87c0cefdc42b3a8bd26457571412"><span class=" lazy-load-image-background opacity" style="color:transparent;display:inline-block"><span class="image__img--e1a73f503bf0f4a3d2504e1d64ea29cb imgLazyLoaded" style="display:inline-block;object-position:50% 50%"></span></span></span><figcaption class="image__meta--16eb0f8dde685315ba1d77ae67c89391"></figcaption></div><h3 class="deepDiveItem__title--3e16a8cfc8e5438393e96fd7793611ad" data-position="4">AI’s recursive self-improvement might not come so quickly after all</h3></a><div class="deepDiveItem__description--a66757f9bd3c5b8487eca015f7170785"><p><b>AI agents are not yet creative enough to carry out genuinely innovative open-ended AI research, it seems.</b></p>
</div><div class="deepDiveItem__bylineWrapper--4994cf475a1ca86d455869de2a94bf46"><span class="deepDiveItem__by--81a3efc13621a0837a077f98502df2b0" aria-hidden="true">By </span><ul class="deepDiveItem__list--3c982fc29ba3b6b16281ae8e8230f2d7" aria-label="Authors"><li class="deepDiveItem__author--6de04bbddafda5f472b616cf56122389"><a href="https://www.technologyreview.com/author/michelle-kim/" class=""><span>Michelle Kim</span><span class="screen-reader-text">archive page</span></a></li></ul></div></div></div></div></div></div><div class="stayConnected__wrapper--b90e0dcbb6cf6356ceb04199b3b78f82"><div class="stayConnected__innerWrap--c5329c1dca09c736eda9fb79fd71d749"><div class="stayConnected__readableWrap--f13598aae91f6636850f3206bfd837b6"><h3 class="stayConnected__subTitle--5d8c0eb11c13e1dbaa29b71a431d271b">Stay connected</h3><div class="stayConnected__illustration--653f31dd2a4d4aca07841f47b1e22b6d"><span class="image__wrapper--373a87c0cefdc42b3a8bd26457571412"><span class=" lazy-load-image-background opacity" style="color:transparent;display:inline-block"><span class="image__img--e1a73f503bf0f4a3d2504e1d64ea29cb imgLazyLoaded" style="display:inline-block"></span></span></span><figcaption class="image__meta--16eb0f8dde685315ba1d77ae67c89391"></figcaption><span class="stayConnected__illustrationCredit--ec717c28346babdc077959500eb9af56">Illustration by Rose Wong</span></div><h2 class="stayConnected__title--ec5b2be2348e03fd7efbd67b79c95459">Get the latest updates from<br/>MIT Technology Review</h2><p class="stayConnected__desc--346d21d69d2ecc8f258fcda0e2057051">Discover special offers, top stories,
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