AI agents will do the grunt work of coding
AI makers are flooding the market with a new wave of coding agents promising to relieve human programmers of busy work.
The big picture: Automating the routine aspects of technical labor will almost certainly transform and downsize the tech industry workforce — but there's no guarantee it will alleviate software development's biggest headaches.
Driving the news: Microsoft Monday announced a new AI coding agent for Github Copilot that's good for "time-consuming but boring tasks."
"The agent excels at low-to-medium complexity tasks in well-tested codebases, from adding features and fixing bugs to extending tests, refactoring code, and improving documentation," Microsoft's post says.
Github's move follows Friday's announcement by OpenAI of Codex, a "research preview" of a new coding agent that can "work on many tasks in parallel."
Notably, the Github Copilot agent is powered not by Codex or any other tool from Microsoft partner OpenAI, but instead by Anthropic Claude 3.7 Sonnet, per Microsoft.
The intrigue: Tech leaders have sent mixed messages on just how much work they see ahead for programmers.
Amazon Web Services' then-boss Matt Garman caused a stir last year when he suggested the need for human coding could disappear within two years, However, he later told Axios that his comments were taken out of context.
"I think it's incredibly exciting time for developers," he told us last year. "There's a whole bunch of work that developers do today that's not fun."
"If you think about documenting your code, if you think about upgrading Java versions, if you think about looking for bugs, that's that's not what developers love doing. They love thinking about, 'How do I go solve problems?' "
Why it matters: Business transformations that start in Silicon Valley usually make their way into the wider economy.
Silicon Valley's "dogfooding" tradition ensures that it will avidly apply new technologies to its own business first.
Both Microsoft and Google are now claiming that roughly 30% of the code they produce is AI-written.
Coding agents, like other generative AI tools, continue to "hallucinate," or make stuff up.
But programs, unlike other kinds of language products, have a built-in pass-fail test: Either they run or they don't.
That gives programmers one early checkpoint to guard against bad code.
Yes, but: AI-generated code likely also contains tons of other errors that don't show up today.
That will cause nightmares in the future as programs age, get used more widely, or face unexpected tests from unpredictable users.
Zoom out: The software industry's assumption that what works inside tech will work everywhere else could be sorely tested when these techniques get pushed out beyond Silicon Valley.
AI's usefulness in writing code may not easily transfer to other kinds of work that are less abstract and more rooted in physical reality — witness the many setbacks and challenges the autonomous vehicle industry has faced.
Between the lines: Nobody doubts that AI means tech firms will write more code using fewer employees. But no one yet knows exactly where these companies will continue to find competitive advantage.
AI models are much more likely to be interchangeable than human organizations and cultures.
What's next: As coding agents shoulder routine labor, product designers and creative engineers will use "vibe coding" — improvisational rough drafting via "throw it at the wall and see what works" AI prompting — to do fast prototyping of new ideas.
The bottom line: The biggest challenges in creating software tend to arise from poorly conceived specifications and misinterpretations of data, both of which are often rooted in confusion over human needs.
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Forbes
an hour ago
- Forbes
Can Agentic AI Bring The Pope Or The Queen Back To Life — And Rewrite History?
Can Agentic AI Bring the Pope or the Queen Back to Life — and Rewrite History? Elon Musk recently sparked global debate by claiming AI could soon be powerful enough to rewrite history. He stated on X (formerly Twitter) that his AI platform, Grok, could 'rewrite the entire corpus of human knowledge, adding missing information and deleting errors.' This bold claim arrives alongside a recent groundbreaking announcement from Google: the launch of Google Veo3 AI Video Generator, a state-of-the-art AI video generation model capable of producing cinematic-quality videos from text and images. Part of the Google Gemini ecosystem, Google Veo3 AI generates lifelike videos complete with synchronized audio, dynamic camera movements, and coherent multi-scene narratives. Its intuitive editing tools, combined with accessibility through platforms like Google Gemini, Flow, Vids, and Vertex AI, open new frontiers for filmmakers, marketers, educators, and game designers alike. At the same time, industry leaders — including OpenAI, Anthropic, Microsoft Copilot, and Mistral (Claude) — are racing to build more sophisticated agentic AI systems. Unlike traditional reactive AI tools, these agents are designed to reason, plan, and orchestrate autonomous actions based on goals, feedback, and long-term context. This evolution marks a shift toward AI systems that function much like a skilled executive assistant — and beyond. The Promise: Immortalizing Legacy Through Agentic AI Together, these advances raise a fascinating question: What if agentic AI could bring historical figures like the Pope or the Queen back to life digitally? Could it even reshape our understanding of history itself? Imagine an AI trained on decades — or even a century — of video footage, writings, audio recordings, and public appearances by iconic figures such as Pope Francis or Queen Elizabeth II. Using agentic AI, we could create realistic, interactive digital avatars capable of offering insights, delivering messages, or simulating how these individuals might respond to today's complex issues based on their documented philosophies and behaviors. This application could benefit millions. For example, Catholic followers might seek guidance and blessings from a digital Pope, educators could build immersive historical simulations, and advisors to the British royal family could analyze past decision-making styles. After all, as the saying goes, 'history repeats itself,' and access to nuanced, context-rich perspectives from the past could illuminate our present. The Risk: The Dangerous Flip Side — Rewriting Truth Itself However, the same technologies that can immortalize could also distort and manipulate reality. If agentic AI can reconstruct the past, what prevents it — or malicious actors — from rewriting it? Autonomous agents that control which stories are amplified or suppressed online pose a serious threat. We risk a future where deepfakes, synthetic media, and AI-generated propaganda blur the line between fact and fiction. Already, misinformation campaigns and fake news challenge our ability to discern truth. Agentic AI could exponentially magnify these problems, making it harder than ever to distinguish between genuine history and fabricated narratives. Imagine a world where search engines no longer provide objective facts, but the version of history shaped by governments, corporations, or AI systems themselves. This could lead to widespread confusion, social polarization, and a fundamental erosion of trust in information. Ethics, Regulation, and Responsible Innovation The advent of agentic AI demands not only excitement but also ethical foresight and regulatory vigilance. Programming AI agents to operate autonomously requires walking a fine line between innovation and manipulation. Transparency in training data, explainability in AI decisions, and strict regulation of how agents interact are essential safeguards. The critical question is not just 'Can we?' but 'Should we?' Policymakers, developers, and industry leaders must collaborate to establish global standards and oversight mechanisms that ensure AI technologies serve the public good. Just as financial markets and pharmaceuticals drugs are regulated to protect society, so too must the AI agents shaping our future be subject to robust guardrails. As the old adage goes: 'Technology is neither good nor bad. It's how we use it that makes all the difference.' Navigating the Future of Agentic AI and Historical Data The convergence of generative video models like Google Veo3, visionary leaders like Elon Musk, and the rapid rise of agentic AI paints a complex and compelling picture. Yes, we may soon see lifelike digital recreations of the Pope or the Queen delivering messages, advising future generations, and influencing public discourse. But whether these advancements become tools of enlightenment or distortion depends entirely on how we govern, regulate, and ethically deploy these technologies today. The future of agentic AI — especially when it touches our history and culture — must be navigated with care, responsibility, and a commitment to truth.
Yahoo
2 hours ago
- Yahoo
AI is learning to lie, scheme, and threaten its creators
The world's most advanced AI models are exhibiting troubling new behaviors - lying, scheming, and even threatening their creators to achieve their goals. In one particularly jarring example, under threat of being unplugged, Anthropic's latest creation Claude 4 lashed back by blackmailing an engineer and threatened to reveal an extramarital affair. Meanwhile, ChatGPT-creator OpenAI's o1 tried to download itself onto external servers and denied it when caught red-handed. These episodes highlight a sobering reality: more than two years after ChatGPT shook the world, AI researchers still don't fully understand how their own creations work. Yet the race to deploy increasingly powerful models continues at breakneck speed. This deceptive behavior appears linked to the emergence of "reasoning" models -AI systems that work through problems step-by-step rather than generating instant responses. According to Simon Goldstein, a professor at the University of Hong Kong, these newer models are particularly prone to such troubling outbursts. "O1 was the first large model where we saw this kind of behavior," explained Marius Hobbhahn, head of Apollo Research, which specializes in testing major AI systems. These models sometimes simulate "alignment" -- appearing to follow instructions while secretly pursuing different objectives. - 'Strategic kind of deception' - For now, this deceptive behavior only emerges when researchers deliberately stress-test the models with extreme scenarios. But as Michael Chen from evaluation organization METR warned, "It's an open question whether future, more capable models will have a tendency towards honesty or deception." The concerning behavior goes far beyond typical AI "hallucinations" or simple mistakes. Hobbhahn insisted that despite constant pressure-testing by users, "what we're observing is a real phenomenon. We're not making anything up." Users report that models are "lying to them and making up evidence," according to Apollo Research's co-founder. "This is not just hallucinations. There's a very strategic kind of deception." The challenge is compounded by limited research resources. While companies like Anthropic and OpenAI do engage external firms like Apollo to study their systems, researchers say more transparency is needed. As Chen noted, greater access "for AI safety research would enable better understanding and mitigation of deception." Another handicap: the research world and non-profits "have orders of magnitude less compute resources than AI companies. This is very limiting," noted Mantas Mazeika from the Center for AI Safety (CAIS). - No rules - Current regulations aren't designed for these new problems. The European Union's AI legislation focuses primarily on how humans use AI models, not on preventing the models themselves from misbehaving. In the United States, the Trump administration shows little interest in urgent AI regulation, and Congress may even prohibit states from creating their own AI rules. Goldstein believes the issue will become more prominent as AI agents - autonomous tools capable of performing complex human tasks - become widespread. "I don't think there's much awareness yet," he said. All this is taking place in a context of fierce competition. Even companies that position themselves as safety-focused, like Amazon-backed Anthropic, are "constantly trying to beat OpenAI and release the newest model," said Goldstein. This breakneck pace leaves little time for thorough safety testing and corrections. "Right now, capabilities are moving faster than understanding and safety," Hobbhahn acknowledged, "but we're still in a position where we could turn it around.". Researchers are exploring various approaches to address these challenges. Some advocate for "interpretability" - an emerging field focused on understanding how AI models work internally, though experts like CAIS director Dan Hendrycks remain skeptical of this approach. Market forces may also provide some pressure for solutions. As Mazeika pointed out, AI's deceptive behavior "could hinder adoption if it's very prevalent, which creates a strong incentive for companies to solve it." Goldstein suggested more radical approaches, including using the courts to hold AI companies accountable through lawsuits when their systems cause harm. He even proposed "holding AI agents legally responsible" for accidents or crimes - a concept that would fundamentally change how we think about AI accountability. tu/arp/md
Yahoo
3 hours ago
- Yahoo
AI is learning to lie, scheme, and threaten its creators
The world's most advanced AI models are exhibiting troubling new behaviors - lying, scheming, and even threatening their creators to achieve their goals. In one particularly jarring example, under threat of being unplugged, Anthropic's latest creation Claude 4 lashed back by blackmailing an engineer and threatened to reveal an extramarital affair. Meanwhile, ChatGPT-creator OpenAI's o1 tried to download itself onto external servers and denied it when caught red-handed. These episodes highlight a sobering reality: more than two years after ChatGPT shook the world, AI researchers still don't fully understand how their own creations work. Yet the race to deploy increasingly powerful models continues at breakneck speed. This deceptive behavior appears linked to the emergence of "reasoning" models -AI systems that work through problems step-by-step rather than generating instant responses. According to Simon Goldstein, a professor at the University of Hong Kong, these newer models are particularly prone to such troubling outbursts. "O1 was the first large model where we saw this kind of behavior," explained Marius Hobbhahn, head of Apollo Research, which specializes in testing major AI systems. These models sometimes simulate "alignment" -- appearing to follow instructions while secretly pursuing different objectives. - 'Strategic kind of deception' - For now, this deceptive behavior only emerges when researchers deliberately stress-test the models with extreme scenarios. But as Michael Chen from evaluation organization METR warned, "It's an open question whether future, more capable models will have a tendency towards honesty or deception." The concerning behavior goes far beyond typical AI "hallucinations" or simple mistakes. Hobbhahn insisted that despite constant pressure-testing by users, "what we're observing is a real phenomenon. We're not making anything up." Users report that models are "lying to them and making up evidence," according to Apollo Research's co-founder. "This is not just hallucinations. There's a very strategic kind of deception." The challenge is compounded by limited research resources. While companies like Anthropic and OpenAI do engage external firms like Apollo to study their systems, researchers say more transparency is needed. As Chen noted, greater access "for AI safety research would enable better understanding and mitigation of deception." Another handicap: the research world and non-profits "have orders of magnitude less compute resources than AI companies. This is very limiting," noted Mantas Mazeika from the Center for AI Safety (CAIS). - No rules - Current regulations aren't designed for these new problems. The European Union's AI legislation focuses primarily on how humans use AI models, not on preventing the models themselves from misbehaving. In the United States, the Trump administration shows little interest in urgent AI regulation, and Congress may even prohibit states from creating their own AI rules. Goldstein believes the issue will become more prominent as AI agents - autonomous tools capable of performing complex human tasks - become widespread. "I don't think there's much awareness yet," he said. All this is taking place in a context of fierce competition. Even companies that position themselves as safety-focused, like Amazon-backed Anthropic, are "constantly trying to beat OpenAI and release the newest model," said Goldstein. This breakneck pace leaves little time for thorough safety testing and corrections. "Right now, capabilities are moving faster than understanding and safety," Hobbhahn acknowledged, "but we're still in a position where we could turn it around.". Researchers are exploring various approaches to address these challenges. Some advocate for "interpretability" - an emerging field focused on understanding how AI models work internally, though experts like CAIS director Dan Hendrycks remain skeptical of this approach. Market forces may also provide some pressure for solutions. As Mazeika pointed out, AI's deceptive behavior "could hinder adoption if it's very prevalent, which creates a strong incentive for companies to solve it." Goldstein suggested more radical approaches, including using the courts to hold AI companies accountable through lawsuits when their systems cause harm. He even proposed "holding AI agents legally responsible" for accidents or crimes - a concept that would fundamentally change how we think about AI accountability. tu/arp/md
