Etiket: Machine Learning

  • OpenAI Dev Day 2025: ChatGPT becomes the new app store — and hardware is coming

    In a packed hall at Fort Mason Center in San Francisco, against a backdrop of the Golden Gate Bridge, OpenAI CEO Sam Altman laid out a bold vision to remake the digital world. The company that brought generative AI to the mainstream with a simple chatbot is now building the foundations for its next act: a comprehensive computing platform designed to move beyond the screen and browser, with legendary designer Jony Ive enlisted to help shape its physical form.

    At its third annual DevDay, OpenAI unveiled a suite of tools that signals a strategic pivot from a model provider to a full-fledged ecosystem. The message was clear: the era of simply asking an AI questions is over. The future is about commanding AI to perform complex tasks, build software autonomously, and live inside every application, a transition Altman framed as moving from "systems that you can ask anything to, to systems that you can ask to do anything for you." 

    The day’s announcements were a three-pronged assault on the status quo, targeting how users interact with software, how developers build it, and how businesses deploy intelligent agents. But it was the sessions held behind closed doors, away from the public livestream, that revealed the true scope of OpenAI’s ambition — a future that includes new hardware, a relentless pursuit of computational power, and a philosophical quest to redefine our relationship with technology.

    From chatbot to operating system: The new 'App Store'

    The centerpiece of the public-facing keynote was the transformation of ChatGPT itself. With the new Apps SDK, OpenAI is turning its wildly popular chatbot into a dynamic, interactive platform, effectively an operating system where developers can build and distribute their own applications.

    “Today, we're going to open up ChatGPT for developers to build real apps inside of ChatGPT,” Altman announced during the keynote presentation to applause. “This will enable a new generation of apps that are interactive, adaptive and personalized, that you can chat with.”

    Live demonstrations showcased apps from partners like Coursera, Canva, and Zillow running seamlessly within a chat conversation. A user could watch a machine learning lecture, ask ChatGPT to explain a concept in real-time, and then use Canva to generate a poster based on the conversation, all without leaving the chat interface. The apps can render rich, interactive UIs, even going full-screen to offer a complete experience, like exploring a Zillow map of homes.

    For developers, this represents a powerful new distribution channel. “When you build with the Apps SDK, your apps can reach hundreds of millions of chat users,” Altman said, highlighting a direct path to a massive user base that has grown to over 800 million weekly active users

    In a private press conference later, Nick Turley, head of ChatGPT, elaborated on the grander vision. "We never meant to build a chatbot," he stated. "When we set out to make ChatGPT, we meant to build a super assistant and we got a little sidetracked. And one of the tragedies of getting a little sidetracked is that we built a great chatbot, but we are the first ones to say that not all software needs to be a chatbot, not all interaction with the commercial world needs to be a chatbot."

    Turley emphasized that while OpenAI is excited about natural language interfaces, "the interface really needs to evolve, which is why you see so much UI in the demos today. In fact, you can even go full screen and chat is in the background." He described a future where users might "start your day in ChatGPT, just because it kind of has become the de facto entry point into the commercial web and into a lot of software," but clarified that "our incentive is not to keep you in. Our product is to allow other people to build amazing businesses on top and to evolve the form factor of software."

    The rise of the agents: Building the 'do anything' AI

    If apps are about bringing the world into ChatGPT, the new "Agent Kit" is about sending AI out into the world to get things done. OpenAI is providing a complete "set of building blocks… to help you take agents from prototype to production," Altman explained in his keynote. 

    Agent Kit is an integrated development environment for creating autonomous AI workers. It features a visual canvas to design complex workflows, an embeddable chat interface ("Chat Kit") for deploying agents in any app, and a sophisticated evaluation suite to measure and improve performance.

    A compelling demo from financial operations platform Ramp showed how Agent Kit was used to build a procurement agent. An employee could simply type, "I need five more ChatGPT business seats," and the agent would parse the request, check it against company expense policies, find vendor details, and prepare a virtual credit card for the purchase — a process that once took weeks now completed in minutes. 

    This push into agents is a direct response to a growing enterprise need to move beyond AI as a simple information retrieval tool and toward AI as a productivity engine that automates complex business processes. Brad Lightcap, OpenAI's COO, noted that for enterprise adoption, "you needed this kind of shift to more agentic AI that could actually do things for you, versus just respond with text outputs." 

    The future of code and the Jony Ive bBombshell

    Perhaps the most profound shift is occurring in software development itself. Codex, OpenAI's AI coding agent, has graduated from a research preview to a full-fledged product, now powered by a specialized version of the new GPT-5 model. It is, as one speaker put it, "a teammate that understands your context." 

    The capabilities are staggering. Developers can now assign Codex tasks directly from Slack, and the agent can autonomously write code, create pull requests, and even review other engineers' work on GitHub. A live demo showed Codex taking a simple photo of a whiteboard sketch and turning it into a fully functional, beautifully designed mobile app screen. Another demo showed an app that could "self-evolve," reprogramming itself in real-time based on a user's natural language request. 

    But the day's biggest surprise came in a closing fireside chat, which was not livestreamed, between Altman and Jony Ive, the iconic former chief design officer of Apple. The two revealed they have been collaborating for three years on a new family of AI-centric hardware.

    Ive, whose design philosophy shaped the iPhone, iMac, and Apple Watch, said his creative team’s purpose "became clear" with the launch of ChatGPT. He argued that our current relationship with technology is broken and that AI presents an opportunity for a fundamental reset.

    “I think it would be absurd to assume that you could have technology that is this breathtaking, delivered to us through legacy products, products that are decades old,” Ive said. “I see it as a chance to use this most remarkable capability to full-on address a lot of the overwhelm and despair that people feel right now.”

    While details of the devices remain secret, Ive spoke of his motivation in deeply human terms. “We love our species, and we want to be useful. We think that humanity deserves much better than humanity generally is given,” he said. He emphasized the importance of "care" in the design process, stating, "We sense when people have cared… you sense carelessness. You sense when somebody does not care about you, they care about money and schedule." 

    This collaboration confirms that OpenAI's ambitions are not confined to the cloud; it is actively exploring the physical interface through which humanity will interact with its powerful new intelligence.

    The Unquenchable Thirst for Compute

    Underpinning this entire platform strategy is a single, overwhelming constraint: the availability of computing power. In both the private press conference and the un-streamed Developer State of the Union, OpenAI’s leadership returned to this theme again and again.

    “The degree to which we are all constrained by compute… Everyone is just so constrained on being able to offer the services at the scale required to get the revenue that at this point, we're quite confident we can push it pretty far,” Altman told reporters. He added that even with massive new hardware partnerships with AMD and others, "we'll be saying the same thing again. We're so convinced… There's so much more demand." 

    This explains the company’s aggressive, multi-billion-dollar investment in infrastructure. When asked about profitability, Altman was candid that the company is in a phase of "investment and growth." He invoked a famous quote from Walt Disney, paraphrasing, "We make more money so we can make more movies." For OpenAI, the "movies" are ever-more-powerful AI models.

    Greg Brockman, OpenAI’s President, put the ultimate goal in stark economic terms during the Developer State of the Union. "AI is going to become, probably in the not too distant future, the fundamental driver of economic growth," he said. "Asking ‘How much compute do you want?’ is a little bit like asking how much workforce do you want? The answer is, you can always get more out of more." 

    As the day concluded and developers mingled at the reception, the scale of OpenAI's project came into focus. Fueled by new models like the powerful GPT-5 Pro and the stunning Sora 2 video generator, the company is no longer just building AI. It is building the world where AI will live — a world of intelligent apps, autonomous agents, and new physical devices, betting that in the near future, intelligence itself will be the ultimate platform.

  • OpenAI announces Apps SDK allowing ChatGPT to launch and run third party apps like Zillow, Canva, Spotify

    OpenAI's annual conference for third-party developers, DevDay, kicked off with a bang today as co-founder and CEO Sam Altman announced a new "Apps SDK" that makes it "possible to build apps inside of ChatGPT," including paid apps, which companies can charge users for using OpenAI's recently unveiled Agentic Commerce Protocol (ACP).

    In other words, instead of launching apps one-by-one on your phone, computer, or on the web — now you can do all that without ever leaving ChatGPT.

    This feature allows the user to log-into their accounts on those external apps and bring all their information back into ChatGPT, and use the apps very similarly to how they already do outside of the chatbot, but now with the ability to ask ChatGPT to perform certain actions, analyze content, or go beyond what each app could offer on its own.

    You can direct Canva to make you slides based on a text description, ask Zillow for home listings in a certain area fitting certain requirements, or ask Coursera about a specific lesson's content while dit plays on video, all from within ChatGPT — with many other apps also already offering their own connections (see below).

    "This will enable a new generation of apps that are interactive, adaptive and personalized, that you can chat with," Altman said.

    While the Apps SDK is available today in preview, OpenAI said it would not begin accepting new apps within ChatGPT or allow them to charge users until "later this year."

    ChatGPT in-line app access is already rolling out to ChatGPT Free, Plus, Go and Pro users — outside of the European Union only for now — with Business, Enterprise, and Education tiers expected to receive access to the apps later this year.

    Built atop common MCP standard

    Built on the open source standard Model Context Protocol (MCP) introduced by rival Anthropic nearly a year ago, the Apps SDK gives third-party developers working independently or on behalf of enterprises large and small to connect selected data, "trigger actions, and render a fully interactive UI [user interface]" Altman explained during his introductory keynote speech.

    The Apps SDK includes a "talking to apps" feature that allows ChatGPT and the underlying GPT-5 or other "o-series" models piloting it underneath to obtain updated context from the third-party app or service, so the model "always knows about exactly what you're user is interacting with," according to another presenter and OpenAI engineer, Alexi Christakis.

    Developers can build apps that:

    • appear inline in chat as lightweight cards or carousels

    • expand to fullscreen for immersive tasks like maps, menus, or slides

    • use picture-in-picture for live sessions such as video, games, or quizzes

    Each mode is designed to preserve ChatGPT’s minimal, conversational flow while adding interactivity and brand presence.

    Early integrations with Coursera, Canva, Zillow and more…

    Christakis showed off early integrations of external apps built atop the Apps SDK, including ones from e-learning company Coursera, cloud design software company Canva, and real estate listings and agent connections search engine, Zillow.

    Altman also announced Apps SDK integrations with additional partners not demoed officially during the keynote including: Booking.com, Expedia, Figma and Spotify and in documentation, said more upcoming partners are on deck: AllTrails, Peloton, OpenTable, Target, theFork, and Uber, representing lifestyle, commerce, and productivity categories.

    The Coursera demo included an example of how the user onboards to the external app, including a new login screen for the app (Coursera) that appears within the ChatGPT chat interface, activated simply by a text prompt from the user asking: "Coursera can you teach me something about machine learning"?

    Once logged in, the app launched within the chat interface, "in line" and can render anything from the web, including interactive elements like video.

    Christakis explained and showed the Apps SDK also supports "picture-in-picture" and "fullscreen" views, allowing the user to choose how to interact with it.

    When playing a Coursera video that appeared, he showed that it automatically pinned the video to the top of the screen so the user could keep watching it even as they continued to have a back-and-forth dialog in text with ChatGPT in the typical input/output prompts and responses below.

    Users can then ask ChatGPT about content appearing in the video without specifying exactly what was said, as the Agents SDK pipes the information on the backend, server-side, from the connected app to the underlying ChatGPT AI model. So "can you explain more about what they're saying right now" will automatically surface the relevant portion of the video and provide that to the underlying AI model for it to analyze and respond to through text.

    In another example, Christakis opened an older, existing ChatGPT conversation he'd had about his siblings' dog walking business and resumed the conversation by asking another third-party app, Canva, to generate a poster using one of ChatGPT's recommended business names, "Walk This Wag," along with specific guidance about font choice ("sans serif") and overall coloration and style ("bright and colorful.")

    Instead of the user manually having to go and add all those specific elements to a Canva template, ChatGPT went and issued the commands and performed the actions on behalf of the user in the background.

    After a few minutes, ChatGPT responded with several poster designs generated directly within the Canva app, but displayed them all in the user's ChatGPT chat session where they could see, review, enlarge and provide feedback or ask for adjustments on all of them.

    Christakis then asked for ChatGPT to turn one of the slides into an entire slide deck so the founders of the dog walking business could present it to investors, which did it in the background over several minutes while he presented a final integrated app, Zillow.

    He started a new chat session and asked a simple question: "based on our conversations, what would be a good city to expand the dog walking business."

    Using ChatGPT's optional memory feature, it referenced the dog walk conversation and suggested Pittsburgh, which Christakis used as a chance to type in "Zillow" and "show me some homes for sale there," which called up an interactive map from Zillow with homes for sale and prices listed and hover-over animations, all in-line within ChatGPT.

    Clicking a specific home also opened a fullscreen view with "most of the Zillow experience," entirely without leaving ChatGPT, including the ability to request home tours and contact agents and filtering by bedrooms and other qualities like outdoor space. ChatGPT pulls up the requested filtered Zillow search as well as provides a text-based response in-line explaining what it did and why.

    The user can then ask follow-up questions about the specific property — such as "how close is it to a dog park?" — or compare it to other properties, all within ChatGPT.

    It can also use apps in conjunction with its Search function, searching the web to compare the app information (in this case, Zillow) with other sources.

    Safety, privacy, and developer standards

    OpenAI emphasized that apps must comply with strict privacy, safety, and content standards to be listed in the ChatGPT directory. Apps must:

    • serve a clear and valuable purpose

    • be predictable and reliable in behavior

    • be safe for general audiences, including teens aged 13–17

    • respect user privacy and limit data collection to only what’s necessary

    Every app must also include a clear, published privacy policy, obtain user consent before connecting, and identify any actions that modify external data (e.g., posting, sending, uploading).

    Apps violating OpenAI’s usage policies, crashing frequently, or misrepresenting their capabilities may be removed at any time. Developers must submit from verified accounts, provide customer support contacts, and maintain their apps for stability and compliance.

    OpenAI also published developer design guidelines, outlining how apps should look, sound, and behave. They must follow ChatGPT’s visual system — including consistent color palettes, typography, spacing, and iconography — and maintain accessibility standards such as alt text and readable contrast ratios.

    Partners can show brand logos and accent colors but not alter ChatGPT’s core interface or use promotional language. Apps should remain “conversational, intelligent, simple, responsive, and accessible,” according to the documentation.

    A new conversational app ecosystem

    By opening ChatGPT to third-party apps and payments, OpenAI is taking a major step toward transforming ChatGPT from a chatbot into a full-fledged AI operating system — one that combines conversational intelligence, rich interfaces, and embedded commerce.

    For developers, that means direct access to over 800 million ChatGPT users, who can discover apps “at the right time” through natural conversation — whether planning trips, learning, or shopping.

    For users, it means a new generation of apps you can chat with — where a single interface helps you book a flight, design a slide deck, or learn a new skill without ever leaving ChatGPT.

    As OpenAI put it: “This is just the start of apps in ChatGPT, bringing new utility to users and new opportunities for developers.”

    There remain a few big questions, namely: 1. what happens to all the data from those third-party apps as they interface with ChatGPT and its users…does OpenAI get access to it and can it train upon it? 2. What happens to OpenAI's once much-hyped GPT Store, which had been in the past promoted as a way for third-party creators and developers to create custom, task-specific versions of ChatGPT and make money on them through a usage-based revenue share model?

    We've asked the company about both issues and will update when we hear back.

  • OpenAI unveils AgentKit that lets developers drag and drop to build AI agents

    OpenAI launched an agent builder that the company hopes will eliminate fragmented tools and make it easier for enterprises to utilize OpenAI’s system to create agents.

    AgentKit, announced during OpenAI’s DevDay in San Francisco, enables developers and enterprises to build agents and add chat capabilities in one place, potentially competing with platforms like Zapier.

    By offering a more streamlined way to create agents, OpenAI advances further into becoming a full-stack application provider.

    “Until now, building agents meant juggling fragmented tools—complex orchestration with no versioning, custom connectors, manual eval pipelines, prompt tuning, and weeks of frontend work before launch,” the company said in a blog post.

    AgentKit includes:

    • Agent Builder, which is a visual canvas where devs can see what they’ve created and versioning multi-agent workflows

    • Connector Registry is a central area for admins to manage connections across OpenAI products. A Global Admin console will be a prerequisite to using this feature.

    • ChatKit enables users to integrate chat-based agents into their user interfaces.

    Eventually, OpenAI said it will build a standalone Workflows API and add agent deployment tabs to ChatGPT.

    OpenAI also expanded evaluation for agents, adding capabilities such as datasets with automated graders and annotations, trace grading that runs end-to-end assessments of workflows, automated prompt optimization, and support for third-party agent measurement tools.

    Developers can access some features of AgentKit, but OpenAI is gradually rolling out additional features, such as Agent Builder. Currently, Agent Builder is available in beta, while ChatKit and new evaluation capabilities are generally available. Connector Registry “is beginning its beta rollout to some API and ChatGPT Enterprise and Edu users.

    OpenAI said pricing for AgentKit tools will be included in the standard API model pricing.

    Agent Builder

    To clarify, many agents are built using OpenAI’s models; however, enterprises often access GPT-5 through other platforms to create their own agents. However, AgentKit brings enterprises more into its ecosystem, ensuring they don’t need to tap other platforms as often.

    Demonstrated during DevDay, the company stated that Agent Builder is ideal for rapid iteration. It also provides developers with visibility into how the agents are working.

    During the demo, an OpenAI developer made an agent that reads the DevDay agenda and suggests panels to watch. It took her just under eight minutes.

    Other model providers saw the importance of offering developer toolkits to build agents to entice enterprises to use more of their tools. Google came out with its Agent Development Kit in April, expanding multi-agent system building “in under 100 lines of code.” Microsoft, which runs the popular agent framework AutoGen, announced it is bringing agent creation to one place with its new Agent Framework.

    OpenAI customers, such as the fintech company Ramp, stated in a blog post that its teams were able to build a procurement agent in a few hours instead of months.

    “Agent Builder transformed what once took months of complex orchestration, custom code, and manual optimizations into just a couple of hours. The visual canvas keeps product, legal, and engineering on the same page, slashing iteration cycles by 70% and getting an agent live in two sprints rather than two quarters,” Ramp said.

    AgentKit’s Connector Registry would also enable enterprises to manage and maintain data across workspaces, consolidating data sources into a single panel that spans both ChatGPT and the API. It will have pre-built connectors to Dropbox, Google Drive, SharePoint and Microsoft Teams. It also supports third-party MCP servers.

    Another capability of Agent Builder is Guardrails, an open-source safety layer that protects against the leakage of personally identifiable information (PII), jailbreaks, and unintended or malicious behavior.

    Bringing more chat

    Since most agentic interactions involve chat, it makes sense to simplify the process for developers to set up chat interfaces and connect them with the agents they’ve just built.

    “Deploying chat UIs for agents can be surprisingly complex—handling streaming responses, managing threads, showing the model thinking and designing engaging in-chat experiences,” OpenAI said.

    The company said ChatKit makes it simple to embed chat agents on platforms and embed these into apps or websites.

    However, some OpenAI competitors have begun thinking beyond the chatbot and want to offer agentic interactions that feel more seamless. Google’s asynchronous coding agent, Jules, has introduced a new feature that enables users to interact with the agent through the command-line interface, eliminating the need to open a chat window.

    Responses

    The response to AgentKit has mainly been positive, with some developers noting that while it simplifies agent building, it doesn’t mean that everyone can now build agents.

    Several developers view Agent Kit not as a Zapier killer, but rather as a tool that complements the pipeline.

    Zapier debuted a no-code tool for building AI agents and bots, called Zapier Central, in 2024.

  • Beyond Von Neumann: Toward a unified deterministic architecture

    A cycle-accurate alternative to speculation — unifying scalar, vector and matrix compute

    For more than half a century, computing has relied on the Von Neumann or Harvard model. Nearly every modern chip — CPUs, GPUs and even many specialized accelerators — derives from this design. Over time, new architectures like Very Long Instruction Word (VLIW), dataflow processors and GPUs were introduced to address specific performance bottlenecks, but none offered a comprehensive alternative to the paradigm itself.

    A new approach called Deterministic Execution challenges this status quo. Instead of dynamically guessing what instructions to run next, it schedules every operation with cycle-level precision, creating a predictable execution timeline. This enables a single processor to unify scalar, vector and matrix compute — handling both general-purpose and AI-intensive workloads without relying on separate accelerators.

    The end of guesswork

    In dynamic execution, processors speculate about future instructions, dispatch work out of order and roll back when predictions are wrong. This adds complexity, wastes power and can expose security vulnerabilities. Deterministic Execution eliminates speculation entirely. Each instruction has a fixed time slot and resource allocation, ensuring it is issued at exactly the right cycle.

    The mechanism behind this is a time-resource matrix: A scheduling framework that orchestrates compute, memory and control resources across time. Much like a train timetable, scalar, vector and matrix operations move across a synchronized compute fabric without pipeline stalls or contention.

    Why it matters for enterprise AI

    Enterprise AI workloads are pushing existing architectures to their limits. GPUs deliver massive throughput but consume enormous power and struggle with memory bottlenecks. CPUs offer flexibility but lack the parallelism needed for modern inference and training. Multi-chip solutions often introduce latency, synchronization issues and software fragmentation.

    In large AI workloads, datasets often cannot fit into caches, and the processor must pull them directly from DRAM or HBM. Accesses can take hundreds of cycles, leaving functional units idle and burning energy. Traditional pipelines stall on every dependency, magnifying the performance gap between theoretical and delivered throughput.

    Deterministic Execution addresses these challenges in three important ways. First, it provides a unified architecture in which general-purpose processing and AI acceleration coexist on a single chip, eliminating the overhead of switching between units. Second, it delivers predictable performance through cycle-accurate execution, making it ideal for latency-sensitive applications such as large langauge model (LLM) inference, fraud detection and industrial automation. Finally, it reduces power consumption and physical footprint by simplifying control logic, which in turn translates to a smaller die area and lower energy use.

    By predicting exactly when data will arrive — whether in 10 cycles or 200 — Deterministic Execution can slot dependent instructions into the right future cycle. This turns latency from a hazard into a schedulable event, keeping the execution units fully utilized and avoiding the massive thread and buffer overheads used by GPUs or custom VLIW chips. In modeled workloads, this unified design delivers sustained throughput on par with accelerator-class hardware while running general-purpose code, enabling a single processor to fulfill roles typically split between a CPU and a GPU.

    For LLM deployment teams, this means inference servers can be tuned with precise performance guarantees. For data infrastructure managers, it offers a single compute target that scales from edge devices to cloud racks without major software rewrites.

    Comparison of traditional Von Neumann architecture and unified deterministic execution. Image created by author.

    Key architectural innovations

    Deterministic Execution builds on several enabling techniques. The time-resource matrix orchestrates compute and memory resources in fixed time slots. Phantom registers allow pipelining beyond the limits of the physical register file. Vector data buffers and extended vector register sets make it possible to scale parallel processing for AI operations. Instruction replay buffers manage variable-latency events predictably, without relying on speculation.

    The architecture’s dual-banked register file doubles read/write capacity without the penalty of more ports. Direct queuing from DRAM into the vector load/store buffer halves memory accesses and removes the need for multi-megabyte SRAM buffers — cutting silicon area, cost and power.

    In modeled AI and DSP kernels, conventional designs issue a load, wait for it to return, then proceed — causing the entire pipeline to idle. Deterministic Execution pipelines loads and dependent computations in parallel, allowing the same loop to run without interruption, cutting both execution time and joules per operation.

    Together, these innovations create a compute engine that combines the flexibility of a CPU with the sustained throughput of an accelerator, without requiring two separate chips.

    Implications beyond AI

    While AI workloads are an obvious beneficiary, Deterministic Execution has broad implications for other domains. Safety-critical systems — such as those in automotive, aerospace and medical devices — can benefit from deterministic timing guarantees. Real-time analytics systems in finance and operations gain the ability to operate without jitter. Edge computing platforms, where every watt of power matters, can operate more efficiently.

    By eliminating guesswork and enforcing predictable timing, systems built on this approach become easier to verify, more secure and more energy-efficient.

    Enterprise impact

    For enterprises deploying AI at scale, architectural efficiency translates directly into competitive advantage. Predictable, latency-free execution simplifies capacity planning for LLM inference clusters, ensuring consistent response times even under peak loads. Lower power consumption and reduced silicon footprint cut operational expenses, especially in large data centers where cooling and energy costs dominate budgets. In edge environments, the ability to run diverse workloads on one chip reduces hardware SKUs, shortens deployment timelines and minimizes maintenance complexity.

    A path forward for enterprise computing

    The shift to Deterministic Execution is not merely about raw performance; it represents a return to architectural simplicity, where one chip can serve multiple roles without compromise. As AI permeates every sector, from manufacturing to cybersecurity, the ability to run diverse workloads predictably on a single architecture will be a strategic advantage.

    Enterprises evaluating infrastructure for the next five to 10 years should watch this development closely. Deterministic Execution has the potential to reduce hardware complexity, cut power costs and simplify software deployment — while enabling consistent performance across a wide range of applications.

    Thang Minh Tran is a microprocessor architect and inventor of more than 180 patents in CPU and accelerator design.

  • Replacing coders with AI? Why Bill Gates, Sam Altman and experience say you shouldn’t.

    In the race to automate everything – from customer service to code – AI is being heralded as a silver bullet. The narrative is seductive: AI tools that can write entire applications, streamline engineering teams and reduce the need for expensive human developers, along with hundreds of other jobs. 

    But from my point of view as a technologist who spends every day inside real companies’ data and workflows, the hype doesn’t match up with the reality. 

    I’ve worked with industry leaders like General Electric, The Walt Disney Company and Harvard Medical School to optimize their data and AI infrastructure, and here’s what I’ve learned: Replacing humans with AI in most jobs is still just an idea on the horizon. 

    I worry that we're thinking too far ahead. In the past two years, more than a quarter of programming jobs have vanished. Mark Zuckerberg announced he is planning to replace many of Meta’s coders with AI. 

    But, intriguingly, both Bill Gates and Sam Altman have publicly warned against replacing coders. 

    Right now, we shouldn’t count on AI tools to successfully replace jobs in tech or business. That’s because what AI knows is inherently limited by what it has seen – and most of what it has seen in the tech world is boilerplate.

    Generative AI models are trained on large datasets, which typically fall into two main categories: publicly available data (from the open internet), or proprietary or licensed data (created in-house by the organization, or purchased from third parties). 

    Simple tasks, like building a basic website or configuring a template app, are easy wins for generative models. But when it comes to writing the sophisticated, proprietary infrastructure code that powers companies like Google or Stripe, there’s a problem: That code doesn’t exist in public repositories. It’s locked away inside the walls of corporations, inaccessible to training data and often written by engineers with decades of experience.

    Right now, AI can’t reason on its own yet. And it doesn’t have instincts. It’s just mimicking patterns. A friend of mine in the tech world once described large language models (LLMs) as a "really good guesser." 

    Think of AI today as a junior team member — helpful for a first draft or simple projects. But like any junior, it requires oversight. In programming, for example, while I’ve found a 5X improvement for simple coding, I’ve found that reviewing and correcting more complicated AI-produced code often takes more time and energy than writing the code myself. 

    You still need senior professionals with deep experience to find the flaws, and to understand the nuances of how those flaws might pose a risk six months from now. 

    That’s not to say AI shouldn’t have a place in the workplace. But the dream of replacing entire teams of programmers or accountants or marketers with one human and a host of AI tools is far premature. We still need senior-level people in these jobs, and we need to train people in junior-level jobs to be technically capable enough to assume the more complex roles one day. 

    The goal of AI in tech and business shouldn’t be about removing humans from the loop. I’m not saying this because I’m scared AI will take my job. I’m saying it because I’ve seen how dangerous trusting AI too much at this stage can be. 

    Business leaders, no matter what industry they’re in, should be aware: While AI promises cost savings and smaller teams, these efficiency gains could backfire. You might trust AI to perform more junior levels of work, but not to complete more sophisticated projects. 

    AI is fast. Humans are smart. There’s a big difference. The sooner we shift the conversation from replacing humans to reinforcing them, the more we’ll reap the benefits of AI. 

    Derek Chang is founding partner of Stratus Data.

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    This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. EV tax credits are dead in the US. Now what? Federal EV tax credits in the US officially came to an end yesterday. Those credits, expanded and extended in the 2022 Inflation Reduction…

  • Unlocking AI’s full potential requires operational excellence

    Talk of AI is inescapable. It’s often the main topic of discussion at board and executive meetings, at corporate retreats, and in the media. A record 58% of S&P 500 companies mentioned AI in their second-quarter earnings calls, according to Goldman Sachs. But it’s difficult to walk the talk. Just 5% of generative AI pilots…

  • The Download: OpenAI’s caste bias problem, and how AI videos are made

    This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. OpenAI is huge in India. Its models are steeped in caste bias. Caste bias is rampant in OpenAI’s products, including ChatGPT, according to an MIT Technology Review investigation. Though CEO Sam Altman boasted…