Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders.<\/em> Subscribe Now<\/em><\/p>\n\n\n\n Here\u2019s an analogy: Freeways didn\u2019t exist in the U.S. until after 1956, when envisioned by President Dwight D. Eisenhower\u2019s administration \u2014 yet super fast, powerful cars like Porsche, BMW, Jaguars, Ferrari and others had been around for decades.\u00a0<\/p>\n\n\n\n You could say AI is at that same pivot point: While models are becoming increasingly more capable, performant and sophisticated, the critical infrastructure they need to bring about true, real-world innovation has yet to be fully built out.\u00a0<\/p>\n\n\n\n \u201cAll we have done is create some very good engines for a car, and we are getting super excited, as if we have this fully functional highway system in place,\u201d Arun Chandrasekaran, Gartner distinguished VP analyst, told VentureBeat.\u00a0<\/p>\n\n\n\n This is leading to a plateauing, of sorts, in model capabilities such as OpenAI\u2019s GPT-5: While an important step forward, it only features faint glimmers of truly agentic AI. <\/p>\n\n\n\n Power caps, rising token costs, and inference delays are reshaping enterprise AI. Join our exclusive salon to discover how top teams are:<\/p>\n\n\n\n Secure your spot to stay ahead<\/strong>: https:\/\/bit.ly\/4mwGngO<\/p>\n\n\n\n \u201cIt is a very capable model, it is a very versatile model, it has made some very good progress in specific domains,\u201d said Chandrasekaran. \u201cBut my view is it\u2019s more of an incremental progress, rather than a radical progress or a radical improvement, given all of the high expectations OpenAI has set in the past.\u201d\u00a0<\/p>\n\n\n\n To be clear, OpenAI has made strides with GPT-5, according to Gartner, including in coding tasks and multi-modal capabilities.\u00a0<\/p>\n\n\n\n Chandrasekaran pointed out that OpenAI has pivoted to make GPT-5 \u201cvery good\u201d at coding, clearly sensing gen AI\u2019s enormous opportunity in enterprise software engineering and taking aim at competitor Anthropic\u2019s leadership in that area.\u00a0<\/p>\n\n\n\n Meanwhile, GPT-5\u2019s progress in modalities beyond text, particularly in speech and images, provides new integration opportunities for enterprises, Chandrasekaran noted.\u00a0<\/p>\n\n\n\n GPT-5 also does, if subtly, advance AI agent and orchestration design, thanks to improved tool use; the model\u00a0can call third-party APIs and tools and perform parallel tool calling (handle multiple tasks simultaneously). However, this means enterprise systems must have the capacity to handle concurrent API requests in a single session, Chandrasekaran points out. <\/p>\n\n\n\n Multistep planning in GPT-5 allows more business logic to reside within the model itself, reducing the need for external workflow engines, and its larger context windows (8K for free users, 32K for Plus at $20 per month and 128K for Pro at $200 per month) can \u201creshape enterprise AI architecture patterns,\u201d he said.\u00a0<\/p>\n\n\n\n This means that applications that previously relied on complex retrieval-augmented generation (RAG) pipelines to work around context limits can now pass much larger datasets directly to the models and simplify some workflows. But this doesn\u2019t mean RAG is irrelevant; \u201cretrieving only the most relevant data is still faster and more cost-effective than always sending massive inputs,\u201d Chandrasekaran pointed out.\u00a0<\/p>\n\n\n\n Gartner sees a shift to a hybrid approach with less stringent retrieval, with devs using GPT-5 to handle \u201clarger, messier contexts\u201d while improving efficiency.\u00a0<\/p>\n\n\n\n On the cost front, GPT-5 \u201csignificantly\u201d reduces API usage fees; top-level costs are $1.25 per 1 million input tokens and $10 per 1 million output tokens, making it comparable to models like Gemini 2.5, but seriously undercutting Claude Opus. However, GTP-5\u2019s input\/output price ratio is higher than earlier models, which AI leaders should take into account when considering GTP-5 for high-token-usage scenarios, Chandrasekaran advised.\u00a0<\/p>\n\n\n\n Ultimately, GPT-5 is designed to eventually replace GPT-4o and the o-series (they were initially sunset, then some reintroduced by OpenAI due to user dissent). Three model sizes (pro, mini, nano) will allow architects to tier services based on cost and latency needs; simple queries can be handled by smaller models and complex tasks by the full model, Gartner notes.\u00a0<\/p>\n\n\n\n However, differences in output formats, memory and function-calling behaviors may require code review and adjustment, and because GPT-5 may render some previous workarounds obsolete, devs should audit their prompt templates and system instructions. <\/p>\n\n\n\n By eventually sunsetting previous versions, \u201cI think what OpenAI is trying to do is abstract that level of complexity away from the user,\u201d said Chandrasekaran. \u201cOften we\u2019re not the best people to make those decisions, and sometimes we may even make erroneous decisions, I would argue.\u201d<\/p>\n\n\n\n Another fact behind the phase-outs: \u201cWe all know that OpenAI has a capacity problem,\u201d he said, and thus has forged partnerships with Microsoft, Oracle (Project Stargate), Google and others to provision compute capacity. Running multiple generations of models would require multiple generations of infrastructure, creating new cost implications and physical constraints.\u00a0<\/p>\n\n\n\n OpenAI claims it reduced hallucination rates by up to 65% in GPT-5 compared to previous models; this can help reduce compliance risks and make the model more suitable for enterprise use cases, and its chain-of-thought (CoT) explanations support auditability and regulatory alignment, Gartner notes.\u00a0<\/p>\n\n\n\n At the same time, these lower hallucination rates as well as GPT-5\u2019s advanced reasoning and multimodal processing could amplify misuse such as advanced scam and phishing generation. Analysts advise that critical workflows remain under human review, even if with less sampling.\u00a0<\/p>\n\n\n\n The firm also advises that enterprise leaders:\u00a0<\/p>\n\n\n\n No doubt, agentic AI is a \u201csuper hot topic today,\u201d Chandrasekaran noted, and is one of the top areas for investment in Gartner\u2019s 2025 Hype Cycle for Gen AI. At the same time, the technology has hit Gartner\u2019s \u201cPeak of Inflated Expectations,\u201d meaning it has experienced widespread publicity due to early success stories, in turn building unrealistic expectations.\u00a0<\/p>\n\n\n\n This trend is typically followed by what Gartner calls the \u201cTrough of Disillusionment,\u201d when interest, excitement and investment cool off as experiments and implementations fail to deliver (remember: There have been two notable AI winters since the 1980s).\u00a0<\/p>\n\n\n\n \u201cA lot of vendors are hyping products beyond what products are capable of,\u201d said Chandrasekaran. \u201cIt\u2019s almost like they\u2019re positioning them as being production-ready, enterprise-ready and are going to deliver business value in a really short span of time.\u201d\u00a0<\/p>\n\n\n\n However, in reality, the chasm between product quality relative to expectation is wide, he noted. Gartner isn\u2019t seeing enterprise-wide agentic deployments; those they are seeing are in \u201csmall, narrow pockets\u201d and specific domains like software engineering or procurement.<\/p>\n\n\n\n \u201cBut even those workflows are not fully autonomous; they are often either human-driven or semi-autonomous in nature,\u201d Chandrasekaran explained.\u00a0<\/p>\n\n\n\n One of the key culprits is the lack of infrastructure; agents require access to a wide set of enterprise tools and must have the capability to communicate with data stores and SaaS apps. At the same time, there must be adequate identity and access management systems in place to control agent behavior and access, as well as oversight of the types of data they can access (not personally identifiable or sensitive), he noted.\u00a0<\/p>\n\n\n\n Lastly, enterprises must be confident that the information the agents are producing is trustworthy, meaning it\u2019s free of bias and doesn\u2019t contain hallucinations or false information.\u00a0<\/p>\n\n\n\n To get there, vendors must collaborate and adopt more open standards for agent-to-enterprise and agent-to-agent tool communication, he advised. <\/p>\n\n\n\n \u201cWhile agents or the underlying technologies may be making progress, this orchestration, governance and data layer is still waiting to be built out for agents to thrive,\u201d said Chandrasekaran. \u201cThat\u2019s where we see a lot of friction today.\u201d<\/p>\n\n\n\n Yes, the industry is making progress with AI reasoning, but still struggles to get AI to understand how the physical world works. AI mostly operates in a digital world; it doesn\u2019t have strong interfaces to the physical world, although improvements are being made in spatial robotics.\u00a0<\/p>\n\n\n\n But, \u201cwe are very, very, very, very early stage for those kinds of environments,\u201d said Chandrasekaran.\u00a0<\/p>\n\n\n\n To truly make significant strides requires a \u201crevolution\u201d in model architecture or reasoning. \u201cYou cannot be on the current curve and just expect more data, more compute, and hope to get to AGI,\u201d she said.\u00a0<\/p>\n\n\n\n That\u2019s evident in the much-anticipated GPT-5 rollout: The ultimate goal that OpenAI defined for itself was AGI, but \u201cit\u2019s really apparent that we are nowhere close to that,\u201d said Chandrasekaran. Ultimately, \u201cwe\u2019re still very, very far away from AGI.\u201d<\/p>\n Daily insights on business use cases with VB Daily<\/strong><\/p>\n If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI.<\/p>\n \n\t\t\t\t\tThanks for subscribing. Check out more VB newsletters here.\n\t\t\t\t<\/p>\n An error occured.<\/p>\n<\/p><\/div>\n
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\n<\/div>GPT-5 improves in three key areas<\/h2>\n\n\n\n
Bye-bye previous GPT versions (sorta)<\/h2>\n\n\n\n
New risks, advice for adopting GPT-5<\/h2>\n\n\n\n
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Agents don\u2019t just need more compute; they need infrastructure<\/h2>\n\n\n\n
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