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pected to collapse 90 from 73 billion to 8 billion these companies used to be the greatest cash machines ever built now they re borrowing money to keep the data center lights on and what are they getting for that 650 billion ai services generate roughly 35 billion in total revenue or 5 of what s being spent on infrastructure there are dreams of more of course but the business models of ai have yet to resonate especially for consumers apple didn t miss the ai revolution it just bet that the winners won t be the ones who build the infrastructure they ll be the ones who own the customer and no one else on earth owns the best customers apple owns the best customers because it makes the best devices thanks to its integration of hardware and software and as i recounted above it is somehow fifty years on the only company of its kind there is however an emerging threat that apple is seeking to head off again from bloomberg apple inc awarded rare bonuses to iphone hardware designers this week aiming to stem a wave of departures to ai startups like openai that are building their own devices the company granted out of cycle bonuses worth several hundred thousand dollars to many members of its iphone product design team according to people with knowledge of the matter apple s leadership has grown increasingly concerned about the number of engineers being poached by potential rivals openai which has tapped former apple design chief jony ive to help design a new generation of ai centric products has emerged as a particular threat openai s hardware division is run in part by apple veteran tang tan he used to oversee the iphone product design team that s receiving the bonuses tan s group at openai has hired several dozen apple engineers and not just ones who worked on the iphone the startup has lured employees who helped develop the ipad apple watch and vision pro openai isn t just hiring designers the company is also building out operations capabilities to be able to actually make the upcoming ive designed device at scale presumably in china still many are wondering about the status of openai s hardware device given the news about sora from the wall street journal openai is planning to pull the plug on its sora video platform a product it released to great fanfare last year that has since fallen from public view the move is one of a number of steps openai is taking to refocus on business and coding functions ahead of a potential initial public offering as soon as the fourth quarter of this year ceo sam altman announced the changes to staff on tuesday writing that the company would wind down products that use its video models in addition to the consumer app openai is also discontinuing a version of sora for developers and won t support video functionality inside chatgpt either openai is in the middle of a strategy shift to redirect the company s computing resources and top talent toward so called productivity tools that can be used by both enterprises and individual users last week openai announced that it was combining its chatgpt desktop app coding tool codex and browser into one superapp the company expects the consolidated product to align its employees around a single vision in fact cutting sora but keeping the hardware initiative fits this strategy shift sora along with the also indefinitely delayed adult mode were products that drive more attention which lends itself to the more traditional consumer business model of advertising productivity on the other hand is a much better fit for enterprise where anthropic is making major gains the problem however is that most consumers aren t willing to pay for software what they are willing to pay for are devices this was the secret of the iphone from 2016 s everything as a service apple has arguably perfected the manufacturing model most of the company s corporate employees are employed in california in the design and marketing of iconic devices that are created in chinese factories built and run to apple s exacting standards including a substantial number of employees on site and then transported all over the world to consumers eager for best in class smartphones tablets computers and smartwatches what makes this model so effective and so profitable is that apple has differentiated its otherwise commoditizable hardware with software software is a completely new type of good in that it is both infinitely differentiable yet infinitely copyable this means that any piece of software is both completely unique yet has unlimited supply leading to a theoretical price of 0 however by combining the differentiable qualities of software with hardware that requires real assets and commodities to manufacture apple is able to charge an incredible premium for its products openai is approaching this space from the opposite direction it has a massive consumer user base for chatgpt and an impressively large number of subscribers it is also adding advertising however to truly monetize consumers the most attractive business model is the apple model integrated hardware and software apple s real ai threat the truth is that apple s lack of investment in ai was always going to be a short to medium term win the company doesn t have to spend on infrastructure and everyone still needs a device the real threat is in the long term what happens if ai becomes so good that it obviates traditional user interfaces or to put it another way what if the point of integration that is most compelling is not a traditional operating system and hardware device but rather ai and a dedicated device if this threat materializes it won t be with openai s initial offering the smartphone is the ultimate form factor and does so many jobs that depend on its flexibility and capability and 3rd party ecosystem that no new entrant could hope to compete indeed google and android is arguably a bigger threat for this reason however just how capable might ai be not just next year but in five years or ten years if ever a better interaction paradigm were to succeed the smartphone surely it will be rooted in ai and apple by giving up now won t be in the game this absolutely is not a prediction indeed if i had to bet i would bet on apple keeping its place first there is the likelihood that the smartphone thanks to its screen connectivity and battery life is in fact the best device for ai and that furthermore ai will be just one capability alongside everything a smartphone already does second to the extent that ai inference moves to the edge apple has a big advantage thanks to its industry leading chips third apple always has the option of opening up its devices to allow for much deeper integration with 3rd party ai providers other than openai in order to effectively fight off a potential threat it s also worth noting that openai has in its relatively short life managed to frame itself as a competitor to basically everyone in tech from google to meta to microsoft only to find itself forced to pivot in the face of anthropic and its focused approach on coding and productivity in the enterprise the audacity of taking on everyone is impressive the effectiveness of fighting everyone for everything may be less so still there is an angle here for openai and a point of vulnerability for apple the company made it fifty years with no one truly competing with its integrated business model the fate of its next fifty years may rest on the question of just how compelling ai ends up being and if openai can out apple the original agents over bubbles monday march 16 2026 listen to podcast watch on youtube listen to this post log in to listen there is a weird paradox in terms of ai prognostication on one hand you don t want to be the one to completely dismiss the most terrifying doomsday scenarios who wants to be found out to be foolishly optimistic at the same time there is also pressure to give credence to the possibility that we are in a bubble and all of this hype and spending is going to go belly up while i have argued against the former i have very much been on board with the latter making the case that bubbles can be good sitting here in march 2026 however on the morning of nvidia s gtc i ve come to a different conclusion i don t think we re in a bubble which paradoxically maybe is the truest evidence we are llm paradigms over the last couple of weeks first in the context of nvidia s earnings and then last week in the context of oracle s i ve talked about three llm inflection points chatgpt the first llm inflection point was the november 2022 launch of chatgpt which hardly needs an explanation yes transformer based large language models were introduced in 2017 and the capabilities were both impressive and growing but under appreciated stratechery started an interview series with daniel gross and nat friedman in october 2022 under the premise that there was an incredible new technology that was sorely lacking for product applications and startup energy needless to say that was entirely flipped on its head just weeks later chatgpt opened the eyes of the world to what llms were capable of but the initial versions had two flaws that have stuck in many people s minds particularly those convinced that we are in a bubble the first flaw is that llms frequently got things wrong and worse would hallucinate when it didn t know the answer this made llms feel like something of a parlor trick amazing when they work but not something that you can count on the second was related to the first even in that flawed state llms were tremendously useful but you needed to have an idea of what to use them for and you needed to proactively take care to manage mistakes and verify the output in case it was hallucinated o1 the second llm inflection point was the release of openai s o1 model in september 2024 by that point llms had improved tremendously both thanks to new foundation models and also because of continued improvements in post training that meant that the stream of tokens that constituted an answer in chatgpt or claude was now much more likely to be right and they were somewhat less likely to hallucinate what made o1 different however was that it reasoned over its answer before delivering it to you i explained in an update at the time the big challenge for traditional llms is that they are path dependent while they can consider the puzzle as a whole as soon as they commit to a particular guess they are locked in and doomed to failure this is a fundamental weakness of what are known as auto regressive large language models which to date is all of them reasoning models self evaluate they work through an answer and then consider if the answer is correct or if they should consider other alternatives to put it in terms of the weaknesses i identified above they were internally proactive in terms of managing mistakes reducing the burden on the user to continually actively guide the llm and the results were remarkable from my perspective if the brilliance of chatgpt was in making llms much more readable and useful the brilliance of o1 was in making llms much more reliable and essential opus 4 5 anthropic released opus 4 5 on november 24 2025 to relatively little fanfare then at some point in december claude code with opus 4 5 suddenly seemed to be able to do things that were never possible previously openai released gpt 5 2 codex around the same time on december 18 and it was similarly capable people had been talking about agents for a while suddenly however both claude and codex were actually accomplishing tasks some of which took hours and doing them correctly that bit about the opus 4 5 model s release date is interesting however the key thing about agentic workloads is that they are about more than the model or using the model recursively like o1 rather a critical component of making agentic workloads work is the harness i e the software that actually controls the model to put it another way claude code and openai s codex actually abstract the user away from the model you give instructions to an agent which actually directs the model critically the agent can also use other deterministic tools as well which means that it can verify its results to put it in the context of coding in paradigm one an llm would generate code in paradigm two an llm would think about the code it was generating and iterate towards a better answer in this paradigm an agent directs a model to generate code then checks to see if the code actually works and if it doesn t tries again all without the user needing to be involved in other words many of the biggest flaws from the original chatgpt have been substantially mitigated at least for verifiable use cases like coding llms are much more likely to be right the first time they reason over their results to increase their chances and now agents actively verify the results without humans needing to be in the loop that leaves one flaw actually figuring out what to use these for the decreased need for agency the reason i ve been writing about these three inflection points over the last couple of weeks has been to explain why it is that the industry is so compute constrained and why the massive investment in capex by the hyperscalers is justified the first paradigm required a lot of compute for training but inference actually answering a question was relatively efficient you simply sent the user whatever the model spit out the second paradigm dramatically increased the amount of computing needed for inference for two reasons first generating an answer required a lot more tokens because all of the reasoning required tokens in addition to the answer itself second the fact that reasoning made the models so much more useful meant that they were used more which drove increased token usage in its own right it s the third paradigm however that has truly tipped the scales in favor of capex expenditure not being speculative investment but rather badly needed investment in meeting demand that far exceeds supply first generating an answer will often entail multiple calls to a reasoning model second the agent itself needs compute and that compute and the tools the agent uses is better done by cpus than gpus third agents are another step function increase in usefulness which means they are going to be used even more than even reasoning models in a chatbot it s how this third point will be manifested that i think is under appreciated after all far more people use chatbots than use agents and i would make the case that most people are not using chatbots as much as they should it s been a question of agency to get the most from ai requires actually taking the initiative to use ai i wrote in 2024 s mkbhd s for everything large language models are intelligent but they do not have goals or values or drive they are tools to be used by well anyone who is willing and able to take the initiative to use them i don t think either brownlee or i particularly need ai or to put it another...
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