Uncertainty is escalating. From the White House to the claims made by AI company DeepSeek, predicting the future has never been more difficult. We see potential for unintended side-effects of US policy creating opportunities for China to increase its global influence; but, at the same time, how the US conducts itself over Ukraine will impact events both in Korea and Taiwan. None of this is helped by the huge boost to China’s reputation as an AI powerhouse provided by DeepSeek. However, on closer examination, we find that Beijing’s claims are substantially overstated. Against this backdrop, we see further restrictions related to AI exports and an acceleration of the bifurcation of the technology industry into 2 separate camps as China pushes harder for technology independence. Those in the middle will be forced to choose.

• Chaos and opportunity: Trump’s deliberately chaotic approach to governance has thrown up proposals in a range of policy areas which run counter to maintaining a tech lead over China.
• Tariffs & withdrawals: The imposition of tariffs on allies as well as policies like the watering down of anti-trust enforcement may help China secure an advantage over the US.
• Russia, Ukraine & Taiwan: The Trump administration’s stance on Russia/Ukraine will have an impact on events in both the Korean peninsula and the Taiwan Strait. Furthermore, changes to the US factsheet on Taiwan could indicate Washington’s preparedness to use it as a bargaining chip in a bigger game with China.
• DeepSeek: shocked the world in being able to match the performance of Western competitors but is not novel in terms of performance or intelligence.
• DeepSeek efficiencies are where the real surprises lie. DeepSeek’s claims seem to suggest that it is many orders of magnitude cheaper to both train and run inference than Western peers.
• The reality: RFM’s analysis suggests that DeepSeek’s claims are misleading and that the reality is that DeepSeek has something more like a 2x-4x advantage in training (rather than 100x) and a 0x – 10x advantage in inference depending on the task being inferenced.
• DeepSeek’s IP: is in its methods not the models it has placed in open source. This is how we think DeepSeek was able to obtain an export license for R1 from the Chinese state.
• China’s motivations: DeepSeek is operating with state approval leading to the question of China’s motivations. RFM sees three possibilities with PR and propaganda being the most likely where we think China has succeeded way beyond its own expectations.
• Build the wall 2.0. The now unfolding ideologically-driven bifurcation of the Internet will accelerate the ongoing shift from global standards to one for China, and one for the West with everyone else being forced to pick a side. Long-term growth will suffer as a consequence.

Selling a car has never made economic sense for Apple but selling an infotainment unit does. It also gives Apple the ability to monetise digital services in the vehicle at the expense of the OEM. Apple has found a way to safely run the instrument cluster on the iPhone opening the way for a complete takeover of the digital cockpit. This will exclude OEMs from the digital vehicle leaving them with very little with which to offset revenue losses from lower vehicle shipments.

• Apple has become the largest threat. Until 2022, Apple was non-threatening to the OEMs as it was using CarPlay primarily to drive loyalty to its ecosystem. With its 2022 move into the instrument cluster, Apple signalled an intent to run the car on the phone.
• Reliability and safety. The problem is that the smartphone is not reliable enough to be considered safe to run critical vehicle systems such as the instrument cluster, presenting Apple with a problem. The solution to this problem was presented at WWDC 2024
• CarPlay 3.0. is how Apple can take control of the instrument cluster via the iPhone and still meet the reliability and safety requirements of the automotive industry. Apple splits the instrument cluster into 4 layers and renders the vital layers with Apple software and hardware on the infotainment unit while running non-critical layers from the iPhone. These are amalgamated by Apple software on the infotainment and then passed to the OEM system to add overlay items like indicators and hazard warnings.
• Software Development: is executed by the OEM using an Apple software development kit (SDK). Apple offers a lot of flexibility in terms of user experience elements but at the end of the day, the instrument cluster is clearly an Apple product with OEM customisations. This greatly reduces the OEM’s digital relevance in the vehicle and threatens to cut them out from the vehicle digital services economy entirely.
• Consumer preferences. Apple has stated that 79% of US consumers will only consider buying CarPlay-enabled vehicles but a 2023 survey from McKinsey puts the figure at closer to 40%. Regardless of which figure is most accurate, Apple has already gained a meaningful amount of market power which could be enough to convince the OEMs to integrate more Apple technology into their vehicles.
• Infotainment takeover. The logical end game for Apple is to have the OEM buy the entire infotainment unit from Apple and integrate it into their vehicles. Unlike the Apple Car, this is a viable business model that would fit well with Apple’s other businesses and give Apple the ability to monetise digital vehicle services at the expense of the vehicle maker. From an OEM perspective, this is best avoided.

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It turns out that AI needs a lot more than attention to create hyper-intelligent machines. RFM believes that the missing link is causality which means that the machines still have no understanding of anything that they do. This prevents the development of any form of AGI and there is no sign that the causality problem will be fixed anytime soon. However, even without causality, there are many use cases for AI-enabled by the advances in language performance and the ability to make sense of unstructured data. Unfortunately, this is not enough to meet the hype that surrounds AI and so some form of correction is needed to bring expectations and reality back into balance.

• What AI is: AI describes a range of technologies that enable machines to make decisions. They range from statistics at one end, through rules-based software, deep learning and now, generative AI. Generative AI is a subset of AI and is defined as AI that is capable of generating content such as words, images and now, video.
• What AI is not: Large Language Models (LLMs) have enabled machines to converse in natural language which has led to an extraordinary increase in anthropomorphism. This is the attribution of human characteristics to non-human objects which in this case are statistics-based algorithms. It is here where expectations for AI and reality begin to diverge.
• Causality: is by far the biggest limitation of generative AI as these models have no causal understanding of what it is that they are doing. This is what causes the machines to invent facts, make simple mistakes and remain unaware that they are doing so.
• Reasoning: this is a hot area of debate where owners of models claim they can reason, and the sceptics disagree. Reasoning is crucial because it will be the first sign of LLMs being able to understand causality. The balance of empirical evidence suggests that the machines remain unable to reason and there is no evidence to suggest that it will be solved anytime soon.
• Use cases: despite the limitations, RFM sees many use cases given that generative AI represents a large step forward in both the ability to use natural language and data characterisation, storage and retrieval. These use cases do not replace humans but make them more productive meaning that the workforce is not going to be replaced although some adaption will be needed in many industries.
• AI bubble: There is little doubt that valuations and expectations are too high. The flood of capital into generative AI has been driven by intense public excitement. The result is becoming a flood of supply of LLM services all of which are roughly equivalent in terms of performance meaning competition and falling prices. It is this that RFM expects will trigger the reset to reality. It is important to note that this reset will not be nearly as harsh as autonomous driving because generative AI has products now where autonomous driving still has nothing.

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The bubble remains inflated but there are signs that generative AI is not taking over as had been forecast. The economics of AI remain very good even if prices fall by 92% but this means the industry would be 1/12^th of the size previously envisaged. This combined with ongoing problems with AI’s reliability and veracity is likely to cause the bubble to pop and reset expectations to reality.

• AI Bubble: There is very little doubt that AI is currently in a bubble as super-intelligent machines are as far off today as they were 10 years ago. However, generative AI does offer genuine improvements in abilities that have a large number of revenue-generating use cases.
• Usage & search: The first sign is that usage of ChatGPT has stopped growing and it is having no impact on the search market. This is not the trajectory of a revolution and disruption.
• AI economics: $20 per user/month is the current benchmark and at this price, a lot of money will be made. However, RFM thinks that the ever-increasing number of models will mean that the price becomes something closer to $20 per user/year, a decline of 92%. At this price monetisation via advertising becomes a realistic option and many may choose this route.
• The pin: Price erosion of 92% is likely to collapse the business plans of most generative AI start-ups meaning that they will need to raise more money after having missed their targets. This will trigger down rounds and falling valuations causing the bubble to deflate. This will look very similar to what happened with autonomous driving and bike sharing.
• Inference at the edge: is a no-brainer in RFM’s opinion as the economics are far better for the service provider. This is because the service provider does not have to support inference as it is executed on the user’s device.
• Privacy and security: inference at the edge also offers better security and privacy options which are likely to become crucial in the provision of LLM-powered services.
• Customisation: Running models on devices also raises the possibility of substantial customisation where a model on the user’s device is finely tuned to that user’s requirements and preferences.
• Black box & entanglement: are old problems with deep learning and are becoming real issues with generative AI. This is because no one really knows how generative AI does what it does or can promise that it won’t go crazy. This causes real problems when regulators are demanding verifiability and guarantees that models won’t go off the rails.
• Deflation: There is a large revenue opportunity to be had but expectations and valuations need to be adjusted downwards as super-intelligent AI looks to be as far away now as it was 10 years ago.

RFM subscribers will receive their copy by email. Alavan Independent has also published this view in Heteronomics.

China/US: A Chip Off An Old Block (Alastair Newton).

Contrary to much of the media coverage, the launch of Huawei’s Mate 60 Pro likely does not mark a major SMIC breakthrough in chip manufacturing but is almost certainly geopolitically, not commercially, motivated. Further similar — and self-harming — moves by Beijing may well follow.

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There is concern that Microsoft and Google have already lost their edge in AI. The free availability of Meta’s LlaMa models has triggered a wave of innovation in the open-source community. Open-source models are available that are close in performance to those being offered by the big players implying that the lower end of generative AI quickly commoditises. This means that the moats that Google and Microsoft have already managed to create will become key differentiators and RFM thinks that they will protect them while they work out how to adapt to open-source competition.

• Genie escaped. Until recently, building LLMs was only possible for those with lots of cloud compute resources and big bank balances. However, the “leak” of Meta’s foundation model to the market has enabled open source to begin tinkering from which there has been a steady flow of results. This is what underpins fears that the leaders in AI have already lost their edge.
• Self-destructive innovation. The irony is that it is innovations from both Microsoft and Google that have enabled the open-source community to break the dependence on the cloud and begin fine-tuning LLMs at the edge. This has resulted in highly capable models being made available for free to anyone who wants them.
• Innovations: LoRa & Chinchilla. LoRa is a technique invented by Microsoft that enables LLMs to be trained on devices with a fraction of the storage or compute power that was previously required. Chinchilla was invented by Google and is a tweak to training methodology that allows smaller models to perform better than others many times their size.
• Galloping commoditisation. The combination of a freely available foundation model and its weights with these new techniques is how open source is managing to train models on Macs, laptops and PCs with powerful graphics cards. This means that the basic end of this market is going to commoditise extremely quickly meaning that other factors such as developer relationships and user numbers will be extremely important in determining winners and losers.
• Moats. RFM finds that both Google and Microsoft have substantial moats that will protect them from competition from the open-source community. These moats have been created from the fact that both already have well over 1bn users and the fact that ChatGPT is becoming a development platform in its own right. Meta is building its own moat in the open-source community.
• Machiavellian Meta. RFM is convinced that Meta’s “leak” of its LlaMa foundation models and their weights was no accident. “Leaking” LlaMa to open source has meant that LlaMa is now the de facto standard for open-source development and a platform in its own right. These innovations will all be available to Meta to use in its products and services meaning that it has effectively outsourced R&D at almost zero cost.

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The 4^th hype cycle is in full swing but large language models (LLMs) do have a lot to offer. They won’t deliver superhuman intelligence, but they can greatly enhance the productivity of humans as well as their ability to use language as a user interface. These systems are very expensive to develop which is probably a major reason why it is the large companies like Microsoft (OpenAI) and Google that currently lead the race. The AI ecosystem is at a very early stage and remains dominated by Nvidia but for how long remains to be seen.

• 4^th hype cycle. Generative AI has for the 4^th time in 60 years triggered dreams of super-intelligent machines being just around the corner. Generative AI offers a big step forward in terms of ingesting, parsing and retrieving data but it continues to suffer from problems that are very unlikely to be eradicated in the foreseeable future. Hence when superintelligence fails to materialise, there is going to be another round of despondency and depression.
• No breakthrough. The techniques to create generative AI are not new and mostly stem from Google’s 2017 transformer neural network architecture. In 2012, deep learning suddenly started to work thanks to enough data and processing power becoming available. Generative AI looks like a repeat of 2012 rather than a genuine breakthrough that will change the world.
• Use case 1: Enterprise. RFM’s testing reveals that LLMs are extremely good at ingesting huge amounts of data and being able to usefully collate and amalgamate data to produce useful and easy-to-use answers to inquiries. This has substantial use cases within the enterprise and may even render some current corporate data solutions obsolete.
• Use case 2: Automotive: where the current smartphone-like user interface is woefully inadequate. Voice has always been the leading contender but has not been anything like good enough. Generative AI could substantially improve the ability to control vehicles with voice and offers a way back for OEMs to reclaim some of the ground that they have already ceded.
• Generative AI is expensive but right now no one seems to care. RFM finds that for services with millions of users, the cost of inference will be far greater than the cost of training. This creates a substantial advantage in running inference in end devices rather than in the cloud.
• Generative AI players: Despite its recent gaffes, Google is a leader in the generative AI space alongside Microsoft (OpenAI) having created a lot of the innovation that everyone is now using. Meta Platforms, Baidu and Midjouney make up 2^nd place with others like Alibaba, Amazon and SoundHound nipping at their heels. Apple & Samsung are nowhere to be seen.
• The AI ecosystem is at a very early stage and is currently dominated by Nvidia which has 85%+ share of the platform used to train AIs. However, the popularity of ChatGPT could move the control point away from Nvidia and greatly weaken its position.

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Despite all indications to the contrary, China has abruptly changed course by ending Covid Zero and working to get the economy back on track. There are plenty of reasons to doubt a long-term turnaround, but we look set for a big bounce over the next year or two. At the same time, the USA has managed to get The Netherlands and Japan to increase restrictions, but these may be restricted to military applications as opposed to more broadly as the USA desires. China will set its own standards wherever it can and the result will be fragmentation and lower long-term growth for all.

• Damascene moment: President Xi has abruptly changed course by repealing Covid Zero and moving to get the economy back on track. Concern about the economy was almost certainly the main catalyst though civil protest also played a part.
• China’s return: Vice-Premier Liu He has been on a charm offensive at Davos pushing economic recovery and reforms. He was met with scepticism but in the short to medium term, we think that economic stimulus in Q2 2023 will be followed by a sustained economic recovery.
• Middle-income trap. Demographic data, productivity and high debt levels may push China into the middle-income trap which could see GDP growth averaging just 3-4%. This would be very bad news for its ambition to rival the USA as an economic and technological world power.
• No unilateral control: The USA is unable to control the semiconductor equipment market at 10nm to 18nm unlike it can at the leading edge. These nodes use Deep Ultraviolet (DUV) where equipment can be sourced from The Netherlands or Japan. This is why the USA has been negotiating with these countries feverishly over the last few months.
• Netherlands and Japan: on January 28^th 2023, The Netherlands and Japan agreed to increase restrictions on China but we believe that these two countries will limit themselves to targeting technologies that can be used for military purposes as opposed to technology more broadly.
• The new restrictions will 1) affect the non-leading edge nodes (18nm – 10nm), 2) be delayed in implementation, 3) be targeted to military applications as opposed to the USA which targets everything and 4) may also target compound semiconductors.
• Lagging reality. Although there has been watering down from Japan and The Netherlands, there are other actions the USA can take. Many Chinese companies are already abandoning the advanced nodes and moving to 28nm – 45nm where there is still plenty of business.
• Build the wall 2.0. The Balkanisation of the internet looks more certain than ever. The new restrictions will only make China push harder for self-reliance, meaning that it will create its own standards wherever it can. The splitting of the global network into two pieces inevitably means less growth for all of the technology sector in the long term.

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The China/USA rivalry waxes yet more fierce. The combination of the 20^th CCP Congress and additional export controls underlines again that semiconductors are central to this struggle. We think the USA intends to do to China what it did to Huawei and that China’s ability to mitigate the impact is limited. Hence, China is likely to focus on lagging-edge technologies and set its own standards wherever it can. The result will be fragmentation and lower long-term growth for all.

• Party over progress: The 20^th Congress of the CCP saw the Standing Committee filled with Xi loyalists as opposed to leaders chosen for their competence. This cements President Xi’s grip on the party and could mean doubling down on big policy blunders like Covid Zero.
• 3 watershed moments: This, combined with the USA’s increased restrictions on semiconductors, and its widening attacks on all of the Chinese technology industry represent three watershed moments that will impact the rivalry for years to come.
• Taiwan: These watershed moments increase the risk of a Chinese invasion of Taiwan where we believe that the early 2025 timeline may prove to be all too prescient.
• New front opens: We think that the new restrictions announced on 7^th October 2022 represent a substantial increase in aggression. The USA now seems to be intending to hobble the development of China’s technology industry, especially where it competes head-to-head with the USA like AI. We also see economic development as a whole being targeted.
• Additional Export Controls are effectively a blockade on modern semiconductors below 18nm in size. This will ensure China remains 10 or more years behind the leading edge. Furthermore, they will limit China’s ability to make chips, buy chips as well as access the talent of US persons.
• China’s limited options, the three Rs: Recreate the semiconductor ecosystem, reverse engineer or copy it or restrict the export of vital materials to force a relaxation of the restrictions are China’s main options for retaliation. They are unlikely to be successful.
• The lagging reality. The failure of retaliation is likely to mean that China will remain restricted to the lagging edge where there is still plenty of business to be done especially at 28nm. Hence it is here where we expect China to shift its investment despite this meaning that it will fall further and further behind the USA and the rest of the world.
• Build the wall 2.0. The Balkanisation of the internet looks more certain than ever. The new restrictions will only make China push harder for self-reliance, meaning that it will create its own standards wherever it can. The splitting of the global network into two pieces inevitably means less growth for all of the technology sector in the long term.

China has taken its foot off the regulatory pedal but the dire state of the economy, largely as a result of Covid zero, is going to hamper any recovery. China also continues to languish in semiconductors and not content with its superiority, the USA has decided to increase the pressure. Older semiconductors and technologies where China is doing well are now being targeted to hamper its rise. The result is likely to be the separation of the Internet into two pieces which is optimal for no one.  

• Balance of pain: The regulatory crackdown has wreaked havoc on the technology industry with big players having lost at least half of their value. The Chinese state has pulled back as it has realised that it has gone too far but there is still no confidence in a recovery.
• Covid zero: is a large factor in this lack of confidence as sudden lockdowns and restrictions are playing havoc with the Chinese economy. Fortunately, there is space for economic stimulus but without a relaxation of Covid zero, there is unlikely to be enough of a recovery to bring back belief in the Chinese technology sector.
• Semis: China has no takers: China offers the best terms to build a fab. On a purely economic basis, everyone should be building their fabs in China but there are no takers. This demonstrates how little trust exists between the West and China.
• Semis: China continues to languish: With 7/10 years of the Made in China 2025 program elapsed, China is still failing to launch. Just 6.6% of domestic semiconductor consumption is met by Chinese companies of which a tiny fraction at best is coming from the 14nm node and 0% from anything more advanced.
• Semis: The eagle swoops: There has been an 18-month lull, but the current administration is now showing its talons. In many ways, this administration has become more aggressive than the last one and is making much more use of its semiconductor dominance to slow China’s rise.
• Semis: Aggression spreads far and wide: This includes new restrictions on advanced AI chips being sold to China as well as potential restrictions on less advanced semiconductor technologies like 14nm equipment and deep ultraviolet lithography.
• Other technologies: While China really struggles in semiconductors, it is a world leader in other areas which eventually could allow it to become a challenger for global technology leadership. However, the USA is leveraging its dominance in semiconductors to slow China down in any way it can. AI is the most recent example.
• Build the wall: The result of this ideological struggle is likely to be a Balkanisation of the Internet with separate and incompatible standards for China and the rest of the world. This is bad news for everyone as the total opportunity will fall far short of its potential.