In 2025, the autonomous vehicle (AV) race accelerated sharply. Waymo’s weekly rides increased more than fivefold in just under a year, putting it neck and neck with China’s Apollo Go in cumulative autonomous miles. Meanwhile, Tesla launched its robotaxi pilot mid-year and is currently gearing up for mass production of its Cybercab, targeting 2 million units annually. 

In our previous note, Driving Towards Autonomy (2024), we provided a primer on the autonomous vehicle space. This report builds on that foundation, examining key developments over the past year. We explore the surge in autonomous ride-hailing, expanding partnerships, unit economics and why we believe the AV sector is now at an inflection point.

Autonomous ride-hailing surge

Ride-hailing has seen strong momentum over the last 12 months. Waymo’s cumulative autonomous miles reached 100 million in July 2025 (Figure A), supported by more than 250,000 trips per week across five major US cities and a fleet of over 1,500 vehicles.

Figure A. Waymo’s cumulative autonomous miles 

Source: Waymo, X

Waymo has also taken market share from other ride hailing services, surpassing Lyft in San Francisco in late 2024 (Figure B). Waymo now plans to add an additional 2,000 Jaguar I-PACE vehicles in 2026 and expand its services to more cities. Additionally, their new integration plant in Mesa, announced in May 2025, is expected to be capable of building tens of thousands of fully autonomous Waymo vehicles per year when fully operational.

Figure B. Waymo market share versus competitors in San Francisco

Source: Bond, Trends – Artificial Intelligence 2025

Chinese autonomous mobility companies have also made strong progress, led by Baidu’s Apollo Go, often described as the Waymo of China. As of May 2025, Apollo Go had completed 11 million cumulative ride-hailing trips, slightly ahead of Waymo’s 10 million at the same time (Figure C). In Q2 2025 alone it delivered 2.2 million rides, representing 148% year-on-year growth. Since February 2025, Apollo Go has been operating fully driverless across China with no safety drivers and now serves 16 cities globally.

Figure C. Ride-Hailing Trips: Apollo Go versus Waymo

Sources: Waymo, Baidu, AlphaTarget

Technology, safety and scaling

Two main architectures have emerged in the autonomous race. Waymo, along with most peers, have adopted a multi-sensor fusion approach, typically combining cameras, LiDAR and radar with high-definition (HD) maps built in advance for each city. These detailed maps (lanes, signs, kerbs, crossings), which can provide centimeter-level accuracy, enable the vehicle to  localise precisely. Typically, these services run in geo-fenced areas with precise mapping and good network coverage, with the latter facilitating remote tele-operation as required. 

Tesla, by contrast, uses primarily cameras with an end-to-end neural-network approach and does not use HD maps for localisation. Note that both approaches use maps for navigation and routing, but Waymo and similar architectures depend on pre-built HD maps to precisely localise the vehicle and validate its sensor-based perception, while Tesla’s system determines position and driving decisions directly from camera feeds, supplemented by a microphone for detecting emergency sirens.

Waymo’s approach offers strong redundancy and precise localisation, which underpins proven commercial deployments. Waymo has also published data showing its vehicles are safer than the average human driver across several key metrics in the areas where it operates, with ‘91% fewer serious injury or worse crashes’ (Figure D). According to a TIME article in June 2025, Waymo’s Director of Field Safety, Matthew Schwall, believes the company’s autonomous vehicles are technically capable of operating safely in any American town, but widespread rollout depends on building local infrastructure and public trust.

Figure D. Waymo safety data

Source: Waymo

However, Waymo’s vehicles are still far from perfect drivers. There have been numerous reports of unpredictable behaviour, such as unexpectedly stopping in traffic. A Reuters article in August 2025 highlighted a Waymo driving into a flood and the passenger had to find a way out. Police said if the person had died, it could have led to a “serious criminal incident”. This highlights that the technology still needs refinement and has not quite yet reached the performance levels needed for widespread deployment. Additionally, the need to map out each new territory in incredible detail before autonomous operations has raised questions about its ability to scale at a meaningful pace. That said, Waymo is making progress on this front, with their 6^th generation vehicle “on track to begin operating without a human behind the wheel in about half the time”.

“Once we can make it basically work in a few cities in America, we can make it work anywhere in America. Once we can make it work in a few cities in China, we can make it work anywhere in China.”

Elon Musk, Tesla CEO (Q1 2025)

One of the benefits of Tesla’s approach is that fewer sensor types simplify hardware and lower costs. The approach is also more generalisable because it does not require detailed mapping before it expands to a new city. Tesla has taken the route of rolling out its autonomous technology as an Advanced Driver Assistance System. Thus, Tesla’s customers are able to use its Full Self Driving (Supervised) solution and report back on interventions, which allows the company to iteratively improve its generalised autonomy solution. Tesla thus also has orders of magnitude more data than other autonomous mobility players given its large existing fleet, allowing it to capture a long-tail of scenarios. Its vertical integration also means it can iterate and optimise faster.

“LiDAR is a fool’s errand, and anyone relying on LiDAR is doomed”

Elon Musk, Tesla CEO (April 2019)

Many, however, question whether a camera-only system can meet the safety threshold  for driverless operations. Musk argues that a multi-sensor approach increases complexity, potentially reducing signal-to-noise ratio (Figure E). Tesla’s “photons to control” approach utilises raw camera feeds, with their high dynamic range providing rich visual information, for both model training and inference at run-time. 

Figure E. Elon Musk’s posts on autonomous technology

Source: X

In June 2025, Tesla soft-launched its robotaxi service in Austin, with many riders reporting that the vehicles felt smoother than Waymos (e.g. fewer instances of phantom braking). However, the rollout is at a very small scale and by invitation only, so it is likely that participants were to some extent biased. Additionally, there were safety monitors in the passenger seat, though Musk has said these will be removed by the end of the year.

Musk has also outlined exceptionally ambitious scaling targets. On the Tesla Q2 2025 earnings call, he stated that he expects the number of autonomous vehicles in operation to grow at a hyper-exponential rate and projected that Tesla would offer autonomous ride-hailing to half the US population by the end of the year. He has also previously said that its new Cybercab vehicle, designed for ride-hailing, is targeted for volume production in 2026, targeting  2 million units per year and potentially 4 million at full scale. These targets illustrate the scale of Tesla’s ambition relative to its peers, who generally speak in terms of thousands of units rather than millions.

Currently, the jury is still out on which approach will ultimately prove superior. Tesla must demonstrate it can safely operate its robotaxis without safety monitors and show it can scale meaningfully, even if not fully to its most ambitious targets. Meanwhile, Waymo will need to refine its technology to reduce occasional unpredictable behaviour and improve the efficiency of scaling operations over time. Both companies face critical milestones and the path to broad autonomous adoption will hinge on how well each can meet these challenges.

Expansion via partnerships

“Leveraging our partners’ local market presence, we can accelerate market entry across different continents and achieve faster deployment while maintaining a cost-efficient asset-light business model.”

Robin Yanhong Li, Baidu CEO (Q2 2025)

To accelerate deployment, autonomous driving companies are increasingly forming partnerships with established ride-hailing services and automotive players. In the US, Waymo continues to collaborate with both Uber and Lyft across multiple cities, integrating its autonomous vehicles directly into their ride-hailing apps. In Japan, Waymo announced a partnership in late 2024 with taxi platform GO and operator Nihon Kotsu, where vehicles will initially be manually driven in Tokyo to refine local performance before autonomous operations begin. Waymo has also expanded its OEM relationships, including with Hyundai, whose IONIQ 5 SUV will join the Waymo One fleet and Toyota, with which it is co-developing technologies to enhance both personally owned vehicles and future fleet models.

Similarly, Apollo GO has also struck a range of partnerships to further expand its operations. For instance, in August 2025 it partnered with Lyft to deploy autonomous rides across Europe, with initial deployments planned for Germany and the UK in 2026, pending regulatory approval. The agreement sees the fleet scaling to thousands of vehicles in the following years, with Apollo Go providing the vehicles and comprehensive technical support and Lyft owning the operational value chain. It also struck a similar deal with Uber in July 2025 for a range of markets outside of the US and mainland China. Initial target markets are Asia and the Middle East, with Dubai emerging as a key hub given its goal for at least 25% of all trips to be driverless by 2030.

These partnerships with ride-hailing platforms allow autonomous driving companies to focus on their core expertise while leveraging partners’ established user bases and operational capabilities. This creates natural synergies and removes the need for autonomous mobility players to spend heavily and compete on customer acquisition. Going forward, we would expect these types of partnerships to continue and become a standard go-to-market channel.

Chinese autonomous players are also increasingly looking to shift to an asset-light approach, choosing not to own the vehicles themselves. This model reduces capital intensity and transfers fleet management to partners better equipped to maximise utilisation and minimise operating costs, which should ultimately enable more efficient scaling.

The road to profitability

“Looking into the longer-term, we see a clear path to profitability as hardware and labour costs keep coming down and our growing operational scale brings more efficiencies.”

Robin Yanhong Li, Baidu CEO (Q1 2025)

A key question for the autonomous vehicle industry is if and when robotaxi operations can achieve profitability. While removing the human driver eliminates a major cost, the high expense of autonomous hardware and ongoing operating overheads, including the need for tele-operators, have historically made positive unit economics difficult to achieve.

A Wall Street Journal analysis in December 2024 estimated the cost of a Waymo vehicle at roughly US$125,000, comprising about US$50,000 for the Jaguar I-PACE base car and the remainder for autonomous components, including LiDAR, cameras, radar and external audio receivers (EARs). Assuming US$15–20 billion in capital investment and a fleet of 20,000 vehicles, the analysis suggested a breakeven period of around 10 years.

“With 13 cameras, 4 LiDAR, 6 radar and an array of external audio receivers (EARs), our new sensor suite is optimised for greater performance at a significantly reduced cost, without compromising safety.”

Satish Jeyachandran, Vice President of Engineering Waymo (August 2024)

Although Waymo faces less pressure to cut costs given its parent Alphabet has significant resources, cost reductions have been an important focus. When it announced its 6^th generation Waymo Driver in August 2024, cost optimisation was a key highlight. Additionally, its partnership with Hyundai, adding the more affordable IONIQ 5 relative to the Jaguar I-PACE, suggests a focus on improving unit economics.

“RT6 is now running at a meaningful scale across multiple cities. And its unit cost is below $30,000, far better than anyone else on the planet.”

Robin Yanhong Li, Baidu CEO (Q1 2025)

In contrast, Baidu’s Apollo Go has achieved meaningfully lower vehicle costs and already reached positive unit economics in China, despite significantly lower average fares. In our discussions with another Chinese autonomous player, they have also seen significant cost reductions in recent times on multiple fronts. For instance, the hardware has become much more affordable, especially since they were able to move from expensive mechanical LiDAR to the more affordable solid-state and semi-solid state LiDAR. Additionally, as the AV industry continues to expand, the supply chain has benefitted from economies of scale, driving down costs. Operating expenses have also fallen through more efficient algorithms that consume less power and by reducing remote safety supervision to one operator per 30 vehicles.

This provider estimated total daily costs of around RMB300 (US$42) for its next generation vehicle, including OPEX and depreciation (Figure F). With taxi drivers in Chinese markets earning up to RMB600 (US$82) per day and autonomous fleets capable of continuous operation, they too appear well positioned to achieve positive unit economics. As fleets scale, fixed overheads should become an even smaller share of total costs, paving the way for profitability at the organisational level. 

Figure F. Autonomous vehicle unit cost for a Chinese autonomous mobility company

Source: Chinese autonomous mobility company

Tesla’s Cybercab is also set to be highly cost effective. Musk has stated the cost was expected to be below $30k, with no pedals, steering wheel or driver dashboard. It is designed for a “gentle ride”, with lower top end speed and more efficient tires than its other models, optimised fully for cost per mile, estimated at around $0.3-$0.4.

While vehicle costs are falling, early market data shows strong willingness to pay. A study by Obi found that Waymo rides were priced roughly 30–40% higher than competing services (Figure G). Among surveyed riders who had taken a Waymo, 70.2% said they preferred it, 16.7% were indifferent and 13.2% favoured traditional ride-hailing apps.  

Figure G. Price comparison between Waymo and other ride-hailing apps

Source: Obi, The Roach Ahead: Pricing Insights On Waymo, Uber and Lyft
Notes: n: 88,998, Dates: 25 March – 25 April 2025, Place: San Francisco

When respondents were asked why they preferred the Waymo, 52% of respondents said it was because they did not need to speak to the driver (Figure H). It is also likely that some riders feel safer without a human driver, though that was not an option on the questionnaire. We assume these factors can to some extent explain its growing market share and its ability to command a premium.

Figure H. Survey for why riders preferred Waymo

Source: Obi, The Roach Ahead: Pricing Insights On Waymo, Uber and Lyft

“We are moving towards a future where taking a robotaxi will be half the cost of taking a taxi today.”

Robin Yanhong Li, Baidu CEO (July 2022)

Although riders are currently willing to pay a premium, we do not expect elevated pricing to persist. In the long run, we would expect fares to fall materially below current ride-hailing rates as cost savings from removing human labour and using purpose-built vehicles are competed away. However, lower prices should drive significantly higher adoption and utilisation rates, as autonomous services become more accessible to a broader segment of consumers.

We still expect healthy margins over time, however. We do not anticipate the autonomous driving market to resemble today’s car industry with over 100 brands and structurally low profitability. Once leading players achieve scale, accumulate large amounts of proprietary driving data and demonstrate superior safety performance, barriers to entry should rise sharply, limiting competition and supporting healthy margins. At the same time, we do not foresee monopoly-like outcomes, as large national governments are unlikely to allow a single foreign operator to dominate domestic mobility infrastructure and are expected to support local champions.

Sizing the opportunity

Autonomous vehicles will potentially impact multiple trillion-dollar markets including transportation, logistics and urban infrastructure. This potential stems from fundamental changes to cost structures and asset utilisation across these sectors, although the early stage of the technology makes precise market sizing challenging.

Since autonomous technologies are still in the very early stages of their rollout, the estimates for total addressable market (TAM) vary significantly across forecasts. For instance, according to Fortune Business Insights, the global autonomous vehicle market was valued at just over US$1.9 trillion in 2023 and is expected to grow to US$13.6 trillion by 2030  (representing a CAGR of 32.3%)! On the other end of the spectrum, Precedence Research has a relatively conservative estimate for the Autonomous Vehicle Market at US$158.3 billion in 2023, set to grow to US$2.75 trillion by 2033 (Figure I).

Figure I: Autonomous Vehicle Market Size, 2023 to 2033

Source: Precedence Research

Conclusion

The autonomous vehicle (AV) market remains nascent, with leading robotaxi fleets currently numbering around 1,000–1,500 vehicles and most new partnerships involving only a few thousand units. This is orders of magnitude smaller than the global vehicle total, estimated at 1.6 billion. However, growth is accelerating as technology advances, regulatory support increases and partnerships proliferate across regions. Crucially, we see emerging positive unit economics as a catalyst, giving companies the confidence and economic capacity to scale more aggressively. We therefore believe the industry is now at an inflection point and we would not be surprised to see fleet sizes doubling or tripling annually for many years to come.

At AlphaTarget, we invest our capital in the most promising disruptive businesses at the forefront of secular trends; and utilise stage analysis and other technical tools to continuously monitor our holdings and manage our investment portfolio. AlphaTarget produces cutting-edge research and our subscribers gain exclusive access to information such as the holdings in our investment portfolio, our in-depth fundamental and technical analysis of each company, our portfolio management moves and details of our proprietary systematic trend following hedging strategy to reduce portfolio drawdowns.  To learn more about our research service, please visit alphatarget.com/subscriptions/.

“[T]he larger driver of our everyday activity is the fact that the software industry is completely changing with AI. It feels like the AI era of the past two years just makes the SaaS era feel like sleeping days.” 

Ivan Zhao, Notion CEO (11 August 2025, The Verge)

“It’s been eight of the most exciting months, I think, of my career.”

Marc Benioff, Salesforce CEO (29 August 2025, The Logan Bartlett Show)

An agentic software future

The current artificial intelligence (AI) revolution has ignited widespread debate about its transformative potential across industries, with software playing a central and pivotal role in these changes. Many observers express concerns that AI could disrupt the very foundations of software as we know it, rendering the current software paradigm obsolete. This fear stems from the belief that AI’s generative capabilities could automate code generation to the point where bespoke applications are created with ease, bypassing the need for ongoing subscriptions to SaaS platforms. Additionally, some industry experts foresee a future where traditional software becomes redundant, as instead of interacting with a range of siloed software applications, users will interact with an agentic interface through which they can express their intent and have agents work in the background to obtain the desired outcome.

“I think the notion that business applications exist, that’s probably where they’ll all collapse, right, in the agent era.”

Satya Nadella, Microsoft CEO (12 December 2024, BG2)

High-profile voices have amplified these fears. In a recent social media post (Figure A), Musk suggested that phones and computers would effectively become edge nodes for AI, directly rendering pixels without the need for a traditional operating system or conventional apps. In that same exchange, he agreed with Replit’s CEO, Amjad Masad, who commented that AI could generate apps on demand, with agents eating traditional apps. Replit itself exemplifies this shift: its vibe coding (i.e., “rapid prototyping”) tool allows users to build and deploy apps in a fraction of the time compared to traditional software development, collapsing traditional coding barriers and enabling anyone to be a software developer.

Figure A: Elon Musk’s post on X

Source: X

We believe the AI revolution will significantly impact existing software, but that does not necessarily mean the demise of established software vendors. Presently, vibe coding tools excel at code generation and prototyping, yet have fallen short in delivering fully polished, production-ready products. We suspect that time will come too, but even then, incumbents possess moats that cannot easily be overcome in the short run. These include network effects, deeply embedded workflows and troves of user data. For instance, CRM is not just a tool but a living ecosystem of customer relationships and integrations that vibe coding cannot simply replicate. However, existing software will have to evolve significantly to meet user expectations if it is to stay relevant in the long run.  

In an August 2025 report, Gartner forecasts the future evolution of AI in enterprise software, as shown below (Figure B). Although timelines are highly uncertain, we think this forecast provides a good outline of the many steps (and hurdles) and time taken before we get to a world where agentic software is mainstream.

·     Stage 1. By the end of 2025, the majority of enterprise apps will have embedded AI assistants to simplify tasks and enhance user productivity. AI assistants are generally human triggered.

·       Stage 2. By 2026, 40% of enterprise apps will incorporate task-specific AI agents capable of handling complex, end-to-end processes independently, such as automating cybersecurity responses or other specialised functions.

·     Stage 3. By 2027, one-third of agentic AI implementations will have different agents collaborating within individual applications, combining diverse skills to manage intricate tasks.

·    Stage 4. By 2028, there will be AI agent ecosystems that collaborate across multiple apps and business functions. One-third of user interactions will occur through agentic front ends.

·         Stage 5. By 2029, at least 50% of knowledge workers will be able to create AI agents on demand.

Figure B: Gartner’s forecast for agentic AI in enterprise apps

Source: Gartner (August 2025)

An example of this agentic evolution is the introduction of Salesforce’s Agentforce, which it launched in September 2024. This AI-powered autonomous agent platform connects to enterprise data and handles tasks across sales, service, marketing and commerce. Agentforce has seen rapid adoption, with over 12,500 deals closed by July 2025, of which 6,000 were paid. Below is an illustration of its enterprise stack (Figure C), composed of a data layer (Data Cloud), a core application layer (Customer 360) and the agentic layer (Agentforce) which orchestrates work on top. We think this is a good illustration of where the enterprise stack is heading, though we acknowledge that there will likely be variations across different companies and industries. A key challenge for incumbent companies like Salesforce however will be adapting these innovations around legacy systems rather than building AI-native solutions from scratch.

Figure C: Salesforce’s Agentforce

Source: Salesforce

The fruits of thy labour

“[T]he overall addressable market went from software to software plus labour, which is 10 times bigger.”

Yamini Rangan, HubSpot CEO (5 May 2025, Grit)

It is important to note that the rise of AI is not just a challenge for software vendors to overcome in order to stay relevant, but also a significant opportunity. Those who innovate early and integrate AI capabilities into their products can offer more advanced services, effectively monetising the additional value they create. In other words, the leaders in this AI-driven evolution can potentially charge for the extra “intelligent” work their software does, creating significant new revenue streams.

At the same time, there is an open question about how much of this value will be captured by the software vendors relative to the infrastructure and model providers e.g. Microsoft and OpenAI, who supply the inference capabilities. Although inference is getting cheaper, with some estimating it is becoming 10x cheaper per year, the amount of inference needed per task is also growing extremely quickly, especially as models become more advanced and do more work. In an article by The Wall Street Journal, they provided an estimate of tokens needed per task below, showing how inference requirements increase drastically with task complexity.  

• Basic chatbot Q&A: 50 to 500 tokens

• Short document summary: 200 to 6,000 tokens

• Basic code assistance: 500 to 2,000 tokens

• Writing complex code: 20,000 to 100,000+ tokens

• Legal document analysis: 75,000 to 250,000+ tokens

• Multi-step agent workflow: 100,000 to one million+ tokens

The article also commented on how this is impacting margins: Ivan Zhao, CEO of productivity software company Notion, said his business had a gross margin of 90% two years ago, fairly standard for SaaS companies. However, approximately 10 percentage points of that margin now goes to the AI companies that power its latest offerings.

It is difficult to forecast the long-term margin impact of inference on SaaS companies, as it will depend on several factors. These include the market dominance of hardware and model providers, which determine their pricing power. Another determinant of inference costs will be the extent to which hardware and software efficiencies improve relative to inference demand. However, if the history of traditional cloud computing by big tech is any guide, inference providers are likely to capture a healthy share of the economics.

Still, we think innovative software vendors will see more upside than downside. They will be able to offer significantly more value to their clients by automating work and reducing labour costs, value that can be monetised at a strong premium. Additionally, as agent-driven systems become embedded in everyday workflows, almost like training an employee who learns a company’s specific needs, vendors could become much harder to dislodge, strengthening their moats. This, in turn, should allow them to raise prices and improve margins. ServiceNow, for instance, exemplifies this opportunity, with the company’s NowAssist AI products commanding 20%+ price premiums over standard offerings, while driving significant revenue growth.

Monetisation revamp

As software increasingly performs work through AI inference, software companies will also need to restructure their monetisation models. SaaS companies will need to shift away from their reliance on seat-based revenue towards usage-based revenue to align monetisation with value delivered. However, this transition is fraught with challenges. Seat-based pricing is more straightforward, scaling with user count. Usage-based models, however, introduce unpredictability, as costs fluctuate with consumption, making forecasting difficult for customers and businesses. It requires real-time systems to track potentially unbounded spend, unlike monthly batch processes. Complex discount structures for enterprise contracts add further intricacy, and data must be stored accurately and accessibly to maintain financial integrity and future pricing flexibility.

Operationally, incentive structures will transform. Sales teams must prioritise high-usage clients over seat counts, necessitating new compensation models. Product teams need to focus on unlocking use cases that drive usage, requiring agility to adapt to evolving customer needs. This transformation demands top-down leadership from the CEO to align sales, product, and finance teams. The right pricing model is still being figured out, with no universal solution yet, and we expect this will take time as AI models and hardware continue to rapidly improve. As seen with Salesforce’s Agentforce, it has reportedly changed its pricing model three times in a single year, reflecting rapid experimentation to find the optimal approach.

Despite challenges, usage-based pricing offers immense opportunity. By aligning monetisation with usage, companies achieve better product-market fit, capturing more value as customers derive greater benefit, fostering a flywheel of growth and innovation.

The agentic organisations

“I am on a mission to make Salesforce an agentic enterprise.”

Marc Benioff, Salesforce CEO (29 August 2025, The Logan Bartlett Show)

In addition to evolving their products and monetisation models, software vendors will also need to rethink their organisational structures. As Marc Benioff from Salesforce highlighted, it’s not just about making the product agent-driven, but about making the entire organisation agent-driven. In a recent podcast, he mentioned reducing Salesforce’s support staff from around 9,000 to 5,000 thanks to AI agents, reducing costs substantially.  

Likewise with sales, AI agents are reshaping how leads are managed and converted. Benioff explained that Salesforce had accumulated over 100 million uncontacted leads over 26 years due to insufficient human resources. However, now agents call back every person who calls them, handling more than 10,000 leads weekly, engaging in conversations and funnelling them into their pipeline. This highlights how agents are not just helping cut costs, but also helping grow revenues more efficiently. We imagine there will be many more use cases where agents will enhance efficiencies significantly. We therefore see significant upside potential for companies that can transform themselves to become more agent driven.

Sizing the opportunity

Cutting-edge software firm Palantir estimates it will be able to grow its revenue by 10x over the next 5 years, while simultaneously reducing headcount. This is a testament to the potential efficiency gains of AI. Although most firms will likely not be as operationally efficient as Palantir, we believe the potential for software to automate services and displace human labour will still lead to a substantial TAM expansion. According to Battery Ventures’ 2024 State of the OpenCloud report, the AI software TAM is worth approximatley US$4 trillion (Figure D).

Figure D: The AI Software Market Opportunity

Source: Battery Ventures, State of the OpenCloud Nov 2024

Conclusion

The current AI paradigm poses one of the greatest challenges the software industry has faced, forcing vendors to rethink many aspects of their business. Yet it also represents a powerful accelerant. Vendors could see substantial revenue uplifts by monetising the intelligent work their software performs, while also capturing significant operational efficiencies within their own organisations. Existing moats give incumbents valuable time to adapt, and as software evolves from being a critical tool to performing critical workflows, these moats may in fact deepen, making vendors harder to displace. However, Darwinism will take effect. The winners will be those agile enough to adapt quickly and embrace AI holistically, not just at the product level, but across their entire organisations.

In our view, companies that are already geared towards usage-based pricing models, will naturally benefit in this evolving AI landscape. We believe usage will rise as AI performs more tasks and we are already seeing signs of strong growth for software infrastructure companies, where usage based revenue models are native. Seat-based models on the other hand may face headwinds as businesses become more efficient and may as a consequence see less seat growth. We are therefore more cautious on vendors that remain seat-based without a clear path to transition toward usage-based models, particularly where their products have limited potential to perform meaningful work that could be monetised.

Additionally, we have a preference for infrastructure software businesses with deeply embedded workflows. Such companies are less easily disrupted than application software vendors and have more breathing room to evolve without immediate competitive threats. We also see cybersecurity as a particularly promising area. As companies expand their AI capabilities, their attack surfaces and vulnerabilities will grow. This will create more demand for advanced cybersecurity solutions. With adversaries also leveraging AI, cybersecurity firms that can help clients secure these new environments are well-positioned to benefit.

At AlphaTarget, we invest our capital in some of the most promising disruptive businesses at the forefront of secular trends; and utilise stage analysis and other technical tools to continuously monitor our holdings and manage our investment portfolio. AlphaTarget produces cutting-edge research and our subscribers gain exclusive access to information such as the holdings in our investment portfolio, our in-depth fundamental and technical analysis of each company, our portfolio management moves and details of our proprietary systematic trend following hedging strategy to reduce portfolio drawdowns.  To learn more about our research service, please visit alphatarget.com/subscriptions/.