Open-weight AI is becoming one of the more interesting second-order plays on the AI boom. Morgan Stanley research argues that cheaper, more flexible models could accelerate AI adoption rather than simply destroying the economics of the existing AI industry. Its latest work also highlights software companies that own valuable enterprise data, context, security and workflow infrastructure.
For UK retail investors, the key distinction is between open-weight and closed-weight AI models.
An open-weight model makes its trained model parameters available for others to download, customise and run themselves. That does not necessarily mean the entire model development process is ‘open source’. Companies can still restrict licences or keep training data and other components proprietary.
The attraction is straightforward: businesses can potentially run models themselves, fine-tune them for specific applications, avoid being tied to a single AI provider and reduce inference costs.
Morgan Stanley argues that falling AI costs could produce a Jevons Paradox effect: as the cost of producing useful AI intelligence falls, businesses may consume vastly more of it. The result could therefore be more AI workloads rather than simply lower industry revenues.
That is an important investment distinction.
Why open-weight models could be bullish for AI
| Potential benefit | What it means for investors |
| Lower inference costs | More AI applications become economically viable |
| Customisation | Enterprises can tailor models to proprietary workflows |
| Deployment flexibility | Companies can run models across cloud or on-premise infrastructure |
| More experimentation | Developers can test more AI applications |
| Higher AI utilisation | Falling costs could increase total compute demand |
| Multi-model architecture | Enterprises can combine several models rather than depend on one provider |
Morgan Stanley’s August research concluded that open and closed models are likely to coexist rather than one completely displacing the other. It also argued that lower model prices could increase demand for cloud infrastructure. Hilo Research
That creates three broad investment beneficiaries: compute infrastructure, hyperscalers and software companies.
1. Nvidia: still the infrastructure king
Nvidia (NASDAQ:NVDA) remains arguably the cleanest listed way of playing rising AI inference demand.
The important point is that open-weight models don’t necessarily mean fewer GPUs. Inference becomes cheaper, but cheaper inference can encourage dramatically greater usage. Research from Ornn, for example, suggests open-weight workloads can remain economically attractive on older Nvidia GPUs, potentially extending their useful lives rather than rendering them obsolete immediately. Ornn
Nvidia’s valuation also looks less extreme than it did during earlier phases of the AI boom. One October comparison put forward PE at about 22.7x, alongside substantial expected earnings growth. Stockopedia’s own rolling 12-month PE data puts Nvidia on 15.2x.
Investment case: the volume of AI compute could rise faster than price per token falls.
Risk: Nvidia is already an enormous market-cap company and expectations remain exceptionally high.
2. Meta: perhaps the most direct open-weight winner
Meta Platforms (NASDAQ:META) is particularly interesting because it has embraced open-weight models through its Llama ecosystem while simultaneously spending aggressively on AI infrastructure.
Morgan Stanley has previously rated Meta Overweight and identified it as its top pick among the hyperscalers in its open-weight analysis, with the argument that its proprietary ecosystem and low cost-to-serve give it an advantage.
Meta also has a huge distribution advantage through Facebook, Instagram and WhatsApp.
The latest numbers illustrate the scale of the opportunity — and the risk. Meta plans up to $135bn-$145bn of 2026 capital expenditure, while it has recently announced plans to raise as much as $95bn through stock offerings to help finance its AI expansion.
Investment case: open models lower AI costs while Meta owns massive consumer distribution and data.
Risk: AI capex is becoming enormous and is already putting an enormous strain on free cash flow.
3. Amazon and Alphabet: the cloud toll roads
Amazon (NASDAQ:AMZN) and Alphabet (NASDAQ:GOOG) may benefit irrespective of which AI model wins.
That’s because open-weight models still need somewhere to run.
Morgan Stanley argues that falling token prices could be offset for hyperscalers by rising inference volumes, better GPU utilisation and additional cloud infrastructure revenue. Its August research specifically maintained Overweight views on Meta, Amazon and Alphabet.
For UK investors, this is an important way of reducing model-selection risk. You don’t have to predict whether Meta’s Llama, an OpenAI model, Anthropic’s models or Chinese open-weight alternatives eventually dominate.
You own the infrastructure on which multiple models can run.
4. Enterprise software: the overlooked winners
This is perhaps the most interesting part of Morgan Stanley’s thesis.
The bank argues that falling intelligence costs could make proprietary enterprise assets more valuable, including business context, trusted data, telemetry, secure execution, transaction processing and compliance capabilities.
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Its latest analysis identifies CrowdStrike (NASDAQ:CRWD), Datadog (NASDAQ:DDOG), Microsoft (NASDAQ:MSFT), Atlassian (NASDAQ:TEAM), Cloudflare (NYSE:NET), MongoDB (NASDAQ:MDB), Nutanix (NASDAQ:NTNX), Palantir (NASDAQ:PLTR), Palo Alto Networks (NASDAQ:PANW), SAP (ETR:SAP) and ServiceNow (NASDAQ:NOW) among the strongest potential beneficiaries.
The logic is powerful.
If AI intelligence becomes cheaper, the scarce asset may no longer be the model. It may be the data and workflow surrounding the model.
For example:
- ServiceNow can embed AI into enterprise workflows.
- Palantir combines AI with proprietary customer data and operational systems.
- CrowdStrike can use AI within security telemetry.
- MongoDB provides the database infrastructure applications need.
- Cloudflare can provide the network and edge infrastructure around AI applications.
- SAP owns deep enterprise processes and data.
The problem is valuation. Some of these companies already price in enormous AI growth.
Top open-weight AI stocks to consider
Valuations are indicative forward multiples from recent October 2026 market data and should be treated as snapshots rather than precise entry valuations.
| Stock | Open-weight opportunity | Indicative forward PE | Snapshot view |
| Nvidia | Rising inference/compute demand | ~23x | Top infrastructure play |
| Meta | Llama + distribution + AI capex | ~21x | Morgan Stanley top hyperscaler pick |
| Alphabet | Cloud + Gemini + AI distribution | ~24x | Strong risk/reward |
| Amazon | AWS inference + cloud | ~31x | Long-term winner |
| Microsoft | Azure + enterprise AI | ~27x | Quality compounder |
| ServiceNow | AI embedded in workflows | ~30x | Enterprise beneficiary |
| SAP | Enterprise data/workflows | ~30x* | European AI play |
| Palantir | Proprietary data + AI deployment | >100x | High-risk/high-reward |
| MongoDB | AI application/database demand | ~53x | Higher-risk software play |
| Cloudflare | Edge AI + inference infrastructure | >200x | Speculative |
*Valuation measures vary considerably according to earnings definitions and reporting periods.
The bull case vs bear case
| Bull case | Bear case |
| Cheaper models dramatically expand AI usage | Model commoditisation destroys software pricing |
| Open models stimulate inference demand | AI spending eventually slows |
| Enterprise adoption accelerates | Companies struggle to generate AI ROI |
| Proprietary data becomes more valuable | Open models reduce software differentiation |
| Cloud providers monetise rising workloads | Token prices fall faster than volumes rise |
| Older GPUs remain useful for inference | Nvidia faces tougher competition |
There is already evidence that investors are becoming more sensitive to the financing requirements of the AI boom. Recent market volatility has highlighted concerns over enormous hyperscaler capex, AI infrastructure debt and the possibility of slower spending growth.
That makes valuation discipline increasingly important.
Sharesify investor verdict
Open-weight AI should not automatically be viewed as a threat to AI stocks. It could actually mark the transition from an expensive technology experiment into a mass-market computing cycle.
The most attractive investment idea is therefore not necessarily finding the company with the best open model. It is owning businesses that benefit when AI becomes cheaper and more widely used.
UK retail investors might think of stock ideas in this way:
1. Meta — strong combination of open-weight exposure, distribution and valuation.
2. Nvidia — strongest direct beneficiary of expanding AI compute.
3. Alphabet — attractive combination of AI, cloud and balance-sheet strength.
4. Amazon — AWS provides exposure to whichever models ultimately win.
5. Microsoft — enterprise distribution and Azure provide considerable AI optionality.
Among the higher-risk second-tier opportunities, ServiceNow, SAP, Cloudflare and MongoDB offer exposure to the idea that enterprise data and workflows become more valuable as AI intelligence gets cheaper. Palantir has perhaps the strongest strategic positioning but also among the most demanding valuations.
The key message for investors is simple: open-weight models may reduce the price of AI, but that could increase the size of the AI market. That makes compute, cloud, proprietary data and enterprise workflow infrastructure potentially more important — not less.
This is an investment analysis rather than personal financial advice. Valuations can change rapidly, particularly across AI stocks.
Disclaimer: The author Steven Frazer has a personal interest in Nvidia and Palantir.
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