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The 10 Question (10Q) framework is the longstanding foundation of our stock selection process on Long Term Global Growth. Many of the traits this framework looks for would appear on just about any growth-investor’s shopping list. High rates of revenue growth, competitive advantage, and worthwhile returns on capital are examples. These traits are certainly unusual in companies, but our prioritisation of them is not.
Where I think our stock selection framework is most differentiated is in our prioritisation of adaptability. The payoffs to this characteristic are material.
And because adaptability is the least tangible of our 10Q criteria, and arguably the hardest to identify ex ante, it is likely to be the least efficiently priced by the market.
That makes identifying adaptability a distinctly high-leverage stock-picking activity for LTGG. And I would argue that we are increasingly well-positioned to establish differentiated insight into this characteristic. Our regard for corporate adaptability is therefore a growing competitive advantage for LTGG.
Pay-offs to adaptability
To underline how crucial adaptability is to the outlier returns we seek on LTGG, it is helpful to consider the examples of Amazon, NVIDIA and Tencent. These companies have all been among the top contributors in LTGG’s history, and they are all still held in the portfolio today.
Source: Revolution. As at 31 March 2026. US Dollar. Long Term Global Growth Composite. Inception: 29 February 2004. Some stocks may not have been held for the full period
The relevant observation here is what proportion of these companies’ current revenue comes from lines of business which were either non-existent or very nascent when LTGG first invested on behalf of our clients; opportunities that were not incorporated into total addressable market calculations or valuation models at the time.
For Tencent, approximately one third of current revenue now comes from advertising, fintech, and cloud services, all of which were embryonic when LTGG purchased the stock for clients in 2009. At that time, the growth narrative centred on Tencent’s dominant social media and gaming businesses.
For Amazon, roughly 60 per cent of revenue now comes from AWS, advertising, third-party retail, and Prime. When LTGG first invested in 2004, Amazon was overwhelmingly a first-party online retail business. AWS and the advertising business did not exist, yet those adjacencies have materially extended Amazon’s growth runway. Indeed, these two businesses dominate Amazon’s profitability today.
Finally, NVIDIA’s datacentre business, which powers AI, now accounts for over 80 per cent of revenue, overtaking graphic chips for gaming which underpinned our original investment thesis when the team initiated a holding in 2016. The business line that powered NVIDIA to become the most valuable company in the world, adding more than three and a half trillion dollars to its market capitalisation over the past three years, was only a minor adjacency less than a decade ago.
This materiality of second acts or adjacencies is not just an idiosyncrasy of the LTGG portfolio. Hamilton Helmer, author of 7 Powers, has cited research on second acts showing that approximately half of profits for the S&P 100 ultimately come from lines of business the companies were not in at IPO. Separately, a Bain study found that second acts accounted for around one-third of total market value creation between 2008 and 2018.
Adaptability as a multistage rocket, the academic proof
Source: James Allen and Chris Zook (Bain & Company), “When Your Business Needs a Second Growth Engine”, Harvard Business Review, May-June 2022. *1,000 large public companies.
What this work demonstrates at the broader market level mirrors what we repeatedly observe at the LTGG portfolio level: adaptability – the ability to expand into new opportunities; to layer on additional S-curves of growth beyond the core business – is a crucial component in the world’s most valuable companies. It is how they extend their growth runways and continue to surprise the market. That is why adaptability is one of the most important characteristics we demand from our holdings.
Adaptability and mispricing
Where we have conviction that a company is genuinely adaptable there is a compelling case that the stock will be structurally underpriced. That’s the holy grail for a long-term, low-turnover growth investor.
Adaptability is fertile ground for mispricing for quite intuitive reasons. It is relatively easy to model the first S-curve of growth for a company once the core business has been established, because extrapolation is straightforward. By contrast, those second, third, and fourth S-curves – which have meaningfully extended the growth runways of LTGG’s top performance contributors – tend to be heavily discounted in valuation models as long as they remain nascent and uncertain. This creates a powerful opportunity for any stock picker who is able to establish greater conviction in those subsequent S-curves coming through.
One of my favourite examples of this mispricing comes from one of our industry’s Bibles on equity valuation: Michael Mauboussin and Alfred Rappaport’s Expectations Investing. Mauboussin, deservedly among the most respected thinkers on equity valuation today, conducted a valuation exercise in the book’s first edition, which concluded Amazon was overvalued at what amounts to around $0.50 per share on a split-adjusted basis. Mauboussin and Rappaport’s attempts to model Amazon’s growth could not justify the company’s valuation at less than 470x today’s share price. That is because this and most valuation exercises fail to capture the vastly evolving opportunity sets and new business lines Amazon’s adaptability can unlock.
It is a shame that this example was scrapped from later editions of the book – I’d argue any resource on equity valuation worth its salt should teach the critical lesson that adaptability can be dramatically mispriced. That mispricing creates a tremendous stock picking opportunity. A significant portion of our work on LTGG is about trying to capitalise on the upside arising from mispriced adaptability, by avoiding our industry’s fixation on ‘total’ addressable markets and target prices, and by explicitly requiring adaptability as a condition for a company’s inclusion in client portfolios.
But this commonplace mispricing of adaptability is also why LTGG strives to remain humble about our own ability to quantify upside in cases where our 10Q research suggests we are dealing with an adaptable company (based on the ingredients discussed below). I don’t pick on Mauboussin and Rappaport because they are alone in their errors. Quite the opposite. While LTGG’s investment track record shows the strategy has been good at capturing upside – delivering outperformance in more than 90 per cent of rolling 5-year periods since inception in 2004 – we have simultaneously been terrible at estimating upside with anything approximating accuracy. Specifically, we have tended to dramatically underestimate the upside which all the top contributors in LTGG went on to deliver (in both operational growth and share price terms).
Since LTGG’s long-run outperformance has relied on a small handful of outlier stocks, this is a sobering lesson: it reminds us that if we took our own valuation upside models too seriously, without acknowledging the inherent limits on our analysis, we would have destroyed tremendous value for our clients.
Where our industry tends to obsess over sell discipline, we must retain the humility to recognise that anchoring such ‘discipline’ solely to our quantitative models of upside when appraising an adaptable company would be to hang our hats on the flimsiest of hooks. If it leads you to sell Amazon at fifty cents a share, it’s not discipline: it’s value destruction.
Identifying adaptability ex ante
It is easy to observe in hindsight that Amazon, NVIDIA, and Tencent have proven adaptable, given the revenue and profit growth arising from their second and third acts. It is also easy to vaguely gesture at ‘special cultures’ as proxies for adaptability – but we can and must get more specific:
After more than two decades of prioritising identifying adaptability, we believe there are common ingredients that have underpinned our most adaptable holdings, and we can observe these in companies ex ante if we care to look. I outline those ingredients here, before exploring LTGG’s practical advantages in identifying them.
1. Ingredients in adaptability
In our opinion, the three ingredients that facilitate corporate adaptability are:
While none can be read directly off financial statements, all are identifiable company attributes that proper bottom-up research can unearth.
It is easier to layer on new growth opportunities when a company benefits from an inherently flexible underlying asset or competency that can be leveraged in various directions. Without this foundation, expansion into adjacencies requires pure diversification, where base rates for success are poor. Tencent’s growth, for example, is underpinned by its network of more than a billion WeChat users – this user base has been the launch pad for multiple business lines including Tencent’s Mini Programmes, Official Accounts, fintech growth and gaming. Likewise, compute capacity Amazon built for retail was later leveraged into AWS, while retail customer relationships and data have likewise been leveraged into advertising.
The second helpful ingredient is aligned founder leadership. More than 70 per cent of LTGG holdings still have founders involved, whether as CEOs, board members, or significant shareholders. And 63 per cent of our holdings are directly founder-run. Founder leadership brings a longer time horizon and the moral authority required to take risks and rally an organisation behind new opportunities.
A salaried public-market CEO with an average tenure of around three years is primarily incentivised to defend what has already been built. To avoid a failed stint as CEO, the rational course of action is to focus on running the core business well, with minimal near-term disruption. But founders have different priorities.
This was exemplified by David Vélez, the founder of Nubank, last year. Vélez dismissed half the C-suite and radically restructured the organisation because he concluded that creeping bureaucracy was slowing the company down. Notably, he took this action during a period in which nothing was wrong with the core business. Indeed, Nubank was delivering more than 80 per cent returns on equity in its core Brazilian market and posting very strong growth at the time.
Yet Vélez was concerned that slowness would prevent the company from planting sufficiently compelling seeds of future opportunity to extend the growth runway over the next decade and beyond.
The final ingredient is a business structure that supports agility. These tend to be lean structures with minimal layers of middle management, reducing friction in decision-making so companies can pivot quickly. AppLovin, NVIDIA, CATL, Reddit, and Nubank (after last year’s changes) all exemplify this characteristic.
With such structures, decisions do not need to pass through multiple layers of bureaucracy before they can be actioned. That enhances innovation quality, because decisions are not dragged down by the lowest common denominator in consultation. It also aids the ability to act opportunistically, thanks to speed. But just as importantly, it makes it easier for top-level management to directly identify when experiments are not working and therefore kill them quickly rather than leaning into them for too long.
Companies cannot unearth mispriced adjacencies unless they can experiment, and they cannot safely experiment unless they are well-placed to terminate failed experiments promptly, minimising damage done.
Such flat structures are typically paired with devolved decision-making to underlying teams, as seen at Roblox. These structures would be untenable otherwise, given high numbers of direct reports into the founder/CEO when middle-management is reduced.
Importantly, organisations can only function this way if they protect high talent density. Founders cannot successfully devolve decision-making to small teams unless the individuals in those teams are All Star players. But All Star players don’t like to work with mediocre teammates, so talent standards must be strictly upheld. This importance of talent density is why Adyen’s senior management insists on direct involvement in the interview process for every new hire, regardless of role.
2. Our edge in identifying those ingredients
Our long-term horizon of five to ten years helps us in two important ways:
First, our investment timeframe shapes what is decision-relevant in the first place. We are naturally interested in potential second acts because we care about growth runways far beyond the next year or two. If a line of business could become material over a five- or ten-year horizon but is unlikely to move the needle over a one- to three-year horizon, that is precisely where our interest lies, and where most of the market looks away.
Source: Baillie Gifford & Co. Long Term Global Growth representative portfolio average based on since inception turnover (29 February 2004) to 31 March 2026. *Source: Refinitiv Workspace, Bloomberg as at April 2025. Stock market holding period is derived through calculating 1/turnover ratio. The turnover is the value of equities traded/market capitalisation based on blended returns of S&P500 and MSCI EAFE.
For context, average holding periods in our industry have fallen to 6 months as of 2025 (compared to approximately 8 years for LTGG). Meanwhile, around 75 per cent of flows in equity markets are driven by high-frequency, quant or hedge fund strategies, which are likewise short-term (often far shorter than 6 months). It follows that the bulk of the market will be structurally indifferent to markers of adaptability, which will only pay off over the long term.
That means we have an advantage here, even if we cannot claim to somehow be better at analysing the nuances of adaptability in companies. We need only bother to identify adaptability in the first place. That is a hurdle we can simply step over – no jumping required.
Second, the reputation surrounding Baillie Gifford’s long-term horizon affords us increasingly helpful access to company management teams. It allows us to spend quality time with founders discussing strategic enablers of second or third-act opportunities, which they don’t bother spelling out in shareholder letters, earnings calls, or investment conferences because they know the market’s time horizon is not long enough to care. While investor access to founders or CEOs may be par for the course among asset managers of Baillie Gifford’s scale, the nature of that access and how we use it – guided by our time horizon – can be a source of genuinely differentiated insight.
I expect this will become an increasingly important LTGG differentiator in the AI era because the information germane to judging adaptability tends to be missing from LLMs’ training- or search data. It requires real conversations with founders and on-the-ground assessment of company cultures.
Underappreciated adaptability in the portfolio today
Samsara, Roblox, and AppLovin are some less well-known LTGG holdings where adaptability is central to the investment thesis.
Samsara sells cloud-based software to optimise the operation of physical assets, such as vehicles and equipment. It is a perfect example of how an inherently flexible underlying asset functions as a foundation for adaptability. The company began by establishing a strong position in vehicle telematics but has successfully expanded into five additional business lines within just ten years of founding.
The inherently flexible asset underpinning this adaptability is the vast quantity of data Samsara collects in the normal course of business. The company has now amassed trillions of proprietary data points across hundreds of millions of customer workflows and billions of physical assets. Both the asset network and the associated data continue to grow, forming a foundation for successive adjacencies which materially expand Samsara’s growth opportunity as new product development opportunities are unlocked with greater data and network scale. We’ve seen this recently with the successful launch of asset tagging and the differentiated route mapping product, neither of which was possible for Samsara a few years ago. The cadence of expansion into new product lines has picked up, with Samsara launching more new products over the last 18 months than it did over the previous 3 years. This adaptability, supported by the growing data asset and network, means Samsara’s growth runway is structurally underpriced.
Image courtesy of Roblox
Meanwhile, Roblox exemplifies both long-term founder leadership and a nimble business structure. Founder Dave Baszucki has one of the longest time horizons we have ever encountered. He persisted with Roblox for more than a decade before the business achieved meaningful scale, enduring a prolonged period in which it could reasonably have been judged a failure. That persistence reflects Baszucki’s orientation toward maximising long-term value even if that requires foregoing short-term payoffs. He continues to run the company against these priorities.
Adaptability at Roblox is further reinforced by its organisational structure, comprised of small, highly autonomous ‘mini companies’ who report directly to the founder, sidestepping the usual middle-management layers. Baszucki functions as a portfolio manager across these mini companies, ensuring alignment with the overarching strategy while preserving local autonomy.
Finally, AppLovin provides a compelling illustration of how founder alignment combined with a nimble business structure can reinforce adaptability. Founder/CEO Adam Foroughi retains operational and voting control, with substantial economic alignment through his more than 10 per cent shareholding. In recent years, Foroughi has obsessed over keeping AppLovin’s headcount lean despite industry-leading rates of revenue growth. This leanness enables a flat structure in which senior leaders report directly to Foroughi. That, in turn, enables rapid experimentation. And equally important, it allows the company to abandon failed experiments quickly (divesting or discontinuing three notable businesses in the past 3 years), so it can focus efforts on those second or third acts, which are bearing fruit. AppLovin is now entering the steep part of the S-curve in its expansion from gaming advertising into e-commerce advertising, opening access to an advertiser pool an order of magnitude larger than its original market.
Conclusion
When making long-term investment decisions under conditions of uncertainty, it is tempting to focus on broad thematic narratives that purport to define the future direction of industries. Growth investors love to do this. Indeed, it underpins the momentum-driven and thematic investment philosophies favoured by many of LTGG’s high-growth equity peers.
But as bottom-up stock pickers, LTGG is guided by a simpler observation: it is ultimately companies that build the future. Rather than attempting to predict that future with confidence, our task is to identify companies with favourable odds of leaning into the next big shift when it occurs.
In that sense, adaptability plays a central futureproofing role in the LTGG portfolio. It is also difficult to price and easy to underappreciate – which is precisely why it remains a powerful source of long-term outperformance when correctly identified.
Annual past performance to 31 March each year (%)
| 2022 | 2023 | 2024 | 2025 | 2026 | |
| Long Term Global Growth Composite (gross) | -17.5 | -17.5 | 27.1 | 8.5 | 5.0 |
| Long Term Global Growth Composite (net) | -18.1 | -18.1 | 26.2 | 7.7 | 4.3 |
| MSCI ACWI Index | 7.7 | -7.0 | 23.8 | 7.6 | 20.5 |
Annualised returns to 31 March 2026 (%)
| 1 year | 5 years | 10 years | 20 years | Since Inception* | |
| Long Term Global Growth Composite (gross) | 5.0 | -0.3 | 15.4 | 11.4 | 12.3 |
| Long Term Global Growth Composite (net) | 4.3 | -1.0 | 14.6 | 10.6 | 11.5 |
| MSCI ACWI Index | 20.5 | 10.0 | 11.9 | 8.2 | 8.8 |
Source: Revolution, MSCI. US dollars. Net returns have been calculated by reducing the gross return by the highest annual management fee for the composite. 1 year figures are not annualised. *29 February 2004.
Past performance is not a guide to future returns.
Legal notice: MSCI makes no express or implied warranties or representations and shall have no liability whatsoever with respect to any MSCI data contained herein. The MSCI data may not be further redistributed or used as a basis for other indexes or any securities or financial products. This report is not approved, endorsed, reviewed or produced by MSCI. None of the MSCI data is intended to constitute investment advice or a recommendation to make (or refrain from making) any kind of investment decision and may not be relied on as such.
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The views expressed should not be considered as advice or a recommendation to buy, sell or hold a particular investment. They reflect opinion and should not be taken as statements of fact nor should any reliance be placed on them when making investment decisions.
This communication was produced and approved in May 2026 and has not been updated subsequently. It represents views held at the time of writing and may not reflect current thinking.
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