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Term: Distributed to Paid-In Capital (DPI) - Finance"DPI stands for Distributed to Paid-In Capital. It is a key performance metric that measures the actual cash money a fund has returned to its investors compared to the total money the investors put in. It is also known as the cash-on-cash return or realisation multiple." - Distributed to Paid-In Capital (DPI) - Finance DPI matters because it answers a simple but decisive question: how much cash has actually come back to investors, not how much value exists on paper. In private equity and venture capital, that distinction is critical because realised proceeds, not unrealised marks, determine whether limited partners have recovered capital, booked gains, or are still waiting for exits. In practical terms, DPI is a liquidity measure disguised as a return metric. A fund can look attractive on paper through rising valuations, yet still deliver weak cash outcomes if exits are slow. DPI strips away that uncertainty by focusing only on cumulative distributions relative to capital paid in, which is why it is often treated as the clearest view of realised performance. Definition and formula The standard formulation is . Cumulative distributions are the total cash, and in some cases stock or equivalent proceeds, that a fund has returned to its investors over time. Paid-in capital is the amount investors have actually contributed when capital was called. That ratio is usually expressed as a multiple rather than a percentage. A DPI of means investors have received back exactly the amount they put in. A DPI above means the fund has distributed more than the capital invested, while a DPI below means the fund has not yet returned all contributed capital. What the metric means in practice DPI is often described as a cash-on-cash return because it compares money received to money contributed. If a fund has a DPI of , it has distributed for every invested. That is not a forecast and not an estimate; it is a historical record of realised capital returned to investors. This makes DPI especially useful for investors who care about whether gains have been monetised. In private markets, unrealised net asset value can rise for years without producing cash. DPI therefore captures a more conservative and often more meaningful view of progress, particularly for limited partners that need distributions to meet liabilities, recycle capital, or report tangible progress to their own stakeholders. How to read the components Each element of the ratio has a precise role. Distributions include all realised cash flows back to investors, usually after the fund has exited portfolio holdings or paid income from holdings such as dividends. Paid-in capital is the denominator because it measures what investors have actually funded, not merely what they committed on paper. In industry usage, the denominator is sometimes described as called capital or capital paid in, depending on reporting conventions. The economic meaning is the same: the metric compares actual capital deployed by investors with actual cash returned to them. Some managers report gross DPI and net DPI, where the latter may reflect fees, expenses, or carried interest more directly, which can produce a more investor-specific picture of realised proceeds. Relationship with other private market metrics DPI becomes more informative when read alongside TVPI and RVPI. TVPI combines realised and unrealised value, so it captures total value relative to paid-in capital, whereas RVPI focuses on the residual, unrealised portion still held in the portfolio. DPI, by contrast, isolates the realised part of the story. That separation matters because two funds can have the same TVPI while telling very different stories. One may have already returned much of the capital and still hold a smaller residual portfolio, while another may rely heavily on unrealised marks with little cash actually distributed. DPI reveals which of those outcomes is more liquid and therefore more certain. Schools of thought and interpretation There are two broad ways practitioners use DPI. The first treats it as a pure realisation measure: a cash metric that is intentionally agnostic about future value. The second treats it as a performance signal that should be judged against fund age, strategy, and market cycle. A young venture fund with low DPI may simply be early in its life, while a mature buyout fund with the same figure may look under-distributed relative to peers. This is why benchmarks are usually contextual rather than absolute. Venture capital tends to show slower DPI progression because exits take longer and value often compounds in a smaller number of late-stage outcomes. Buyout funds typically generate distributions earlier because operating control and debt structures can accelerate monetisation. As a result, the same DPI number can imply very different performance depending on the asset class and vintage year. Tensions, limitations, and common misunderstandings DPI is powerful, but it is not a complete measure of fund quality. It says nothing directly about the speed of returns, so it does not capture time value in the way IRR does. It also says nothing about remaining embedded value, which means a fund with modest DPI may still have strong unrealised upside. On the other hand, a high DPI does not guarantee superior overall performance if the remaining portfolio has been written down or if the realised returns took too long to materialise. Another common mistake is to treat DPI as a proxy for total return. It is only one part of the private markets toolkit. A fund with and low residual value is different from a fund with and a very strong unrealised book. The first has distributed more cash; the second may still have more economic value left to harvest. Why DPI still matters DPI remains central because the private markets industry ultimately lives or dies by realised cash outcomes. Limited partners commit capital years before exits occur, and they must distinguish between valuation gains and money already returned. DPI gives them a direct answer to that operational question, which is why it has become one of the headline metrics in fund reporting and benchmarking. It also matters because the market has become more disciplined about liquidity. In periods when exit markets are slow, distributions can lag even for funds with strong underlying assets. In that environment, DPI helps investors judge whether a manager is converting portfolio value into actual proceeds, rather than merely accumulating paper appreciation. That makes it a practical measure of credibility as well as performance. Bottom line for investors DPI is best understood as the realised-return lens for private funds. It tells investors how much cash they have received back for each unit of capital they put in, and it does so without relying on forecasts, marks, or valuation narratives. Used properly, the metric is most useful when paired with fund age, strategy, TVPI, RVPI, and IRR. Used alone, it can be misleading; used in context, it is one of the cleanest measures of whether a fund has turned paper success into cash in investors' hands.
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Quote: David Frankel - Founder Collective"There are so many founders. I think there are fewer entrepreneurs." - David Frankel - Founder Collective The real tension is not whether AI can produce extraordinary companies, but whether the market can tell genuine enterprise creation apart from capital-fuelled imitation. Frankel's view, grounded in a long seed investing record and the current AI cycle, is that too many people now call themselves founders because the label has become cheap, while the harder, rarer discipline of entrepreneurship remains scarce . That distinction matters because the present market is rewarding access, speed and narrative compression, not just execution. In a boom, the difference between those who start companies and those who build enduring businesses becomes easy to ignore, yet it is exactly that difference that determines whether capital is compounding or merely chasing momentum . Why the market confuses founding with entrepreneurship Frankel's argument starts from the observation that startup formation has been normalised. Programmes, networks and a culture of repeated company creation have made founding more accessible than at almost any point in the last two decades . That accessibility is not automatically a bad thing. It expands the pool of talent, lowers social friction and helps more people test ideas quickly. But it also means the title of founder can be acquired faster than the habits that make a company viable. Entrepreneurship still requires fortitude, resilience, the ability to recruit, a tolerance for uncertainty and the stamina to keep going when enthusiasm is no longer enough . The modern ecosystem can produce many people who are willing to start; it produces far fewer who are willing, or able, to absorb the grind of building through repeated ambiguity. That gap matters more in AI than in previous waves because the cost of making something look impressive has dropped sharply. Tools, models and outsourced expertise allow small teams to seem larger, faster and more capable than they are. As a result, the market can confuse presentation with substance. Frankel's warning is not nostalgic. He is not arguing for some vanished era of harder founders. He is pointing out that when technical leverage increases, the premium on judgement, focus and persistence rises as well . A company can now reach a polished demo, a credible launch and even real revenue with astonishing speed, but those milestones do not prove that the team can defend a market position once competition intensifies. AI raises the ceiling while compressing the middle The underlying strategic problem in the current cycle is concentration. Frankel sees AI as the most important technology wave of his career, yet he also believes that the financial outcome will be brutally uneven . A small number of companies will become immense, while a long tail of others will be washed out by capital intensity, valuation pressure and undifferentiated positioning . His backstory as a seed investor explains why he is so focused on this asymmetry. In the past, early entry could produce large multiples because the valuation gap between seed and scale was wide enough to absorb mistakes. Now, many rounds are priced as if the winner is already known, which means the scale of success required to justify an investment rises as the entry price rises . This is why he is sceptical of the fashionable claim that price no longer matters. In theory, if a company becomes sufficiently huge, the purchase price becomes irrelevant. In practice, venture arithmetic is unforgiving. The return needed from a high-priced seed bet is much harder to achieve than from a modestly priced one, especially when later rounds, dilution and follow-on funding are taken into account . Frankel's resistance to uncapped notes and overheated seeds is not conservatism for its own sake. It is a recognition that capital efficiency still governs outcomes even in a cycle where investors talk as if only access to the 'true winners' matters . Why small funds still have a role The broader backstory also clarifies why Frankel defends small seed funds even as mega-platforms dominate headlines. Founder Collective's model depends less on owning a company at all costs and more on identifying founders early enough that the fund can still generate meaningful returns from a handful of wins . That approach is easy to dismiss in a market obsessed with scale, but it remains logically coherent. A small fund does not need every outcome to be a decacorn; it needs a few exceptional companies to become large enough to return the vehicle . By contrast, large funds increasingly need the very biggest outcomes simply to matter economically. Frankel's critique is that the larger the platform, the more it begins to behave like a distribution business dressed up as judgment. In that world, the investor's role can drift from conviction to access, and access alone is a fragile basis for enduring venture returns . That does not mean he ignores large rounds or refuses to co-invest with bigger firms. It means he reads those situations differently. He has described small cheques in large rounds as a kind of insurance policy, a way for founders to preserve a relationship with an investor who will still care even if the company stops being strategically material to a mega-fund . The implication is subtle but important: in venture, sponsorship is not identical to support. A founder may think they are buying a long-term ally when in fact they are buying a temporary seat. Frankel's preference for smaller funds and earlier pricing is partly a way of preserving genuine alignment rather than merely competitive positioning . Entrepreneurs, not just founders, survive the cycle The deeper philosophical thread in Frankel's backstory is that he is still selecting for the same trait he valued before AI became the organising narrative of the market: edge. That edge can be domain knowledge, technical intensity, a rare founder pairing or a deep understanding of a neglected market . He does not treat entrepreneurship as a generic ability to start something. He treats it as a scarce combination of insight and persistence. That is why he is comfortable backing teams with unusual backgrounds, including second-time founders or operators who have lived inside a problem for years . The value lies not just in what they build, but in how long they can remain credible while building it. The CEO and CTO split he emphasises also fits this framework. A good technical founder may build the product, but the CEO has to recruit, persuade, raise capital and keep the organisation coherent as it scales . AI does not remove that burden. If anything, it intensifies it, because a smaller team can now produce more output, which means each hiring mistake or strategic misread has greater consequence. Frankel's interest in founder alchemy, trust and complementary skills is therefore not sentimental. It is a practical response to a market in which technology amplifies capability but does not eliminate the need for leadership . Why the distinction matters for the next decade Frankel's statement about fewer entrepreneurs than founders also helps explain his broader view of the AI economy. He expects enormous productivity gains, major changes in professional services and serious disruption across software, yet he does not predict universal job destruction . Instead, he anticipates a widening gap between those who learn to use AI well and those who do not . That is another version of the same theme. The market will not be split only between AI companies and non-AI companies, but between people and firms that convert AI into durable advantage and those that simply use the label while remaining structurally unchanged. In that sense, the real backstory is not about a slogan. It is about a filter. AI will expose which teams are genuinely entrepreneurial because it will reward speed, adaptation and clarity while punishing anything that depends on status, branding or complacency. There is also a larger market implication. If AI lowers the cost of starting, the number of founders will keep rising. If it raises the productivity of the best teams, the gap between the merely active and the truly entrepreneurial will widen. That is why Frankel's line lands so hard. It separates formation from creation, motion from momentum and activity from endurance . In a market crowded with people launching companies, the scarce resource is not the ability to incorporate or raise an early round. It is the ability to keep building after the easy part is over, when the product is real, the market is competitive and the narrative no longer carries itself. Seen that way, Frankel's comment is not a put-down of the current generation. It is a diagnosis of the era. The AI boom has made founding easier, faster and more visible, but entrepreneurship remains difficult, patient and brutally selective. The market may be producing more startups than ever, yet the scarcity that determines long-run value has not changed. It still lives in judgement, stamina and the willingness to keep earning the right to exist after the initial excitement fades .
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Term: Total Value to Paid-In Capital (TVPI) - Finance"TVPI (Total Value to Paid-In Capital) measures a fund's total relative performance. It is a multiple showing the sum of cash given back to investors plus the current worth of remaining assets, divided by the actual money investors put into the fund." - Total Value to Paid-In Capital (TVPI) - Finance TVPI matters because private market returns are not fully captured by cash alone. A fund can distribute little in the early years and still show strong underlying value, while another can hand back cash quickly yet leave weak residual assets behind. TVPI is designed to combine both sides of that picture into one multiple, making it a central shorthand for how much value a private equity or venture capital fund has created relative to the money investors have actually paid in. In practical terms, TVPI answers a simple question with a lot of nuance hidden inside it: for each unit of paid-in capital, how much total value exists today? That total value is the sum of cumulative distributions already returned to investors and the current value of remaining holdings, usually measured as residual net asset value. The denominator is paid-in capital, meaning actual capital called and contributed, not merely capital committed on paper. Core definition and formula The standard formula is . In some sources, residual value is described as remaining NAV, unrealised value, or the fair value of the still-held portfolio. The same identity is often expressed as , where DPI captures realised cash returned and RVPI captures the unrealised balance still in the fund. That decomposition is analytically useful because it prevents the headline multiple from becoming a black box. If a fund has returned in cash and still holds assets worth of paid-in capital, its TVPI is . The number looks strong, but it also reveals that most of the outcome still depends on assets that have not yet been sold. What the metric means in substance TVPI is often described as the total return multiple or overall fund value multiple because it blends realised and unrealised performance into one figure. A TVPI of means the fund has, on a combined basis, merely matched the money investors have put in. A figure above indicates value creation, while a figure below implies capital destruction on a total-value basis. In the private funds context, this matters because the timing of returns can vary dramatically. Early in a fund's life, DPI may be low because few exits have occurred, yet RVPI may be high if the portfolio has appreciated. Later in the life of the fund, the balance may shift: DPI rises as exits happen, while RVPI falls as the remaining portfolio is sold down. TVPI sits across both phases and gives LPs a way to compare funds that are at different stages of maturity. Why the denominator matters One subtle but important point is that TVPI is built on paid-in capital, not committed capital. That distinction matters because LPs usually commit more than is called at any one time, and committed but uncalled capital has not yet been deployed into assets or fees. Using paid-in capital keeps the metric tied to the capital base that has actually entered the fund and has therefore been exposed to performance outcomes. This also explains why TVPI is best understood as a fund-level multiple rather than a time-adjusted return measure. It does not say when value was created, only how much value exists relative to what has been contributed. That is why analysts often pair TVPI with IRR, which does incorporate timing, and with DPI, which focuses on actual cash returned. How practitioners read it In due diligence, TVPI is often treated as the broadest single performance snapshot. LPs use it to gauge whether a manager has generated value overall, whether or not that value has yet been realised as cash. GPs may also use it as a headline figure because it can present a stronger picture than DPI alone when the portfolio still contains meaningful unrealised gains. Yet the metric only becomes genuinely informative when read alongside its components. A high TVPI with a low DPI means the fund's reported success is still largely paper value. A high TVPI with a high DPI is more robust because much of the multiple has already been converted into cash. Conversely, a modest TVPI can still be attractive if it is driven by a strong DPI in a fund that has already returned capital early. Schools of thought and debates There are two broad ways of interpreting TVPI. The first treats it as a convenient summary of economic value, especially for comparing funds in the same vintage, strategy, or stage of life. The second treats it more sceptically, as a valuation-dependent multiple that can flatter portfolios whose remaining assets are marked aggressively. Both views are valid, and the tension between them is part of why the metric remains debated. The main criticism is that TVPI contains unrealised value, and unrealised value depends on marks. Those marks may be disciplined and conservative, or they may be optimistic and slow to adjust. For that reason, a fund with a very strong TVPI may still be carrying latent write-down risk if market conditions deteriorate or if exit assumptions prove too aggressive. There is also a methodological debate about whether TVPI is too blunt to stand alone. Supporters argue that it is the right level of aggregation for fund reporting because LPs need a broad measure of how much value the manager has created. Critics argue that it can obscure the composition of that value, especially when a fund has returned relatively little cash but is carrying a large residual stake that may or may not monetise at the stated value. Mathematical interpretation and related metrics TVPI is a simple ratio, but its interpretation is richer than the formula suggests. If denotes cumulative distributions, residual value, and paid-in capital, then . The related metrics are and , which yields the identity . That identity is more than notation. It shows that TVPI is a full-value measure, DPI is a realised-return measure, and RVPI is the unrealised balance still waiting to be proven. In practice, these three metrics let investors separate cash already earned from value that remains contingent on future exits. Why the term still matters TVPI remains important because private markets are still characterised by lumpy exits, long holding periods, and valuation marks that matter long before cash is distributed. Public market investors can often look at a quoted price and know exactly where they stand. Private market investors cannot. TVPI gives them a disciplined way to track the combined effect of distributions and residual assets while the fund is still alive. It also remains central because it fits the needs of LP reporting. A single multiple is easier to communicate than a full ledger of cash flows and marks, yet it is still rich enough to connect realised and unrealised performance. For that reason, TVPI is often the first number an LP scans, even if it should never be the last number they use. Ultimately, the value of TVPI lies in its balance. It is more complete than DPI, because it includes assets still on the books. It is more grounded than an abstract growth story, because it is anchored to capital actually paid in. And it is more decision-useful than a raw valuation figure, because it tells investors how much of that valuation has already been converted into realised economic return.
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Global Advisors News Brief - August 15 2026Read the full brief at the link Headlines for the last 24hrs
Time window: 2026-08-14T05:00:33.115Z to 2026-08-15T05:00:33.115Z |
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Quote: David Frankel - Founder Collective"[AI] is the wave of our lives. I feel that way, by the way. If I look at internet, SaaS, mobile, AI, nothing looks the same. And will there be roadkill from this wave? Oh my God, there's going to be a lot." - David Frankel - Founder Collective AI is compressing product cycles, inflating capital intensity and widening the gap between companies that can turn capability into distribution and those that cannot. That is why the current wave can be both transformational and destructive at the same time: the technology improves quickly, but the market clears far more brutally, leaving a long tail of funded businesses unable to justify the prices paid for them . Why the boom feels bigger than prior waves Frankel places AI ahead of the internet, SaaS and mobile as a force in his investing lifetime, not because every company in the category will be a winner, but because the ceiling on the best outcomes is higher and the pace of change is faster . The historical comparison matters. Over the past 25 years, he argues, there have been fewer than 100 sustainably large technology companies above roughly $10 billion in value, and the median of the top 500 companies created in that period is around $2.6 billion . That means the modal outcome in every great wave has still been middling relative to the rare giants. AI is likely to repeat that pattern, only with greater speed and more capital chasing the same small set of large wins . The implication is uncomfortable for founders and investors alike. A great technology shift does not guarantee broad success for the companies formed around it. It can instead create a narrow funnel in which a handful of businesses become generational, while the rest become what Frankel calls roadkill . The phrase is stark, but the underlying mechanism is familiar: when a category becomes the focus of intense consensus, valuations rise faster than the underlying certainty of durable differentiation. The market begins to treat participation in the wave as a substitute for competitive edge, even though participation alone is not enough . The seed market problem is really a maths problem Frankel's scepticism about frothy seed pricing is not emotional; it is arithmetic. If the entry valuation rises sharply, the eventual winner must be far larger, or much faster, for the fund maths to work. He pushes back directly against the idea that price no longer matters in AI. In his view, price always matters, because the required exit multiple changes with the starting point . An uncapped note, a lofty cap, or a seed round priced as if it were a later stage all compress the margin of safety and make even a good company a less attractive venture investment . This explains why he is wary of the current fashion for large seed rounds led by multi-stage funds. Those firms can write big cheques, but their involvement often changes the power dynamics for everyone else. Frankel describes a recurring pattern in which a startup takes money from a prominent platform, only to discover later that the internal champion has left, the company is no longer strategic, and the founder is left looking for a second source of support . That is why he sees Founder Collective's smaller cheques as useful not because they dominate the round, but because they remain relevant after momentum investors move on . Discipline is becoming a strategic differentiator The venture market increasingly rewards fund size, access and speed, yet Frankel still argues for discipline at seed. He does not deny that larger platforms can win, especially when a company is already on the path to being one of the very biggest . What he disputes is the notion that scale alone is enough to produce durable venture outperformance. Mega-funds need the very largest outcomes because their fixed costs and cheque sizes require huge capital returns, whereas a boutique seed fund can still generate excellent returns from a more modest, but genuinely elite, company . That distinction gives context to his comments on Founder Collective's strategy. The firm has always been willing to sacrifice some upside by staying small, avoiding management-fee maximisation and keeping a framework that favours founder quality over momentum . He admits that this discipline has caused the firm to miss certain businesses that later became obvious successes, but he treats those misses as the cost of not turning the firm into a size-driven machine . The argument is not that larger funds are wrong in all cases. It is that a small fund can still be a rational, coherent business when it is built around seed economics rather than later-stage asset gathering . Why founder quality still outranks category fashion Even in an AI cycle, Frankel insists that the real edge lies in founder quality, complementary teams and deep domain knowledge . The most interesting startups are not necessarily the ones that declare themselves AI companies first; they are often the ones where people with long industry experience can now use modern tools to attack a persistent pain point. A veteran SAP consultant, for example, who has spent years inside a broken workflow may suddenly be able to build a platform that was previously too expensive or too difficult to create . The capability is new, but the insight is old. That combination is more durable than theme-chasing . Frankel also gives unusually high priority to the relationship between founder and market. He wants a founder who can sell, a technical co-founder who can do what he calls magic, and a partnership marked by trust rather than sameness . That emphasis matters because AI lowers the barrier to building, but not the barrier to convincing, distributing and retaining customers. A model can be copied, but trust, timing and judgement remain hard to replicate. In that sense, AI may actually increase the value of operators who understand a niche deeply enough to see what the market has missed . AI changes the size of the company, not just the size of the market One of the most important strategic shifts in the discussion is the claim that AI enables meaningful businesses to be built by very small teams . That is not just a story about lower headcount; it is a story about a new operating baseline. With coding, support, research, analytics and even some creative tasks partially automated, a startup can begin with more leverage than earlier generations could access . This resembles the effect cloud computing had on infrastructure, but higher up the stack: the product can be launched without assembling the same volume of human labour first . The same logic helps explain why Frankel does not expect mass unemployment, despite expecting significant labour disruption. He anticipates a widening divide between those who learn to use AI well and those who do not . That divide may be especially sharp between younger workers who are already using voice, model-based tools and code assistants, and older workers whose workflows are more fixed . The labour market consequence is not a simple replacement story. It is a reallocation story in which routine tasks disappear, service expectations rise and high-trust human judgement becomes more valuable in the situations where mistakes are expensive . The next battleground is not only models Frankel is also explicit that today's apparent winners are not guaranteed to remain so. He argues that technology platforms rarely stay on top forever, and that the next disruption could come from China, from open models, or from a computing shift that is not yet mainstream . His photonic-computing comments are especially revealing because they show how he thinks about the stack: the model layer is important, but chips, energy and data-centre architecture may be equally decisive over time . If optical or photonic chips materially reduce energy cost, then current assumptions about what constitutes an unassailable advantage could change quickly . That broader view also explains his concern about US research spending and regulation. He sees public R and D, universities and agencies such as DARPA as the upstream source of future commercial breakthroughs, and worries that underinvestment could erode American advantage . At the same time, he views China's faster experimentation and lighter practical constraints as a serious competitive force, especially in areas that require rapid iteration . The strategic tension is clear: if the best ideas come from open-ended research but the fastest deployment happens elsewhere, then national advantage will depend on whether the US can preserve both invention and implementation . Why the most useful venture metric is liquidity, not paper value Frankel's attention to secondary sales and DPI is another sign of how pragmatic his framework has become . In a market where companies can remain private for years while still changing shape underneath investors, paper gains can become misleading . He is more interested in real cash returned than in theoretical value, and that is why he is open to partial liquidity when a company is clearly successful . The point is not to be anti-growth. It is to recognise that waiting for a perfect exit may be inferior to realising some return while preserving upside . This concern dovetails with his comments on the speed of innovation cycles. If products can be made obsolete before liquidity arrives, then the venture business becomes more dependent on timing, optionality and fund construction . Smaller funds can still work, but only if they remain disciplined about entry, selective about ownership and realistic about how much of the portfolio will actually become meaningful . Frankel's position is therefore not nostalgic. It is adaptive. He is willing to accept that AI will create enormous wealth, but he refuses to confuse that fact with an assumption that every investor, fund or startup in the category will share it . The long view: transformation and destruction are linked The deepest thread in Frankel's view is that technological revolutions always produce both creation and elimination. AI will likely make software more abundant, teams smaller, scientific work faster and services more accessible . It will also compress the life expectancy of companies, intensify competitive pressure and expose valuations that were justified more by narrative than by economics . That is why he can sound both enthusiastic and severe in the same breath. The opportunity is real, but so is the waste . For founders, the practical lesson is to build something with genuine edge rather than merely joining the category. For investors, the lesson is to respect price, preserve discipline and treat current leaders as temporary rather than permanent . For the broader market, the lesson is simpler still: AI is not just a growth story. It is a sorting mechanism. The companies that understand their customers, own a real workflow and adapt faster than the model cycle can become extraordinary. The rest may help prove just how ruthless a great wave can be .
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![“[AI] is the wave of our lives. I feel that way, by the way. If I look at internet, SaaS, mobile, AI, nothing looks the same. And will there be roadkill from this wave? Oh my God, there's going to be a lot.” - Quote: David Frankel - Founder Collective](https://globaladvisors.biz/wp-content/uploads/2026/08/20260812_19h00_GlobalAdvisors_Marketing_Quote_DavidFrankel_GAQ.png)
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Term: Recurrent neural network (RNN) - Artificial intelligence"A recurrent neural network (RNN) is a type of artificial intelligence model designed to process sequential data by saving a short-term memory of past inputs. Unlike standard networks that treat each piece of data independently, an RNN feeds the output of a step back into the network as input for the next step." - Recurrent neural network (RNN) - Artificial intelligence Sequential data creates a modelling problem that plain feedforward networks handle awkwardly: the meaning of each new input depends on what came before it. A recurrent neural network meets that problem by carrying forward a hidden state, so the model can use a short-term representation of earlier inputs when processing the current step. That design makes RNNs especially relevant wherever order matters, from speech and text to sensor streams and other time-based sequences . The practical meaning is straightforward. Rather than treating each token, frame, or observation as independent, an RNN updates an internal memory as it moves through the sequence. The same set of parameters is reused at every step, which lets the network apply a consistent rule to arbitrarily long inputs without increasing the parameter count with sequence length . In applied settings, this is why RNNs were long associated with language modelling, transcription, translation, sentiment analysis, and forecasting tasks that depend on temporal context . How the mechanism works At each time step , an RNN receives an input vector and a previous hidden state , then computes a new hidden state . A common compact specification is , with an output such as . Here maps inputs to the hidden layer, carries recurrent information forward, maps the hidden state to outputs, and , are bias terms . This formulation matters because the hidden state acts as the model's working memory. The network does not store a perfect record of the past; it compresses past information into a fixed-size state that is updated repeatedly as new data arrives. In effect, the RNN is unrolled across time into a chain of identical cells, each using the same weights but a different time index, which is the standard way to understand how recurrence and parameter sharing fit together . Training usually relies on backpropagation through time, in which the unrolled network is treated as a deep structure spanning all time steps. Gradients are propagated through the sequence so that the model can learn how earlier inputs influenced later errors . This training method is one reason RNNs were powerful but also difficult to optimise in practice, because long chains of repeated transformations can make gradients shrink or grow too much as they move backward through time . Why RNNs were important The main appeal of an RNN is that it builds sequence awareness directly into the architecture. That makes it suitable for language, where a word's meaning depends on previous words, and for time series, where current values are shaped by trends and lags in the past . RNNs therefore became a natural choice for machine translation, speech recognition, text generation, handwriting synthesis, and forecasting problems where the order of observations cannot be ignored . Another advantage is flexibility over input length. Because recurrence reuses parameters at every step, the same network can process short sequences and long sequences without redesigning the architecture for each possible length . That property helped RNNs become a general-purpose tool for variable-length sequence data, which is one reason they appeared in early voice assistants and other systems that had to absorb streams of language or audio . Core debates and technical limits The same mechanism that gives RNNs memory also creates their biggest weakness. In standard forms, the hidden state must compress everything relevant from the past into a single vector, so distant information can be lost or diluted as the sequence grows longer . This makes plain RNNs less reliable on tasks that require very long-range dependencies, such as tracking a subject introduced many steps earlier in a paragraph or a regime change that began far back in a financial series . That limitation led to several schools of thought. One camp treated the plain RNN as the cleanest expression of sequential modelling and tried to improve it with better optimisation and feature engineering. Another camp moved towards gated variants, such as long short-term memory networks and gated recurrent units, which were designed to preserve information more effectively across long spans. A third camp eventually shifted attention to attention-based architectures, especially transformers, which avoid step-by-step recurrence and instead model dependencies more directly across positions. The result is not that RNNs were wrong, but that their inductive bias is most useful when recency, order, and local continuity are more important than very long-distance recall . There is also a conceptual debate about what 'memory' means in an RNN. In strict terms, the model does not remember in a human sense; it stores a learned summary in the hidden state and updates that summary according to its parameters . For some tasks, that summary is enough. For others, especially those involving richer context or complex cross-token relationships, the compressed state becomes a bottleneck rather than a strength . What the term means in practice today In modern AI, RNNs remain important as a teaching model and as a practical baseline for sequential learning. They clarify the logic of state, recurrence, and temporal dependence in a way that makes later architectures easier to understand . In production systems, they are less dominant than they once were, but they still matter where compute constraints, streaming data, or established legacy pipelines favour compact recurrent models over larger attention-based systems . The term also matters because it describes a broader design principle rather than a single fixed implementation. Any system that feeds its prior state forward to influence later predictions is using recurrence in some form, even if the exact cell structure differs across variants . That is why RNNs remain a reference point in machine learning discussions: they sit at the intersection of sequence modelling, memory, optimisation, and architectural trade-offs, and they explain why order-sensitive data demands more than ordinary static classification .
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Global Advisors News Brief - August 14 2026Read the full brief at the link Headlines for the last 24hrs
Time window: 2026-08-13T05:00:33.075Z to 2026-08-14T05:00:33.075Z |
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Quote: David Frankel - Founder Collective"Are we headed for another dot crash? Definitely. It's not a question of if. When, nobody knows." - David Frankel - Founder Collective The central tension is between technological truth and financial timing. AI can be transformational and still leave a trail of mispriced companies, inflated expectations and broken portfolios, because capital markets reward narrative far faster than they reward durable economics. David Frankel's warning lands in that gap: the technology may be real, the opportunity may be enormous, and the crash may still be unavoidable . Why the warning matters Frankel's position is not that AI is a mirage. He treats it as the most consequential technology wave of his venture career, with the capacity to produce a small number of genuinely giant companies while destroying a much larger number of hopeful entrants . That matters because the market tends to flatten very different kinds of company into the same theme. A startup using AI well, a startup built around AI infrastructure, and a startup merely borrowing AI language for fundraising can all look identical in a pitch deck, even though their economics and survival prospects are radically different . The background to that view is historical concentration. Frankel repeatedly returns to the idea that venture outcomes are not normally distributed in the way many current investors would prefer. Dealroom's summary of the discussion notes his argument that the most valuable companies of the last quarter century were exceedingly rare, and that the median value of the top 500 companies created over that period was about $2.6 billion . In practical terms, that means the AI wave does not need dozens of $10 billion winners to look impressive; it only needs a handful. But it also means that most of the capital already deployed into the category will not be rewarded at the same level. The crash can be real without invalidating the technology Frankel's most important distinction is between a good technological thesis and a bad market structure. He does not argue that AI progress will stop. He argues that valuations, round structures and investor behaviour are likely to outrun what the underlying businesses can support . That is why he is willing to say a dot-com style crash is coming, while also insisting that the underlying wave is still the opportunity of a generation . In his framing, the crash is not a rebuttal to AI; it is a consequence of everyone trying to own the same future at once. This is a familiar pattern in technology cycles. First comes a genuine capability shift. Then capital floods in. Then investor discipline weakens because no one wants to miss the next category-defining company. Then the market starts financing too many lookalikes at prices that assume all of them will become category leaders . Frankel's language is blunt because he thinks the distortion is already visible. He suggests that a large share of today's AI startups are effectively roadkill in waiting, not because they are useless, but because the market is treating a very narrow top tier as if it were broadly available . Seed investing has become a different business The quote also makes more sense when placed inside the economics of seed venture. Frankel argues that small funds can still work because their return requirements are different from those of larger firms . A boutique seed fund can be made whole by owning a meaningful stake in an outcome that would barely move a mega-fund. By contrast, bigger platforms increasingly need exposure to the very largest companies in order to justify their own scale . That is why he sees seed as crowded but not dead: the stage still works if the fund size, entry price and ownership model remain coherent . His scepticism is aimed at a specific intermediate category: the enlarged seed fund that is too big to be nimble and too small to dominate later rounds . Those firms often depend on access, branding and reserve power, but Frankel thinks they can lose the intimacy that matters most at the point of company formation. The irony is that the current market sometimes treats capital scale as proof of strength, when in practice it can become a weakness if the firm can no longer support founders once the next financing decision arrives . Price, ownership and the mathematics of dilution Frankel is also pushing back against the idea that price no longer matters if the company is a future winner. His objection is simple: it is still a ratio problem. The higher the entry valuation, the larger the outcome required to generate the same return . That means uncapped notes, oversized seed rounds and momentum pricing all compress the margin for error. A company can be excellent and still be a bad venture investment if the entry price is too aggressive . This is why he is comfortable missing some deals. Founder Collective's model is explicitly disciplined, even if that means leaving upside on the table . The firm may invest smaller cheques, act as a back-pocket insurer for founders and accept that some later growth will accrue to larger funds . Frankel would rather preserve a framework than become a momentum investor by default. That choice is not cost-free, and he admits it can look foolish in retrospect . But it keeps the firm aligned with the type of outcome it can actually monetise. What changes inside the company There is another layer to the warning: AI changes company formation itself. Frankel argues that more people can now start companies, but fewer possess the entrepreneurial stamina required to build them through a full cycle . That distinction matters because an AI-enabled market lowers the friction to launch, yet does not lower the emotional cost of persistence. Many teams can look like founders at the start, but far fewer can sustain the pressure, ambiguity and constant reinvention demanded when the tide turns . He also sees AI reducing the minimum size of a viable company. Very small teams can now do work that previously required much larger organisations, and that will create new forms of efficiency as well as new forms of concentration . But the stronger consequence may be psychological rather than operational. If a tiny team can now ship like a much larger one, investors may start demanding compressed growth timelines from everyone else, even where that makes no strategic sense . The market then mistakes AI speed for universal speed, and penalises companies that are strong but not spectacular. Why incumbency may be shorter than it looks The crash warning also reflects Frankel's broader belief that no current leader is safe for long. He expects today's apparent AI incumbents to be challenged by new model architectures, Chinese competitors, open-source systems and eventually shifts in compute itself . That is one reason he is interested in photonic computing and other technologies that could change the cost base beneath Nvidia's current dominance . The message is not that one company will definitely lose, but that every layer of the stack is provisional. That same logic shapes his view of regulation and state capacity. Frankel worries that the United States may underinvest in basic research, while China may continue to compress experimentation cycles through faster deployment and looser constraints . If that is true, then the next turn in AI may not merely be a better model, but a different industrial geography. The market implication is stark: a company that looks dominant in one cycle can become just another historical footnote if the capital, policy and research environment changes underneath it. Why the warning is also a strategy note Frankel's phrasing sounds like a prediction, but it also functions as guidance. If a crash is coming, then investors should care less about being seen in the hottest rounds and more about whether the business can survive a changed financing environment . That pushes the best capital towards founders with domain knowledge, real product edge and enough discipline to build through volatility rather than merely ride it . It also rewards firms that can remain active when valuations are less fashionable and liquidity is more constrained . The deeper strategic point is that AI is not a single market. It is a series of overlapping markets: models, tooling, infrastructure, application layers, professional services, data, workflow software and compute . Some of those layers will see spectacular concentration. Others will be commoditised. Some will expand because costs fall. Others will contract because new tools displace old ones. Frankel's warning cuts through the fantasy that all of these outcomes can be financed at once and still produce acceptable returns. In his view, the wave is real, but the market's current enthusiasm is too broad to be sustainable . That is why the phrase 'another dot crash' should be read less as a prophecy of technological failure and more as a statement about discipline. In periods of intense innovation, capital often confuses participation with conviction. Frankel is arguing for a harder standard: if the next decade will reward a small number of extraordinary companies, then the burden on investors is to identify genuine edge, price it honestly and accept that most of the category will not make it through intact .
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Term: Analysis of Variance (ANOVA) - Statistics"ANOVA stands for Analysis of Variance. It is a statistical test used to check if the average values (means) of three or more groups are different from each other. Instead of running multiple t-tests - which increases the chance of a false-positive error - ANOVA looks at the data all at once to see if the group differences are real or just random luck." - Analysis of Variance (ANOVA) - Statistics Group comparison becomes fragile the moment the analyst moves beyond two samples, because repeated pairwise testing inflates the probability of a false positive. ANOVA addresses that problem by asking a single omnibus question: do the observed differences among group means exceed what would be expected from ordinary sampling variation alone? The method is built around variance partitioning, so it compares how far group averages sit from the overall mean against how much spread remains inside each group . That framing matters in practice because the test is designed to separate signal from noise when the explanatory variable is categorical and the outcome is numerical. In the standard one-way case, there is one factor with multiple levels and one response variable, and the null hypothesis states that all population means are equal. A significant result does not prove that every group differs from every other group; it only shows that at least one mean is inconsistent with the rest . How ANOVA works The mechanical idea is simple even if the bookkeeping is not. ANOVA decomposes total variation into a between-group component and a within-group component, then forms an F ratio from the corresponding mean squares. In symbolic form, a one-way model is often written as , where is the observation in group , is the grand mean, is the effect of group , and is the residual error . The test statistic is usually expressed as , where is the between-group mean square and is the within-group mean square . If the groups are genuinely different, the numerator should be large relative to the denominator, because the group means will be pulled apart more than can be explained by random scatter within groups. The resulting value is then compared with an distribution with suitable degrees of freedom to obtain a p-value . Another way to see the logic is through the sum of squares. The total sum of squares is partitioned into between-group and within-group parts, often written as . The between-group term measures how far each group mean sits from the grand mean, while the within-group term measures how far individual observations sit from their own group mean. This partition is why ANOVA is often described as a model of variation rather than simply a test of means . Why variance, not direct mean comparison The choice to work through variance is not a cosmetic one. If one were to compare several means by running many t-tests, each test would carry its own chance of error, and the overall false-positive rate would rise quickly. ANOVA avoids that by pooling the comparison into one test, which preserves the intended type I error rate much more effectively than an uncorrected battery of pairwise tests . That is why the method is usually treated as the first gate in a wider inferential workflow rather than as the final word on which specific groups differ. This also explains a common misunderstanding. ANOVA does not directly tell the analyst which treatments, categories, or conditions differ from each other. A significant omnibus result only justifies further investigation, usually with post hoc procedures that adjust for multiple comparisons. In applied work, this distinction is crucial: the test answers whether there is evidence of any difference, while follow-up analysis answers where that difference lies . Assumptions and their practical meaning Classical ANOVA rests on three core assumptions: independence of observations, approximately normal residuals within each group, and equal variances across groups. These are often presented as technical requirements, but they have ordinary practical meanings. Independence means one measurement should not mechanically determine another, normality concerns the shape of the error distribution in each group, and homogeneity of variance means the groups should have roughly comparable spread . The equal-variance condition is especially important because the ratio implicitly assumes that any within-group variation is measuring the same underlying error scale across groups. When that assumption is badly violated, the nominal p-value can be misleading, particularly if sample sizes are also uneven. In such cases, analysts may use a robust alternative such as Welch's ANOVA, transform the response, or model the structure differently rather than forcing the classical test to fit the data . Major schools of thought Historically, ANOVA is associated with Fisher's experimental design tradition, where randomisation, replication, and blocking are central to credible inference. In that school, the method is not merely a computational test but part of a disciplined way of structuring experiments so that treatment effects can be separated from nuisance variation . The appeal of that approach is its clarity: design the study well, then let the variance decomposition do the inferential work. A second tradition treats ANOVA as a special case of the general linear model. In that view, the same algebra underlies t-tests, one-way ANOVA, factorial designs, and regression with categorical predictors. This unifying perspective is attractive because it shows that the method is not a standalone ritual, but a particular parameterisation of linear modelling with indicators for group membership . It also makes extensions such as two-way ANOVA and interactions easier to understand, since the analyst is simply adding structure to the model . A third perspective is more pragmatic and less doctrinal. Here the emphasis is on whether the question asks for an ordered comparison of several means, whether the sample sizes are adequate, and whether the assumptions are plausible enough to justify the method. This school is less interested in defending ANOVA as a universal solution and more interested in using it as one tool among others, alongside robust tests, mixed models, or non-parametric alternatives where appropriate . Tensions, limitations, and common misuses One tension is that ANOVA is often described as a test of means, yet it is operationalised through variance. That wording can mislead users into thinking the procedure directly measures average differences in a simple way. In fact, the logic is inferential and indirect: means matter because their separation changes the variance structure, and the test statistic captures that change through a ratio of estimated error terms . Another limitation is that statistical significance can be overinterpreted. A small p-value indicates that the observed pattern would be unlikely if all population means were equal, but it does not say that the effect is practically important, causally identified, or stable across samples. In applied settings, the analyst still needs effect sizes, confidence intervals, diagnostic checks, and subject-matter judgement to decide whether the difference is meaningful . There is also a recurring temptation to treat ANOVA as if it were immune to poor design. It is not. If groups differ systematically in ways that were never controlled, if observations are dependent, or if variance heterogeneity is severe, the tidy algebra can produce a false sense of certainty. That is why serious use of the method begins with the design of the comparison, not with the software output . Why it still matters ANOVA remains important because many real questions are still group-comparison questions: do teaching methods produce different outcomes, do treatments differ, do factories produce products with the same mean quality, or do policy regimes lead to distinct average results? The method offers a compact answer to those questions while controlling the error rate better than ad hoc multiple testing . Its longevity comes from that combination of statistical discipline and practical usefulness. It also matters because it teaches a broader lesson about inference. Differences in raw averages are not enough; one must ask whether those differences exceed the background variation that naturally appears in data. ANOVA formalises that idea in a way that is portable across disciplines, from medicine and psychology to agriculture, engineering, and business research . The method endures because the underlying problem endures: real decisions usually depend on whether apparent differences are genuine or merely the noise of sampling.
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Global Advisors News Brief - August 13 2026Read the full brief at the link Headlines for the last 24hrs
Time window: 2026-08-12T07:00:52.108Z to 2026-08-13T07:00:52.108Z |
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