Setting a fair value on an early-stage tech company is in many cases a cumbersome exercise. Neither a too low nor too high valuation is in the best interest of any stakeholder, neither existing owners nor potential investors. Setting the right price is all about knowing what to look for from a company lifecycle perspective.
With a wide array of approaches to setting the value of a company, knowing which ones are suitable for the lifecycle phase of the company should be a rule of thumb. While listed companies can be broken down with quantitative metrics, setting the right value of a startup is more often based on a vision and belief that the company is able to execute. As Matthew Schubring told Forbes: “A good valuation is 75% art and 25% science because it takes into account the story behind the numbers of a business.
Taking a lifecycle approach to valuations is in my book a good approach to maintaining that balance between art and science.
1. As a newly started company, the facts about the company are limited, and the value is set on future expectations, rather than present facts.
In this phase, understanding the size of the target market and what share is considered addressable at which price may serve as a confirmation that there is a target market with potential value to capture, but the true value of the company is believing that the team is able to execute on their plan to penetrate that given market. One may get a sense of the potential future value of the company based on the usual suspects of multiples such as price/earnings, and price/sales, but at this stage, these should be considered a theoretical future rather than a definitive indicator of a company’s value. At the conception stage, funding is usually from the entrepreneurs themselves, together with friends fools, and family, and it is, therefore, reasonable that the value of the company is set based on faith, hope, and love.
2. As a company proceeds to the next phase of its lifecycle, the initial vision is paired with some initial investments, that may act as anchoring points for both present and future valuations.
Setting a fair price on the work that has gone into the company should act as a basis for a cost-based valuation of the company. This may include but should not be limited to money spent on external services (development, legal, accounting services, etc.), the amount of time invested in the company (at a reasonable hourly rate), data integrations, and existing contracts, just to name some metrics.
Since there is still a high degree of uncertainty related to potential future profits, looking at the proposed valuation at the time of investment and the potential future value at a given point in time is a useful way to do a reality check of the value of a company. As an example, if you acquire a 10% share of the company for $20 million and expect a 7x return on invested capital after 5 years, is it plausible that the company may be worth $700 million five years later?
3. When the company starts to have profits to show for, it is time to fire up the spreadsheets and start looking at valuation multiples. In this stage, what is the company’s value object in relation to the equity story? If the company is raising capital based on its ability to amass a large user base, what is the average value per user, and what is the user acquisition cost? Are you raising capital as a B2B SaaS company, the price to profit multiple should be calculated based on the business model’s ability to scale.
To verify the numbers, a peer group analysis is a useful method. Compare valuation multiples with similar companies. If you do not have a transaction database of your own, there are a lot of great resources online such as Crunchbase.
4. Reaching the IPO-stage, one should expect that the quantitative factors outweigh the qualitative, but as the demand for tech IPOs seems insatiable, the excitement of potentially getting in early on the next big thing brings us full circle back to faith hope, and love as the approach to pricing a company.
Tesla is perhaps the foremost example of how a company is able to defy logic when it comes to the connection between fundamentals and market performance. But this heat is even more intense when it comes to new tech stocks hitting the market.
Companies like Tesla, Facebook, Netflix, Amazon, Represent some of the fastest-growing valuations the world has ever seen. Their growth is exponential, their assets are almost ephemeral, and they shun all traditional economic models for valuations. The investments required to scale are almost negligible, and their market size is often unlimited. However, these are rare cases. After all, there is a reason why there is one Facebook.
Traditional valuation methods are not only still valid, but it is more important than ever to understand the underlying value of a company rather than getting carried away by the excitement of potentially getting in early on the next big thing.
Even though various approaches are more suited for different stages of a company’s lifecycle, some elements should be present no matter the funding stage. Learning how to calculate and set the price on risk and risk mitigation is in my perspective a prerequisite for almost any valuation. No matter the sophistication of the calculated valuation and expected returns, those numbers are only as good as your assumptions. Knowing how to quantify uncertainties and assess the underlying risk of those uncertainties is a powerful tool for both investors and entrepreneurs.
When the homework is done, and it is time to enter price negotiations, make sure to know your worth. This applies to both entrepreneurs as well as investors. If all parties are a bit dissatisfied after sealing the deal, the price is most likely right at the time being. After all, at the end of the day, the value of the company is determined by future success.
Arendalsuka er over, og alle er enige om at det har vært en fin tur. Blant de mange debattscenene er nok en gang digitalisering og særlig kunstig intelligens et yndet tema. Her har det vært mulig å bryne seg de store temaene som de etiske, regulatoriske og samfunnsmessige konsekvensene og mulighetene knyttet til AI. IKT-Norge står opp for anledningen og tar til orde for en bred politisk satsing på kunstig intelligens med en ambisjon om å bli ledende nasjon innen faget.
Men når bakrusen etter en uke med paneldebatter og sene kvelder der diskusjonene fortsetter stadig mer visjonært ettersom man går inn i de sene nattetimer er over tegner vår evne til å omsette ord til handling et stadig mindre optimistisk bilde for vår digitale fremtid og evne til å ta i bruk kunstig intelligens i praksis.
Ifølge Abelias omstillingsbarometer sakker stadig akterut sammenlignet med våre naboland, og avstanden mellom festtaler og virkelighet øker dermed for hver dag som går. Områder der vi en gang briljerte preges i dag av middelmådighet, og vi taper kampen om de beste hodene. Som resultat ligger vi etter i bruk av ny teknologi og vår omstillingsevne går fra vondt til verre.
Det har lenge vært et mantra at digitalisering i større grad må inn på toppledelsens, virksomhetsstyrenes og den politiske agendaen. Uten å undervurdere viktigheten av nettopp dette er det desto viktigere å sørge for at det er noen som kan gjøre jobben når ledelsen kommer entusiastisk tilbake fra ledersamling med AI som tema, klare for å transformere sin egen virksomhet ved bruk av teknologi.
Ryggmargsrefleksen vil for mange være at her trenger vi en ny løsning eller plattform, men det er viktig å minne seg på at digital transformasjon ikke er en anskaffelse, men en kontinuerlig prosess. For å lykkes med denne transformasjonen er det avgjørende med et langtidsperspektiv på både investeringer i teknologi og nødvendig kompetanse.
På samme måte som et bygg vil kollapse dersom en slurver med fundamentet, gjelder det samme for digitaliseringen. Å utvikle på toppen av gammel og utdatert teknologi vil skape mer trøbbel enn nytte på sikt. Det kreves kunnskap om teknologien for å skape systemer som er trygge, fleksible og fremtidsrettede nok til å kunne bære den digitale omstillingen.
Når alle virksomheter skal bli datadrevne øker også det digitale trusselbildet, og angriperne blir stadig mer sofistikerte. Der data kan sees på som en kilde til innovasjon og innsikt fra virksomhets perspektiv vil digitale angripere se på data som en angrepsvektor. Maskinlæringsmodeller som ChatGPT benytter seg i stor grad av åpne data, og kriminelle har for lengst lært seg å fôre disse modellene med feilaktige datasett gjennom såkalt «data poisoning» for ondsinnede formål.
Det holder med andre ord ikke å ha lært seg å bruke ChatGPT til enkle spørringer eller klarer å skrive noen enkle kodelinjer etter et onlinekurs på Codecademy. Det er behov for en kombinasjon av bransjeerfaring, kombinert med digital dybdekompetanse som evner å sette seg inn i både de forretningsmessige problemene som skal løses, men også evner å se helheten og kompleksiteten i det digitale utviklingsarbeidet.
Det er derfor paradoksalt at mens næringslivet skriker etter teknologikompetanse, og det er tre ganger så mange søkere som det er studieplasser til informatikkfagene går antall studieplasser ned. Dette til tross for at det også er en økning i antall søkere.
Det finnes ingen lettvinte løsninger for å dekke kompetansegapet, og for hvert år vi somler med å opprette tilstrekkelig mange studieplasser innen informatikkfaget vil ledetiden mot å fylle kompetansegapet øke. Som en konsekvens risikerer vi at konkurransekraften til norske virksomheter svekkes.
I år er det kunstig intelligens som driver det digitale hype-toget, i fjor var det metaverse, og neste år er det kanskje noe annet. At digitale trender kommer og går er en del av teknologiens natur og endrer ikke det faktum at programvare fortsetter å spise verden med uforminsket styrke og vi er avhengig av teknologikompetanse for å henge med.
Samtidig kan det tidvis virke som om vi her hjemme er mest opptatt av hvorvidt vi skal bruke AI eller KI som akronym for kunstig intelligens fremfor å sørge for at vi utvikler og tiltrekker oss den rette spisskompetansen.
Norwegian fintech is following the downward trend of the sector globally, as indicated by a survey conducted by the Ontogeny Group on behalf of Finansforbundet. Simultaneously, as revenue growth levels off and access to capital for the sector dries up, the accumulated deficits are increasing. This set of challenges will undoubtedly lead to more companies in the sector throwing in the towel in the times to come.
This is despite the fact that banks (which fintech was supposed to challenge) are making more money than ever before.
The report highlights macro trends as possible explanatory variables for the downward trend, but there is more than just tougher economic times behind the steep decline. Similar to the global fintech sector, it has been revealed that delivering financial services without being a financial institution is not as straightforward as initially thought.
During the emergence of the fintech sector, a number of new regulations have come into play. While regulations like PSD2 aimed to reduce barriers to entry and level the playing field for financial services competition, other regulatory changes impose stricter requirements for customer identification and consumer protection in the presentation and distribution of financial products and services.
At the same time, PSD2 didn’t bring about the seismic shift in consumer behavior that many hoped for. The allowance for national interpretations of technical standards led to unnecessary friction related to banks’ technical interfaces and resulted in services like initiating recurring payments via third parties being interpreted as not falling under the regulatory framework. The biggest impact of PSD2 didn’t favor the fintech sector; instead, it acted as a catalyst for incumbent banks to revamp their online and mobile banking platforms to proactively raise barriers of entry for potential non-bank competitors.
Many fintech companies have had a sudden awakening, finding themselves subjected to the same requirements as full-fledged banks to operate under what were meant to be lighter licenses and concessions, such as payment service providers, fund distributors, or loan agents.
Since fintech companies aren’t full-fledged banks, they don’t have access to the money-printing capabilities that banks have during times of rising interest rates and the spread between deposit and lending rates. Instead, they must rely on off-balance sheet income sources like various fees, subscription models, and kickback agreements. The challenge is that these are fragile revenue models that tend to engage in a race to the bottom as soon as one player decides to lower prices or eliminate fees entirely, similar to how Skandiabanken did in the Norwegian market. Furthermore, these revenues are vulnerable to regulatory changes, such as the tightening of commission sizes for fund distribution under MiFID II or the IFR regulation that placed limits on interchange income from credit card transactions.
As a result, margins are squeezed, and the only path to profitability is achieving scale and critical mass. This is particularly challenging in a small country like Norway, where the domestic market size is limited.
Many have attempted to overcome this by rapidly expanding into new geographies, but they’ve quickly realized that fintech has limited global scalability and relies on local adaptations to address not only local interpretations of financial regulations but also varying user behaviors and cultural differences. Even Vipps, with grand plans to digitize payment services across Europe and seemingly limitless resources, has yet to demonstrate a successful international expansion.
There are certainly honorable exceptions, with the report highlighting Fixrate and Fundingpartner as notable examples that have both found ways to participate in a business model where interest rate margins drive profitability without transitioning to full-scale banks.
Others have tried to pivot from the end-user market to becoming technology and platform providers for banks. Whether this will be a winning strategy for the remaining fintech companies pursuing this survival strategy remains to be seen. However, it’s a market with a limited pool of potential customers, and decision-making processes are often lengthy and unpredictable.
It’s also important to note that the fintech phenomenon and sector were born out of dissatisfaction and mistrust of the financial sector and banks in the aftermath of the 2008 financial crisis. In Norway, the banking sector has been able to thrive due to a high level of trust among the population, which is evident in the relatively low rate of customer mobility. Although a survey by Accenture indicates that this trust is declining, a customer mobility rate of only 7 percent that switches banks annually and the limited penetration of challenger banks like Revolut and Lunar in the Norwegian market suggest that the broader market in Norway remains loyal to their bank and is reluctant to adopt new digital financial services from smaller and lesser-known players.
As the report shows, despite signs of optimism last year, things have gone from bad to worse for the fintech sector, and all indications suggest that it will become even tougher in the times ahead. While this brief assessment primarily targets the Norwegian fintech sector, the majority of the underlying obstacles are universally applicable to the fintech sector as a whole.
For existing and aspiring fintech entrepreneurs, it will be more important than ever to have a solid grasp of their business model and a near-obsessive focus on the problem they are solving for whom, and whether there are individuals willing to pay for it.
Artificial intelligence is the talk of the town in every industry these days, and construction is no exception. Digitalization is imperative to improve operational efficiency in the Architecture, Engineering, and Construction (AEC) industry in the future, and AI is one of the most impactful technologies to make that happen. Here’s how.
As opposed to more traditional IT, which is more geared toward rule-based, predictable processes as we see in manufacturing. AI has the potential to deliver dynamic digitalization at scale where operations are similar, but not identical, as is the case in construction.
This may instinctively be perceived as an- overly techno-centric and audacious statement, but if we dive into the range of potential use cases for AI and algorithmic design tools as a broad term in construction, this may be closer to reality than our initial gut reflex might think.
Just like the introduction of the internet gave birth to online banking, and the launch of the smartphone brought technology into our everyday lives in a manner we might never have imagined in advance, AI has the transformative power to usher in an age of digital transformation in the AEC industry.
Looking at the entire AEC value chain, AI has a part to play in every step of the way from design throughout construction all the way to building maintenance and eventual decommissioning.
Designers and architects may be some of the early adopters of advanced data modeling tools in the industry, having used model-driven design for decades already. With solutions like Spacemaker – now Autodesk Forma, Spacio, Arealize, and Parallelo, just to name a few coming out of the Norwegian contech startup scene, designers can generate a wide range of optimized building design suggestions in seconds. Further on, tools like Consigli may evaluate whether those designs are up to speed with current technical standards and regulations.
To take this even further, technology can also help in sourcing the right products and materials for any project – a process that is tedious and time-consuming. A fine-tuned language model like we see in ChatGPT can cut research time, suggesting the best product matches and even answering questions relating to product performance.
The use of AI for increased bidding intelligence may increase the value of historical data, as contracts will be able to analyze vast amounts of data to both more precisely predict the probability of certain bids, but also heighten the quality of tenders by using AI to check for potential inconsistencies.
However, where things really get exciting is when AI is applied to the construction phase. This is where the virtual world of data meets the hard physical reality of a construction project. The amount of data produced at any given construction project is staggering, but this data is most often entered in retrospect ad used for reporting purposes.
By capturing and processing data in near real-time, solutions like Ditio will be able to present those data as actionable insights to site managers, thus directly impacting project performance on a granular level.
At Skanska, we are currently using those data sets to automate and optimize mass hauling. Thus, reducing fuel usage and lowering CO2 emissions. Initial research found that machines on an average civil construction site are idling about half the time. Much of this time is when haulers are standing in line or waiting on another machine to complete its task. To reduce this time, the underlying algorithms enable “live, dynamic routing” that reorganizes circulation routes and staging patterns on-site to minimize idling. This solution, which will be available for the entire industry through Ditio, will be able to suggest optimal routing and machine fleet composition day by day, as environmental conditions change.
Based on the same data sets, a new set of machine learning algorithms may be utilized for predictive maintenance of equipment and vehicles as well. Such a system would take haulers specific duties and cargo into account. For example, trucks that are used to haul boulders will need checkups more often than ones used to haul soil. By being able to predict more precisely when a machine will break down using both machine and project data, it is possible to avoid a halt in production due to unexpected machine failures, as well as reduce unnecessary repairs.
AI can also be used to identify potential safety hazards before they occur, improving job site safety This may be everything from using image recognition on drone footage to predict potential safety hazards to check for proper use of PPE (personal protective equipment) by workers on site.
Those same images me be utilized to automatically track project progress and performance, as well as to check for structural integrity in concrete constructions are just some examples of how data may be leveraged to increase operational performance in the construction phase.
And that is without even touching upon the topic of robotics, exemplified through Spot from Boston Dynamics and Siteprint from HP. While it is the robot’s physical presence that impresses us, it is the software and internal algorithms that enable their performance. But that’s a topic that deserves a blog post on its own.
Although there are a multitude of promising use cases, getting started with AI in the construction industry is not something that is done overnight. The IT landscape in the industry tends to be fragmented and is characterized by data stuck in siloed applications. A long-term commitment to a consistent data platform that maintains a single source of truth is imperative to establish a common ground of trustworthy and unambiguous data. This will require investments in both technology and competence.
There has been, and still is a cultural barrier to technology adoption in the industry. According to Constructiondive, there are concerns related to AI’s potential to eliminate jobs. A 2023 survey by Pew Research found that 32% of U.S. workers think AI will hurt the workforce more than help, while just 13% believe the reverse.
However, it is important to note that construction is an industry where the workforce has been shrinking, and AI may prove to be a necessary ingredient to close the gap and let contractors handle more work with a decreasing talent pool.
At the end of the day, using AI should never be a goal by itself, but a means to provide an algorithmic approach to a dynamic environment. To reap the potential benefits of AI in construction, it is crucial to combine technological insight with deep industry knowledge to identify high-impact use cases, thus avoiding chasing solutions looking for problems.
Leading pioneers in metaverse development are abandoning the concept. Does this spell the end of the promised land of interoperability between digital realms and immersive experiences that connect the physical and digital world, or is merely the initial hype dying off as development continues unhinged from tabloid headlines?
This week, several leading corporations announced that they will either abandon or significantly scale down their metaverse initiatives. The Wall Street Journal was the first to report that “Walt Disney Co. has eliminated its next-generation storytelling and consumer-experiences unit, the small division that was developing metaverse strategies.
This follows after Microsoft also announced that it will be scaling down its commercial metaverse plans, shifting to an industrial metaverse late last year. After forming the Industrial Metaverse Core team in October, Microsoft is layingoff all 100 workers, according to The Information.
Although scaling down, Microsoft claims that it remains committed to the industrial metaverse according to an official statement given to Forbes. By doing one thing and saying another, does it spell that Microsoft still has a clear vision for the industrial metaverse, or is the statement merely a communication hedge against being put on the wrong side of history if the metaverse proves to actually be the future?
Tencent and Bytedance are also taking a significant portion of their metaverse bets off the table. Tencent had ambitious plans to build both virtual reality software and hardware at an “extended reality” XR unit it launched in June last year, for which it hired nearly 300 people. Now, sources familiar with the matter state that Tencent is cutting back on its XR investments as metaverse falters
The remaining question is the metaverse a stillborn concept rather than the promised land of our digital futures?
One of the core challenges with the metaverse as a concept is that no one really has a uniform understanding of what it is. There are as many definitions of the metaverse as there are self-proclaimed metaverse evangelists. And those definitions are often deliberately vague, ambiguous, and jam-packed with every tech buzzword known to man as well as anecdotal suggestions of everything that is wrong with the current internet. Often delivering a message that is difficult to disagree with, but a long way from future promises to actual implementation.
As an example, even in an area where the promises of an interconnected metaverse are most prominent, namely gaming, it is easy to state that the future should allow gamers to transfer in-game items and identities across gaming worlds, but in reality, it raised numerous challenges. Would it make sense to bring along your firearms from Fortnite to World of Warcraft without disrupting the gameplay in the said world? How would such items render if the game worlds use different graphics engines? What about legal agreements regulating commercial and IPR terms across game worlds?
Further on, many of the building blocks that make up what is called the industrial metaverse undoubtedly have potential. Using virtual reality for training purposes, augmented reality for site inspections, and elaborate digital twins to replicate and simulate complex civil engineering projects as well as building performance shows great promise. But that does not imply that there needs to be an industrial metaverse in place to harness the effects of each of these underlying technologies.
Ditio aims to make construction sites more sustainable with artificial intelligence and is raising money for a new issue. As new investors, Dovetail signs on backed by Ferd Impact, together with Construct Venture.
In a short time, the newcomer Ditio has established itself as a leading supplier of real-time data and AI solutions for digital management of projects in the Norwegian construction industry. Among other things, the company develops artificially intelligent tools for tracking, planning, and optimization of mass transport, which will help make construction sites more sustainable.
The company has now brought in new capital to ensure further growth and international expansion. The new investors are Dovetail backed by Ferd Impact, as well as Construct Venture – OBOS and AF Gruppen‘s joint industrial venture setup. Skanska Digital – which has been an investor since 2019 – continues as the largest shareholder.
The ownership team is strengthened with both industrial and international software expertise, which fuels the company’s growth ambitions. Ditio will not only use artificial intelligence to streamline the process of cutting emissions but will become the preferred software supplier for both large and small players in the global construction market.
– We had our first meeting with Ditio in February last year, and everything we have learned about them over the past year has made us confident that we are a good match, says Codruta Gamulea Berg, Partner at Dovetail, which invests in technology-based companies. – We have spoken to many of the company’s customers and are extremely impressed by the feedback. It is rare for a product to be so successful in an industry with a low degree of digitization, where it has been notoriously difficult to gain access to – and value from – data.
When Construct Venture – with OBOS and AF Gruppen as owners enters as owner, it will be a significant commitment from some of the biggest players in the industry.
As part of the due diligence process, AF Anlegg tested Ditio’s software on one of their projects, with very good feedback. We have strong faith in Ditio commercially and feel that the solution has high industrial relevance, and with this investment want to contribute to a gathering of forces within construction to make the industry’s resource use and environmental impact more efficient, says Jan Fossgård in Construct Venture.
Ditio gives customers the opportunity to stream real-time data from the construction site and provides a full overview of everything from time registration to follow-up of subcontractors, load lists, location, and equipment. Skanska became Ditio’s first major customer a few years ago, and since then signed up the majority of major players in the Norwegian market as active customers.
– It is nice to see that we are a preferred supplier for the larger contractors, who have often grown from the systems that suited them when the requirements, the complexity, and the company were smaller. We seem to have cracked a code that provides a positive experience for construction workers to collect data in real-time, as a natural part of the workflow. It is particularly rewarding to see how customers use Ditio data to develop advanced analytics for better control over resource use, profitability, and CO2 emissions, says Erik Aadland, CEO Ditio.
It is the sustainability aspect that is particularly interesting for Skanska, which has committed to becoming climate neutral by 2045, and must halve its emissions already by 2030.
– As an example, construction machinery accounts for a fifth of the greenhouse gas emissions from the construction industry. Integrating technologies such as route optimization through machine learning and artificial intelligence in real-time management of construction machinery gives the driver control over the load, recommends optimal speed, and reduces inefficient idle time. Which results in better utilization of the machines and lower greenhouse gas emissions, says Christoffer Hernæs, CDO at Skanska and chairman of Ditio.
Ditio’s new mass hauling solution goes beyond reducing idle time, but also streamlines the process of ensuring that non-contaminated soil and rock masses are recovered or utilized, while contaminated masses are transported to approved landfills.
Once again, digital transformation has become the talk of the town, following the recent advancements in generative artificial intelligence. However, the path from words to execution is long and cumbersome, and few digital transformation efforts fail to produce the expected outcomes. Why is that so, and what can we do about it?
According to an often-cited study from McKinsey state that 70 percent of digital transformation efforts fail. The reasons are many, and not surprisingly, McKinsey lists lack of management support, poor or nonexistent collaboration across silos, lack of employee engagement, and accountability as some of the main culprits. This is supported by Bain, which in a study of 100 companies states that 75 percent of those surveyed settle for dilution of value and mediocre performance. A mere 5 percent of companies involved in digital transformation efforts reported that they had achieved or exceeded the expectations they had set for themselves.
Despite statistics that repeatedly show the need for a different approach to digital transformation, resources are still flowing into digital transformation initiatives. According to an Accenture study of 1350 global businesses, investments in digital reinvention exceeded $100 billion between 2016 and 2018. Not surprisingly, the majority of respondents reported that their investments failed to materialize and resulted in poor returns.
These are in my opinion the main reasons why digital transformation fails.
1. Lack of shared direction
If no one agrees on where they are going, why they are heading in that direction, and what they aim to achieve, it is close to impossible to succeed. Lack of direction starts at the top, and all too often, C-level executives avoid acknowledging that they are not on the same page. This may be due to a lack of common understanding of the task at hand, a lack of technological insight, or downright competing priorities. If top managers aren’t on the same page, it is a downright guaranteed failure.
To ensure that top managers are on the same page, over-communicate and articulate not just what you’re trying to achieve, but the problem you’re solving. A common pitfall after reaching this stage is to sit down and create a game-plan with ambitious deadlines and even more ambitious goals. The problem is that those numbers rarely convince the employees of the company why they are suddenly urged to change.
Instead of presenting an endless barrage of charts, boxes, and figures, leaders should embrace storytelling as a tool to inspire and engage the organization. Spreadsheets and powerpoints may serve a purpose for strategic planning or budgeting, but in order to take the strategy from the boardroom and to the whole of the organization, those key figures and objectives should be translated into a compelling story that gets everyone on the same page.
2. Digital replication over digital transformation
When asked to define digital transformation, I often emphasize the last part of the term telling us in clear sight that we should prepare to transition from one state to another. Nevertheless, all too many times, digital transformation efforts end up replicating and marginally improving manual processes in a digital context.
In order to break free of this approach, I often encourage people to ask themselves, imagine there was no paper. Would you design your processes the same way you have today? Most likely you would utilize the possibilities given by technology to design a frictionless process with as few steps as possible. This is unfortunately not always the case when it comes to the digitization of legacy business processes. As we evolve from the use of IT for process assistance and caseworker support to intelligent automation, the need for a new approach to digitization is crucial. Rather than replicating obsolete practices, we should embrace the opportunity to reinvent existing business practices.
3. Digital becomes a goal itself
I may be tempted to argue that the whole concept of a digital strategy is the sworn enemy of a successful digital transformation. All too often, digital strategies divert attention away from goals that provide shareholder value, such as increased revenues, reduced costs, or increased customer satisfaction.
The digital economy disrupts many of the core concepts of traditional financial theories such as economic rent, the profit earned in excess of a company’s cost of capital, as well as redefines traditional value chains by making distribution intermediaries obsolete. Digital offers absolute transparency and a potentially limitless choice, which on average creates more value for customers than for corporations. An inevitable outcome of technological innovation is according to The Zero Marginal Cost Society by Jeremy Rifkin that the marginal cost of products and services will eventually move close to zero. This will at best shift the profit pool of any industry, and favor those who are able to capture value from new revenue sources.
In order to overcome this, leaders must invest sufficient time to really understand how digital is impacting their industry, and how to benefit from these changes. This points back to the importance of a shared direction, unless everyone is on the same page when it comes to understanding digital, it is difficult to harvest any returns from digital transformation efforts.
5. Lack of talent
The ability to succeed with digital transformation all depends on the people of your organization. Lack of experience and/or expertise in digital transformation leads to basic mistakes such as unrealistic goals, lack of a clear strategy, underestimating risk, and not allocating enough resources. It may be tempting to fill the gaps through outside hires, but no matter how many experts you acquire from the outside, nothing gets done without leading change within the existing organization.
It is widely recognized that digital change requires employees to learn new skills, but in addition to learning new skills, focus equally on unlearning old habits and obsolete practices. In my many years as a management consultant, the phrase “this is how we’ve always done it” was all too often the go-to explanation for why certain tasks were performed that way. When challenging existing practices, make sure to identify and break down any informal organizational hierarchy that either knowingly or unconsciously directs a web of resistance to change.
Change management is not only limited to top-level management but needs to be embraced by middle management as well. An important question to ask oneself is whether you have any change agents at the middle management level in place. Change agents are those employees that are passionate about new technology and the opportunities that arise in times of uncertainty. Many start out as digital advocates and, over time, develop into experienced business transformers. They recognize the impact of digital and they’re driven to help their organizations adapt.
6. Inability to scale
Digital initiatives are often conceived in a small part of the organization as a pilot initiative, and all too often there’s a stark divide between the digital capabilities supporting the pilot and the capabilities available to support scaling it.
This may be due to the fact that there is a skill gap between various parts of the organization, or in my opinion due to a lack of understanding on how a successful pilot is “handed over to production”.
Instead of separating the entities doing innovation sprints and the entities taking care of business as usual, one should assign initiatives to cross-functional teams that are both expected to continuously improve their operations, but perhaps most importantly, given the necessary autonomy to make decisions along the way. It is important to note that autonomy does not spell absolute freedom, autonomy is only productive when paired with quantifiable goals, KPIs, and deadlines. Some may argue that adhering to strict deadlines is a relic of the past and the enemy of agile development where it is done when it is done reigns supreme. Setting a time to market requirements encourage autonomous/agile teams of putting a “minimum viable product” in front of customers quickly and improve it incrementally based on real-world customer data. Unless the effort is time-boxed in this way, teams naturally gravitate back toward the habit of overdesigning solutions that are obsolete upon delivery. This burns precious time and resources.
Since digital initiatives by nature often transcend traditional organizational silos, this requires an organizational culture where it is not only accepted but celebrated when digital innovations break free of their existing organizational boundaries. As a direct consequence, the organization’s operational model must be sufficiently agile to adapt to a constantly changing environment.
7. Assuming that digital transformation is a one-off
This one is in my opinion one of the biggest pitfalls when it comes to digital transformation. All to often digitalis transformation is treated like procuring a physical server or an ERP system that is placed in a basement somewhere and tucked away in the balance sheet as a depreciating asset.
This mindset directly counteracts building the necessary internal skillsets, as it is viewed as a temporary job that can be solved by hiring development capacity as external consultants. The problem with this approach is that, while the intention to avoid raising internal fixed costs makes perfect sense from a financial perspective, organizations become dependent on external expertise that is often more than twice as expensive as establishing your own in-house development capacity, but the operational risk increase as core competence related to your technical platform has their loyalty elsewhere.
This is not to say that one should build everything in-house. Every decision regarding expanding one’s technical platform should include a make vs. buy alternative but establishing a strong internal IT technical department is necessary to get ahead. There’s no silver bullet to this, but this is how we did it in Sbanken.
Apart from development capabilities, leaders must acknowledge that once digital becomes an integral part of their business, the organization must prepare for a state of perpetual change and continuous improvement. The organization needs to be agile, while still staying true to long-term goals and overall vision. There must be a culture that allows employees to challenge the existing and always seek to reinvent the organization. To do so, the organization must embrace life-long learning as a core value and invest in acquiring and developing talent. But above all, top management must be prepared to put words into action and acknowledge that digital transformation is hard.
Den siste tiden har det vært nærmest umulig å ikke få med seg mengdene av AI (kunstig intelligens)-genererte bilde, skapt av tjenester som DALL-E og NightCafe. Og aktiviteten nærmest eksploderte denne måneden med tjenestene CHatGPT og Lensa.
Men hvilke implikasjoner vil denne teknologien ha ut over en jevn strøm av AI-generert innhold og profilbilder i sosiale medier?
Mange teknologier har vært spådd å kunne føre til den neste teknologiske paradigmeskiftet slik internett gjorde på midten av 90-tallet og smarttelefonen gjorde i 2008, og generativ AI viser alle tegn til å kunne bli nettopp den neste store digitale teknologirevolusjonen.
Kunstig intelligens gjennomsyrer allerede de fleste aspekter av våre liv, enten det er ved innlogging via ansiktsgjenkjenning, scanning av en faktura i mobilbanken, spamfilteret til epostkontoen din eller Spotify sin «Discovery Weekly» funksjon. Der alle disse er eksempler på hvordan kunstig intelligens analyserer og gi mening basert på noe som allerede finnes, ligger det revolusjonerende potensialet i generativ AI at den er i stand til å skape noe nytt.
Selv om resultatene i dag har varierende kvalitet, vil denne teknologien utvilsomt utfordre en rekke bransjer og funksjoner. Åpenbare kandidater som fotografer, designere og tekstforfatterede ble raskt identifisert. Jeg gjorde selv et eksperiment der jeg gjennom et sett med spørsmål fikk ChatGPT til å forfatte et blogginnlegg der flere lesere ikke innså at de hadde lest en maskingenerert tekst før de kom til konklusjonen av innlegget, nemlig at innlegget var skrevet utelukkende av ChatGPT.
Men teknologien har et potensiale som strekker seg langt forbi de åpenbare bruksområdene.
Forrester peker på et forestående kappløp innen utvikling av generativ AI for bedrifter, og peker blant annet på hvordan generativ AI kan endre entreprenørbransjen gjennom bedre innsikt og oppfølging av komplekse kontrakter, eller gjennom bildegjenkjenning raskt identifisere tegn på feil og mangler i byggeprosessen som eksempelvis sprekker i betong. Alternativt generere store mengder syntetiske data som viser kjennetegn på sprekker i betong som opplæringsmateriell.
Koding er et annet område der virtuelle agenter som ChatGPT kan ha et stort potensial.
Verktøy som Github copilot fungere som en slags autocomplete for utvikling og kan bidra til å øke produktiviteten til utviklere og bidra til at bedrifter kan få mer ut av kompetansen de allerede har. Ettersom en gjenganger i NHO sitt kompetansebarometer er den vedvarende og økende mangelen på teknologikompetanse, hvor halvparten av bedriftene oppgir et udekket behov for IKT-kompetanse er dette særlig interessant.
I Skanska har vi prøvd ut ChatGPT til dette formålet og ser at tjenesten klarer å generere relativt presise databasespørringer og enkel kode basert på input i klartekst. Samtidig er det viktig å minne seg selv på at assistert utvikling ved bruk av virtuelle agenter vil ikke erstatte utviklere. Teknologien er relativt umoden og egner seg best til svært enkle operasjoner, og generer fortsatt mange feilaktige resultater. Noe som har resultert i at Stack Overflow har valgt å forby kode skrevet av ChatGPT.
En annen utfordring som reiser seg ved utstrakt bruk av teknologien er håndhevelse av opphavsrett. Generativ AI skaper basert på mønstergjenkjenning, og disse mønstrene kan i mange tilfeller kommer fra materiale beskyttet av opphavsrett. På hvilket abstraksjonsnivå er dette inspirert av, og når er det kopiert av? Jeg ba selv DALL-E gjenskape «The garden of earthly delights by Hieronymus Bosch in the style of Henri Matisse” og fikk tilbake fire unike resultater som på hver sine måter hadde klare referanser til kildematerialet i både motiv og stil.
Men ikke alle er like begeistret for teknologien. I kjølvannet av lanseringen av ChatGPT har lærerne sendt bekymringsmelding til Stortinget om den bruken av kunstig intelligens i skriftlige leveranser der de hevder at teknologien kan undergrave integriteten til utdanning og dermed devaluere graden man oppnår ved fullført utdanning.
Å kjempe mot ny teknologi slik Landslaget for norskundervisning byr opp til har historisk sett vist seg å være en dårlig tilnærming. Teknologiens natur er å utfordre tradisjonelle metoder. Selv om luddittene organiserte seg for å sabotere vevemaskiner i England på tidlig 1800-tall i frykt for at automatisering ville ta fra dem arbeidet klarte de ikke stanse den industrielle revolusjon.
Teknologiens uforutsigbare natur er både hva som gjør den spennende, men også hva som gjør at den produserer feilaktige og av og til moralsk tvilsomme resultater.
Utviklingen og etter hvert vidstrakt bruk av kunstig intelligens er uunngåelig, og som Captain Picard i Star Trek fikk erfare i møte med den kybernetiske rasen Borg er motstand nytteløst. Bruken av teknologi er hva som definerer mennesker som art, og vi trenger å forstå teknologien for å kunne utnytte fordelene, og samtidig finne måter å forhindre baksidene.
Generative AI has been one of the hot topics in the field of Artificial Intelligence this fall, and last week we got to try out a new addition to the growing landscape of generative AI solutions with ChatGPT from OpenAI. Like many others, I’ve been playing around with the solution the last couple of days in order to cut through the hype and see for myself whether this is a big deal or not.
The big deal about GPT-3 is that it is a very large and powerful language processing model, which makes it capable of generating human-like text and performing a wide range of language-based tasks. Because of its size and capabilities, GPT-3 has the potential to revolutionize the field of natural language processing, and it has already been used to create a variety of language-based applications, such as chatbots and virtual assistants.
GPT-3, or Generative Pretrained Transformer 3, is a state-of-the-art language processing model developed by OpenAI. It is trained on a large amount of text data and can generate human-like text in a variety of languages and formats. It is also capable of performing a wide range of language-based tasks, such as translation, summarization, and question answering.
OpenAI is an artificial intelligence research laboratory consisting of the for-profit corporation OpenAI LP and its parent company, the non-profit OpenAI Inc. OpenAI is known for its work on advanced AI algorithms, including GPT-3 (Generative Pretrained Transformer 3), which is considered one of the most powerful language processing models in the world.
The disruptive potential of GPT-3 and other advanced language processing models is significant. These models have the potential to revolutionize many industries and fields, including natural language processing, artificial intelligence, and machine learning. For example, GPT-3 and other large language models could be used to develop more advanced chatbots and virtual assistants, which could improve customer service and support in a variety of industries. In addition, these models could be used to improve machine translation, summarization, and other language-related tasks, making them more accurate and efficient. Overall, the disruptive potential of GPT-3 and other advanced language processing models is significant and could greatly impact many different fields and industries.
Here are some examples of how GPT-3 and other advanced language processing models could be used:
Chatbots and virtual assistants: These models could be used to develop more advanced chatbots and virtual assistants that are capable of holding more natural and human-like conversations. These applications could be used in customer service and support, or in other areas where communication with customers or clients is important.
Machine translation: These models could be used to improve machine translation systems, making them more accurate and efficient. This could be particularly useful for businesses that need to translate large amounts of text or documents, such as technical manuals or legal documents.
Summarization: These models could be used to improve text summarization, allowing users to quickly and easily generate summaries of long documents or articles. This could be useful for researchers, journalists, or anyone else who needs to quickly digest a large amount of text.
Question answering: These models could be used to develop more advanced question answering systems, which could be used in a variety of applications, such as search engines or virtual assistants.
There are several challenges associated with GPT-3 and other advanced language processing models. One of the main challenges is the sheer size and complexity of these models, which can make them difficult to train and maintain. Because of their size, these models require large amounts of data and computational power to train, which can be expensive and time-consuming.
Another challenge is the potential for bias in these models. Because these models are trained on large amounts of text data, they can potentially inherit biases from the data they are trained on. For example, if a model is trained on a dataset that contains gender or racial biases, the model may generate text that reflects those biases. This can be a major concern, as it could lead to discriminatory or offensive output from the model.
Another challenge is the potential for misuse of these models. Because these models are capable of generating human-like text, there is a risk that they could be used for nefarious purposes, such as creating fake news or impersonating real people. This could lead to a variety of negative consequences, including misinformation and confusion.
Overall, while GPT-3 and other advanced language processing models have significant potential, they also come with a number of challenges that need to be addressed.
This blog post was not written by me, but by ChatGPT itself without any editing of the text starting from the second paragraph, based on the following questions presented to the chat agent:
What is the big deal about chatgpt?
What is openai and chatgpt?
What is the disruptive potential of the technology?
Can you give some examples?
What are the challenges of this technology?
Although there are some repetitions, and some generic answers, the results are downright impressive and exceed what I have seen so far by any virtual chat agent.
The construction industry is known for being a laggard when it comes to digitalization, the industry is now picking up the pace.
Several factors are driving this change of pace ranging from evolving client expectations, where factors like energy efficiency, IoT, and connected objects are going from nice-to-have to must-have.
Advancements in technology play a significant role in both explaining why the construction industry has been a digital laggard, but also why now is the time for digitalization at scale. Looking at digital frontrunners, their similarities are often b2c industries where self-service and pure digital delivery models have played a significant role in their digital agendas.
For the construction industry, the ability to connect the physical world to the digital has long been a barrier to widescale digitalization. With technologies available on the market allowing both data capture through sensors, drones, telemetry, and more as well as methods and solutions to drive real-time actionable insights and automation.
A new generation of workers is also driving the adoption of digital tools on the sites. At the same time, user expectations are shifting towards simplicity and ease of use. A productivity tool on your smartphone is no longer compared with the ERP system used back at HQ, it is compared to every other app installed on that phone. Without a state-of-the-art user experience, widespread adoption is unlikely.
Ditio collects data from large complex construction sites to enhance profitability, site insights, and accurate reporting. At the core of the software lies a unique approach to mass hauling, enabling improved efficiency and reduced climate footprint due to optimal route planning for mass hauling. Ditio has raised seed capital from Skanska. Read more at Ditio.
7Analytics utilize artificial intelligence to calculate and predict the flow of surface water and the risk of flooding based on future weather predictions. The solution allows engineers, contractors, municipalities, and property developers to develop future projects in an efficient and sustainable way. 7Analytics has raised $2.5 million in a seed round of funding led by sustainability-focused VC firm Momentum Partners, with participation from Construct Venture and Obos. Read more at 7Analytics.
VirtualHouse allows the building contractor to test and perform QA on equipment and software from technical subcontractors before they gain access to the building. It provides a safer and more transparent construction process, a building with fewer errors at handover, and more cost-effective operation for the client. Read more at VirtualHouse.
Steer is a Norwegian technology startup that provides advanced autonomous and remote control solutions to any kind of construction equipment. Read more at Steer.
Digiquip promises a complete overview of equipment, personnel, and projects in one platform. Learn more at Digiquip.
Tørn is an online marketplace where building materials and supply retailers, wholesalers, and manufacturer can sell their dead stock and surplus to consumers and craftsmen. Tørn facilitates a seamless purchase experience between sellers and buyers through its platform with inbuilt payment, shipping, and customer support. Read more at Tørn.
Catenda is a collaboration platform centered around the BIM model for visual collaboration in 3D models and auto-generated 2D floor plans. With strong API capabilities at the core of the platform, Catendas Bimsync allows integrations to leading industry BIM tools such as Revit and Solibri. Catenda has has Spring Capital, and Egis as notable investors. Read more at Catenda.
Consigli delivers a platform for data driven engineering, planning and and optimization throughout the entire value chain from design to building operations. Consigli promise to reduce material usage, energy consuption and deliver higher quality in execution. Consigli has raised capital from TRK Group and Møller Eiendom among others. Read more at Consigli.
Cobuilder offers a platform of integrated solutions that address the specific information management needs of construction industry actors by enabling collaboration and seamless data exchange internally and within any third-party software. Read more at Cobuilder.
Imerso promise to be the world’s smartest construction monitoring platform to help clients to save costs, accelerate deliveries, and drive productivity across their projects. Through 3D scanning Imerso allows clients to capture the built environment and create As-built documentation as well as deliver automatic BIM inspection. Imerso has Nemetscheck as a notable investor. Read more at Imerso.
Novorender delivers a solution allowing sharing of advanced 3D models between stakeholders in a construction project regardless of 3D formats and file sizes. Novorender promise to dramatically increase the value of existing 3D models during the development of large projects through increased availability of 3D models. Read more at Novorender.
Spacio offers a cloud-based platform for building design and optimization. the solution aims to factor in floor plans, adaptive parking, energy, space allocation, noise, daylight, energy, stormwater, and more. read more at Spacio.
Vrex is a VR technology company. Their product is a collaboration tool, VREX, for BIM and CAD in Virtual Reality. It allows you to work virtually with anyone around the world, integrated with your issue management systems. Vrex reduces costs through better reviews in less time, which means fewer costly mistakes and less travel time. Vrex has Construct Venture and Skagerak Maturo as notable investors. Read more at Vrex.
nLink designs and develops mobile robots that solve real-life problems, replacing repetitious work tasks in difficult and dangerous environments. Notable investors are equipment manufacturer Hilti. Read more at nLink.
InfoBric offers several solutions such as access control, fleet management, blast management, and work hour tracking for sustainable, compliant, and efficient construction sites. Infobric is wholly owned by Summa Equity. Read more at Infobric.
Infraspace is a cloud-based platform for early-stage infrastructure planning and design. By generating all possible design outcomes through an algorithmic approach, the platform suggests the optimal solution based on a weighted priority of cost, quality, and sustainability factors. Read more at Infraspace
Emerald Geomodelling is a service provider specializing in providing the missing link between complex geophysical and sparse geotechnical data in order to reduce risk in infrastructure projects. By combining project experience with efficient and accurate machine learning routines, the company provides full 3D coverage with quantified precision. Read more at Emerald Geomodelling.
Bygr is a collaboration tool for residential development that aim to streamline communications related to change request, added building options and after-market between end-customers, property developers, main contractors, and sub-contractors. Bygr has raised 40 MNOK in several rounds, and notable investors include Construct Venture and Kistefos. Read more at Bygr.
Birdflocks is a collaboration and networking platform for the AEC industry where stakeholders can share common challenges, joint project opportunities and more. Read more at Birdflocks.
Kvist Solutions delivers a software solution to simplify BREEAM certification throughout the building process. read more at Kvist.
Ei Solutions delivers an analysis engine that generates the sustainability insights the industry needs to comply with the EU-taxonomy’s screening requirements with just a click. This provides an overview of potential red flags instantly for real estate owners in order to target the sustainability issues that really matter in the portfolio. Read more at Ei Solutions.
Reduzer helps developers design and build buildings and infrastructure while keeping environmental impacts organized and helping contractors reduce impacts. All environmental data, goals, and certifications are captured and presented in an easy-to-use collaborative workspace. read more at Reduzer.
Rebartek delivers prefabricated, tailor-made reinforcement cages. By combining parametric structural design and robotics, the company provides a safer construction site, faster installation, less waste, and lower cost. Read more at Rebartek.
dPlan is a project management tool with an emphasis on VDC (Virtual Design and Construction) processes and lean project management. Read more at dPlan.
Sparkel is a collaboration platform centered around BIM for better communication, workflow management, and change order management. Read more at Sparkel.
Perlo automatically collects, standardizes, and manages project information during the building phase across the relevant third-party systems in a single channel to help property developers and contractors master the core business of building. Read more at Perlo.
Tendro delivers software for procurement processes in the construction industry through a communication and delegation platform that integrates the builder, entrepreneur, and subcontractor into the same platform. Read more at Tendro.
Parallello is a platform that utilizes parametric design to generate optimally and all possible floor plans. Read more at Parallello.
Datatrees is a parametric design agency that delivers a building modeling tool for fast building design and a similar product for lighting design. Read more at Datatrees.
Arealize is an online tool to generate floor plans automatically. Read more at Arealize.
Sirken offers surplus materials from building sites through retrofitted shipping containers. Read more at Sirken.
Order Control is an order management and delivery calendar tool that automatically cross-references material prices and tracks service levels to reduce time spent with order management as well as reduce material waste. Read more at Order Control.
Skanska Digital has developed several solutions for both civil and building construction ranging from data capture and reporting for drill and blast tunneling, systematic completion, and sensor-based data capture that is yet to be launched in the market as commercial products. Stay tuned for more.
An honorable mention to Spacemaker, although no longer a Norwegian startup by definition. The 240 MUSD acquisition by Autodesk gave the construction tech segment the locomotive that is needed to spark investor interest and growth.
This is my first take on getting the full picture of the Norwegian Contech scene, if you are missing any companies on the list, please let me know in the comments below. Please note that I have limited the scope to exclude companies that are primarily (although they may have some use in the AEC value chain) in the proptech category and/or deliver solutions for building maintenance and operations.
As technology advance, so does cyber security threats and the sophistication of attacks. The usual suspects like traditional malware phishing and ransomware are ever-present and growing in extent, but there are also some new attack vectors on the rise.
The rapid deployment of connected devices such as smart appliances and sensors is also becoming a cyber security concern. SonicWall report a 77% increase in IoT attacks in the first half of 2022. With the expansion of IoT, security risks also grow. IoT vendors are notorious for implementing little to no security on their devices.
This is because, as they are often not used to store sensitive data directly, manufacturers haven’t always been focused on keeping them secure with frequent security patches and updates. That has changed recently, as it’s been shown that even when they don’t store data themselves, attackers can often find ways to use them as gateways to access other networked devices that might.
By 2023, analysts at Gartner predict, there will be 43 billion IoT-connected devices in the world, and every connected device should be treated as a potential attack vector and vulnerability.
Artificial intelligence (AI) may be opening up new opportunities and markets for businesses of all sizes, but also opens up new opportunities for malicious usage. Cybercriminals are already identified as one of those opportunities to play the system through a process called data poisoning.
This is performed by somehow injecting corrupted data, or somehow influencing the data used to train machine learning models in a sense that over time produces false outcomes.
Technologies including autocomplete, chatbots, spam filters, intrusion detection systems, financial fraud prevention, and even medical diagnostic tools are all susceptible to data poisoning attacks as they make use of online training or continuous-learning models.
As an example, they can do a lot just by changing data for a recommendation engine. From there, they can get someone to download a malware app or click on an infected link.
Another potential threat is a way to circumvent biometric identity verification by tampering with the underlying machine learning algorithms. A facial recognition system used for authentication might be manipulated to permit anyone wearing a specific pair of glasses to be classified as any correct user.
And don’t even get me started on the potential threats and fraud risks in the metaverse. Regardless of how the metaverse will play out, the fact is that corporations, governments, brands, creators, and early adopters are exploring the platforms. While they may differ as stakeholders, their risk profiles are somewhat similar, depending on the definition of the metaverse.
Common for all definitions of the metaverse, identity is a central component, and identity theft and profile hijacking may have wide-reaching consequences, particularly if said identity is portable and interoperable across various metaverse platforms as metaverse evangelists are proposing.
In the VR/AR/XR version of the metaverse, the physical world and the digital world become more integrated. This proposes a new set of risks where the distinction between cyber and physical gets wiped out.
Among the most disconcerting of the potential cybersecurity threats in the metaverse is the risk of biometric hacking. Because the metaverse functions through VR/AR, users will need to wear VR headsets and, potentially, other VR/AR technologies, such as haptic gloves, thus be able to gain access to sensitive data regarding users’ physical conditions.
In the web3 version of the metaverse, the list of potential exploits and attack vectors is never-ending. The blog Web 3 Is Doing Just Great has put together a comprehensive timeline of the most prominent web3 hacks and exploits, and the reading is abysmal.
As a result, billions have been siphoned, or downright stolen from users in various web3 platforms through hacks, scams, and pyramid schemes in disguise.
Regardless of the technology, cyber security is at the end of the day an arms race, and the line of defense is only as good as access to skillsets and talent.
A shortage of cyber security professionals is a challenge for both corporations and nation-states. The need to invest in cyber security expertise, both diverse and specialized should be a top priority for both policymakers and IT departments.
Angrep på datasystemer er allerede i dag et våpenkappløp mellom kriminelle og de som jobber med IT-sikkerhet, og metodene blir stadig mer sofistikerte og kreative. Med en stadig eskalerende krig i Europa som både utspiller seg på bakken og i den digitale verden.
Der Østre Toten kommune heller tok kostnaden med å reparere skadene som følge av angrepet, delte e24 tidligere i høst urovekkende statistikk som viste at hele 6 av 10 virksomheter som ble utsatt for løsepengevirus velger å betale hackerne. Ikke bare bidrar dette til å finansiere krig og ytterligere kriminalitet, men vil i mange tilfeller ikke løse problemet, men kun lede til nye trusler om å lekke sensitiv data med tilhørende tilleggskrav om mer penger.
Det kan være fristende at enkelthendelser og statistikk som også inkludere alt fra de minste bedriftene ikke er sammenlignbart med en større virksomhet, men da kan det være verdt å minne seg selv på hvordan Nicolai Tangen ble rundlurt av innleide hackere som i løpet av kort tid fikk full kontroll over hans personlige maskin.
NBIM sitt valg om å dele dette åpent med omverdenen er et forbilledlig eksempel på proaktivt sikkerhetsarbeid. Både metoden, men også åpenheten om utfallet. Å fortelle historien åpent videre fremfor å dokumentere hendelsen i et konfidensielt notat til toppledelse, revisor og styre er også noe flere burde etterstrebe. Det er derfor nødvendig at alle bedrifter tar inn over seg alvoret og sørger for at den digitale sikkerhet er ivaretatt på flere nivåer i organisasjonen.
På samme måte som vi tar våre forhåndsregler i den fysiske verden, må det samme tankesettet også gjennomsyre utviklingen og bruken av digitale løsninger. Vi må slutte å snakke om cyber-kriminalitet som noe eget fenomen. Et ran er et ran, enten det skjer på åpen gate eller gjennom et løsepengevirus. Det er ikke som om kriminelle miljøer trekker en skillelinje mellom ordinær kriminalitet og datakriminalitet, det digitale rom åpner bare for en utvidelse og skalering av den kriminelle virksomheten.
Det er derfor avgjørende viktig å styrke sikkerhetsforståelse både blant ansatte på alle nivåer og brukere av digitale tjenester. Digital sikkerhet er ikke noe som settes bor til IT-avdelingen. Det er alles ansvar.
Hold programvare oppdatert og bli kvitt teknisk gjeld. Sanering og modernisering av gamle systemer bør prioriteres fremfor å skru på gamle løsninger. Utdatert programvare er en gullgruve for datakriminelle, og det mest kjente eksemplet på dette er Wannacry løsepengeviruset som rammet virksomheter i over hundre land, inkludert Norge. Britiske NHS var en av de som ble rammet da de anså det som for kostbart å oppgradere fra en utdatert versjon av Windows XP som hadde sikkerhetshull i seg. Angrepet endte opp med å koste NHS over 92 millioner britiske pund.
Et lappeteppe av sikkerhetsoppdateringer bidrar til økt kompleksitet og gjør det vanskeligere å oppdage sikkerhetsbrudd i tide før skaden skjer. Bruk av skytjenester bør sees på som noe som reduserer risikoen for sikkerhetsbrudd fremfor noe nytt og skremmende. Maskinlæring og kunstig intelligens kan bidra til å oppdage avvik langt mer effektivt og hurtigere enn gjennom menneskelig oppfølging.
Design for sikkerhet. Eller tenk som en kjeltring om du vil. I Norge er vi vokst opp i et samfunn som er basert på tillit der man ikke skal plage andre, man skal være grei og snill, og for øvrig kan man gjøre hva man vil. Men når teknologien gjør verden mindre møter vi en virkelighet der denne tilliten ikke er til stede i like stor grad, og få har hørt om kardemommeloven.
Når digitale tjenester utvikles, er det avgjørende å forstå hvordan kriminelle miljøer tenker og legge inn sikkerhet iboende i koden. Kreative hackere ansettes av kriminelle miljøer og er godt skolert når det kommer til å forstå mulighetene og begrensningene som ligger i teknologien.
Printere, møteromselektronikk, telefonisystem, kort sagt alt som er koblet på nettet kan være en vei inn til kritiske systemer. Fremveksten av det industrielle tingenes internett vil gi næringslivet store muligheter, men også en rekke nye trusler som følge av millioner av nye oppkoblede sensorer og endepunkter.
Ved innføring og utvikling av nye systemer og løsninger, utstyr deg med en god porsjon produktiv paranoia og tenk hvordan noen kan tenkes å utnytte dette til ondsinnede formål.
Ha en beredskapsplan. God styring at digital sikkerhet handler i stor grad om å være forberedt på hva man gjør når det skjer, ikke hvis det skjer. Ta jevnlig backup av virksomhetskritiske data, etabler gode rutiner for disaster recovery. Proaktivt sikkerhetsarbeid må prioriteres og gis tilstrekkelig plass i virksomhetens risikostyring.
Gjennomfør sikkerhetsøvelser. På samme måte som man gjennomfører brannøvelser med jevne mellomrom bør det være like naturlig å gjennomføre tilsvarende øvelser når det kommer til digital sikkerhet og trener på egen responsevne når krisen er et faktum. I tillegg til beredskapsøvelser, gjør som Oljefondet og lei inn hackere til å forsøke å bryte seg inn.
Avslutningsvis er det viktig å være bevisst på at kriminelle trenger kun å lykkes med å bryte sikkerheten én gang, mens du selv som virksomhet må lykkes med å holde sikkerheten 100 prosent av gangene. Denne erkjennelsen bør lede til kontinuerlig arbeid med digital sikkerhet på alle nivåer i virksomheten.
If it looks like a duck, acts like a duck, and quacks like a duck, then it probably is a duck. Fintech was for a long time believed to disrupt financial services without being subject to the same requirements as banks. As the market matures and access to capital becomes scarcer, all signs point towards the market coming to terms with the fact that many fintechs are in fact closer to financial services companies than software.
As the market overall has taken a downturn, forward ARR valuation multiples have seen a steady decline through 2022, and fintech has taken by far the worst hit in terms of valuation multiples. It is without a doubt that software companies in general will have to reevaluate and recalibrate their valuations. However, as software as a whole has declined, fintech valuation multiples have literally plummeted.
This graph from the excellent blog post A framework for navigating Down Markets by leading fintech-investors a16z shows how fintech has fallen from having premium multiples to the bottom-tier of valuation multiples.
Has fintech valuation been over-inflated and based on the profit pool of the financial services industry rather than sustainable business models that create value over time?
Their explanations may be many, but the consequences are nevertheless the same.
First of all, of all venture capital raised last year, the fintech segment amounted to about 20% of venture capital raised last year. The fintech sector may simply have had further to fall even more as investors came to terms with the fact that fintechs cannot achieve the growth entrepreneurs’ pitch decks promised.
Data from CB Insights shows that global fintech funding drops in terms of both the number of deals and total volume. However, it is worth noting that while funding has dropped nearly 70 percent since the top in 2021, funding levels are still on par with Q4 2020.
Market uncertainty and rising interest rates are here to stay (at least for a while), and fintechs are not excepted from being affected by this. Given the diversity of the fintech sector, the impact of shifting interest rates is likely to vary. Nevertheless, market turmoil is unlikely to favor fintechs.
This puts a squeeze on fintech lenders, as well as players in the BNPL that are both highly dependent on wholesale funding. At the same time as financing becomes more expensive, consumers are finding themselves in an economic squeeze amongst rising interest rates and inflation, resulting in soaring losses for unsecured lenders and BNPL players. This is not the only obstacle facing the BNPL space. Regulators have stated an urgent need to regulate BNPL products the same way traditional unsecured lending products are regulated.
Fintechs in the wealth management space is as likely to be affected as the young, new-to-the-market consumers they court feel confident in their market approach as the economy tightens, and the urge to buy meme stocks becomes less appealing.
Challenger banks are also struggling on their path toward profitability.
The research also discovered that in the US, less than “a handful” of the country’s leading 85 neo-banks have reached a breakeven point, and several are losing as much as $140 per customer annually.
Alongside the growth of the sector, competition has also increased, making it harder to stand out as a new entrant in the market. Not only have incumbents invested in their digital offerings, but as many as one in three challenger banks are digital subsidiaries launched by incumbents in the financial services industry. Thus are able to combine deep industry knowledge, lower cost of capital for lending, as well as piggybacking on established brands in the market.
As growth has been the primary metric for success, many challenger banks have rapidly expanded in new geographies and have been obsessed with opening as many accounts as possible without thinking about how to make them profitable, while at the same time operating with affiliate programs and customer onboarding benefits that might not be financially sustainable in the long run.
Even though market sentiment affects various players differently. While players like Revolut announce new feature expansions like instant messaging and extended crypto functionality to enable its user to pay with crypto with their Revolut cards like Chime are resorting to cost-cutting measures by laying off 12 percent of their staff.
As we enter the inevitable recession that has been looming on the horizon through 2022, the fintech space will have its acid test on its staying power and a force to be reckoned with for incumbent financial institutions.
Although a shift in the market sentiment undoubtedly represent a wake up call for the industry, it is not all doom and gloom. There is still a plethora of problems to be solved in the financial services industry. Although incumbents have awakened from their slumber, small niche fintech players are avle to move faster, and tend to be more socialized in their approach.
Even though the fintech term is a wide umbrella covering various business models and concepts, a majority of leading fintech players inhibit core economics derived from financial services businesses. Businesses that are capital intensive, slowed down by compliance, and tend to have lower multiples.
Det sies at en gjennomsnittlig voksen person tar 35 000 valg i løpet av en dag. Bare i løpet av denne korte artikkelen vil den gjennomsnittlige leser flere ganger gjennom teksten velge hvorvidt det er interessant nok til å lese videre, eller om notificationen som nettopp tikket inn på smarttelefonen virket mer interessant, eller hvorvidt det allerede nå er nok tekst og på tide og bla gjennom TikTok for litt enkel underholdning.
Mange av merkevarene som har lykkes har én ting til felles. De evner å holde det enkelt. Alt fra Den første T-Forden som var mulig å bestille i hvilken farge du ville så lenge den var svart til Google sitt enkle og minimalistiske søkefelt på en tid der internett var dominert av startsider med utallige linker og kategorier. Fellestegnet for begge disse selskapene var at dette var første byggesten for en skalerbar plattformstrategi og forretningsmodell.
Manglende enkelhet har også en konsekvens på tvers av enhver bedrifts verdikjede. Det samme året som Apple lanserte den første iPhonen, lanserte Nokia på sin side 10 ulike modeller. Ikke bare fører dette til unødvendig mange valg, men også økte kostnader på tvers av verdikjeden i form alt fra økt kompleksitet hele veien fra produksjon til forhandler til kapitalbinding i form av behovet for spesialiserte komponenter og lagringskapasitet.
Konkurransen i digitale flater er konkurransen om brukerens oppmerksomhet, og kompleksitet og unødvendige valg er antitesen til en attraktiv og relevant brukeropplevelse.
En analyse fra The Standish Group har gått gjennom 2000 softwareprosjekter fra 1000 selskaper med formål å avdekke hvor stor andel av de funksjonene som er utviklet faktisk blir brukt. Analysen viser til at kun 7 prosent av funksjonaliteten brukes «alltid», og så mye som 45 prosent av funksjonaliteten brukes aldri.
Utfordringen er at mange virksomheter har på plass prosesser, strukturer og regelverk som favoriserer et tankesett der alle eventuelle brukssituasjoner må beskrives i detalj før første spadetak blir tatt. Som eksempel har det i mange år vært snakk om innovative offentlige anskaffelser, men fortsatt må leverandørene forholde seg til og forplikte seg til å levere på kravtabeller som er lange som et vondt år og beskriver all tenkelig funksjonalitet som er samlet inn i kravspesifikasjonsfasen.
På motsatt ende av skalaen finner vi Instagram som er et av de beste eksemplene på å tenke stort, starte i det små og skalér raskt. Med et team på 13 personer skalerte de en enkel idé til 27 millioner brukere på 18 måneder. Selv om dette et unikt tilfelle som er tilnærmet umulig å kopiere, så er det mye som kan læres av måten Instagram evnet å avgrense omfanget, fokusere på hva som skal være kjernefunksjonalitet og fremfor alt sette gjennomføringsevne som viktigste parameter for suksess.
Tenk stort og ha et bevisst forhold til hvor du skal. En langsiktig visjon skal bidra til et felles samlingspunkt, og trenger ikke være detaljert. Start i det små og identifiser de første stegene mot et fremtidig mål. Sett stramme tidsfrister for å skape et behov for å avgrense. Fokuser på å levere en første versjon fremfor å bruke for mye tid på å analysere eventuelle fremtidige steg i detalj før du har hentet erfaringer fra første leveranse.
Forstå samtidig mulighetene som ligger i teknologien. Apple og Spotify forstod dette og gjorde det enkelt å kjøpe musikk lovlig digital i en tid der etablerte aktører kjempet mot utviklingen. Videre har Spotify gjort det enklere enn noen sinne å lytte til den musikken du liker ved å sette sammen en personalisert spilleliste til alle sine millioner av brukere basert på kunstig intelligens. Der gårsdagens digitale løsninger inneholdt store mengder valg og funksjoner for å favne et bredt spekter av brukere kan relevant bruk av data bidra til automatisert personalisering.
For oss som en bank er det et viktig premiss for oss å inspirere våre kunder å ta kloke valg for fremtiden. Forskning viser at vi mennesker er langt fra utelukkende rasjonelle individer, og tar ofte irrasjonelle valg som er i strid med de prinsippene vi har. Enten det dreier seg om hvor mye lån en evner å betjene eller hvor mye en skal sette av til langsiktig sparing så er personlig økonomi for mange komplisert og strider mot vår naturlige søken etter raske belønninger. Da handler det om gjøre det enkelt å velge klokt.
Teknologioptimisme driver verden fremover og er en viktig egenskap i møte med ny teknologi. Det gir rom for å utforske det nye og utfordre etablerte sannheter. Samtidig er det like viktig å være bevisst teknologiens svakheter, samt evne å forestille seg hvordan teknologien kan utnyttes til ondsinnede formål. Ingen ting er fullkomment, minst av alt teknologi. Blokkjedeteknologien representerer en teknologisk revolusjon når det kommer til å bevise digitalt eierskap, men uten å ta teknologiens og det omkringliggende økosystemets fundamentale svakheter på alvor vil vi neppe få nok tillit til teknologien til å se bred adopsjon.
I over et tiår har det blitt spådd at kryptovaluta og den underliggende teknologien vil løse globale problemer som å skape et mer rettferdig finansmarked på tvers av landegrenser, bidra til finansiell inkludering for alle som ikke har tilgang på tradisjonelle finansielle tjenester, danne grunnlaget for desentraliserte digitale identiteter og mye mer, senest i forrige uke av E24s faste kryptospaltist.
Men ingen ting er ufeilbarlig, ei heller blokkjedeteknologi og etter hvert som årene har gått og teknologien har blitt mer moden, har også teknologiens begrensninger blitt mer og mer åpenbare.
E24s spaltist tar ikke feil ved å påpeke at internasjonale pengeoverføringer fremstår arkaiske og fremmedgjørende i en digital tid både med tanke på overføringshastighet, vekslingskurs og gebyrstrukturer. Der noe av dette kan forklares av utdatert teknologi og høye gebyrer satt av fordyrende mellomledd opererer fortsatt krypto i et uregulert marked uten de samme krav til sanksjons- og antihvitvaskingskontroller. En masseadopsjon av blokkjedebaserte betalinger vil kreve at disse også underlegges de samme reguleringer som tradisjonelle banker og betalingsforetak, med tilhørende reduksjon i hastighet og økning i kostander.
Det er her også viktig å ta med seg at krypto har helt siden unnfangelsen vært dypt fundamentert i troen på troen på et fullkomment desentralisert finansielt system for en digital tidsalder. Et finansielt system født ut av asken av finanskrisen der en dyp mistillit til etablerte institusjoner som sentralbanker, styrende myndigheter og tradisjonelle banker. Her har kryptovaluta vært ansett som en teknologidrevet frelse fra regjerende institusjoners kontroll og ulikhet og urettferdighet.
For alle som følger kryptovaluta er denne uken særlig signifikant ettersom den neste største kryptovalutaen ethereum gjennomgår en fundamental endring i konsensusalgoritmene for å redusere energibehovet som kreves for validering av transaksjoner, men dog ikke uten å ofre litt av blokkjedens desentraliserte natur. Overgangen fra såkalt proof of work til proof of stake betyr i praksis at i stedet for at hvem som helst kan bidra til å verifisere en ethereum-transaksjon, blir dette forbeholdt utvalgte noder der kravet er å eie minimum 32 ETH, tilsvarende 48 tusen USD. Der alle dyr en gang var like, vil innen utgangen av denne uken noen dyr være likere enn andre.
E24s spaltist har helt rett i at mange av motargumentene mot kryptovaluta kommer fra et privilegert ståsted. Vi her i Norge lever i et samfunn basert på tillit. Tillit til hverandre, og tillit til institusjoner. Det er et gode mange ikke har. På den annen side er det i beste fall naivt å utpeke én teknologi som nærmest en frelse fra fundamentale samfunnsproblemer. Vi mennesker har en trang til å søke det fullkomne, og den blinde troen på krypto og blokkjedeteknologiens egenskaper og uante potensiale er intet unntak.
Som for ti år siden gjentas det samme narrativet om blokkjedeteknologiens uante muligheter av en ny generasjon evangelister. Forskjellen er at etter ti år med utprøving og praktisk erfaring med teknologien vet vi også mer om hvilke svakheter den også har. Mangelen på kritiske røster i kryptomiljøet har skapt et ekkokammer der svulstige lovnader får stå uimotsagt helt til de blir etablerte sannheter, og skeptikere har raskt blitt stilnet av frykten for å bli stående på feil side av historien. At krypto og blokkjeder i dag er der internett var på midten av nittitallet har nærmest etablert seg som en vedtatt sannhet, og hvem vil vel ende opp i samme kategori som de som spådde at internett ville floppe?
Artificial intelligence is without a doubt the most transformative digital technology around. While the topic has been off the radar for popular science for some time, the alleged self-awareness of Google’s LaMDA created headlines a few weeks ago. However, that’s far from the only breakthrough in the path toward true artificial intelligence.
Last month, DeepMind unveiled a new “generalist” AI model called Gato. What separates this model from Ai models like AlphaGO and GPT3, is that where those were highly specialized models, Gato can perform more than 600 various tasks, ranging from playing Atari video games, captioning images, chatting, and stacking blocks with a real robot arm.
Unlike ordinary AI, artificial general intelligence (AGI) wouldn’t require giant troves of data to learn a task. Whereas ordinary artificial intelligence has to be pre-trained or programmed to solve a specific set of problems, general intelligence can learn through intuition and experience.
Others claim that Gato’s performance at its designated tasks is mediocre at best. but that misses the point in my mind, it doesn’t matter if the model at this point is dumb as a brick, after all, we humans used to live in caves and scavenge for nuts and berries. What’s important is whether this is a step toward so-called strong AI or AGI. This would be a single AI system — or possibly a group of linked systems — that could be applied to any task or problem. Unlike narrow AI algorithms, knowledge gained by general AI can be shared and retained among system components. In a general AI model, the algorithm that can beat the world’s best at Go would be able to learn chess or any other game, as well as take on additional tasks. AGI is conceived as a generally intelligent system that can act and think much like humans.
It is a stretch to call Gato a full-fledged AGI, but it is undoubtedly a major breakthrough in the inveitable path towards artificial general intelligence.
At kryptomarkedet har kollapset dette halvåret er i skrivende stund et ubestridelig faktum. For kryptovaluta henger det bratte fallet tett sammen med mekanismene som har drevet frem den sterke veksten – tro, håp og grådighet.
Siden toppen i november 2021 har toneangivende kryptovalutaer som bitcoin og ethereum falt med over 50 prosent, et betydelig brattere fall enn tradisjonelle finansmarkeder. For mange av oss har kollapsen i kryptovalutaer vært en lenge varslet katastrofe, drevet frem av en serie hendelser som har bidratt til stadig flere skyer på himmelen for kryptomarkedene. Ingen ting kan vokse i evig tid, og kryptovaluta er intet unntak fra denne regelen.
For de troende har krypto og særlig bitcoin helt siden unnfangelsen vært dypt fundamentert i troen på troen på et fullkomment desentralisert finansielt system for en digital tidsalder. Et finansielt system født ut av asken av finanskrisen der en dyp mistillit til etablerte institusjoner som sentralbanker, styrende myndigheter og tradisjonelle banker. Her har kryptovaluta vært ansett som en teknologidrevet frelse fra regjerende institusjoners kontroll og ulikhet og urettferdighet.
De troene har på veien fått følge av de håpefulle. Unge investorer som opplever at toget er gått for å kunne bli rik på aksjemarkedet på samme måte som tidligere generasjoner. Forsterket av finfluensere i sosiale medier som presenterer drømmen om passiv inntekt og en ekstravagant livsstil. Fellestrekket mellom majoriteten av disse, de har tilsynelatende skaffet sin formue gjennom investeringer i krypto, og er overentusiastiske når det kommer til å oppfordre andre til å gjøre som dem. En iver som handler om mer enn å bare vise seg frem, men har sterke økonomiske motiver. Mer om dette senere.
For de håpefulle som har vært heldige å komme inn på rett tidspunkt i memecoins som Dogecoin har håpet blitt konvertert til tro ved at tusenlapper over natten har blitt til millioner baserte på de enorme svingningene i denne type coins. Troen er og forblir sterk hos de som har opplevd et mirakel, og for de som har smakt på regnbuen er det vanskelig å se for seg at dette skal snu.
Symbiosen mellom tro og håp har over tid fungert selvforsterkende, og skapt et nærmest urokkelig positivt sentiment rundt krypto som fremtidens penger, finans, kunst og spill. For de som har stått på utsiden har dette manifestert seg frykt. Frykten for å gå glipp av noe, tydelig representert av Spetalen som angivelig begrunnet sitt inntog i krypto med at jeg orker ikke å se at Røkke tjener penger og ikke jeg.
Men ikke minst stilnet skeptikere gjennom frykten for å bli stående på feil side av historien. At krypto i dag er der internett var på midten av nittitallet har nærmest etablert seg som en vedtatt sannhet, og hvem vil vel ende opp i samme kategori som de som spådde at internett ville floppe?
Men størst av alt er grådigheten. Markedet for krypto har klare pyramidelignende mekanismer, der verdiskapingen i markedet handler om å opprettholde en tilstrekkelig høy etterspørsel til at kursene stadig stiger og nye investorer kommer til. For å oppnå dette har det etablert seg et økosystem der alle som allerede er investert er incentivert og motivert for å holde sirkuset på veien.
Ikke bare vil verdien av deres investeringer stige, men nær samtlige børser for kjøp- og salg av krypto opererer med affiliate programmer, der eksisterende kunder belønnes for å verve venner til å handle krypto. Den norske kryptobørsen NBX opplyser at vervebonusen er opptil 150 kroner for hver ny kunde som registrerer seg. Men ikke nok med det, videre opplyses det om at Når vennene dine handler på NBX, får du 20% av gebyrene de betaler det første året.
Kryptomarkedene slik de fungerer i dag kjennetegnes av: De som kom inn tidlig har mest å vinne på videre vekst. Størstedelen av verdiskapingen skjer ved at nye investorer kommer til. Promotører som viser frem sin rikdom og livsstil, og tjener penger på å verve nye investorer. Lovnader om passiv inntekt og en frykt for å gå glipp av noe ved å avstå fra å kjøpe.
Alt dette får meg til å tenke på ordtaket, hvis det ser ut som en and, høres ut som en and og oppfører seg om en and, er det antakelig en and.
Men ingen ting kan vokse uforstyrret til himmels. Kryptomarkedene opplever nå en perfekt storm som har sørget for et bratt fall i verdien på samtlige kryptovalutaer. Visjonen om kryptovaluta som en motsyklisk aktivaklasse er ettertrykkelig motbevist, kryptomarkedene har korrelert med utviklingen på verdens børser, krypto er alt annet enn en trygg havn i møte med stigende renter og økende inflasjon.
For de som ikke allerede har hørt om metaverse og gjort seg opp en mening om det, så er det angivelig det neste store. Det vil snu opp end på alle våre sosiale interaksjoner og våre digitale liv, og krypto er på et eller annet vis en avgjørende brikke i denne fremtiden. Hvorfor og hvordan er opp til hver enkelt å tolke seg frem til. Men til tross luftige lovnader presentert av evangelister og promotører (som alle er sterkt finansielt motivert) for å fremsnakke det kryptobaserte metaverse, forteller data fra den underliggende blokkjeden en annen historie. Til tross en verdsettelse på 2,3 milliarder USD, har plattformen knappe 1500 aktive brukere totalt, og ikke mer enn 822 månedlig aktive brukere. Dette gir en verdsettelse per bruker på mellom 15 til 28 millioner USD. Til sammenligning er verdsettelse per bruker for TikTok rundt 360 USD.
Kryptovaluta har vært gjennom en rekke opp- og nedturer fra den tid jeg startet å følge det som da var en kuriositet og en revolusjonerende teknologisk innovasjon som gjorde det mulig å overføre verdier digitalt mellom to parter uten innblanding fra en tredjepart.
På veien har både nye troende og trosretninger kommet og gått, men aldri har utbredelsen vært så omfattende som nå, og fallhøyden tilsvarende stor. På samme måte som formuer store formuer blitt skapt på veien mot toppen, er det tilsvarende summer som i disse dager blir destruert. Det var kryptomarkedenes desentraliserte natur som var predikert som frelsen fra institusjonenes kontroll, men mye kan tyde på at en full desentralisering uten noen form for sentralisert reguleringen er kryptomarkedenes akilleshæl fremfor frelse.
There has been no shortage of challenger banks competing for the customer’s attention in the past decade. However, it is far between the ones that manage to turn a profit. As capital becomes more scarce, and valuations are declining, challenger banks with no clear business models other than nifty features and a niche target audience, are facing a bumpy road ahead.
Since the rise of challenger banks began a decade ago, the number of challenger banks has grown rapidly, and also their ability to attract users, with more than 1 billion user accounts around the world. After a peak year of funding in 2021, the total valuation for the challenger bank segment is estimated to be more than $300 billion.
Challenger banks have played a crucial role in setting the agenda for banking innovations in the past decade. Spearheading mobile-first user experiences experience, open banking, personalization, and increased customer centricity, challenger banks acted as a catalyst for change in the industry. However, the perceived success of challenger banks is only on the surface.
The research also discovered that in the US, less than “a handful” of the country’s leading 85 neo-banks have reached a breakeven point, and several are losing as much as $140 per customer annually.
Alongside the growth of the sector, competition has also increased, making it harder to stand out as new entrants in the market. Not only have incumbents invested in their digital offerings, but as many as one in three challenger banks are digital subsidiaries launched by incumbents in the financial services industry. Thus are able to combine deep industry knowledge, lower cost of capital for lending, as well as piggybacking on established brands in the market.
As growth has been the primary metric for success, many challenger banks have rapidly expanded in new geographies and have been obsessed with opening as many accounts as possible without thinking about how to make them profitable, while at the same time been operating with affiliate programs and customer onboarding benefits that might not be financially sustainable in the long run.
At this point, the research points to challenger banks shifting their focus from get reach towards get rich, a mindset shift that can be difficult to orchestrate for companies that have built their entire culture around user attraction and growth.
Without turning a profit, many challenger banks will struggle to survive, no matter how many clients they serve. Some may be consolidated by competitors or incumbents, but as the market for challenger banks is maturing, challenger banks need to realize that they are in fact banks, and without profit, they will not have the right to live and will ultimately dissolve.
According to unconfirmed sources, Apple has allegedly acquired credit-scoring startup Credit Kudos. The deal marks yet another addition to Apple’s venture into the expanded payments space. What are the potential implications for incumbent banks and the payment industry as a whole?
At its core, Credit Kudos offers an Open Banking and machine learning-based credit score that it says enables lenders to increase acceptance of previously declined customers and reduce defaults. it is a challenger to incumbent credit rating agencies such as Experian, Bisnode and Equifax.
Credit Kudos’ technology uses a combination of machine learning and Open Banking-gathered transaction data to predict an individual’s likelihood of repayment. According to an interview with Finextra, the model has been trained on transaction data and loan outcomes collected for more than six years and enables lenders to score all applicants, not just those with credit history.
This acquisition follows several moves that show that Apple has its eyes set on the payment industry. Ever since the launch of Apple Pay back in 2014, Apple has continued their push towards the mobile payment market by safeguarding NFC access on their smartphones as well as P2P payments and launching an Apple-branded credit card to compensate for slow adoption rates of mobile payments.
Following Apples acquisition of Mobeewave back in 2020, this acquisition outlines Apples broader ambitions in the extended payment ecosystem. The jury is still out on the potential implications, as Apple are holding their cards close to their chest.
The acquisition will allow Apple to gather and consolidate user data through APIs, and a broader access to user data will undoubtedly strengthen Apples position towards mobile wallet dominance. This is a subject I have covered previously and may lead to disintermediation of the customer interface, allowing Apple to act as a gatekeeper for potential cross-sales through every banking interactions. This scenario may be a costly outcome for incumbents. Looking at how other industries like the hospitality industry where players like Expedia take commissions between 10 to 15 percent have been disintermediated; there is likely a willingness to pay to be the default payment option if third-party wallets become dominant players for digital payments.
Some news outlets point towards the ongoing open banking consolidation trend, following VISA and Mastercard’s shopping spree in acquiring API-aggregators Tink and Nordic API Gateway/Aiia. It is also speculated whether deal could pave the way for to arrive on the other side of the Atlantic. Apple’s credit card has been available in the US since August 2019. The API could help the company determine whether to approve a UK resident’s application for an Apple Card.
Cnbc also points out that the deal could have serious implications for some of Credit Kudos’ clients, which include the London-based fintech firms Curve and Fronted.
However, the big question is if this is a move towards the lucrative Buy Now Pay Later (BNPL) Space as a natural extension to the payment transaction. It is already rumored that Apple is developing their own buy now pay later solution that is allegedly named Apple Pay later.
I drew the comparison to Square following Apple’s acquisition of Mobeewave, and how that acquisition put Apple in direct competition with Square, a provider of smartphone-enabled POS terminals for small businesses. By allowing the smartphone, itself to become a payment terminal, Apple effectively eliminates the need for external peripherals such as the ones provided by Square and iZettle, and lets buyers tap either their phone or credit card directly on the merchant’s phone to process the payment.
The real value of Square lies in its unique positioning in its customer value chain. By placing themselves in the midst of their customer’s income stream, Square is able to leverage this position to provide small business loans. Through square cash, merchants will receive a loan offer based on their card sales, and the ability to repay it automatically with a percentage of their daily card sales through Square.
Since then Square has put USD 29 billion on the table to purchase buy now pay later company Afterpay in order to get in on the action and complement Square’s existing payment ecosystem. Square plans to integrate Afterpay into its existing Seller and Cash App business units, so that even “the smallest of merchants” can offer buy now, pay later at checkout.
In the time to come, we should expect to see buy now pay later transition from the checkout process at online retailers and become an integrated offering from digital wallets such as the announced Apple Pay Later, PayPal’s, Pay in Four, and the upcoming integration of Afterpay in the Cash app. For incumbent banks and credit institutions, this is bad news, as banks are not in the driver’s seat in the digital wallet department.
For incumbents, it is imperative to rethink how credit is offered. The lines across traditional credit products are already blurring, as banks offer loans against open credit card lines and fintechs offer installment-based credit cards or debit cards with pay later features. Loan origination, therefore, needs to be agnostic of the product through which credit is being delivered.
The key takeaway from all of this is that in a digital world, the customer is always right, and developing customer-centric offerings that reduce friction for both consumers as well as merchants is a critical factor.
Challenging the status quo is one of the hardest part of leading change. In a time when as good as all industries are facing radical changes over the next decade, innovation is necessary for survival. However, all too often, organizations and leaders are stuck in a “wait and see” mode. Rather than taking a leap of faith into the unknown, incumbents all too often find themselves on the steady decline into obsolescence by doing nothing.
The challenge many leaders face is how to prioritize new business opportunities when there are still a number of unknowns in the business case. The result is often a wait and see approach, where initiatives with a predictable solid business case based on linear projections that show a reasonable ROI prevails. Innovation, which is by nature riddled with uncertainty fall victim to the need for more information and analysis, and often ends up as an academic study. Wasting both potential market opportunities as well as internal resources.
When the impact of technology is shaping both customer behavior as well as potential business models, complacency becomes a significant vulnerability and should be considered among top strategic risks for any company out there.
Disruptive innovation gets its potential from incumbent complacency. Where what may be seen as an inferior product is good enough for underserved customers, and slowly improves until it is able to challenge existing paradigms. By the time the actual consequences of disruptive innovation is evident enough to fit the business case template, it may already be too late to act.
The challenge is to recognize the actual risks of doing nothing.
Customer behavior shifts fast, while at the same time, developing solutions that fit customer needs takes time. In order to keep track of customer behavior, companies must recognize changes in customer behavior at a much earlier stage. The winner takes it all dynamics of a digital economy favors fast movers, and the inability to act may result in arriving at the battlefield long after the battle is over.
The characteristics of an exponential world also mean that the stakes are raised. Potential returns are higher for those who succeed, and the potential downsides for those who are left behind are correspondingly devastating. The ability to shift your mindset to actively pursuing opportunities for exponential growth is crucial in order to both recognize which opportunities are worth pursuing as well as when to take a leap of faith. Attempting to do a little bit of everything is equally unwise as doing nothing.
At the end of the day, complacency is challenging to overcome. It is often the by-product of not only past success, but also a steady state of current success. Overcoming this requires leaders to push parts of the organization into the deep end of the pool. In order to succeed it is crucial to have the right people on board that recognize that innovation will be an uphill battle, and is prepared to overcome both trip wires and take some punches along the way.
Both leaders and middle managers should embrace the concept of productive paranoia. Productive paranoia is the ability to be hyper-vigilant about potentially bad events that can hit your company and then turn that fear into preparation and clearheaded action. This does not mean that you should sit around and fear what may happen, it requires you to act on that knowledge.
Investing in innovation may seem expensive as returns are often uncertain from a business case perspective, but the alternative of doing nothing will most likely be even more expensive in the end. In order to overcome organizational complacency and be able to navigate uncertain waters, leaders should replace the fear of the unknown with a profound fear of doing nothing.