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Since its inception in 2003, LinkedIn has succeeded in becoming the leading online platform for professionals to connect, seek career opportunities and learn. However, despite acting as a bridge between recruiters and job seekers through innovative algorithms and personalized models significant challenges persist. Most notably, mismatches in job recommendations, time consuming and inefficient screening processes and limited career development options. In order to help LinkedIn overcome the aforementioned inefficiencies, our team has examined, analyzed, created and evaluated a Gen AI solution which can assist it in changing from a static networking site to a real time, holistic, personalized career ecosystem.

Currently, LinkedIn at its core is a two way platform connecting job seekers and recruiters. Via the integration of GenAI it is feasible to surpass this transactional model by analyzing users’ profiles, patterns, learning interests, engagement data and goals and assess dynamically skill gaps, recommend accurate learning opportunities relating to the current job market trends and even simulate AI interview scenarios for each job advertisement. On the other hand, employers can receive access to a rich AI generated dashboard of summaries for each candidate and provide for each one predictive fit scores (culture fit, hard skills match, etc.) thus making faster and more accurate decisions and lowering associated costs.

According to industry benchmarks, AI predictive analytics can improve job matching accuracy by up to 25% (Recruiterflow, 2025), while simultaneously automated screening tools can reduce hiring cycles by 30% (SHRM, 2023). Additionally, personalization enhanced by AI can result in the rise of user engagement and happiness by around 20% (LinkedIn Talent Blog, 2023). In return, increased user retention and addition leads to more data which contributes to the generation of even better suggestions (network effects).

However, in our research we also recognize the necessity of responsible AI implementation. As a component of Microsoft’s ecosystem, LinkedIn must comply with the EU AI Act and GDPR. Only through transparent algorithms and bias mitigation practices can trust and fairness be maintained.

During our analysis, we decided to visualize our concept creating two prototypes using Lovable AI:

• The Job Seeker UI, which depicts how users scroll and interact with personalized job suggestions, customized learning paths improve their skill and AI feedback on how to enhance their profiles. 
• The Recruiter UI, which demonstrates AI candidate lists with match scores and profile summaries for each one.

These prototypes, in combination, show how GenAI can be implemented into LinkedIn’s existing interface to add value for all stakeholders.

To conclude, Generative AI can greatly strengthen LinkedIn’s role as an ecosystem driver in the digital economy.  While maintaining ethics and compliance, not only does it complement its current advantages but can create new revenue streams. Although possible bias and regulations can not be overlooked, Microsoft’s well tested infrastructure and LinkedIn’s scale can be decisive factors in overcoming these obstacles. Ultimately, LinkedIn can become an AI compass for recruiters and job seekers and by enabling better matches can continue to play a pivotal role in the digital economy.

Team 21

Ben Seidensticker

Georgios Trikkas Britt

Jacintha Boender

Mayke Heijmans

References

LinkedIn Talent Blog. (2023, May 17). Helping recruiters save time and increase candidate engagement with AI. https://www.linkedin.com/business/talent/blog/talent-acquisition/helping-recruiters-save-time-increase-candidate-engagement-with-ai

OpenAI. (2025). Reimagining LinkedIn: How Generative AI can create the perfect match  [Digital illustration]. Generated with DALL·E via ChatGPT on October 16, 2025.

Recruiterflow. (2025, January 15). AI candidate matching: A complete guide. https://recruiterflow.com/blog/candidate-matching

Seidensticker, B., Trikkas Britt, G., Boender, J., & Heijmans, M. (2025, October 17). Innovating Business Models ‘Leveraging GenAI to Transform LinkedIn’s Business Model‘. BM01BIM Information Strategy. Rotterdam School of Management, Erasmus University.

Society for Human Resource Management (SHRM). (2023, July 21). The evolving role of AI in recruitment and retention. https://www.shrm.org/labs/resources/the-evolving-role-of-ai-in-recruitment-and-retention

5/5 (1)

Ever since the inception of human history, people have developed numerous methods of exchanging value to fulfill needs which they could not cover on their own. The barter economy which was a main characteristic of earlier societies, meant that goods were swapped directly based on mutual needs. However, currencies were created as economies grew more complex. Ranging from precious metals like gold and silver to symbolic coins and paper money, these forms have always had the common goal of making transactions as easy, convenient, and efficient as possible.

During the past century, innovations in technology constantly reshaped how payments were conducted: 

• In 1918, the U.S. Federal Reserve utilized telegraph wire to mark one of the first electronic transfers. 
• The standardization of secure payments between banks internationally was rendered a reality thanks to the creation of the SWIFT network in 1973.
• By the 1980s and 1990s, online banking and debit cards were connected directly to individual holder’s accounts making digital payments feasible.

In every recent case, a central agent such as a government or bank entity has played a crucial role in regulating, supervising, backing and ensuring trust in these systems.

However, in a turn of events, this paradigm has been challenged since 2009 with the emergence of Bitcoin. This cryptocurrency combines a set of appealing features: a peer-to-peer electronic cash system with the disintermediation of banks, advanced security through cryptography and a decentralized blockchain structure. Thus, it promises transparency, safety, sovereignty and independence from central authorities.

Since the introduction of Bitcoin via a whitepaper signed by the still unknown figure named Satoshi Nakamoto, numerous cryptocurrencies have gained popularity, ignited by technological idealism and speculative frenzies. For example, Bitcoin’s historically high prices at $20000 and $68000, in December 2017 and November 2021 respectively dominated the news headlines and indicated that millionaires can be made overnight. Yet the crypto world has also been stunned by scandals and disappointments. The Luna crash in May 2022 from approximately $119 to virtually zero ($0.0001) resulted in a $40 billion wipe out. The collapse of FTX in November 2022 exposed systemic fraud and undermined confidence in the entire crypto industry.

Overall, despite periodic bull runs, there is strong evidence signaling that cryptocurrencies are unlikely to be adopted at a mainstream level. Firstly, they can be considered as speculative assets due to their high volatility and abrupt price fluctuations. This violates two fundamental requirements for a nationwide type of currency: stability and reliability. For instance, in a span of 7 months (November 2021 to June 2022) Bitcoin’s value more than halved from just over $57000 to around $24000.

Regulation and security concerns cast additional doubts. Insufficient ID verification processes are thought to enable money laundering, tax evasion and scams, while incidents like the Coincheck hack in 2018 when hackers stole roughly 523–560 million NEM tokens prove that successful cyberattacks are possible. Moreover, the theoretical risk of a 51 percent attack (whoever controls more than 50% of the computational power in a network and essentially controls the whole network) raises serious questions about long term fallback solutions when bad actors join forces.

Another dimension of difficulty is added when environmental and scalability worries are taken into account. The constant need to verify transactions and information through mining can result in the consumption of significant levels of electricity. For example, Bitcoin mining alone uses more energy than Denmark on an annual basis.

Amid speculation and predictions about the longevity and viability of crypto, central banks are looking to create their own form of digital currency (Central Bank Digital Currency – CBDC). Unlike crypto, they are backed by established governments and institutions, which are known to the public and can be held accountable. Simultaneously, since central banks primarily control the monetary policy they have the technical expertise and experience to control the supply of digital coins and therefore minimize volatility.

Based on recent reports conducted by central banks around the globe, CBDCs can also be used in conjunction with cash and other digital payments allowing easy integration in the current banking system and ensuring trust. By further providing offline payment options, financial inclusion is strengthened and the consequences of cyber attacks are limited.

Countries are already developing CBDCs. Most notably, China banned cryptocurrencies in 2021 while promoting the rollout of the digital yuan. The European Central Bank and the FED are actively seeking feedback from major stakeholders with the provision of reports and key findings to the public. But apart from the development of a digital euro and dollar, nations from Nigeria to the Bahamas have launched their own projects. These efforts depict not only a response to the shortcomings of crypto but also a strategic approach to modernize money while preserving regulatory oversight.

In the long run, I believe cryptocurrencies have set an extraordinary foundation for designing, developing and implementing digital currency but will at best survive as a niche form of investment for risk-tolerant investors. Despite the promising blockchain technology underlying them and the revolutionary proposals of how a decentralized, transparent, peer-to-peer economy can be created, the environmental, scalability and cybersecurity concerns outweigh the benefits. The real revolution in money may not come from Bitcoin or Ethereum but from central banks themselves.

References

Atlantic Council. (2025). Central Bank Digital Currency Tracker. https://www.atlanticcouncil.org/cbdctracker/

Briola, A., Vidal-Tomás, D., Wang, Y., & Aste, T. (2022). Anatomy of a stablecoin’s failure: The Terra-Luna case. arXiv. https://arxiv.org/abs/2207.13914

Chamanara, S., Ghaffarizadeh, S. A., & Madani, K. (2023). The environmental footprint of Bitcoin mining across the globe: Call for urgent action. Earth’s Future, 11, e2023EF003871. https://doi.org/10.1029/2023EF003871

IMF. (2024). Central Bank Digital Currency Adoption (FTN063). https://www.imf.org/-/media/Files/Publications/FTN063/2024/English/FTNEA2024005.ashx

Investopedia. (n.d.). Bitcoin’s price history. https://www.investopedia.com/articles/forex/121815/bitcoins-price-history.asp

Kohli, V., Chakravarty, S., Chamola, V., Singh, K. S., Sangwan, K., & Zeadally, S. (2022). An analysis of energy consumption and carbon footprints of cryptocurrencies and possible solutions. arXiv. https://arxiv.org/abs/2203.03717

Penser. (n.d.). CBDC projects across the globe. https://www.penser.co.uk/cryptocurrencies/cbdc-projects-across-the-globe/

Reuters. (2024, September 17). Central bank digital currency momentum growing, study shows. https://www.reuters.com/markets/currencies/central-bank-digital-currency-momentum-growing-study-shows-2024-09-17/

Statmuse. (n.d.). Bitcoin value graph November 2021. https://www.statmuse.com/money/ask/bitcoin-value-graph-november-2021